26th week of 2021 patent applcation highlights part 54 |
Patent application number | Title | Published |
20210201133 | METHOD AND APPARATUS WITH NEURAL NETWORK DATA INPUT AND OUTPUT CONTROL - A neural network deep learning data control apparatus includes: a memory; an encoding circuit configured to receive a data sequence, generate a compressed data sequence in which consecutive invalid bits in a bit string of the data sequence are compressed into a single bit of the compressed data sequence, generate a validity determination sequence indicating a valid bit and an invalid bit in a bit string of the compressed data sequence, and write the compressed data sequence and the validity determination sequence to the memory; and a decoding circuit configured to read the compressed data sequence and the validity determination sequence from the memory, and determine a bit in the bit string of the compressed data sequence set for transmission to a neural network circuit, based on the validity determination sequence, such that the neural network circuit omits an operation with respect to non-consecutive invalid bits. | 2021-07-01 |
20210201134 | DATA OUTPUT METHOD, DATA ACQUISITION METHOD, DEVICE, AND ELECTRONIC APPARATUS - A data output method, a data acquisition method, a device, and an electronic apparatus are provided, and a specific technical solution is: reading a first data sub-block, and splicing the first data sub-block into a continuous data stream, wherein the first data sub-block is a data sub-block in transferred data in a neural network; compressing the continuous data stream to acquire a second data sub-block; determining, according to a length of the first data sub-block and a length of the second data sub-block, whether there is a gain in compression of the continuous data stream; outputting the second data sub-block if there is the gain in the compression of the continuous data stream. | 2021-07-01 |
20210201135 | END-TO-END LEARNING IN COMMUNICATION SYSTEMS - An apparatus and method is described including obtaining or generating a transmitter-training sequence of messages for a transmission system, wherein the transmission system includes a transmitter, a channel and a receiver, wherein the transmitter includes a transmitter algorithm having at least some trainable weights and the receiver includes a receiver algorithm having at least some trainable weights; transmitting perturbed versions of the transmitter-training sequence of messages over the transmission system; receiving first receiver loss function data at the transmitter, the first receiver loss function data being generated based on a received-training sequence as received at the receiver and knowledge of the transmitter training sequence of messages for the transmission system; and training at least some weights of the transmitter algorithm based on first receiver loss function data and knowledge of the transmitter-training sequence of messages and the perturbed versions of the transmitter-training sequence of messages. | 2021-07-01 |
20210201136 | Acceleration of Model/Weight Programming in Memristor Crossbar Arrays - A crossbar array includes a number of memory elements. An analog-to-digital converter (ADC) is electronically coupled to the vector output register. A digital-to-analog converter (DAC) is electronically coupled to the vector input register. A processor is electronically coupled to the ADC and to the DAC. The processor may be configured to determine whether division of input vector data by output vector data from the crossbar array is within a threshold value, and if not within the threshold value, determine changed data values as between the output vector data and the input vector data, and write the changed data values to the memory elements of the crossbar array. | 2021-07-01 |
20210201137 | SYSTEM AND METHOD FOR INSPECTING ITEMS - A system for inspecting items in transit through a transit facility, wherein the system comprises a plurality of data collection units located at a plurality of transit facilities; a decision entity, in connection with the data collection unit at a selected one of the transit facilities; and a server connectable to each of the data collection units. The server comprising a data store storing inspection data, obtained from the data collection units, indicative of instances of item inspection at the plurality of transit facilities; and a processor coupled to the data store and operable to update the data store based on data gathered at the data collection units. Wherein, for an item in transit through a transit facility, the system is configured to obtain item data providing an indication of a predicted level of inspection for the item and provide said item data to the decision entity; obtain, from the decision entity, a decided level of inspection for the item; and output a command signal to control inspection of the item in accordance with a final level of inspection assigned to the item, wherein the final level of inspection is selected based on an indication of: (i) the predicted level of inspection for the item, and (ii) the decided level of inspection for the item. | 2021-07-01 |
20210201138 | LEARNING DEVICE, INFORMATION PROCESSING SYSTEM, LEARNING METHOD, AND LEARNING PROGRAM - A model setting unit | 2021-07-01 |
20210201139 | DEVICE AND METHOD FOR MEASURING A CHARACTERISTIC OF AN INTERACTION BETWEEN A USER AND AN INTERACTION DEVICE - A device and a method for measuring a predefined characteristic of an interaction capable of being present between an interaction device and a user comprises an input interface for receiving at least one temporal sequence of data representative of the interaction and a classification module, which is connected to the input interface and processes the temporal sequence of data to output a measure of the predefined characteristic. The classification module has been configured from temporal sequences of learning data marked with the presence or absence of the characteristic. Such a measuring device may be used for detecting the start and/or end of the interaction between the user and the interaction device. | 2021-07-01 |
20210201140 | SAMPLE ANALYSIS APPARATUS AND SAMPLE ANALYSIS PROGRAM - A learning model creation unit | 2021-07-01 |
20210201141 | NEURAL NETWORK OPTIMIZATION METHOD, AND NEURAL NETWORK OPTIMIZATION DEVICE - A neural network optimization method includes: performing first processing of, for each of a plurality of preset layers included in a high precision neural network, performing bit reduction that is processing of reducing bit precision of parameters that constitute the preset layer to derive a degree of influence exerted on the recognition result of the high precision neural network by the bit reduction performed on the layer; and performing second processing of performing the bit reduction on each of at least one of the plurality of preset layers included in the high precision neural network that is identified based on the degree of influence derived for each of the plurality of preset layers to generate a bit reduction neural network. | 2021-07-01 |
20210201142 | ELECTRONIC DEVICE AND CONTROL METHOD THEREOF - An electronic device is provided. The electronic device includes: a memory configured to store first information and second information; and a processor configured to obtain a first weight matrix by loading the first information or obtain a second weight matrix by loading the first information and the second information, based on resource information of the electronic device, wherein the first information includes weights related to the first weight matrix and a first index corresponding to the weights, and the second information includes an additional weight for obtaining the second weight matrix and a second index corresponding to the additional weight. | 2021-07-01 |
20210201143 | COMPUTING DEVICE AND METHOD OF CLASSIFYING CATEGORY OF DATA - A device and method for classifying a category of data are provided. The device includes: a memory storing one or more instructions; and at least one processor configured to execute the one or more instructions stored in the memory to cause the processor to: identify a classification system of a category of data comprising a classification criterion of the category of the data and a plurality of keywords; obtain data comprising at least one sentence; and determine at least one category with respect to the at least one sentence of the data based on the classification system of the category of the data using a neural network that performs classification by unsupervised learning. | 2021-07-01 |
20210201144 | SYSTEMS AND METHODS FOR ARTIFICIAL INTELLIGENCE ENHANCEMENTS IN AUTOMATED CONVERSATIONS - Systems and methods for generating custom client intents in an AI driven conversation system are provided. Additionally, systems and methods for contact updating in a conversation between an original contact and a dynamic messaging system is provided. Additional systems and methods allow for annotation of a response in a training desk. In additional embodiments, systems and methods for model deployment in a dynamic messaging system are provided. In yet additional embodiments, systems and methods for improved functioning of a dynamic messaging system are provided. Further, systems and methods for an automated buying assistant are provided. An additional set of embodiments include systems and methods for automated task completion. | 2021-07-01 |
20210201145 | THREE-DIMENSIONAL INTERSECTION STRUCTURE PREDICTION FOR AUTONOMOUS DRIVING APPLICATIONS - In various examples, a three-dimensional (3D) intersection structure may be predicted using a deep neural network (DNN) based on processing two-dimensional (2D) input data. To train the DNN to accurately predict 3D intersection structures from 2D inputs, the DNN may be trained using a first loss function that compares 3D outputs of the DNN—after conversion to 2D space—to 2D ground truth data and a second loss function that analyzes the 3D predictions of the DNN in view of one or more geometric constraints—e.g., geometric knowledge of intersections may be used to penalize predictions of the DNN that do not align with known intersection and/or road structure geometries. As such, live perception of an autonomous or semi-autonomous vehicle may be used by the DNN to detect 3D locations of intersection structures from 2D inputs. | 2021-07-01 |
20210201146 | COMPUTING DEVICE AND OPERATION METHOD THEREOF - The disclosure relates to an artificial intelligence (AI) system, which imitates functions of the human brain, such as recognition and determination, using a machine learning algorithm such as deep learning, and an application thereof. A computing device includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory to execute the one or more instructions to: using at least one neural network, infer user-preferred item candidates and user tastes based on user information; and select and provide an item suited to the user tastes from among the user-preferred item candidates. | 2021-07-01 |
20210201147 | MODEL TRAINING METHOD, MACHINE TRANSLATION METHOD, COMPUTER DEVICE, AND STORAGE MEDIUM - Embodiments of this application disclose a neural network model training method, a machine translation method, a computer device, and a storage medium. The method includes: obtaining a training sample set including a training sample and a standard tag vector corresponding to the training sample; inputting the training sample into a neural network model including a plurality of attention networks to obtain a feature fusion vector; obtaining a predicted tag vector according to the feature fusion vector, and performing adjustment on a model parameter of the neural network model until a convergence condition is met to obtain a target neural network model. | 2021-07-01 |
20210201148 | METHOD, APPARATUS, AND STORAGE MEDIUM FOR PREDICTING INFORMATION - A method, apparatus, and storage medium for predicting information are described. The method for obtaining a combined model includes obtaining, a to-be-trained image set including N to-be-trained images; extracting a to-be-trained feature set from each to-be-trained image, the to-be-trained feature set comprising a first, second, and third to-be-trained feature, the first to-be-trained feature representing an image feature of a first region, the second to-be-trained feature representing an image feature of a second region, the third to-be-trained feature representing an attribute feature related to an interaction operation, and the first region being smaller than the second region; obtaining a first to-be-trained label and a second to-be-trained label that correspond to the each to-be-trained image; and obtaining a combined model through training according to the to-be-trained feature set in the each to-be-trained image and the first to-be-trained label and the second to-be-trained label that correspond to the each to-be-trained image. | 2021-07-01 |
20210201149 | METHOD, APPARATUS, DEVICE AND STORAGE MEDIUM FOR EMBEDDING USER APP INTEREST - A method, an apparatus, a device and a storage medium for embedding user app interest are provided. The method includes: acquiring a user existing app installation list and a user app installation list within a predetermined time window, where the app includes app ID information and app category information; inputting the existing app installation list and the app installation list within the predetermined time window into a pre-trained user app interest embedding model to obtain a user app interest embedding vector. By combining the user existing app installation list information and the user recent app installation list information, the user app interest embedding vector may simultaneously reflect the user long-term interest and the user short-term interest. | 2021-07-01 |
20210201150 | FRAME SELECTION BASED ON A TRAINED NEURAL NETWORK - Various embodiments describe frame selection based on training and using a neural network. In an example, the neural network is a convolutional neural network trained with training pairs. Each training pair includes two training frames from a frame collection. The loss function relies on the estimated quality difference between the two training frames. Further, the definition of the loss function varies based on the actual quality difference between these two frames. In a further example, the neural network is trained by incorporating facial heatmaps generated from the training frames and facial quality scores of faces detected in the training frames. In addition, the training involves using a feature mean that represents an average of the features of the training frames belonging to the same frame collection. Once the neural network is trained, a frame collection is input thereto and a frame is selected based on generated quality scores. | 2021-07-01 |
20210201151 | METHOD AND DEVICE FOR TRAINING A MACHINE LEARNING ROUTINE FOR CONTROLLING A TECHNICAL SYSTEM - To train a machine learning routine (BNN), a sequence of first training data (PIC) is read in through the machine learning routine. The machine learning routine is trained using the first training data, wherein a plurality of learning parameters (LP) of the machine learning routine is set by the training. Furthermore, a value distribution (VLP) of the learning parameters, which occurs during the training, is determined and a continuation signal (CN) is generated on the basis of the determined value distribution of the learning parameters. Depending on the continuation signal, the training is then continued with a further sequence of the first training data or other training data (PIC | 2021-07-01 |
20210201152 | DOMAIN ADAPTATION OF DEEP NEURAL NETWORKS - Disclosed herein are system, method, and computer program product embodiments for adapting machine learning models for use in additional applications. For example, feature extraction models are readily available for use in applications such as image detection. These feature extraction models can be used to label inputs (such as images) in conjunction with other deep neural network models. However, in adapting the feature extraction models to these uses, it becomes problematic to improve the quality of their results on target data sets, as these feature extraction models are large and resistant to retraining. Approaches disclosed herein include a transfer layer for providing fast retraining of machine learning models. | 2021-07-01 |
20210201153 | WEIGHT INITIALIZATION METHOD AND APPARATUS FOR STABLE LEARNING OF DEEP LEARNING MODEL USING ACTIVATION FUNCTION - Provided is an artificial neural network learning apparatus for deep learning. The apparatus includes an input unit configured to acquire an input data or a training data, a memory configured to store the input data, the training data, and a deep learning artificial neural network model, and a processor configured to perform computation based on the artificial neural network model, in which the processor sets the initial weight depending on the number of nodes belonging to a first layer and the number of nodes belonging to a second layer of the artificial neural network model, and determines the initial weight by compensation by multiplying a standard deviation (σ) by a square root of a reciprocal of a probability of a normal probability distribution for a remaining section except for a section in which an output value of the activation function converges to a specific value. | 2021-07-01 |
20210201154 | ADVERSARIAL NETWORK SYSTEMS AND METHODS - Methods, and systems for determining or inferring user attributes and/or determining information related to user attributes. One of the methods includes: receiving, at each encoder of a plurality of encoders, individual user datasets for a plurality of individual users from a single specified source, the plurality of encoders receiving data from a plurality of data sources; generating a plurality of vectors, wherein generating a plurality of vectors comprises, for each individual user dataset, generating a vector of a specified size, the plurality of vectors forming a shared representation and wherein each of the plurality of encoders comprises a machine learning model trained based at least in part on: a) an encoder's loss, and b) a classifier loss; receiving a query of the shared representation; and providing information from the shared representation in response to the query. | 2021-07-01 |
20210201155 | INTELLIGENT CONTROL METHOD FOR DYNAMIC NEURAL NETWORK-BASED VARIABLE CYCLE ENGINE - An intelligent control method for a dynamic neural network-based variable cycle engine is provided. By adding a grey relation analysis method-based structure adjustment algorithm to the neural network training algorithm, the neural network structure is adjusted, a dynamic neural network controller is constructed, and thus the intelligent control of the variable cycle engine is realized. A dynamic neural network is trained through the grey relation analysis method-based network structure adjustment algorithm designed by the present invention, and an intelligent controller of the dynamic neural network-based variable cycle engine is constructed. Thus, the problem of coupling between nonlinear multiple variables caused by the increase of control variables of the variable cycle engine and the problem that the traditional control method relies too much on model accuracy are effectively solved. | 2021-07-01 |
20210201156 | SAMPLE-EFFICIENT REINFORCEMENT LEARNING - Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sample-efficient reinforcement learning. One of the methods includes maintaining an ensemble of Q networks, an ensemble of transition models, and an ensemble of reward models; obtaining a transition; generating, using the ensemble of transition models, M trajectories; for each time step in each of the trajectories: generating, using the ensemble of reward models, N rewards for the time step, generating, using the ensemble of Q networks, L Q values for the time step, and determining, from the rewards, the Q values, and the training reward, L*N candidate target Q values for the trajectory and for the time step; for each of the time steps, combining the candidate target Q values; determining a final target Q value; and training at least one of the Q networks in the ensemble using the final target Q value. | 2021-07-01 |
20210201157 | NEURAL NETWORK MODEL COMPRESSION WITH QUANTIZABILITY REGULARIZATION - A method, computer program, and computer system is provided for compressing a neural network model. A multi-dimensional tensor corresponding to a set of weight coefficients associated with a neural network is reshaped. A subset of weight coefficients is identified from among the set of weight coefficients. A model of the neural network is compressed based on the identified subset of weight coefficients. | 2021-07-01 |
20210201158 | TRAINING ARTIFICIAL NEURAL NETWORKS BASED ON SYNAPTIC CONNECTIVITY GRAPHS - Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a student neural network. In one aspect, there is provided a method comprising: processing a training input using the student neural network to generate an output for the training input; processing the student neural network output using a discriminative neural network to generate a discriminative score for the student neural network output, wherein the discriminative score characterizes a prediction for whether the network input was generated using: (i) the student neural network, or (ii) a brain emulation neural network; and adjusting current values of the student neural network parameters using gradients of an objective function that depends on the discriminative score for the student neural network output. | 2021-07-01 |
20210201159 | System and Method for Unsupervised Domain Adaptation with Mixup Training - A system and method for domain adaptation involves a first domain and a second domain. A machine learning system is trained with first sensor data and first label data of the first domain. Second sensor data of a second domain is obtained. Second label data is generated via the machine learning system based on the second sensor data. Inter-domain sensor data is generated by interpolating the first sensor data of the first domain with respect to the second sensor data of the second domain. Inter-domain label data is generated by interpolating first label data of the first domain with respect to second label data of the second domain. The machine learning system is operable to generate inter-domain output data in response to the inter-domain sensor data. Inter-domain loss data is generated based on the inter-domain output data with respect to the inter-domain label data. Parameters of the machine learning system are updated upon optimizing final loss data that includes at least the inter-domain loss data. After domain adaptation, the machine learning system, which is operable in the first domain, is adapted to generate current label data that identifies current sensor data of the second domain. | 2021-07-01 |
20210201160 | HYBRID NEURAL NETWORK AND AUTOENCODER - A physics-influenced deep neural network (PDNN) model, or a deep neural network incorporating a physics-based cost function, can be used to efficiently denoise sensor data. To generate the PDNN model, noisy sensor data is used as training data input to a deep neural network and training output is valuated with a cost function that incorporates a physics-based model. An autoencoder can be coupled to the PDNN model and trained with the reduced-noise sensor data which is output from the PDNN during training of the PDNN or with a separate set of sensor data. The autoencoder detects outliers based on the reconstructed reduced-noise sensor data which it generates. Denoising sensor data by leveraging an autoencoder which is influenced by the physics of the underlying domain based on the incorporation of the physics-based model in the PDNN facilitates accurate and efficient denoising of sensor data and detection of outliers. | 2021-07-01 |
20210201161 | METHOD, APPARATUS, ELECTRONIC DEVICE AND READABLE STORAGE MEDIUM FOR CONSTRUCTING KEY-POINT LEARNING MODEL - A method, an apparatus, an electronic device, and a computer readable storage medium for constructing a key-point learning model are provided. The method includes: acquiring labeled data labeling a human-body key-point and unlabeled data that does not label the human-body key-point; training, using the labeled data, an initial prediction model and an initial discriminator in a supervised training way to obtain a first prediction model and a first discriminator; training, using the unlabeled data, the first prediction model and the first discriminator in an unsupervised training way to obtain a second prediction model and a second discriminator; and constructing a key-point learning model according to the second prediction model and the second discriminator. | 2021-07-01 |
20210201162 | METHOD AND SYSTEM FOR GENERATING A TRANSITORY SENTIMENT COMMUNITY - A method and system of generating a transitory sentiment community. The method comprises identifying, in accordance with a supervised trained model, within agglomerated social media content data, content associated with a subject of interest and characterized in accordance with one of a sentiment expressive usage and not a sentiment expressive usage, the subject of interest defined in accordance with at least one text character string; performing, based on an unsupervised trained model in conjunction with content associated with the sentiment expressive usage, a sentiment analysis that determines a sentiment intensity rating associated with at least a portion of the agglomerated social media content data, and generating the transitory sentiment community based at least in part on the sentiment intensity rating. | 2021-07-01 |
20210201163 | Genome Sequence Alignment System and Method - A system is provided that includes a bit vector-based distance counter circuitry configured to generate one or more bit vectors encoded with information about potential matches and edits between a read and a reference genome, wherein the read comprises an encoding of a fragment of deoxyribonucleic acid (DNA) encoded via bases G, A, T, C. The system further includes a bit vector-based traceback circuitry configured to divide the reference genome into one or more windows and to use the plurality of bit vectors to generate a traceback output for each of the one or more windows, wherein the traceback output comprises a match, a substitution, an insert, a delete, or a combination thereof, between the read and the one or more windows. | 2021-07-01 |
20210201164 | METHOD AND SYSTEM FOR IDENTIFYING RELEVANT VARIABLES - The invention relates to a method for identifying variables relevant to a dataset, said variables being derived from a plurality of variables involved in processing the dataset, said method comprising:
| 2021-07-01 |
20210201165 | Internet Of Things (IOT) Big Data Artificial Intelligence Expert System Information Management And Control Systems And Methods - IoT Big Data information management and control systems and methods for distributed performance monitoring and critical network fault detection comprising a combination of capabilities including: IoT data collection sensor stations receiving sensor input signals and also connected to monitor units providing communication with other monitor units and/or cloud computing resources via IoT telecommunication links, and wherein a first data collection sensor station has expert predesignated other network elements comprising other data collection sensor stations, monitor units, and/or telecommunications equipment for performance and/or fault monitoring based on criticality to said first data collection sensor station operations, thereby extending monitoring and control operations to include distributed interdependent or critical operations being monitored and analyzed throughout the IoT network, and wherein performance and/or fault monitoring signals received by said first data collection sensor station are analyzed with artificial intelligence, hierarchical expert system algorithms for generation of warning and control signals. | 2021-07-01 |
20210201166 | Distributed Activity Control Systems For Artificial Intelligence Task Execution Direction Including Task Adjacency And Reachability Analysis - A dynamic, distributed directed activity network comprising a directed activity control program specifying tasks to be executed including required individual task inputs and outputs, the required order of task execution, permitted parallelism in task execution, task adjacency to subsequent tasks, and reachability from each task to other tasks; a plurality of task execution agents, individual of said agents having a set of dynamically changing agent attributes and capable of executing different required tasks; a plurality of task execution controllers, each controller associated with one or more of the task execution agents with access to dynamically changing agent attributes; a directed activity controller for communicating with said task execution controllers for directing execution of said activity control program; and, a communications network supporting communication between said directed activity controller and task execution controllers for directing execution of said directed activity control program using selected task execution agents. | 2021-07-01 |
20210201167 | METHOD OF KNOWLEDGE SHARING AMONG DIALOGUE SYSTEMS, DIALOGUE METHOD AND DEVICE - The present application discloses a method of knowledge sharing between dialogue systems, including: receiving, by a first dialogue system, a knowledge sharing request sent by an external dialogue system, wherein the knowledge sharing request comprises at least feature information of a knowledge point to be shared by the external dialogue system; parsing the knowledge sharing request to determine the feature information of the knowledge point to be shared; and adding the feature information of the knowledge point to be shared to a knowledge base of the first dialogue system to form a shared knowledge base. The embodiment of the present application realizes the sharing of knowledge among different dialogue systems by sharing knowledge points among dialogue systems, and meets the cross-domain dialogue needs of users for dialogue robots to the greatest extent while minimizing costs. | 2021-07-01 |
20210201168 | Method and Apparatus for Outputting Information, Device and Storage Medium - A method and apparatus for outputting information, a device and a storage medium are provided. An implementation of the method may include: acquiring knowledge data from at least one data source; acquiring information related to at least one user and a preset entity set; determining an entity, metadata of the entity and a relationship between entities, based on the related information, the knowledge data and the preset entity set; creating a knowledge graph based on the entity, the metadata and the relationship; and outputting the knowledge graph. | 2021-07-01 |
20210201169 | SYSTEMS AND METHODS FOR COLLECTING AND PROCESSING DATA FOR INSURANCE-RELATED TASKS - Provided herein are systems and methods for collecting and processing data to assist in the processing of insurance-related tasks. The systems and methods described herein may also be useful to guiding a user such as an insurance company representative through interactions according to formalized protocols. In some embodiments, systems and methods may be used to collect and process data associated with an episode such as interaction between a user and a third party. The system may be a data collection and processing system including a knowledge representation module having one or more knowledge graphs and parsing algorithms. The knowledge representation module may be used to process structured and unstructured data and to generate one or more prompts relating to data collection and processing. | 2021-07-01 |
20210201170 | SYSTEM AND METHOD FOR IMPLEMENTING AN ASSESSMENT TOOL - The invention relates to a method and system that implements an assessment tool that assesses regulations. The system comprises: a server interface; a data store; and an assessment engine comprising a computer processor, coupled to the server interface and the data store, programmed to: identify a regulation; divide the regulation into a plurality of rules; convert each rule into a question, test and one or more conditions; present, via a user interface, a first question to a user; receive, via the user interface, a response to the first question; store, in the data store, the response; present, via the user interface, a subsequent question responsive to the response; store, in the data store, the subsequent question and corresponding response; and dynamically generate an audit trail of each question and answer combination. | 2021-07-01 |
20210201171 | Data Management And Bootstrapping Processing For Machine Learning And Classification Development - A system for developing machine learning for use in the radiofrequency domain that produces a robust set of training data for machine learning from a small set of labelled training data that is bootstrapped with unlabeled electromagnetic environment data. A raw signal set is prepared from the labeled data and separately processed for any electromagnetic environment and interference signals as well as for a primary signal by applying the real electromagnetic environment data and then summed to generate a second data set that is larger than the first data set. Feature extraction is used to produce a bootstrapped labelled data set that is larger than the original labelled data set and that can be used as training data for machine language classification. | 2021-07-01 |
20210201172 | QUESTION-ANSWERING LEARNING METHOD AND QUESTION-ANSWERING LEARNING SYSTEM USING THE SAME AND COMPUTER PROGRAM PRODUCT THEREOF - A question-answering learning method including the following steps is provided. Firstly, several classifiers are created according to N1 labeled sentences among N sentences. Then, at least one corresponding sentence type of each of the N2 unlabeled sentences among the N sentences is determined by each classifier. Then, N3 sentences are selected from the N2 unlabeled sentences according to a degree of consistency of determined results of the classifiers, wherein the determined results of the N3 sentences are determined by the classifiers and are inconsistent. Then, N4 mutually complementary sentences are selected as to-be-labeled sentences from the N3 sentences. Then, after the N4 selected to-be-labeled sentences are labeled, several classifiers are re-created according to the N1 labeled sentences and the N4 selected to-be-labeled sentences. Then, at least one of the previously created classifiers is added to the currently created classifiers to be members of the classifiers. | 2021-07-01 |
20210201173 | Monitoring Database Processes to Generate Machine Learning Predictions - Methods and system are presented for monitoring database processes to generate machine learning predictions. A plurality of database processes executed on database implementations can be monitored, wherein the monitoring includes determining a start time, an end time, and a number of rows impacted by portions of the database processes, and the monitored database processes generate instances of machine learning data including at least the number of rows impacted and an associated duration of time. Using a machine learning component and the machine learning data, a duration of time can be predicted for a candidate database process for execution on a database implementation. | 2021-07-01 |
20210201174 | GENERATING QUESTION TEMPLATES IN A KNOWLEDGE-GRAPH BASED QUESTION AND ANSWER SYSTEM - Techniques for generating a natural language question template for an artificial intelligence question and answer (QA) system are disclosed. A graph database query relating to a QA system is parsed using a predefined schema. The parsing includes extracting a first plurality of values from the graph database query relating to a where clause in the graph database query, extracting a second plurality of values from the graph database query relating to a return clause in the graph database query, identifying a QA template rule relating to the graph database query, based on a match clause in the graph database query. A natural language question template is generated based on the first plurality of values, the second plurality of values, and the identified QA template rule. The natural language question template is suitable for use by the QA system as part of generating a response to a natural language question. | 2021-07-01 |
20210201175 | NOTIFICATION PRIORITIZATION BASED ON USER RESPONSES - Methods and systems are described for prioritizing notifications based on user responses. The system may include determining a first score indicative of a first relevance of a notification to a first user at a first client device. The first score is determined based on at least metadata characterizing the notification. The notification is prioritized for the first user based on at least the first score. The notification is presented at the first client device based on at least the prioritization for the first user. A second score is determined that is indicative of a second relevance of the notification to a second user at a second client device. The second score is determined based on at least a response to the notification from the first client device. | 2021-07-01 |
20210201176 | SYSTEM AND METHOD OF MACHINE LEARNING BASED DEVIATION PREDICTION AND INTERCONNECTED-METRICS DERIVATION FOR ACTION RECOMMENDATIONS - A system and method for automatically predicting deviation on a metric of a use-case and deriving interconnections between metrics for generating action recommendations is provided. The system includes a deviation management system | 2021-07-01 |
20210201177 | PREDICTION SYSTEM, PREDICTION METHOD, AND INFORMATION STORAGE MEDIUM - Provided is a prediction system including: a learning model in which a relationship between an action history of each of a plurality of users who used a service in the past and a usage result of the service included in the action history of each of the plurality of users is learned; and at least one processor, the at least one processor being configured to: acquire the action history of a user using the service; predict, based on the action history of the user using the service and the learning model, the usage result of the user using the service; and execute processing corresponding to the usage result predicted. | 2021-07-01 |
20210201178 | MULTI-PHASE CHARACTERIZATION USING DATA FUSION FROM MULTIVARIATE SENSORS - A method for determining a wellbore fluid phase includes receiving, from a first sensor, first fluid data for a fluid flowing through a wellbore. The method also includes receiving, from a second sensor, second fluid data for the fluid. The method further includes determining, based at least in part on the first fluid data, a first fluid property. The method also includes determining, based at least in part on the second fluid data, a second fluid property. The method further includes determining a relationship between the first fluid property and the second fluid property, the relationship based at least in part on respective evaluations of the first fluid property with respect to a first threshold and the second fluid property with respect to a second threshold. The method includes determining, based at least in part on the relationship, a phase of the fluid. | 2021-07-01 |
20210201179 | METHOD AND SYSTEM FOR DESIGNING A PREDICTION MODEL - The invention relates to a method ( | 2021-07-01 |
20210201180 | GENERATING SOLUTIONS FROM AURAL INPUTS - Techniques for generating solutions from aural inputs include identifying, with one or more machine learning engines, a plurality of aural signals provided by two or more human speakers, at least some of the plurality of aural signals associated with a human-perceived problem; parsing, with the one or more machine learning engines, the plurality of aural signals to generate a plurality of terms, each of the terms associated with the human-perceived problem; deriving, with the one or more machine learning engines, a plurality of solution sentiments and a plurality of solution constraints from the plurality of terms; generating, with the one or more machine learning engines, at least one solution to the human-perceived problem based on the derived solution sentiments and solution constraints; and presenting the at least one solution of the human-perceived problem to the two or more human speakers through at least one of a graphical interface or an auditory interface. | 2021-07-01 |
20210201181 | INFERENCING AND LEARNING BASED ON SENSORIMOTOR INPUT DATA - Embodiments relate to performing inference, such as object recognition, based on sensory inputs received from sensors and location information associated with the sensory inputs. The sensory inputs describe one or more features of the objects. The location information describes known or potential locations of the sensors generating the sensory inputs. An inference system learns representations of objects by characterizing a plurality of feature-location representations of the objects, and then performs inference by identifying or updating candidate objects consistent with feature-location representations observed from the sensory input data and location information. In one instance, the inference system learns representations of objects for each sensor. The set of candidate objects for each sensor is updated to those consistent with candidate objects for other sensors, as well as the observed feature-location representations for the sensor. | 2021-07-01 |
20210201182 | METHOD AND APPARATUS FOR PERFORMING STRUCTURED EXTRACTION ON TEXT, DEVICE AND STORAGE MEDIUM - Embodiments of the present disclosure provide a method and apparatus for performing a structured extraction on a text, a device and a storage medium. The method may include: performing a text detection on an entity text image to obtain a position and content of a text line of the entity text image; extracting multivariate information of the text line based on the position and the content of the text line; performing a feature fusion on the multivariate information of the text line to obtain a multimodal fusion feature of the text line; performing category and relationship reasoning based on the multimodal fusion feature of the text line to obtain a category and a relationship probability matrix of the text line; and constructing structured information of the entity text image based on the category and the relationship probability matrix of the text line. | 2021-07-01 |
20210201183 | Training Collaborative Robots through User Demonstrations - The present disclosure provides describes to train a multi policy ML model to control robots in a multi-robot system in collaborating to perform a task. For example, trajectories associated with manipulating an object to perform the collaborative task can be determined and an ML model trained to output control actions for the robots in the multi-robot system to collaborate to complete the task. | 2021-07-01 |
20210201184 | EXPLAINABLE PROCESS PREDICTION - A method and system are provided in which predictions are generated, using one or more machine learning-based prediction models, for one or more process parameters associated with a running process. Explanation-oriented data elements are generated that correspond to the generated predictions and include confidence indicators associated with the generated predictions. The explanation-oriented data elements are presented in one or more dashboards of a visualization platform. The explanation-oriented data elements are representative of an explanation framework for explaining the predicted business process parameters generated by a machine learning-based prediction model and in a manner so that a user can understand and trust the basis for the predictions to facilitate effective and appropriate intervention in a running process. | 2021-07-01 |
20210201185 | ENVIRONMENTAL STATE ANALYSIS METHOD, AND USER TERMINAL AND NON-TRANSITORY MEDIUM IMPLEMENTING SAME - An environmental state analysis method includes obtaining key data that affects an environmental state of a designated place, and determining a degree of influence of the key data on the environmental state of the designated place according to the key data by using an analysis model. The key data includes one or more of environmental protection data, pollution source data, and environmental monitoring data. The environmental state includes one or more of a diffusion speed of harmful gas and a concentration of dust in the air. | 2021-07-01 |
20210201186 | Utilizing Machine Learning to Predict Information Corresponding to Merchant Offline Presence - A machine learning process includes first, second, third and fourth phases. The first phase includes accessing geographical locations and economic traits for merchants. First merchants have offline locations. Second merchants have no offline locations. The second phase includes labeling third merchants as having offline locations and labeling fourth merchants as having no offline locations. The third phase includes training a machine learning model via the economic trait data of the first, second, third, and fourth merchants. A first probability of having the offline location and a second probability of having no offline location are determined via the trained model and for each of the remaining merchants. Fifth merchants whose predicted first probability exceeds a first predefined threshold are labeled as having offline locations. Sixth merchants whose predicted second probability exceeds a second predefined threshold are labeled as having no offline locations. The fourth phase repeats the second and third phases. | 2021-07-01 |
20210201187 | CONTROLLING A QUANTUM COMPUTING DEVICE BASED ON PREDICTED OPERATION TIME - Systems, computer-implemented methods, and computer program products that can facilitate determining a state of a qubit are described. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a relation determining component that can determine relation of a status signal of a quantum computing device to a noise value of the quantum computing device. The system can further include an operation time estimator that can estimate an operation time for the quantum computing device based on the relation of the status signal to the noise value. | 2021-07-01 |
20210201188 | RESONATOR, OSCILLATOR, AND QUANTUM COMPUTER - A resonator, an oscillator, and a quantum computer capable of preventing oscillation conditions for generating a parametric oscillation from becoming complicated are provided. A resonator includes at least one loop circuit in which a first superconducting line, a first Josephson junction, a second superconducting line, and a second Josephson junction are connected in a ring shape, in which critical current values of the first and second Josephson junctions are different from each other. | 2021-07-01 |
20210201189 | CO-SCHEDULING QUANTUM COMPUTING JOBS - Systems, computer-implemented methods, and computer program products to facilitate quantum computing job scheduling are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a scheduler component that can determine a run order of quantum computing jobs based on one or more quantum based run constraints. The computer executable components can further comprise a run queue component that can store the quantum computing jobs based on the run order. In an embodiment, the scheduler component can determine the run order based on availability of one or more qubits comprising a defined level of fidelity. | 2021-07-01 |
20210201190 | MACHINE LEARNING MODEL DEVELOPMENT AND OPTIMIZATION PROCESS THAT ENSURES PERFORMANCE VALIDATION AND DATA SUFFICIENCY FOR REGULATORY APPROVAL - Machine learning model development and optimization tools are provided that ensure performance validation and data sufficiency for regulatory approval. According to an embodiment, a computer implemented method can comprise training a machine learning model to perform an inferencing task on an initial set of data samples included in a sample population. In various embodiments, the model can include a medical AI model. The method further comprises determining, by the system, subgroup performance measures for subgroups of the data samples respectively associated with different metadata factors, wherein the subgroup performance measures reflect performance accuracy of the machine learning model with respect to the subgroups. The method further comprises determining, by the system, whether the machine learning model meets an acceptable level of performance for deployment in a field environment based on whether the subgroup performance measures respectively satisfy a threshold subgroup performance measure. | 2021-07-01 |
20210201191 | METHOD AND SYSTEM FOR GENERATING MACHINE LEARNING BASED CLASSIFIERS FOR RECONFIGURABLE SENSOR - A sensor management system includes a cloud-based sensor configuration system and an electronic device. The electronic device includes a sensor unit. The sensor unit includes configuration data that controls operation of the sensor unit. The configuration data includes a classifier that classifies feature sets generated from sensor signals of the sensor unit. The electronic device sends sensor data to the cloud-based sensor configuration system. The cloud-based sensor configuration system analyzes the sensor data and generates a new classifier customized for the sensor unit based on the sensor data. The cloud-based sensor configuration system sends the new classifier to the electronic device. The electronic device replaces the classifier in the sensor unit with the new classifier. | 2021-07-01 |
20210201192 | METHOD AND APPARATUS OF GENERATING QUESTION-ANSWER LEARNING MODEL THROUGH REINFORCEMENT LEARNING - The present invention relates to a method of operating a question-answer model through reinforcement learning in an apparatus of generating an answer to a question. The method includes: sampling a latent variable from any passage by a first agent; extracting a question-answer dataset from the passage based on the latent variable; determining whether or not to apply the extracted question-answer dataset to learning of a question-answer model generating an answer to any question by a second agent; and applying a change value of performance of the question-answer model as a reward to the first agent and the second agent. | 2021-07-01 |
20210201193 | SYSTEM AND METHOD FOR MANAGING CLASSIFICATION OUTCOMES OF DATA INPUTS CLASSIFIED INTO BIAS CATEGORIES - A method includes receiving, by a processor, bias data categories. A data input from a user for classification in data categories is received. A classification machine learning model is utilized to classify the data input in at least one data category and determine a first confidence probability in a classification outcome. A bias filter machine learning model is utilized to determine a second confidence probability that the classification outcome of classifying the data input into the at least one data category is based on at least one bias characteristic associated with at least one bias data category. A gate machine learning model is utilized to determine when to output the classification outcome of classifying the data input into the at least one data category to a computing device of a user based at least in part on the first confidence probability, the second confidence probability, and a predefined bias threshold. | 2021-07-01 |
20210201194 | METHODS AND SYSTEMS TO CHARACTERIZE THE USER OF A PERSONAL CARE DEVICE | 2021-07-01 |
20210201195 | MACHINE LEARNING MODELS BASED ON ALTERED DATA AND SYSTEMS AND METHODS FOR TRAINING AND USING THE SAME - Data may be abstracted and/or masked prior to being provided to a machine learning model for training. A machine learning model may provide a confidence level associated with a result. If the confidence level is too high, the machine learning model or an application including the machine learning model may refrain from providing the result as an output. In some examples, the machine learning model may provide a “second best” result that has an acceptable confidence level. In other examples, an error signal may be provided as the output. In accordance with examples of the present disclosure, data may be abstracted and/or masked prior to being provided to a machine learning model for training and confidence levels of results of the trained machine learning model may be used to determine when a result should be withheld. | 2021-07-01 |
20210201196 | METHOD AND APPARATUS FOR TRAINING MACHINE READING COMPREHENSION MODEL, AND STORAGE MEDIUM - Embodiments of the present disclosure provide a method and an apparatus for training a machine reading comprehension model, and a storage medium. The method includes: training an initial model to generate an intermediate model based on sample data; extracting samples to be processed from the sample data according to a first preset rule; generating a noise text according to a preset noise generation method; adding the noise text to each of the samples to be processed respectively to generate noise samples; and performing correction training on the intermediate model based on the noise samples to generate the machine reading comprehension model. | 2021-07-01 |
20210201197 | EXPERIENCE ORCHESTRATION - Examples of a digital orchestration system are provided. The system may obtain orchestration data on a real-time basis. The system may identify a plurality of events for offering a plurality of user services across a plurality of user interaction stages. The system may identify a plurality of actions associated with each of the plurality of events. The system may create a recipe associated with each of the plurality of actions. The system may identify and implement the associated recipe. The system may create an event sequence for each of the plurality of user interaction stages. The system may create a user service sequence comprising the plurality of user services associated with the event sequence. The system may generate a user experience result based on the event sequence and the user service sequence. | 2021-07-01 |
20210201198 | METHOD, ELECTRONIC DEVICE, AND STORAGE MEDIUM FOR GENERATING NODE REPRESENTATIONS IN HETEROGENEOUS GRAPH - A method for generating node representations in a heterogeneous graph, an electronic device, and a non-transitory computer-readable storage medium, and relates to the field of machine learning technologies. The method includes: acquiring a heterogeneous graph; inputting the heterogeneous graph into a heterogeneous graph learning model to generate a node representation of each node in the heterogeneous graph, in which the heterogeneous graph learning model generates the node representation of each node by actions of: segmenting the heterogeneous graph into a plurality of subgraphs, in which each subgraph includes nodes of two types and an edge of one type between the nodes of two types; and generating the node representation of each node according to the plurality of subgraphs. | 2021-07-01 |
20210201199 | METHODS AND SYSTEMS FOR GROUPING INFORMED ADVISOR PAIRINGS - A system for customizing informed advisor pairings, the system including a computing device. The computing device is configured to identify a user feature wherein the user feature contains a user biological extraction. The computing device is configured to generate using element training data and using a first machine-learning algorithm a first machine-learning model that outputs advisor elements. The computing device receives an informed advisor element relating to an informed advisor. The computing device determines using output advisor elements whether an informed advisor is compatible for a user. | 2021-07-01 |
20210201200 | DATA CLASS ANALYSIS METHOD AND APPARATUS - Methods, apparatus, and system determine if a data class in a plurality of data classes is separable, such as by determining an average intra-class similarity within each data class, inter-class similarity across all data classes in the plurality of data classes, and determining separability based on the average intra-class similarity relative to the inter-class similarity. Data classes determined to be highly variable may be removed. Pair(s) of data classes not separable from one another may be combined into one class or one of the data classes may be dropped. A hardware accelerator, which may comprise artificial neurons, accelerate performance of the data analysis. | 2021-07-01 |
20210201201 | METHOD AND APPARATUS FOR DETERMINING STORAGE LOAD OF APPLICATION - The present disclosure describes a method and apparatus for determining a storage load of an application. The method includes acquiring statistic data of a storage load, inputting the statistic data, and determining read/write request tracking data of the storage load. The acquired statistic data of a storage load may be generated by an application in a predetermined time period. The determined read/write request tracking data of the storage load may be during the predetermined time period through the pre-trained machine learning model. The statistic data may include global information corresponding to read/write requests generated by the application in a predetermined time period. The read/write request tracking data may be information of each read/write request generated by the application in a predetermined time period. | 2021-07-01 |
20210201202 | SYSTEM AND METHOD FOR TRAINING A MACHINE LEARNING MODEL BASED ON USER-SELECTED FACTORS - In certain embodiments, graphical representations of factors for risk adjustment of a key performance indicator may be presented, and a user selection of a factor subset may be received. Training information may be provided as input to a machine learning model to predict values of the key performance indicator for the selected factor subset. The training information may indicate values of the factor subset associated with a provider. Reference feedback may then be provided to the machine learning model, the reference feedback comprising historic values of the key performance indicator for the provider based on the values of the factor subset that are associated with the provider. The machine learning model may then update portions of the machine learning model based on the reference feedback. The values of the factor subset may then be provided to the updated machine learning model to obtain predicted values of the key performance indicator. | 2021-07-01 |
20210201203 | ENTITY ANALYSIS SYSTEM - A method for building a factual database of concepts and entities that are related to the concepts through a learning process. Training content (e.g., news articles, books) and a set of entities (e.g., Bill Clinton and Barack Obama) that are related to a concept (e.g., Presidents) is received. Groups of words that co-occur frequently in the textual content in conjunction with the entities are identified as templates. Templates may also be identified by analyzing parts-of-speech patterns of the templates. Entities that co-occur frequently in the textual content in conjunction with the templates are identified as additional related entities (e.g., Ronald Reagan and Richard Nixon). To eliminate erroneous results, the identified entities may be presented to a user who removes any false positives. The entities are then stored in association with the concept. | 2021-07-01 |
20210201204 | LEARNING DATA GENERATION DEVICE, LEARNING DATA GENERATION METHOD, AND PROGRAM - A training data generation device ( | 2021-07-01 |
20210201205 | METHOD AND SYSTEM FOR DETERMINING CORRECTNESS OF PREDICTIONS PERFORMED BY DEEP LEARNING MODEL - The disclosure relates to method and system for determining correctness of predictions performed by deep learning model. The method includes extracting a neuron activation pattern of a layer of the deep learning model with respect to the input data, and generating an activation vector based on the extracted neuron activation pattern. The method further includes determining the correctness of the prediction performed by the deep learning model with respect to the input data using a prediction validation model and based on the activation vector. The prediction validation model is a machine learning model that has been generated and trained using training activation vectors derived from correctly predicted test dataset and incorrectly predicted test dataset of the deep learning model. The method further includes providing the correctness of the prediction performed by the deep learning model with respect to the input data for subsequent rendering or subsequent processing. | 2021-07-01 |
20210201206 | Optimizing Data Processing and Feature Selection for Model Training - A method includes training a first machine learning model based on a set of training data and based on the training, determining a first performance metric corresponding to the first machine learning model. The method also includes determining one or more past performance metrics corresponding to one or more machine learning models that were previously trained based on the set of training data. Based on the first performance metric and the one or more past performance metrics, the method includes automatically selecting a second machine learning model to train based on the set of training data. | 2021-07-01 |
20210201207 | Hierarchy Optimization Method for Machine Learning - A method includes receiving a set of training data and selecting a first machine learning platform based on a first optimization function that metrics past machine learning platforms used for training on the set of training data. The method also includes selecting a first algorithm supported by the first machine learning platform based on a second optimization function that metrics past algorithms used for training on the set of training data. Further, the method includes determining one or more hyperparameters supported by the first algorithm based on a third optimization function that metrics past combinations of hyperparameters from the set of hyperparameters used for training on the set of training data. The method also includes training a machine learning model on the set of training data using the first machine learning platform, the first algorithm, and the one or more hyperparameters. | 2021-07-01 |
20210201208 | SYSTEM AND METHODS FOR MACHINE LEARNING TRAINING DATA SELECTION - A system and method are disclosed for running a plurality of simulation tests on a first machine learning model to obtain a plurality of results that are each produced during a respective simulation test, the first machine learning model gradually trained using first training data historically collected over a period of time, the first training data comprising a plurality of first training data sets each including a subset of first training inputs and first target outputs associated with one of a plurality of points in time during the period of time, determining a simulation test of the plurality of simulation tests at which corresponding results of the first machine learning model satisfy a threshold condition, wherein the threshold condition is based on historical data at a first point in time of the plurality of points in time, identifying a first training data set of the plurality of first training data sets on which the first machine learning model used during the determined simulation test was trained, wherein the first training data set on which the first machine learning model used during the determined simulation test was trained is associated with one or more second points in time that precede the first point in time, and determining a subset of target outputs from the identified first training data set on which the first machine learning model used during the determined simulation test was trained, the determined subset of first target outputs to define an amount of second training data to be sufficient to train a second machine learning model. | 2021-07-01 |
20210201209 | METHOD AND SYSTEM FOR SELECTING A LEARNING MODEL FROM AMONG A PLURALITY OF LEARNING MODELS - The invention relates to a method for selecting a learning model defined in particular by parameters and hyperparameters from among a plurality of learning models, implemented by a computing device, said computing device comprising a model selection module and a model repository including a plurality of series of instructions each corresponding to a learning model and each including hyperparameter values, said method comprising a step of selecting a model when the prediction performance value and the classification value are greater than predetermined second threshold and the hyperparameter value is greater than a predefined threshold value. | 2021-07-01 |
20210201210 | BOOKING MANAGEMENT SYSTEM - Secure authentication and delayed transaction processing for booking management systems is provided. Third-party services partner with booking management systems to aggregate and list offerings of the third-party services in a digestible display on a one-stop platform. A booking management system can manage the authentication of payment card information on behalf of any number of such third-party services. The booking management system can maintain and process authentication information associated with traveler payment cards, and provide virtual payment information to the third-party services for delayed transactions. The third-party services may later initiate the delayed transactions using the virtual payment information, without being required to perform authentication processing on the traveler payment card information maintained by the booking management system. | 2021-07-01 |
20210201211 | INFORMATION PROCESSING SYSTEM, INFORMATION PROCESSING APPARATUS, AND NON-TRANSITORY COMPUTER READABLE MEDIUM - An information processing system includes a processor configured to cancel a first reservation of a place made by a first person for a time window in a case where a predetermined condition is met, and in a case where the first reservation has been cancelled, transmit information indicating that the place has become available to a second person who made a second reservation of the place for a time window different from the time window of the first reservation. | 2021-07-01 |
20210201212 | SYSTEM AND METHOD FOR RANKING IN ALTERNATIVE DESTINATION RECOMMENDATION ON RIDESHARING PLATFORMS - Methods, systems, and apparatus, including computer programs encoded on computer storage media, for ranking alternative destination recommendations are provided. One of the methods comprises: receiving a trip request comprising an origin and an initial destination; determining one or more alternative destinations based on one or more features of the initial destination; for each of the alternative destinations, determining, based on a machine-learning classifier, a probability for the rider to select a trip option from the origin to the alternative destination, where the machine-learning classifier is trained to accept input comprising one or more attributes of the trip option and generate output comprising the probability for the rider to select the alternative destination; generating a score for each of the alternative destinations based at least on the probability; and ranking, by the computing device of the ridesharing platform, the alternative destinations based on the scores. | 2021-07-01 |
20210201213 | RESERVATION AND WAITLIST MANAGEMENT USING PRECISION TABLE TURN-TIME ANALYSIS - A system and method for dynamic table turn-time estimation, waitlist management, and reservation allocation. The system for turn-table estimation incorporates data associated with the restaurant and its customers and analyzes it to accurately predict table turn-times, dynamically adjusting the predictions on a table-by-table basis as the data changes. The system for reservation management similarly ingests data associated with the restaurant and its customers and analyzes it to predict table availability, assign reservations on a table-by-table basis, and dynamically re-allocate table-by-table reservations as necessary to improve restaurant operations. The system for waitlist management likewise ingests data associated with the restaurant and its customers to dynamically predict estimated wait times, maximize table occupancy, assign waitlist spots to customers who have not yet arrived at the restaurant, and re-order the waitlist as customers arrive and/or leave the restaurant. | 2021-07-01 |
20210201214 | SYSTEM AND METHOD FOR RECOMMENDING BIDDING BUNDLE OPTIONS IN BIDDING-BASED RIDESHARING - Methods, systems, and apparatus, including computer programs encoded on computer storage media, for bidding-based ridesharing are described. One exemplary method includes: obtaining a price range of a trip request for a rider; determining a plurality of trip setting candidates based on the trip request, and a plurality of price candidates based on the price range; generating a plurality of bidding bundle option candidates based on a plurality of combinations of the plurality of trip setting candidates and the plurality of price candidates; determining, based on a trained machine-learning classifier, a selection probability for the rider to select each of the plurality of bidding bundle option candidates; and ranking the plurality of bidding bundle option candidates based on corresponding probabilities; and transmitting one or more of the plurality of bidding bundle option candidates with top selection probabilities to a terminal device associated with the rider. | 2021-07-01 |
20210201215 | RIDE-SHARE SUPPORT SYSTEM AND RIDE-SHARE SUPPORT METHOD - A ride-share support system includes a getting-on/getting-off point information acquiring unit configured to acquire first getting-on/getting-off point information including a getting-on point or a getting-off point of a first ride-share applicant and second getting-on/getting-off point information including a getting-on point or a getting-off point of a second ride-share applicant, a getting-on/getting-off point distance calculating unit configured to calculate a distance between getting-on/getting-off points which is a distance between the getting-on point or the getting-off point of the first ride-share applicant and the getting-on point or the getting-off point of the second ride-share applicant, and a ride-share matching unit configured to determine ride-sharers of a vehicle by matching a plurality of ride-share applicants while excluding combination of the first ride-share applicant and the second ride-share applicant in a case where the distance between getting-on/getting-off points is equal to or less than a predetermined distance. | 2021-07-01 |
20210201216 | SPORTS AND CONCERT EVENT TICKET PRICING AND VISUALIZATION SYSTEM - A system and method for displaying seat inventory at a venue and facilitating planning of ticket prices for events at the venue is presented. Methods to predict total revenue for an event are described. Also presented are systems and methods for determining at what price and when to release so-called ‘flex’ price tickets during an on-sale using the sales velocity and sales/inquiry ratios. Determining demand of seats from secondary markets is also described with methods to use the demand for either re-pricing the seats in the primary market or presenting ‘best value’ seats to a prospective purchaser. Outputted tickets can be used to initiate entry processes to a gate structure of a venue by unlocking the gate structure or denying access through the gate structure of an invalid outputted ticket. | 2021-07-01 |
20210201217 | RE-CREATING THE SOUND QUALITY OF AN AUDIENCE LOCATION IN A PERFORMANCE SPACE - A computer-based method for reproducing a sound quality in a performance space is provided. The method includes accessing a multi-dimensional sound signature, stored in a computer, for an audience location in a first performance space. The method further includes receiving sound data from a sound input device in a second performance space. The method further includes modifying the sound data to match a sound characteristic of the multi-dimensional sound signature. The method further includes transmitting the modified sound data through a sound output device in the second performance space. | 2021-07-01 |
20210201218 | BAGGAGE DELIVERY SYSTEM AND METHOD - A system and method of delivering a bag to a final destination are described. The system receives a customer request to re-route the bag, the customer request including a final destination and a unique identifier associated with customer-related information. The system identifies the bag based on the customer-related information and re-routes the bag from a first destination to a further destination. The system sends a delivery request to a delivery service provider for the bag to be delivered from the further destination to the final destination. | 2021-07-01 |
20210201219 | INFORMATION PROCESSING APPARATUS, RISK FORECASTING METHOD, AND PROGRAM - An information processing apparatus ( | 2021-07-01 |
20210201220 | METHOD AND SYSTEM FOR ECOLOGICAL OPERATION OF TOTAL PHOSPHORUS EXPORT OF CASCADE HYDROPOWER STATION - A method and system for ecological operation of total phosphorus (TP) export of a cascade hydropower station are provided. The method includes: integrating total power generation and TP export of the cascade hydropower station into a single operation objective, and obtaining an operation objective under different weight ratios; optimizing the obtained operation objective; and obtaining a water level operation process corresponding to total power generation and TP export under the current operation objective through the optimized operation objective. The present invention alleviates an ecological and environmental problem caused by the construction of the hydropower station from a water quality mechanism, and can be widely used, for example, in the ecological optimal operation of cascade hydropower stations in a river basin. | 2021-07-01 |
20210201221 | A SPACE ALLOCATION SYSTEM FOR ALLOCATING SPACE TO OBJECTS WITH MULTI-VARIATE CHARACTERISTICS, AND A METHOD THEREOF - The present invention discloses a space allocation system and a method for allocating space to objects with multi-variate characteristics. In the present invention, intra-container allocation and/or inter-container allocation is performed to generate a combination in which the objects can be placed in one or more pre-defined storage spaces in a plurality of storage containers. The generated combination is further optimized for efficient space allocation to objects with multi-variate characteristics. | 2021-07-01 |
20210201222 | METHOD AND SYSTEM FOR ESTABLISHING AND MANAGING A VIRTUAL FLEET OF CONNECTED VEHICLES - A method and a system for establishing and managing a virtual fleet of connected vehicles are provided herein. The system may include: a computer processor associated with an automotive data server; a data storage associated with said automotive data server, wherein the computer processor is configured to obtain a request to form a virtual fleet being an association of two or more connected vehicles, wherein each of the connected vehicles is communicating with said automotive data server that manages a plurality of connected vehicles wherein automotive data thereof is stored in an anonymized form on said data storage, and wherein the computer processor is further configured to establish a virtual fleet of said two or more connected vehicles, responsive to said request, wherein managing said virtual fleet is carried out via said automotive data server. | 2021-07-01 |
20210201223 | Farming Data Collection and Exchange System - Embodiments of the present invention provide a passive relay device for farming vehicles and implements, as well as an online farming data exchange, which together enable capturing, processing and sharing farming operation data generated during combined use of the farming vehicle and farming implement at a farming business. The farming operation data includes detailed information about individual farming operations, including without limitation the type of farming operation, the location of the farming operation, the travel path for the farming operation, as well as operating parameters and operating events occurring while the farming operation is performed. | 2021-07-01 |
20210201224 | Farming Data Collection and Exchange System - Embodiments of the present invention provide a passive relay device for farming vehicles and implements, as well as an online farming data exchange, which together enable capturing, processing and sharing farming operation data generated during combined use of the farming vehicle and farming implement at a farming business. The farming operation data includes detailed information about individual farming operations, including without limitation the type of farming operation, the location of the farming operation, the travel path for the farming operation, as well as operating parameters and operating events occurring while the farming operation is performed. | 2021-07-01 |
20210201225 | Farming Data Collection and Exchange System - Embodiments of the present invention provide a passive relay device for farming vehicles and implements, as well as an online farming data exchange, which together enable capturing, processing and sharing farming operation data generated during combined use of the farming vehicle and farming implement at a farming business. The farming operation data includes detailed information about individual farming operations, including without limitation the type of farming operation, the location of the farming operation, the travel path for the farming operation, as well as operating parameters and operating events occurring while the farming operation is performed. | 2021-07-01 |
20210201226 | SYSTEMS AND METHODS FOR PREDICTING AND HANDLING SLACK PERIODS - A system for predicting slack periods is provided. The system predicts and detects time intervals for an entity (e.g., a contact center) where the amount of available work (e.g., call volume) is less than what can be handled by the number of employees (e.g., agents) that are scheduled to work during the time intervals. These detected intervals are referred to herein as “slack periods”. When slack periods are predicted or detected, the system encourages the employees to perform QM tasks during the slack periods and can even automatically schedule the QM tasks for the employees. To further encourage the completion of QM tasks during slack periods, the system can provide incentives for the employees to complete the QM tasks. | 2021-07-01 |
20210201227 | SYSTEM AND PROCESS FOR CREATING A PROCESS FLOW CHART HAVING IMPRINTED ANALYTICS - A system and process for creating and displaying a process flow chart wherein analytics are connected to and imprinted within a process flow chart next to an associated step within the subject process being visually displayed. | 2021-07-01 |
20210201228 | SYSTEM AND METHOD FOR IDENTIFYING COMPARABLES - The invention relates to a computer implemented system and method for identification of comparables. The method may comprise: (a) receiving input data from a plurality of data sources for a comparable, (b) generating labeled training data for a function classifier by labeling historical search results for comparables, (c) generating probabilistic training data for the primary product and service classifiers, (d) training the primary product and service classifiers using the labeled training data and the probabilistic training data, (e) determining the functions, products, services, and risks of the comparable using the corresponding classifiers, (f) receiving attributes of a tested party, (g) applying a scoring algorithm to calculate a similarity score for the comparable, (h) generating a recommendation to accept the comparable, reject the comparable, or give additional scrutiny to determine acceptability, and (i) automatically providing a written justification for the decision to accept or to reject the comparable. | 2021-07-01 |
20210201229 | CYBERSECURITY QUANTITATIVE ANALYSIS SOFTWARE AS A SERVICE - A mathematically accurate cybersecurity risk analysis platform which is able to quantify the effects of cybersecurity risk in several inter-related dimensions, and accomplish this within established regulatory and audit-control frameworks, providing risk analyses which are not simply the function of professional judgement or expert opinion. The specific dimensions are between Threats, Risks, Vulnerabilities and Capabilities. | 2021-07-01 |
20210201230 | SYSTEM AND METHOD FOR FULFILLING WORK TASKS VIA MODULAR AUTONOMOUS VEHICLES - A method for fulfilling a work task request using a modular autonomous vehicle is provided. The method includes transmitting a work task request specifying a work request to be performed by the modular autonomous vehicle, and identifying equipment required for performing the work task request. Once the work task request is stored at a server, the method further includes identifying information of the equipment required for performing the work task request, and determining whether equipment of the modular autonomous vehicle corresponds to the equipment required for assigning the work task to the modular autonomous vehicle. Method also includes, upon receiving in-cabin sensing data, assigning or denying the work task to the modular autonomous vehicle for performance of the work task. | 2021-07-01 |
20210201231 | FOOD ORDER MANAGEMENT SYSTEM AND METHOD THEREOF - A food order management system which coordinates the preparation and delivery of food orders at a business establishment using components which predict food order preparation times, delivery times, and other critical time periods in the food ordering process based on a variety of factors, internal to the business establishment and external to the business establishment, that may affect the food ordering process. | 2021-07-01 |
20210201232 | PLANNING SYSTEM USING SPATIAL-BASED VISUALIZATION AIDS - A presentation, organizational and analytical system comprises includes an information database component that allows project specific ability to organize data and observations. A computer system constructs visual overlays of spatially mapped features, quantitative and qualitative attributes and produces an analytical framework where the attributes of any spatial location change in response to changes in the features of a context. | 2021-07-01 |