45th week of 2019 patent applcation highlights part 49 |
Patent application number | Title | Published |
20190340458 | SYSTEM AND METHODS FOR AUTOMATIC SOLAR PANEL RECOGNITION AND DEFECT DETECTION USING INFRARED IMAGING - Methods and systems are provided for detecting a defect in a solar panel. The method includes initially imaging, via an infrared camera, a group of solar panels. Then, identifying, via a computer system configured for solar panel defect detection, the individual solar panels in the group of solar panels. Finally, identifying, via evaluation of an infrared image obtained by the infrared camera, a defect in at least one of the group of solar panels. | 2019-11-07 |
20190340459 | ANALYZING STORAGE SYSTEMS USING MACHINE LEARNING SYSTEMS - A method is used in analyzing a storage system using a machine learning system. Data gathered from information associated with operations performed in a storage system is analyzed. The storage system is comprised of a plurality of components. A bitmap image is created based on the gathered data, where at least one of the plurality of components is represented in the bitmap image. The machine learning system is trained using the bitmap image, where the bitmap image is organized to depict the plurality of components of the storage system. | 2019-11-07 |
20190340460 | TEXT LINE DETECTING METHOD AND TEXT LINE DETECTING DEVICE - A text line detecting method includes: performing a preprocessing operation on an image to be detected to generate connected domains; performing a filtering operation on the connected domains to obtain connected domains that meet a preset requirement; and perform a text line recognizing operation according to a processing result. In the text line detecting method according to the embodiments of the present invention, by means of performing the preprocessing operation and the filtering operation on the image to be detected to obtain the connected domains that meet the preset requirement, and then performing the text line recognizing operation according to the processing result,detection and recognition accuracy of a text line are improved, and detection and recognition efficiencies of the text line are improved. | 2019-11-07 |
20190340461 | LOCATING METHOD AND SYSTEM - A method and system for locating a target object in a target scene. The method may include obtaining a depth image of the target scene. The depth image may include a plurality of pixels. The method may also include, for each of the plurality of pixels of the depth image, determining a first target coordinate under a target coordinate system. The method may further include generating a marking image according to the depth image and the first target coordinates of the plurality of pixels in the depth image. The marking image may represent potential target objects in the depth image. The method may also include determining a locating coordinate of the target object under the target coordinate system according to the marking image. | 2019-11-07 |
20190340462 | ITERATIVELY APPLYING NEURAL NETWORKS TO AUTOMATICALLY IDENTIFY PIXELS OF SALIENT OBJECTS PORTRAYED IN DIGITAL IMAGES - The present disclosure relates to systems, method, and computer readable media that iteratively apply a neural network to a digital image at a reduced resolution to automatically identify pixels of salient objects portrayed within the digital image. For example, the disclosed systems can generate a reduced-resolution digital image from an input digital image and apply a neural network to identify a region corresponding to a salient object. The disclosed systems can then iteratively apply the neural network to additional reduced-resolution digital images (based on the identified region) to generate one or more reduced-resolution segmentation maps that roughly indicate pixels of the salient object. In addition, the systems described herein can perform post-processing based on the reduced-resolution segmentation map(s) and the input digital image to accurately determine pixels that correspond to the salient object. | 2019-11-07 |
20190340463 | SYSTEM AND METHOD FOR MAGNETIC RESONANCE FINGERPRINTING USING A PLURALITY OF PULSE SEQUENCE TYPES - A method for performing magnetic resonance fingerprinting includes acquiring a plurality of MR image datasets using at least two pulse sequence types, the plurality of MR image datasets representing signal evolutions for image elements in a region of interest, comparing the plurality of MR image datasets to a dictionary of signal evolutions to identify at least one parameter of the MR image datasets and generating a report indicating the at least one parameter of the MR image datasets. | 2019-11-07 |
20190340464 | Systems and Methods for Providing an Image Classifier - Systems and methods are provided for image classification using histograms of oriented gradients (HoG) in conjunction with a trainer. The efficiency of the process is greatly increased by first establishing a bitmap which identifies a subset of the pixels in the HoG window as including relevant foreground information, and limiting the HoG calculation and comparison process to only the pixels included in the bitmap. | 2019-11-07 |
20190340465 | SYSTEM AND METHOD OF PREDICTING HUMAN INTERACTION WITH VEHICLES - Systems and methods for predicting user interaction with vehicles. A computing device receives an image and a video segment of a road scene, the first at least one of an image and a video segment being taken from a perspective of a participant in the road scene and then generates stimulus data based on the image and the video segment. Stimulus data is transmitted to a user interface and response data is received, which includes at least one of an action and a likelihood of the action corresponding to another participant in the road scene. The computing device aggregates a subset of the plurality of response data to form statistical data and a model is created based on the statistical data. The model is applied to another image or video segment and a prediction of user behavior in the another image or video segment is generated. | 2019-11-07 |
20190340466 | System and Method for Generating and Processing Training Data - The present disclosure provides generally for a system and method for generating and processing training data, such as when access to training data for a form may be insufficient to effectively train an artificial entity to process the associated form. According to the present disclosure, a computer system may identify and distinguish between content data and background data from a small set of data, such as a handful of authentic forms. The computer system may remove unwanted text, noise, or other portions of an image to create an empty, blank, or scrubbed form with no data. The system may generate training examples or synthetic data from this form, which may be incorporated into training data. In some embodiments, the synthetic data may be generated into a form. The computer system may synthetically generate variations in the original form to simulate variations that may exist with expected incoming data. | 2019-11-07 |
20190340467 | FACILITY LEVEL TRANSACTION-ENABLING SYSTEMS AND METHODS FOR PROVISIONING AND RESOURCE ALLOCATION - The present disclosure describes transaction-enabling systems and methods. A system can include a facility having a core task and a controller. The controller may include a facility description circuit to interpret historical facility parameter values and corresponding outcome values. A facility prediction circuit operates an adaptive learning system to train a facility resource allocation circuit in response to the historical facility parameter values and corresponding outcome values. The facility description circuit further interprets a plurality of present state facility parameter values and the trained facility resource allocation circuit adjusts facility resource values in response. | 2019-11-07 |
20190340468 | Focus-Weighted, Machine Learning Disease Classifier Error Prediction for Microscope Slide Images - A method is described for generating a prediction of a disease classification error for a magnified, digital microscope slide image of a tissue sample. The image is composed of a multitude of patches or tiles of pixel image data. An out-of-focus degree per patch is computed using a machine learning out-of-focus classifier. Data representing expected disease classifier error statistics of a machine learning disease classifier for a plurality of out-of-focus degrees is retrieved. A mapping of the expected disease classifier error statistics to each of the patches of the digital microscope slide image based on the computed out-of-focus degree per patch is computed, thereby generating a disease classifier error prediction for each of the patches. The disease classifier error predictions thus generated are aggregated over all of the patches. | 2019-11-07 |
20190340469 | TOPIC-GUIDED MODEL FOR IMAGE CAPTIONING SYSTEM - Techniques are provided for training and operation of a topic-guided image captioning system. A methodology implementing the techniques according to an embodiment includes generating image feature vectors, for an image to be captioned, based on application of a convolutional neural network (CNN) to the image. The method further includes generating the caption based on application of a recurrent neural network (RNN) to the image feature vectors. The RNN is configured as a long short-term memory (LSTM) RNN. The method further includes training the LSTM RNN with training images and associated training captions. The training is based on a combination of: feature vectors of the training image; feature vectors of the associated training caption; and a multimodal compact bilinear (MCB) pooling of the training caption feature vectors and an estimated topic of the training image. The estimated topic is generated by an application of the CNN to the training image. | 2019-11-07 |
20190340470 | DEEP LEARNING MEDICAL SYSTEMS AND METHODS FOR IMAGE RECONSTRUCTION AND QUALITY EVALUATION - Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image. | 2019-11-07 |
20190340471 | SENSING SYSTEM, SENSOR NODE DEVICE, SENSOR MEASUREMENT VALUE PROCESSING METHOD, AND PROGRAM - A sensing system including multiple sensor node devices and an analysis device, wherein: each of the multiple sensor node devices has a sensor that measures a measurement target and acquires data values, a learning unit that, based on the data values, learns a model used to estimate the data values at an installation position of the sensor, and a communication unit that transmits learning result data indicating a learning result from the learning unit; and the analysis device has a spatial analysis unit that estimates a spatial distribution of the data values based on the learning result data from each of the multiple sensor node devices and the installation positions of the respective sensor node devices. | 2019-11-07 |
20190340472 | SYSTEM OF RECOGNIZING IDENTITY OF OBJECT AND METHOD OF AUTOMATICALLY RECOGNIZING IDENTITY OF OBJECT - A system of recognizing identity of object and method of automatically recognizing identity of object are provided. The method is to shoot a monitoring region for obtaining a monitoring image, recognize an object image in the monitoring image, scan the monitoring region for retrieving each identity data of each wireless badge, determines each image position of each object image in the monitor region, determine each badge position of each wireless badge in the monitor region, and link the object image and the identity data together when the positions match with each other. The system can pair the object image with the identity data instantly, determine whether the object is unregistered, and effectively save the time and human resources required by artificially pairing the object images with the identity data. | 2019-11-07 |
20190340473 | PATTERN RECOGNITION METHOD OF AUTOANTIBODY IMMUNOFLUORESCENCE IMAGE - A pattern recognition method of the immunofluorescence images of autoantibody identification is disclosed. The method includes the following steps: inputting a plurality of original cell immunofluorescence images; conducting an operation of a plurality of convolutional neural networks by a processor, the plurality of convolutional neural networks include a convolution layer, a pooling layer and an inception layer for capturing the plurality of convolution features; conducting a judgment process to obtain the proportions of the antinuclear antibodies morphological patterns; and outputting the recognition results. | 2019-11-07 |
20190340474 | TRANSLATION AND DISPLAY OF TEXT IN PICTURE - A method performed by a mobile terminal may include receiving an image that includes text, translating the text into another language and superimposing and displaying the translated text over the received image. | 2019-11-07 |
20190340475 | PROTECTING PRIVATE INFORMATION PROVIDED ON A TRANSACTION CARD AND/OR A DOCUMENT WITH A REFLECTIVE ELEMENT - A transaction card includes a card body, where the card body includes a surface with a first surface area. The surface of the card body includes private information that encompasses a second surface area of the surface, and the second surface area is less than the first surface area. The transaction card includes a reflective element that is applied to the surface of the card body, and includes a third surface area. The third surface area is based on the first surface area or the second surface area, and the third surface area enables the reflective element to reflect light away from the private information. | 2019-11-07 |
20190340476 | SYSTEM AND METHOD FOR GENERATING A DYNAMIC MACHINE READABLE CODE - Aspects of the present disclosure involve systems, methods, devices, and the like for generating dynamic machine readable codes. In one embodiment, a system is introduced that enables the analysis of user information for the generation of the dynamic machine readable code. In response to the analysis, using middleware on a multi-tier system, user information is embedded onto the dynamic machine readable code. The embedded user information can be captured during the transaction enabling the presentation of customized content which can be used to provide a user friendly interface for the transacting while detecting incorrect account usage. In another embodiment, in conjunction with the dynamic machine readable code, additional user and/or device features are captured during the processing of a transaction such that the combination facilitate fraudulent activity detection. | 2019-11-07 |
20190340477 | INFORMATION PROCESSING METHOD, DEVICE AND STORAGE MEDIUM - A method of an information processing device is provided. The method includes: dividing, by the at least one processor, a predetermined region used for generating a two-dimensional code into an image region and an encoding region that does not overlap the image region; setting, by the at least one processor, a first image in the image region; and setting, by the at least one processor, at least one code element used for storing data information in the encoding region. | 2019-11-07 |
20190340478 | CONFIGURING A SET OF APPLETS ON A BATTERY-LESS TRANSACTION CARD - A transaction card may power on the transaction card using electric current induced from an interaction of the transaction card with an electromagnetic field. The transaction card may establish a communication with a device. The communication may indicate that the transaction card has powered. The transaction card may receive, from the device, a set of instructions to configure a set of applets on the transaction card after notifying the device that the transaction card has powered on. The set of applets to be configured may be related to completing one or more different transactions. The set of applets to be configured may be different than another set of applets already configured on the transaction card. The transaction card may configure the set of applets on the transaction card according to the set of instructions after receiving the set of instructions. | 2019-11-07 |
20190340479 | METHOD AND APPARATUS FOR PROVIDING A COMMUNICATIONS SERVICE USING A LOW POWERED RADIO TAG - A radio tag comprising a first radio and a second radio and a method for providing a communications service are disclosed. For example, the method comprises entering, by a processor of the radio tag, an active state of the radio tag and activating the second radio when a wake-up signal is received, where the second radio draws power from a power source, transmitting, by the processor of the radio tag, data to a device or receiving the data from the device when the radio tag is in the active state, and deactivating, by the processor of the radio tag, the second radio and entering an idle state when the wake-up signal is no longer being received, where only the first radio draws power from the power source for listening for the wake-up signal in the idle state. | 2019-11-07 |
20190340480 | CHEMICAL AND PHYSICAL SENSING WITH A READER AND RFID TAGS - A method of detecting a stimulus can include detecting an output from a radio frequency identification tag including a sensor. A smartphone-based sensing strategy can use chemiresponsive nanomaterials integrated into the circuitry of commercial Near Field Communication tags to achieve non-line-of-sight, portable, and inexpensive detection and discrimination of gas phase chemicals (e.g., ammonia, hydrogen peroxide, cyclohexanone, and water) at part-per-thousand and part-per-million concentrations. | 2019-11-07 |
20190340481 | SECURE CONTACTLESS PAYMENT METHOD AND DEVICE WITH ACTIVE ELECTRONIC CIRCUITRY - A contactless payment device including a wireless communication device; a power source; a processor coupled to the power source; an accelerometer communicatively coupled to the processor and the power source; and an actuator communicatively coupled to the wireless communication device and the processor. The actuator is configured to activate the wireless communication device when the actuator is set in a closed state, and deactivate the wireless communication device when the actuator is set in an open state. The processor is configured to receive an incoming signal from the accelerometer; determine whether the incoming signal corresponds to a pre-programmed signal corresponding to an enabling gesture; and set the actuator in the closed state for a time interval, when the incoming signal corresponds to the enabling gesture. | 2019-11-07 |
20190340482 | TWO-PIECE TRANSACTION CARD CONSTRUCTION - The disclosed embodiments generally relate to transaction card constructions, and particularly, to a two-piece transaction card construction. Disclosed embodiments include a generally planar first card component including a first surface and a generally planar second card component including a second surface wherein the first card component is separate from the second card component. In disclosed embodiments, for example, the generally planar first card component and the generally planar second card component may be configured such that one forms a cavity and the other forms an inlay component configured to be seated within the cavity. In other embodiments the generally planar first card component and the generally planar second card component may be configured such that one forms a container and the other forms a lid configured to close the container. | 2019-11-07 |
20190340483 | Package Sealing Tape Types With Varied Transducer Sampling Densities - A low-cost, multi-function adhesive tape platform with a form factor that unobtrusively integrates one or more transducers and one or more wireless communication devices in an adhesive product system. In an aspect, the adhesive product system integrates transducer and wireless communication components within a flexible adhesive structure in a way that not only provides a cost-effective platform for interconnecting, optimizing, and protecting the constituent components but also maintains the flexibility needed to function as an adhesive product that can be deployed seamlessly and unobtrusively into various applications and workflows, including sensing, notification, security, and object tracking applications, and asset management workflows such as manufacturing, storage, shipping, delivery, and other logistics associated with moving products and other physical objects. | 2019-11-07 |
20190340484 | PAYMENT CARDS AND DEVICES WITH DISPLAYS, CHIPS, RFIDS, MAGNETIC EMULATORS, MAGNETIC ENCODERS, AND OTHER COMPONENTS - A payment card (e.g., credit and/or debit card) or other card or device (e.g., mobile telephone) is provided with a magnetic emulator operable to communicate data to a magnetic stripe read-head. User interfaces are provided in a number of different configurations in order to achieve a number of different functionalities. | 2019-11-07 |
20190340485 | METHOD AND SYSTEM FOR GENERATING A RESPONSIVE COMMUNICATION FROM A CHATBOT TO NETWORK WITH PLANT MONITORING SYSTEMS - A plant monitoring system includes one or more internet of things (IOT) sensors to detect conditions associated with a plant; a database coupled to the sensors; and a chatbot coupled to the database to answer queries from a user about the plant condition. | 2019-11-07 |
20190340486 | PERFORMING MULTIPLY AND ACCUMULATE OPERATIONS IN NEURAL NETWORK PROCESSOR - Embodiments relate to a neural processor circuit including a plurality of neural engine circuits, a data buffer, and a kernel fetcher circuit. At least one of the neural engine circuits is configured to receive matrix elements of a matrix as at least the portion of the input data from the data buffer over multiple processing cycles. The at least one neural engine circuit further receives vector elements of a vector from the kernel fetcher circuit, wherein each of the vector elements is extracted as a corresponding kernel to the at least one neural engine circuit in each of the processing cycles. The at least one neural engine circuit performs multiplication between the matrix and the vector as a convolution operation to produce at least one output channel of the output data. | 2019-11-07 |
20190340487 | Neural Network Architecture for Performing Medical Coding - Mechanisms are provided to implement a medical coding engine to perform medical coding using a neural network architecture that leverages hierarchical semantics between medical concepts. The medical coding engine configures a medical coding neural network to comprise an first layer of nodes comprising preferred terminology (PT) nodes, a second layer comprising lowest level terminology (LLT) nodes, and a third layer comprising weighted values for each connection between each PT node and each LLT node forming a PT node/LLT node connection. Responsive to receiving an adverse event from a cognitive system, a PT node is identified in the first layer associated with a citation from the adverse event. One or more nodes are identified from the second layer based on the identification PT node and a weight associated with the PT node/LLT node connection. A medical code associated with each the one or more LLT nodes is then output. | 2019-11-07 |
20190340488 | COMPRESSION OF KERNEL DATA FOR NEURAL NETWORK OPERATIONS - Embodiments relate to a neural processor circuit that includes a kernel access circuit and multiple neural engine circuits. The kernel access circuit reads compressed kernel data from memory external to the neural processor circuit. Each neural engine circuit receives compressed kernel data from the kernel access circuit. Each neural engine circuit includes a kernel extract circuit and a kernel multiply-add (MAD) circuit. The kernel extract circuit extracts uncompressed kernel data from the compressed kernel data. The kernel MAD circuit receives the uncompressed kernel data from the kernel extract circuit and performs neural network operations on a portion of input data using the uncompressed kernel data. | 2019-11-07 |
20190340489 | NEURAL NETWORK PROCESSOR FOR HANDLING DIFFERING DATATYPES - Embodiments relate to a neural engine circuit that includes an input buffer circuit, a kernel extract circuit, and a multiply-accumulator (MAC) circuit. The MAC circuit receives input data from the input buffer circuit and a kernel coefficient from the kernel extract circuit. The MAC circuit contains several multiply-add (MAD) circuits and accumulators used to perform neural networking operations on the received input data and kernel coefficients. MAD circuits are configured to support fixed-point precision (e.g., INT8) and floating-point precision (FP16) of operands. In floating-point mode, each MAD circuit multiplies the integer bits of input data and kernel coefficients and adds their exponent bits to determine a binary point for alignment. In fixed-point mode, input data and kernel coefficients are multiplied. In both operation modes, the output data is stored in an accumulator, and may be sent back as accumulated values for further multiply-add operations in subsequent processing cycles. | 2019-11-07 |
20190340490 | SYSTEMS AND METHODS FOR ASSIGNING TASKS IN A NEURAL NETWORK PROCESSOR - Embodiments relate to managing tasks that when executed by a neural processor circuit instantiates a neural network. The neural processor circuit includes neural engine circuits and a neural task manager circuit. The neural task manager circuit includes multiple task queues and a task arbiter circuit. Each task queue stores a reference to a task list of tasks for a machine learning operation. Each task queue may be associated with a priority parameter. Based on the priority of the task queues, the task arbiter circuit retrieves configuration data for a task from a memory external to the neural processor circuit, and provides the configuration data to components of the neural processor circuit including the neural engine circuits. The configuration data programs the neural processor circuit to execute the task. For example, the configuration data may include input data and kernel data processed by the neural engine circuits to execute the task. | 2019-11-07 |
20190340491 | SCALABLE NEURAL NETWORK PROCESSING ENGINE - Embodiments relate to a neural processor circuit with scalable architecture for instantiating one or more neural networks. The neural processor circuit includes a data buffer coupled to a memory external to the neural processor circuit, and a plurality of neural engine circuits. To execute tasks that instantiate the neural networks, each neural engine circuit generates output data using input data and kernel coefficients. A neural processor circuit may include multiple neural engine circuits that are selectively activated or deactivated according to configuration data of the tasks. Furthermore, an electronic device may include multiple neural processor circuits that are selectively activated or deactivated to execute the tasks. | 2019-11-07 |
20190340492 | DESIGN FLOW FOR QUANTIZED NEURAL NETWORKS - Methods and apparatus are disclosed supporting a design flow for developing quantized neural networks. In one example of the disclosed technology, a method includes quantizing a normal-precision floating-point neural network model into a quantized format. For example, the quantized format can be a block floating-point format, where two or more elements of tensors in the neural network share a common exponent. A set of test input is applied to a normal-precision flooding point model and the corresponding quantized model and the respective output tensors are compared. Based on this comparison, hyperparameters or other attributes of the neural networks can be adjusted. Further, quantization parameters determining the widths of data and selection of shared exponents for the block floating-point format can be selected. An adjusted, quantized neural network is retrained and programmed into a hardware accelerator. | 2019-11-07 |
20190340493 | NEURAL NETWORK ACCELERATOR - A neural network implementation is disclosed. The implementation allows the computations for the neural network to be performed on either an accelerator or a processor. The accelerator and the processor share a memory and communicate over a bus to perform the computations and to share data. The implementation uses weight compression and pruning, as well as parallel processing, to reduce computing, storage, and power requirements. | 2019-11-07 |
20190340494 | Method, Digital Electronic Circuit and System for Unsupervised Detection of Repeating Patterns in a Series of Events - A method of performing unsupervised detection of repeating patterns in a series (TS) of events (E | 2019-11-07 |
20190340495 | Path Stack Neural Network AI - This is primarily a path finding algorithm using neural networks. The core idea is using a “path stack” of values as well as various modifiers to improve upon the basic ideas of artificial neural networks. Each layer within the stack of numbers represents information about the space that a given actor is existing in. These are modified by things such as personality or situation modifiers to weight the given cells strongly or weakly. This can represent perceived conditions, memories of previous information, or other modifications to the area. The given information within the path stack may be used for other purposes such as choosing an action, acquiring a target, or communicating information to other actors. The use of artificial neural networks and “stacks” of floating point numbers with modifiers will allow for the creation of more responsive and less predictable AI. While its primarily intention is to be useful in turn-based computer games it is by no means limited to this; any information that may be abstracted into a set of individual cells or coordinates can be utilized in such an algorithm. | 2019-11-07 |
20190340496 | INFORMATION PROCESSING APPARATUS AND INFORMATION PROCESSING METHOD - An information processing apparatus includes an inputter, a comparison processor, and an outputter. The inputter inputs, in a neural network, a first data item that is one of data items included in time-series data. The comparison processor performs comparison between a first predicted data item predicted by the neural network and a second data item that is included in the time-series data. The first predicted data item is predicted as a data item first time after the first data item. The second data item is a data item the first time after the first data item. The outputter outputs information indicating warning if an error between the second data item and the first predicted data item is larger than a threshold after the comparison processor performs the comparison. | 2019-11-07 |
20190340497 | Signal Recovery Via Deep Convolutional Networks - Real-world data may not be sparse in a fixed basis, and current high-performance recovery algorithms are slow to converge, which limits compressive sensing (CS) to either non-real-time applications or scenarios where massive back-end computing is available. Presented herein are embodiments for improving CS by developing a new signal recovery framework that uses a deep convolutional neural network (CNN) to learn the inverse transformation from measurement signals. When trained on a set of representative images, the network learns both a representation for the signals and an inverse map approximating a greedy or convex recovery algorithm. Implementations on real data indicate that some embodiments closely approximate the solution produced by state-of-the-art CS recovery algorithms, yet are hundreds of times faster in run time. | 2019-11-07 |
20190340498 | DYNAMICALLY SHAPING AND SEGMENTING WORK UNITS FOR PROCESSING IN NEURAL NETWORK PROCESSOR - Embodiments relate to a neural processor circuit that includes multiple neural engine circuits, a data buffer, and a kernel fetcher circuit. At least one of the neural engine circuits receives multiple sub-channels of a portion of input data from the data buffer. Neural engine circuit further receives a kernel of the one or more kernels from the kernel fetcher circuit, wherein the kernel was decomposed into a corresponding sub-kernel for each sub-channel of the portion of the input data. Neural engine circuit performs a convolution operation on each sub-channel of the portion of the input data and the corresponding sub-kernel. Neural engine circuit accumulates corresponding outputs of each sub-channel portion of the convolution operation to generate a single channel of the output data. | 2019-11-07 |
20190340499 | QUANTIZATION FOR DNN ACCELERATORS - Methods and apparatus are disclosed for providing emulation of quantized precision operations. In some examples, the quantized precision operations are performed for neural network models. Parameters of the quantized precision operations can be selected to emulate operation of hardware accelerators adapted to perform quantized format operations. In some examples, the quantized precision operations are performed in a block floating-point format where one or more values of a tensor, matrix, or vectors share a common exponent. Techniques for selecting the exponent, reshaping the input tensors, and training neural networks for use with quantized precision models are also disclosed. In some examples, a neural network model is further retrained based on the quantized model. For example, a normal precision model or a quantized precision model can be retrained by evaluating loss induced by performing operations in the quantized format. | 2019-11-07 |
20190340500 | FOILING NEUROMORPHIC HARDWARE LIMITATIONS BY RECIPROCALLY SCALING CONNECTION WEIGHTS AND INPUT VALUES TO NEURONS OF NEURAL NETWORKS - Training a neural network according to a training algorithm, which may iteratively perform the following. Scaled connection weight values are called from a memory. Such values span an initial range within or compatible with the limited range of values allowed by hardware. Based on the values called, effective connection weight values are learned. The values learned span an effective range that differs from the initial range. As learning proceeds, the scaled connection weight values are updated by scaling the values learned, so as for the updated values to span a final range that is within the limited range. The training algorithm instructs to store the updated, scaled values on the memory, in view of a next iterative step. | 2019-11-07 |
20190340501 | SPLITTING OF INPUT DATA FOR PROCESSING IN NEURAL NETWORK PROCESSOR - Embodiments of the present disclosure relate to splitting input data into smaller units for loading into a data buffer and neural engines in a neural processor circuit for performing neural network operations. The input data of a large size is split into slices and each slice is again split into tiles. The tile is uploaded from an external source to a data buffer inside the neural processor circuit but outside the neural engines. Each tile is again split into work units sized for storing in an input buffer circuit inside each neural engine. The input data stored in the data buffer and the input buffer circuit is reused by the neural engines to reduce re-fetching of input data. Operations of splitting the input data are performed at various components of the neural processor circuit under the management of rasterizers provided in these components. | 2019-11-07 |
20190340502 | PROCESSING GROUP CONVOLUTION IN NEURAL NETWORK PROCESSOR - Embodiments relate to a neural processor circuit including neural engines, a buffer, and a kernel access circuit. The neural engines perform convolution operations on input data and kernel data to generate output data. The buffer is between the neural engines and a memory external to the neural processor circuit. The buffer stores input data for sending to the neural engines and output data received from the neural engines. The kernel access circuit receives one or more kernels from the memory external to the neural processor circuit. The neural processor circuit operates in one of multiple modes, at least one of which divides a convolution operation into multiple independent convolution operations for execution by the neural engines. | 2019-11-07 |
20190340503 | SEARCH SYSTEM FOR PROVIDING FREE-TEXT PROBLEM-SOLUTION SEARCHING - Various methods and systems for providing query result titles using a problem-solution search engine in a search system are provided. A query is received; the query is identified as a free-text query. Problem description features are identified from the free-text query. The problem description features characterize an issue described in the free-text query. Solution description features associated with product titles are accessed, where solution description features characterize a resolved issue described in a product review. A product associated with the product review is the product that resolved the resolved issue in the product review. The problem description features are compared to the solution description features. A product title is identified as a query result title. The product title is selected based on solution description features of the product title corresponding to the problem description features of the free-text query. The query result title is communicated. | 2019-11-07 |
20190340504 | NEURAL NETWORK METHOD AND APPARATUS - A neural network method and apparatus is provided. A processor-implemented neural network method includes determining, based on a determined number of classes of input data, a precision for a neural network layer outputting an operation result, and processing parameters of the layer according to the determined precision. | 2019-11-07 |
20190340505 | DETERMINING INFLUENCE OF ATTRIBUTES IN RECURRENT NEURAL NET-WORKS TRAINED ON THERAPY PREDICTION - A method and system of determining influence of attributes in Recurrent Neural Networks (RNN) trained on therapy prediction is provided. For each output neuron z | 2019-11-07 |
20190340506 | LEARNING RADIO SIGNALS USING RADIO SIGNAL TRANSFORMERS - Methods, systems, and apparatus, including computer programs encoded on a storage medium, for processing radio signals. In once aspect, a system is disclosed that includes a processor and a storage device storing computer code that includes operations. The operations may include obtaining first output data generated by a first neural network based on the first neural network processing a received radio signal, receiving, by a signal transformer, a second set of input data that includes (i) the received radio signal and (ii) the first output data, generating, by the signal transformer, data representing a transformed radio signal by applying one or more transforms to the received radio signal, providing the data representing the transformed radio signal to a second neural network, obtaining second output data generated by the second neural network, and determining based on the second output data a set of information describing the received radio signal. | 2019-11-07 |
20190340507 | CLASSIFYING DATA - Taxonomy-based architecture data classifying methods and systems are disclosed. A classifier comprises processing nodes arranged in a tree-based architecture. During training mode a classifier module receives descriptions, generates classification predictions, sends the classification predictions to an error calculator to calculate gradients, and receives the gradient while the selector module receives descriptions and annotations associated to the sample piece of data, distributes the descriptions and annotations to child nodes. During testing mode, the classifier module receives descriptions, generates classification predictions and sends the classification predictions to the selector module which receives descriptions and predictions and distributes descriptions to child nodes corresponding to the predictions. | 2019-11-07 |
20190340508 | Computing Device and Computation Method for Neural Network Computation - A computing device includes: a first computing unit configured to perform a first operation on an input first matrix M times, to obtain a second matrix, a second computing unit, configured to perform a second operation on the input second matrix, and a control unit, configured to: control the first computing unit to perform an i | 2019-11-07 |
20190340509 | ACTION SELECTION FOR REINFORCEMENT LEARNING USING NEURAL NETWORKS - Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a system configured to select actions to be performed by an agent that interacts with an environment. The system comprises a manager neural network subsystem and a worker neural network subsystem. The manager subsystem is configured to, at each of the multiple time steps, generate a final goal vector for the time step. The worker subsystem is configured to, at each of multiple time steps, use the final goal vector generated by the manager subsystem to generate a respective action score for each action in a predetermined set of actions. | 2019-11-07 |
20190340510 | SPARSIFYING NEURAL NETWORK MODELS - A technique includes modifying a neural network model to sparsify the model. The model includes a plurality of kernel element weights, which are parameterized according to a plurality of dimensions. Modifying the model includes, in a given iteration of the plurality of iterations, training the model based on a structure regularization in which kernel element weights that share a dimension in common are removed as a group to create corresponding zero kernel elements in the model; and compressing the model to exclude zero kernel element weights from the model to prepare the model to be trained in another iteration. | 2019-11-07 |
20190340511 | SPARSITY CONTROL BASED ON HARDWARE FOR DEEP-NEURAL NETWORKS - Systems, methods, computer program products, and apparatuses to transform a weight space of an inference model to increase the compute efficiency of a target inference platform. A density of a weight space can be determined, and a transformation parameter derived based on the determined density. The weight space can be re-ordered based on the transformation parameter to balance the compute load between the processing elements (PEs) of the target platform, and as such, reduce the idle time and/or stalls of the PEs. | 2019-11-07 |
20190340512 | ANALYTICS FOR AN AUTOMATED APPLICATION TESTING PLATFORM - Machine learning techniques are employed to model test runs of an automated test platform in ways that allow for reliable identification of various types of test behavior such as, for example, whether certain classes of failures can be characterized as test flake. | 2019-11-07 |
20190340513 | OPTIMAL SOLUTION DETERMINATION METHOD, OPTIMAL SOLUTION DETERMINATION PROGRAM, AND OPTIMAL SOLUTION DETERMINATION DEVICE - Provided is an optimal solution determination method for determining optimality of a solution in a combinatorial optimization problem using a computer, including uniformly extracting a plurality of solutions in a solution space of the combinatorial optimization problem as a plurality of first solutions, and estimating a maximum evaluation value in a case where solutions of a number that exceeds the number of the plurality of first solutions are assumed, on the basis of a plurality of first evaluation values respectively corresponding to the plurality of first solutions that are uniformly extracted, as a first maximum evaluation value Z. Further, in a case where a solution candidate (graph G_1) that belongs to a solution space is input (step S18), an evaluation value S_1 corresponding to the graph G_1 is acquired, the acquired evaluation value S_1 is compared with the first maximum evaluation value Z, and it is determined whether the evaluation value S_1 of the input graph G_1 is within a confidence interval of the first maximum evaluation value Z (whether the graph G_1 is a first optimal value or not). | 2019-11-07 |
20190340514 | SYSTEM AND METHOD FOR GENERATING ULTIMATE REASON CODES FOR COMPUTER MODELS - A system and method for generating ultimate reason codes for computer models is provided. The system for generating ultimate reason codes for computer models comprising a computer system for receiving a data set, and an ultimate reason code generation engine stored on the computer system which, when executed by the computer system, causes the computer system to train a base model with a plurality of reason codes, wherein each reason code includes one or more variables, each of which belongs to only one reason code, train a subsequent model using a subset of the plurality of reason codes, determine whether a high score exists in the base model, determine a scored difference if a high score exists in the base model, and designate a reason code having a largest drop of score as an ultimate reason code. | 2019-11-07 |
20190340515 | SENSING AND ACTIVITY CLASSIFICATION FOR INFANTS - Various examples are described for using a movement sensor to detecting an activity of an infant. In an example, an activity classification system includes a sensor configured to measure the activity of an infant and an external monitor. The monitor receives, from a sensor, a time series of data comprising an inertial measurement for each a time period. The monitor determines, from the time series and by using a predictive model, an activity from a list of identified activities. Examples of identified activities are deep sleep, light sleep, sitting, awake, nursing, or bottle feeding. | 2019-11-07 |
20190340516 | SYSTEM AND METHOD FOR QUANTITATIVELY ANALYZING AN IDEA - A system and a computer-implemented method for quantitatively analyzing an idea, for example, a business idea, and generating decision-based contextual recommendations on the idea are provided. The system selectively extracts data sets associated with a context of an idea input, from one or more internal and external data sources. The system computes measurement indices related to market buzz, competition, investor and entrepreneur interest, domain and technology skill, commitment, funding and geography risk, etc., by performing a quantitative analysis of the data sets with reference to configurable thresholds and/or based on predetermined criteria. The system computes an execution risk index using the user-defined parameters, in communication with one or more of the internal and external data sources The system generates a recommendation score based on the measurement indices and the execution risk index for generating decision-based contextual recommendations to arrive at one or more decisions related to the idea. | 2019-11-07 |
20190340517 | A METHOD FOR DETECTION AND CHARACTERIZATION OF TECHNICAL EMERGENCE AND ASSOCIATED METHODS - The present invention is a method for constructing a knowledgebase that can provide analysis and trend prediction of emerging technologies. Metadata and full text are gathered from collections of documents, which can include more than 10 million documents, and are used to build a heterogeneous network of elements related to themes such as technical emergence. Indicators and models are selected that identify network characteristics and trends of interest. The indicators can be derived by applying a combination of citation analyses, natural language processing, entity disambiguation, organization classification, and time series analyses. A metric can be used to evaluate indicator utility. A framework can be sued to generate and validate the indicators. The models can be derived using an automated process. Upon receipt of a query, the indicators and models can be used to apply a scoring process to extracted features to predict a future prominence of an entity. | 2019-11-07 |
20190340518 | SYSTEMS AND METHODS FOR ENRICHING MODELING TOOLS AND INFRASTRUCTURE WITH SEMANTICS - Systems and methods for generating and processing modeling workflows. | 2019-11-07 |
20190340519 | VEHICLE RECOMMENDATIONS BASED ON DRIVING HABITS - Disclosed embodiments provide techniques for providing vehicular recommendations based on driver habits. Embodiments utilize a variety of input data, including, but not limited to, static vehicular data, dynamic vehicular data, and/or environmental data. In embodiments, empirical rules are used to adjust recommended maintenance schedules based on the input conditions. Additionally, the adjusted recommendations along with unscheduled maintenance data are input to a machine learning system, such as a neural network. The machine learning system is used to further revise the maintenance schedule, estimate end of life of the vehicle, and issue recommendations for when to sell a vehicle and recommendations on attributes of a new vehicle for acquisition. | 2019-11-07 |
20190340520 | PREDICTION MODEL GENERATION SYSTEM, METHOD, AND PROGRAM - A prediction model generation system is provided that is capable of generating a prediction model for accurately predicting a relationship between an ID of a record in first master data and an ID of a record in second master data. Co-clustering means | 2019-11-07 |
20190340521 | Intelligent Recommendation Method and Terminal - The present disclosure relates to recommendation methods and terminals. One example method includes receiving, by a terminal, a service request of a recommendation application, where the service request includes a configuration file, the configuration file includes at least an algorithm parameter and portfolio information used to identify an algorithm portfolio structure, and the recommendation application is included in the terminal, invoking, by the terminal, at least one algorithm from an algorithm library of the terminal based on the algorithm parameter and the portfolio information, predicting, by the terminal and based on the at least one algorithm and user data of the terminal, at least one service currently required by a user of the terminal, where the user data of the terminal is stored in a personal database located in the terminal, and displaying, by the terminal, at least one icon corresponding to the at least one service. | 2019-11-07 |
20190340522 | EVENT PREDICTION SYSTEM, EVENT PREDICTION METHOD, RECORDING MEDIA, AND MOVING BODY - Event prediction system includes accumulation unit and model generator. Accumulation unit accumulates a plurality of pieces of data for learning including history information. The history information indicates a situation of moving body at the time of occurrence of an event related to driving of moving body. Model generator generates a prediction model for predicting occurrence of the event with the plurality of pieces of data for learning. The history information includes raster data for learning. The raster data for learning indicates, with a plurality of cells, the situation of moving body at the time of occurrence of the event. | 2019-11-07 |
20190340523 | INFORMATION PROCESSING APPARATUS AND STORAGE MEDIUM - Provided is an information processing apparatus, including a calculation section which calculates a proficiency level of a user for operations performed by the user for achieving a prescribed objective based on history information related to the operations and attribute information related to physical features of the user, and a generation section which generates advice for achieving the objective based on the proficiency level calculated by the calculation section. | 2019-11-07 |
20190340524 | MODEL SELECTION INTERFACE - In one embodiment, a method includes providing, to a client system of a user, a user interface for display. The user interface may include a first set of options for selecting an artificial intelligence (AI) task for integrating into a user application, a second set of options for selecting one or more devices on which the user wants to deploy the selected AI task, and a third set of options for selecting one or more performance constraints specific to the selected devices. User specifications may be received based on user selections in the first, second, and third sets of options. A custom AI model may be generated based on the user specifications and sent to the client system of the user for integrating into the user application. The custom AI model once integrated may enable the user application to perform the selected AI task on the selected devices. | 2019-11-07 |
20190340525 | ITERATIVE GENERATION OF TOP QUALITY PLANS IN AUTOMATED PLAN GENERATION FOR ARTIFICIAL INTELLIGENCE APPLICATIONS AND THE LIKE - A method for improving performance of at least one hardware processor solving a top-k planning problem includes obtaining, in a memory coupled to the at least one processor, a specification of the planning problem in a planning language; obtaining, in a first iteration carried out by the at least one processor, at least one solution to the planning problem; and modifying the planning problem, in the first iteration carried out by the at least one processor, to forbid the at least one solution. The method further includes repeating, by the at least one processor, the obtaining of the at least one solution and the modifying to forbid the at least one solution, for a plurality of additional iterations, after the first iteration, until a desired number, k, of solutions to the planning problem are found or until no further solutions exist, whichever comes first. | 2019-11-07 |
20190340526 | OPTIMIZING AUTOMATED MODELING ALGORITHMS FOR RISK ASSESSMENT AND GENERATION OF EXPLANATORY DATA - Certain aspects involve optimizing neural networks or other models for assessing risks and generating explanatory data regarding predictor variables used in the model. In one example, a system identifies predictor variables compliant with certain monotonicity constraints. The system generates a neural network for determining a relationship between each predictor variable and a risk indicator. The system performs a factor analysis on the predictor variables to determine common factors. The system iteratively adjusts the neural network so that (i) a monotonic relationship exists between each common factor and the risk indicator and (ii) a respective variance inflation factor for each common factor is sufficiently low. Each variance inflation factor indicates multicollinearity among a subset of the predictor variables corresponding to a common factor. The adjusted neural network can be used to generate explanatory indicating relationships between (i) changes in the risk indicator and (ii) changes in at least some common factors. | 2019-11-07 |
20190340527 | GRAPHICAL USER INTERFACE FEATURES FOR UPDATING A CONVERSATIONAL BOT - Various technologies pertaining to creating and/or updating a chatbot are described herein. Graphical user interfaces (GUIs) are described that facilitate updating a computer-implemented response model of the chatbot based upon interaction between a developer and features of the GUIs, wherein the GUIs depict dialogs between a user and the chatbot. | 2019-11-07 |
20190340528 | REASONING ENGINE SERVICES - A reasoning engine is disclosed. Contemplated reasoning engines acquire data relating to one or more aspects of various environments. Inference engines within the reasoning engines review the acquire data, historical or current, to generate one or more hypotheses about how the aspects of the environments might be correlated, if at all. The reasoning engine can attempt to validate the hypotheses through controlling acquisition of the environment data. | 2019-11-07 |
20190340529 | Automatic Digital Asset Sharing Suggestions - Techniques of digital asset management (DAM) are described. A DAM system can obtain a knowledge graph metadata network describing relationships between metadata associated with a user's collection of digital assets (DAs), e.g., images, videos, music, etc. Based on information obtained, e.g., from the user's DA collection and/or the knowledge graph metadata network, the DAM system may provide users with more intelligent (and automated) DA sharing suggestions that are as relevant as possible for a given context. In some embodiments, the sharing suggestions may be based on one or more DAs recently shared with the user from a third party. In other embodiments, a proactive sharing suggestion may be presented to a user based on a detected indication of an intent to share DAs, e.g., based on the extraction of relevant features from an incoming message from a third party (or an outgoing message from the user to a third party). | 2019-11-07 |
20190340530 | SYSTEMS METHODS AND MEDIA FOR AUTOMATICALLY IDENTIFYING ENTREPRENEURIAL INDIVIDUALS IN A POPULATION USING INDIVIDUAL AND POPULATION LEVEL DATA - In some embodiments, systems, methods, and media for automatically identifying entrepreneurial individuals in a population using individual and population level data are provided. In some embodiments, a system is provided, comprising: a database storing: grades and identifying information for classes; a hardware processor configured to: calculate, for each class, a difficulty value based on the grade for each individual; modify grades associated with the individual based on the difficulties; determine a variance using the modified grades; determine an average variance; determine that the variance for a first individual is larger average; determine that the first individual is more likely than average to be entrepreneurial; in response to determining that the first individual is more likely than average to be entrepreneurial, add identifying information of the first student to a second database of potential entrepreneurs. | 2019-11-07 |
20190340531 | PROCESSING SENSOR LOGS - A method of processing sensor logs is described. The method includes accessing a first sensor log and a corresponding first reference log. Each of the first sensor log and the first reference log includes a series of measured values of a parameter according to a first time series. The method also includes accessing a second sensor log and a corresponding second reference log. Each of the second sensor log and the second reference log includes a series of measured values of a parameter according to a second time series. The method also includes dynamically time warping the first reference log and/or and second reference log by a first transformation between the first time series and a common time-frame and/or a second transformation between the second time series and the common time-frame. The method also includes generating first and second warped sensor logs by applying the or each transformation to the corresponding ones of the first and second sensor logs. | 2019-11-07 |
20190340532 | QUANTUM COMPUTER SIMULATOR CHARACTERIZATION - The disclosure describes various aspects of quantum computer simulators. In an aspect, a method for characterizing a quantum computer simulator includes identifying simulator processes supported by the quantum computer simulator, generating, for each simulator process, characteristic curves for different gates or quantum operations, the characteristic curves including information for predicting the time it takes to simulate each of the gates or quantum operations in a respective simulator process, and providing the characteristic curves to select one of the simulator processes to simulate a circuit, quantum program, or quantum algorithm that uses at least some of the gates or quantum operations. In another aspect, a method for optimizing simulations in a quantum computer simulator is described where a simulator process is selected for simulation of a circuit, quantum program, or quantum algorithm based on characteristic curves that predict a time it takes for the simulation to be carried out. | 2019-11-07 |
20190340533 | SYSTEMS AND METHODS FOR PREPARING DATA FOR USE BY MACHINE LEARNING ALGORITHMS - Historical data used to train machine learning algorithms can have thousands of records with hundreds of fields, and inevitably includes faulty data that affects the accuracy and utility of a primary model machine learning algorithm. To improve dataset integrity it is segregated into a clean dataset having no invalid data values and a faulty dataset having the invalid data values. The clean dataset is used to produce a secondary model machine learning algorithm trained to generate from plural complete data records a replacement value for a single invalid data value in a data record, and a tertiary model machine learning clustering algorithm trained to generate from plural complete data records replacement values for multiple invalid data values. Substituting the replacement data values for invalid data values in the faulty dataset creates augmented training data which is combined with clean data to train a more accurate and useful primary model. | 2019-11-07 |
20190340534 | Communication Efficient Federated Learning - The present disclosure provides efficient communication techniques for transmission of model updates within a machine learning framework, such as, for example, a federated learning framework in which a high-quality centralized model is trained on training data distributed overt a large number of clients each with unreliable network connections and low computational power. In an example federated learning setting, in each of a plurality of rounds, each client independently updates the model based on its local data and communicates the updated model back to the server, where all the client-side updates are used to update a global model. The present disclosure provides systems and methods that reduce communication costs. In particular, the present disclosure provides at least: structured update approaches in which the model update is restricted to be small and sketched update approaches in which the model update is compressed before sending to the server. | 2019-11-07 |
20190340535 | SYSTEMS AND METHODS FOR UTILIZING MACHINE LEARNING TO IDENTIFY NON-TECHNICAL LOSS - Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating to a plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss. | 2019-11-07 |
20190340536 | SERVER FOR IDENTIFYING ELECTRONIC DEVICES LOCATED IN A SPECIFIC SPACE AND A CONTROL METHOD THEREOF - A server and a method for controlling a server are provided. The method includes performing a connection with a plurality of electronic devices; transmitting, to the plurality of electronic devices, a control signal for controlling transception of communication signals between the plurality of electronic devices; receiving intensity information of each of the communication signals from the plurality of electronic devices, wherein the intensity information of each of the communication signals is received from another electronic device by the plurality of electronic devices; and clustering the plurality of electronic devices based on the obtained intensity information of the communication signals. | 2019-11-07 |
20190340537 | Personalized Match Score For Places - A personalized score for a place that a user may want to visit is computed and displayed to the user. The score is computed based on at least one of inferred or explicit parameters, using machine learning. The score may be displayed to the user in connection with the place, and in some examples explanations of the underlying factors that resulted in the score are also displayed. Because each user is unique, the score may be different for one person than for another. Accordingly, when a group of friends are deciding on a place to visit, such as a place to eat, the personalized score for a given restaurant may be higher for a first user than for a second user. | 2019-11-07 |
20190340538 | IDENTIFYING ENTITIES USING A DEEP-LEARNING MODEL - In one embodiment, a method includes retrieving a first vector representation of a first entity, with which a user has interacted, and a second vector representation of a second entity, with which the user has not interacted. The first and second vector representations are determined using an initial deep-learning model. A first similarity score is computed between a vector representation of the user and the first vector representation, and a second similarity score is computed between the vector representation of the user and the second vector representation. The second vector representation is updated if the second similarity score is greater than the first similarity score using the initial deep-learning model. An updated deep-learning model is generated based on the initial deep-learning model and on the updated second vector representation. | 2019-11-07 |
20190340539 | TECHNOLOGIES FOR PLATFORM-TARGETED MACHINE LEARNING - Technologies for platform-targeted machine learning include a computing device to generate a machine learning algorithm model indicative of a plurality of classes between which a user input is to be classified and translate the machine learning algorithm model into hardware code for execution on the target platform. The user input is to be classified as being associated with a particular class based on an application of one or more features to the user input, and each of the one or more features has an associated implementation cost indicative of a cost to perform on a target platform on which the corresponding feature is to be applied to the user input. | 2019-11-07 |
20190340540 | ADAPTIVE CONTINUOUS LOG MODEL LEARNING - Systems and methods for adaptive and continuous log model learning can include updating a core model to generate an updated core model, each being a syntactic model and being additive in nature, based on a heterogeneous training log file and updating a peripheral model, that represents a relationship between core models, using a set of existing auxiliary files, that define can define relationship between existing models, and the updated core model to generate an updated peripheral model based on the heterogeneous training log file. Additionally, they can include detecting, with the updated core model and the updated peripheral model, an anomaly within a set of testing logs indicative of information technology system operation to take remedial action on the information technology system based on a most recent model update. | 2019-11-07 |
20190340541 | LAYERED STOCHASTIC ANONYMIZATION OF DATA - Techniques that facilitate layered stochastics anonymization of data are provided. In one example, a system includes a machine learning component and an evaluation component. The machine learning component performs a machine learning process for first data associated with one or more features to generate second data indicative of one or more example datasets within a degree of similarity to the first data. The first data and the second data comprise a corresponding data format. The evaluation component evaluates the second data for a particular feature from the one or more features and generates third data indicative of a confidence score for the second data. | 2019-11-07 |
20190340542 | Computational Efficiency in Symbolic Sequence Analytics Using Random Sequence Embeddings - A method and system of analyzing a symbolic sequence is provided. Metadata of a symbolic sequence is received from a computing device of an owner. A set of R random sequences are generated based on the received metadata and sent to the computing device of the owner of the symbolic sequence for computation of a feature matrix based on the set of R random sequences and the symbolic sequence. The feature matrix is received from the computing device of the owner. Upon determining that an inner product of the feature matrix is below a threshold accuracy, the iterative process returns to generating R random sequences. Upon determining that the inner product of the feature matrix is at or above the threshold accuracy, the feature matrix is categorized based on machine learning. The categorized global feature matrix is sent to be displayed on a user interface of the computing device of the owner. | 2019-11-07 |
20190340543 | SYSTEM AND METHOD FOR OPTIMIZING VEHICLE FLEET DEPLOYMENT - A system and method for optimizing vehicle fleet deployment. The method includes determining a predicted vehicle demand at an upcoming time for at least one geographic location based on current data including current contextual data by applying a demand prediction model to features extracted from the current data, wherein the demand prediction model is trained using machine learning based on historical vehicle demand data and historical contextual data for a plurality of historical geographical locations and times; and generating an optimal fleet movement plan based on the predicted vehicle demand by applying a linear optimization model to cost values, wherein the optimal fleet movement plan is for moving at least one vehicle of a fleet including a plurality of vehicles, wherein the cost values are determined based on the predicted vehicle demand, a current location of each vehicle of the fleet, and a status of each vehicle of the fleet. | 2019-11-07 |
20190340544 | TRIP PLANNING AND IMPLEMENTATION - A network computer system can receive location information indicating a current location of a user, upon detecting a user having arrived at an airport. based at least on the current location of the user, the network computer system can determine a time-to-reach pickup interval. Additionally, the network computer system can receive location information indicating a current location of each of a set of service providers. Based on at least on the current location of at least one service provider of the set of service providers, the network computer system can determine a request time interval. Moreover, based on the request time interval and the time-to-reach pickup interval, the network computer system can determine a computed request time indicating a time to trigger a service request. | 2019-11-07 |
20190340545 | ELECTRIC POWER MANAGEMENT SYSTEM FOR REDUCING LARGE AND RAPID CHANGE IN POWER RECEIVED FROM ELECTRICITY DELIVERY SYSTEM - A long-term predictor circuit predicts a long-term predicted power indicating temporal variations in consumed power of a customer, using a long-term prediction model indicating the variations for each moment of clock times. A short-term predictor circuit predicts a short-term predicted power using a short-term prediction model indicating the variations over a time interval before and after a change in a consumed power of each load apparatus, based on the variations over a time interval immediately before a current time, the short-term predicted power indicating the variations over a time interval immediately after the current time. A controller circuit controls charging and discharging of a battery apparatus by setting a charging power or a discharging power based on the long-term predicted power, and controls discharging of the battery apparatus by setting a discharging power based on the short-term predicted power. | 2019-11-07 |
20190340546 | REAL TIME PERSONAL MOBILITY PLANNER SYSTEM - A mobility planning server is disclosed that is configured to: receive a listing of one or more scheduled activities for a plurality of users; identify a plurality of different types of mobility modes potentially available to transport the users; retrieve, from one or more service providers of the mobility modes, the potential availability of the mobility modes provided by the service provider and any service provider indicated constraints regarding the mobility modes; analyze the one or more scheduled activities, identified mobility modes, the potential availability of the mobility modes, and constraints indicated by any service provider; prepare a mobility plan for each user based on the analysis wherein the mobility plans include a mobility planning server selected mobility mode for each activity; confirm the availability of mobility modes included in the mobility plans; confirm user acceptance of the mobility plans; and schedule a selected mobility mode for each activity. | 2019-11-07 |
20190340547 | RECORDING MEDIUM RECORDING PROGRAM MANAGEMENT PROGRAM AND INFORMATION PROCESSING APPARATUS - A non-transitory computer-readable recording medium stores t herein a program management program for causing a computer to execute processes including: referring to a storage indicating an execution status of a production support program which is executed using production management data; specifying one or more data items which correspond to a data item not used at execution time of the production support program or a data item whose frequency of use at the execution time of the production support program is a predetermined reference value or less, among data items included in the production management data; and outputting the specified one or more data items. | 2019-11-07 |
20190340548 | SYSTEM FOR BUILDING AND UTILIZING RISK MODELS FOR LONG RANGE RISK - A system, method and program product for analyzing long term risk. A system is disclosed that includes a risk system for analyzing long-term risks, including: a risk knowledgebase that includes risk information associated with at least one domain; a risk model builder that builds a representation of a risk model based on inputs from a user interface and the risk knowledgebase, wherein the risk model includes risk factor nodes, risk event nodes and impact nodes; and a risk simulation engine that processes the representation and computes predicted outcomes. | 2019-11-07 |
20190340549 | METHOD AND SYSTEM FOR NETWORK INFRASTRUCTURE SECURITY BREACH MEASUREMENT - Apparatus for assessing threat to at least one computer network in which a plurality of systems ( | 2019-11-07 |
20190340550 | CUSTOMIZED LABOR DEMAND ALLOCATION SYSTEM - Examples provide a system for budget-driven labor demand allocation to roles and/or tasks associated with a selected location. A labor demand splitter analyzes a budget plan for a role and live metric data, including weekly delivery schedules and predicted foot traffic, using a set of per-role configuration criteria, forecast metrics, and per-role metric weights. A month splitter and/or a week splitter allocates hours to each day based on the analysis. An intra-day splitter spreads the hours across a set of time-segments for a selected day based on a selected forecast spreading routine, such as an end-points spreading routine or a mid-point spreading routine. The labor demand splitter performs rounding, smoothing, edge-filling and/or coverage fill to generate a set of per-role allocation hours for each day during a selected time-period. The per-role allocation hours are output if it conforms with the goal hours specified by the budget plan. | 2019-11-07 |
20190340551 | SIGNAGE & ADVERTISING TRACKER & COMPLIANCE/REGULATION APPLICATION - An Interactive application electronic communication device that helps user stay knowledgeable of the latest rules and regulations on signage and advertising for multiple industries targeting specific signage and ordinances. The electronic communication device application helps business owners, marketers and other professionals to plan, market, and be fully informed of and in compliance with local marketing/signage laws and ordinances throughout various cities, counties and states. | 2019-11-07 |
20190340552 | AUTOMATED MANAGING OF A DATA CENTER INSTALLATION - Automated managing of a data center installation is provided. The managing includes evaluating, at least in part by image processing analysis, a captured image of at least a portion of the data center installation to identify a component-related deficiency within the data center installation. One or more measurements within a data center are used to determine an energy penalty due to the identified component-related deficiency within the data center installation, and an action to correct the component-related deficiency within the data center installation is initiated based on the energy penalty exceeding a predefined threshold. | 2019-11-07 |
20190340553 | SYSTEM AND METHOD FOR ENTERPRISE RESOURCE MANAGEMENT INTERFACE - Present embodiments are directed toward systems and methods for enhancing the organization and overall management of resource allocation items in one or more enterprise networks by normalizing corresponding allocation objects, filtering the allocation objects based on one or more attributes of the allocation objects, grouping the allocation objects in a client instance view viewable to an individual, and generating a variety of allocation objects in accordance with the one or more filter configuration inputs and the one or more grouping configuration, improving client instance customization of resource allocations. | 2019-11-07 |
20190340554 | ENGAGEMENT LEVELS AND ROLES IN PROJECTS - The disclosure provides for associating users with roles in projects. Implementations include determining entity features of project entities. The project entities are grouped into projects based on similarities of the entity features between the project entities. From content of the project entities of a project of the projects, occurrences of events with respect to users are determined, where each event corresponds to one or more predefined user activities. The occurrences of the events are analyzed to determine, for each user of a plurality of the users, an engagement level of the user with the project. A role for the project is assigned to the user from predefined roles based on applying a role feature corresponding to the engagement level of the user to a machine learning model that represents the role, and an assignment of the user to the role is incorporated into a project repository. | 2019-11-07 |
20190340555 | TWO-STAGE CONTROL SYSTEMS AND METHODS FOR ECONOMICAL OPTIMIZATION OF AN ELECTRICAL SYSTEM - The present disclosure is directed to systems and methods for economically optimal control of an electrical system. Some embodiments employ generalized multivariable constrained continuous optimization techniques to determine an optimal control sequence over a future time domain in the presence of any number of costs, savings opportunities (value streams), and constraints. Some embodiments also include control methods that enable infrequent recalculation of the optimal setpoints. Some embodiments may include a battery degradation model that, working in conjunction with the economic optimizer, enables the most economical use of any type of battery. Some embodiments include techniques for load and generation learning and prediction. Some embodiments include consideration of external data, such as weather. | 2019-11-07 |
20190340556 | SYSTEMS AND METHODS FOR PROVIDING DYNAMIC VOICE-PROMPT DIALOGUE FOR SITUATIONAL AWARENESS ONBOARD AN AIRCRAFT - A method for providing operational awareness data onboard an aircraft, by a computing device comprising at least a processor and a system memory element, is provided. The method continuously identifies deviations from operational goals of the aircraft, by the processor, based on a current state of the aircraft, a predicted state of the aircraft, and flight crew perception of the current state and the predicted state; and autonomously initiates a dialogue with flight crew onboard the aircraft by providing voice-data prompts for user action onboard the aircraft, by the processor onboard the aircraft, based on the deviations. | 2019-11-07 |
20190340557 | STANDARDIZED GRAPH-BASED FRAMEWORK FOR DETERMINING AN OPTIMAL LICENSE MIX FOR AN ENTERPRISE COMPUTER SYSTEM - Embodiments include a computer-implemented method for determining an optimal license mix for an enterprise computer system in accordance with a standardized graph-based framework. The method includes discovering licensable products of an enterprise computer system in accordance with a standardized graph-based framework, and constructing a licensable product star graph (LPSG) in accordance with the standardized graph-based framework by evaluating each licensable product to identify any license models and any target elements associated with the licensable product. The method further includes constructing a licensable product constellation graph (LPCG) in accordance with the standardized graph-based framework by evaluating each LPSG to group any common target elements of the license models, and determining an optimal license mix for the enterprise computer system based on the LPCG. | 2019-11-07 |