MACHINE LEARNING

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LIQUEFACTION EVALUATION MODEL GENERATION DEVICE, LIQUEFACTION EVALUATION DEVICE, NON-TRANSITORY RECORDING MEDIUM RECORDING LIQUEFACTION EVALUATION MODEL GENERATION PROGRAM, NON-TRANSITORY RECORDING MEDIUM RECORDING LIQUEFACTION EVALUATION PROGRAM, LIQUEFACTION EVALUATION MODEL GENERATION METHOD, AND LIQUEFACTION EVALUATION METHOD

Publication No.: US2022003887A1 06/01/2022

Applicant:

UNIV TOHOKU [JP]

Absstract of: US2022003887A1

A liquefaction evaluation model generation device includes a data acquisition unit configured to acquire training data in which learning vibration data indicating a physical quantity associated with vibrations observed in the ground is defined as a question and learning liquefaction data indicating the degree of liquefaction occurring in the ground where the vibrations associated with the learning vibration data have been observed is defined as an answer, and a machine learning execution unit configured to execute machine learning using the training data and generate a liquefaction evaluation model that is a machine learning model. A liquefaction evaluation device includes a data acquisition unit configured to acquire inference vibration data indicating a physical quantity associated with vibrations observed in the ground and an inference execution unit configured to input the inference vibration data to the above-described machine learning model and cause the machine learning model to output inference liquefaction data indicating the degree of liquefaction occurring in the ground where the vibrations associated with the inference vibration data have been observed.

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METHOD AND SYSTEM FOR PREDICTING PERFORMANCE IN ELECTRONIC DESIGN BASED ON MACHINE LEARNING

Publication No.: US2022004900A1 06/01/2022

Applicant:

AGENCY SCIENCE TECH & RES [SG]

WO_2020112023_A1

Absstract of: US2022004900A1

There is provided a method of predicting performance in electronic design based on machine learning using at least one processor, the method including: providing a first machine learning model configured to predict performance data for an electronic system based on a set of input design parameters for the electronic system; providing a second machine learning model configured to generate a new set of parameter values for the set of input design parameters for the electronic system based on a desired performance data provided for the electronic system; generating, using the second machine learning model, the new set of parameter values for the set of input design parameters for the electronic system based on the desired performance data provided for the electronic system; evaluating the set of input design parameters having the new set of parameter values for the electronic system to obtain an evaluated performance data associated with the set of input design parameters having the new set of parameter values; generating a new set of training data based on the set of input design parameters having the new set of parameter values and the evaluated performance data associated with the set of input design parameters having the new set of parameter values; and training the first machine learning model based on at least the new set of training data. There is also provided a corresponding system for predicting performance in electronic design based on machine learning.

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METHOD AND DEVICE FOR CREATING A MACHINE LEARNING SYSTEM

Publication No.: US2022004806A1 06/01/2022

Applicant:

BOSCH GMBH ROBERT [DE]

DE_102020208309_PA

Absstract of: US2022004806A1

A method for creating a machine learning system which is designed for segmentation and object detection in images. The method includes: providing a directed graph; selecting a path through the graph, at least one additional node being selected from this subset, a path through the graph from the input node along the edges via the additional node up to the output node being selected; creating a machine learning system as a function of the selected path; and training the machine learning system created.

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On-Device Machine Learning Platform

Publication No.: US2022004929A1 06/01/2022

Applicant:

GOOGLE INC [US]

KR_20210134822_PA

Absstract of: US2022004929A1

The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and/or client-provided input data used to generate predictions/inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.

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COGNITIVE ANALYSIS OF A PROJECT DESCRIPTION

Publication No.: US2022004951A1 06/01/2022

Applicant:

IBM [US]

Absstract of: US2022004951A1

An embodiment includes extracting a capability from a dataset representative of a project description of a proposed project using a first machine learning process to form a cluster representative of the capability. The embodiment assigns the capability to a first node of a business operations graph based on a classification result of the capability by a second machine learning process. The embodiment generates a visual indicator based, at least in part, on the assigning of the capability to the first node. The embodiment generates the visual indicator by a process comprising generating a first visual indicator of the capability being assigned to the first node, and a second visual indicator of a development sequence for the capability relative to another capability from the project description based at least in part on an association from the business operations graph between the first node and a second node of the graph.

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HETEROGENEOUS SYSTEM ON A CHIP SCHEDULER WITH LEARNING AGENT

Publication No.: US2022004430A1 06/01/2022

Applicant:

IBM [US]

Absstract of: US2022004430A1

Described are techniques for scheduling tasks on a heterogeneous system on a chip (SoC). The techniques including receiving a directed acyclic graph at a meta pre-processor associated with a heterogeneous system-on-chip and communicatively coupled to a scheduler, where the directed acyclic graph corresponds to a control flow graph of respective tasks associated with an application executed by the heterogeneous system-on-chip. The techniques further including determining, using a learning agent implementing machine learning algorithms, a rank for a respective task in the directed acyclic graph, wherein the learning agent receives as input the directed acyclic graph, constraints associated with the directed acyclic graph, and heuristics regarding previously completed tasks. The techniques further including providing the respective task to the scheduler for execution on the heterogeneous system-on-chip according to the rank.

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Machine Learning For Temperature Compensation

Publication No.: US2022004456A1 06/01/2022

Applicant:

WESTERN DIGITAL TECH INC [US]

CN_113889168_PA

Absstract of: US2022004456A1

A method of temperature compensation to read a flash memory device includes determining a state of the flash memory device. An action is selected with a maximum Q-value from a Q-table for the current state during exploitation. A read operation of a code word from the flash memory device is conducted using one or more parameters according to the selected action. The code word is decoded with an error correction code (ECC) process.

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MAZE-DRIVEN SELF-DIAGNOSTICS USING REINFORCEMENT LEARNING

Publication No.: US2022004448A1 06/01/2022

Applicant:

MITEL CLOUD SERVICES INC [US]

Absstract of: US2022004448A1

Systems and methods are provided for automatedly troubleshooting a computing application (e.g., a cloud-based computing application). An application domain of the computing application is modeled as a two-dimensional array of cells, a first dimension of the array representing components or microservices of the application domain, and a second dimension of the array representing states of the components or microservices, the array including paths between pairs of cells in the array. A troubleshooting goal is defined as a target state of the application domain, the target state corresponding to a target cell in the array. An initial state of the application domain is also provided, the initial state corresponding to an initial cell in the array. A reinforcement-learning-trained machine-learning algorithm can determine a solution path in the array between the initial cell and the target cell. Divergence between a failure case and a solution path indicates a probable failure cause.

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PREDICTIVE SEARCH TECHNIQUES BASED ON IMAGE ANALYSIS AND GROUP FEEDBACK

Publication No.: US2022004604A1 06/01/2022

Applicant:

LOOKY LOO INC [US]

Absstract of: US2022004604A1

The disclosed techniques in artificial intelligence include at least a system and a computer-implemented method for performing a predictive search that compensates for a misclassification. For example, the system can set a user-defined classification of a physical characteristic of the user and retrieve an image captured by a camera device. The system can aggregate binary feedback data submitted by authorized users about the image, predict a classification for the physical characteristic by processing the image with a machine learning (ML) process, and search a database based on a query, which has criteria that includes an indication of the aggregate binary feedback data, the user-defined classification, the predicted classification, and/or data indicative of the user's feedback. The system can then identify a search result that satisfies the query and cause a user device to display a recommendation that includes the search result.

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METHOD AND SYSTEM FOR DYNAMIC LATENT VECTOR ALLOCATION

Publication No.: US2022004896A1 06/01/2022

Applicant:

OATH INC [US]

Absstract of: US2022004896A1

The present teaching relates to method, system, and computer programming product for dynamic vector allocation. Machine learning is conducted using training data constructed based on a target vector having a plurality of feature entries, wherein each of the plurality of feature entries is mapped from at least one original attribute from one or more original source vectors. A feature entry in the target vector is identified based on a first criterion associated with an assessment of the machine learning, for replacing the corresponding at least one original attribute from the one or more original source vectors. At least one alternative attribute from alternative source vectors based on a second criterion is determined, wherein the at least one alternative attribute is to be mapped to the feature entry of the target vector. The feature entry of the target vector is populated based on the at least one alternative attribute.

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PAIN DETERMINATION USING TREND ANALYSIS, MEDICAL DEVICE INCORPORATING MACHINE LEARNING, ECONOMIC DISCRIMINANT MODEL, AND IOT, TAILORMADE MACHINE LEARNING, AND NOVEL BRAINWAVE FEATURE QUANTITY FOR PAIN DETERMINATION

Publication No.: US2022004913A1 06/01/2022

Applicant:

UNIV OSAKA [JP]

JP_2020203121_A

Absstract of: US2022004913A1

A high accuracy information extracting device construction system includes: a feature quantity extraction expression list generating unit for generating a feature quantity extraction expression list; a feature quantity calculating unit for calculating feature quantities of teacher data by means of respective feature quantity extracting expressions; a teacher data supply unit for supplying teacher data; an evaluation value calculating unit for generating information extracting expressions by means of machine learning on the basis of the calculated feature quantities of teacher data and the teacher data, and calculating evaluation values for the respective feature quantity extracting expressions; and a synthesis unit for constructing a high accuracy information extracting device using T weak information extracting parts F(X)t output from the evaluation value calculating unit 15 and confidence levels Ct corresponding thereto.

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DETECTING COGNITIVE BIASES IN INTERACTIONS WITH ANALYTICS DATA

Publication No.: US2022004898A1 06/01/2022

Applicant:

ADOBE INC [US]

Absstract of: US2022004898A1

The present disclosure relates to methods, systems, and non-transitory computer-readable media for determining a cognitive, action-selection bias of a user that influences how the user will select a sequence of digital actions for execution of a task. For example, the disclosed systems can identify, from a digital behavior log of a user, a set of digital action sequences that correspond to a set of sessions for a task previously executed by the user. The disclosed systems can utilize a machine learning model to analyze the set of sessions to generate session weights. The session weights can correspond to an action-selection bias that indicates an extent to which a future session for the task executed by the user is predicted to be influenced by the set of sessions. The disclosed systems can provide a visual indication of the action-selection bias of the user for display on a graphical user interface.

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MACHINE LEARNING PIPELINE FOR PREDICTIONS REGARDING A NETWORK

Publication No.: US2022004897A1 06/01/2022

Applicant:

JUNIPER NETWORKS INC [US]

CN_113886001_PA

Absstract of: US2022004897A1

This disclosure describes techniques that include using an automatically trained machine learning system to generate a prediction. In one example, this disclosure describes a method comprising: based on a request for the prediction: training each respective machine learning (ML) model in a plurality of ML models to generate a respective training-phase prediction in a plurality of training-phase predictions; automatically determining a selected ML model in the plurality of ML models based on evaluation metrics for the plurality of ML; and applying the selected ML model to generate the prediction based on data collected from a network that includes a plurality of network devices.

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IMAGE ENCODER USING MACHINE LEARNING AND DATA PROCESSING METHOD OF THE IMAGE ENCODER

Publication No.: US2022007045A1 06/01/2022

Applicant:

SAMSUNG ELECTRONICS CO LTD [KR]

TW_201924340_A

Absstract of: US2022007045A1

An image encoder for outputting a bitstream by encoding an input image includes a predictive block, a machine learning based prediction enhancement (MLBE) block, and a subtractor. The predictive block is configured to generate a prediction block using data of a previous input block. The MLBE block is configured to transform the prediction block into an enhanced prediction block by applying a machine learning technique to the prediction block. The subtractor is configured to generate a residual block by subtracting pixel data of the enhanced prediction block from pixel data of a current input block.

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SYSTEMS AND METHODS OF APPLICATION OF MACHINE LEARNING TO TRAFFIC DATA FOR VEHICLE RECOMMENDATION

Publication No.: US2022005100A1 06/01/2022

Applicant:

CAPITAL ONE SERVICES LLC [US]

US_11151633_B1

Absstract of: US2022005100A1

According to certain aspects of the disclosure, a computer-implemented method may be used for providing vehicle recommendations based on traffic camera data. The method may include acquiring traffic data from a plurality of traffic cameras and determining vehicle information based on the traffic data. Additionally, calculating average idling time of vehicles based on the traffic data from the plurality of traffic cameras. Additionally, determining accident information based on the traffic data from the plurality of traffic cameras and selecting at least one available vehicle for purchase, wherein the at least one available vehicle matches one or more predetermined requirements based on the determined vehicle information, average idling time, and the determined accident information. Additionally, transmitting a recommendation based on the selected at least one available vehicle for purchase.

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APPARATUS AND METHOD FOR CLUSTERING VALIDATION BASED ON MACHINE LEARNING PERFORMANCE

Publication No.: KR20210158740A 31/12/2021

Applicant:

한국전자통신연구원

Absstract of: KR20210158740A

본 발명은 기계학습 성능 기반 클러스터링 평가 장치 및 그 방법에 관한 것이다. 본 발명에 따른 기계학습 성능 기반 클러스터링 평가 장치는 기계학습 응용 구현에 사용될 데이터, 기계학습 모델, 클러스터링 알고리즘을 수신하는 입력부와, 기계학습 성능 기반 클러스터링 평가 프로그램이 저장된 메모리 및 프로그램을 실행시키는 프로세서를 포함하고, 프로세서는 클러스터링 알고리즘을 적용하여 데이터 클러스터를 도출하고, 데이터 클러스터를 활용하여 기계학습 모델을 학습하고, 구현하고자 하는 응용의 성능 관련 수치를 도출하고, 기계학습 응용 성능에 대한 기대에 대응되는 클러스터링 알고리즘 및 클러스터 수 중 적어도 어느 하나를 출력한다.

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SYSTEMS AND METHODS FOR WEB TRACKER CLASSIFICATION AND MITIGATION

Publication No.: US2021409382A1 30/12/2021

Applicant:

MICROSOFT TECHNOLOGY LICENSING LLC [US]

Absstract of: US2021409382A1

Embodiments described herein are directed to intelligently classifying Web trackers in a privacy preserving manner and mitigating the effects of such Web trackers. As users browse the Web and encounter various Web sites, tracker-related metrics are determined. The metrics are obfuscated to protect the privacy of the user. The obfuscated metrics are provided as inputs to a machine learning model, which is configured to output a classification for the Web trackers associated with the Web sites visited by the user. Depending on the classification, the effects of the Web trackers are mitigated by placing restrictions on the Web trackers. The restrictions for a particular Web tracker may be relaxed based on a level of user engagement a user has with respect to the tracker's associated Web site. By doing so, the compatibility risks associated with tracking prevention are mitigated for Web sites that are relatively important to the user.

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PRIVATE AND FEDERATED LEARNING

Publication No.: US2021409197A1 30/12/2021

Applicant:

IBM [US]

US_2020358599_A1

Absstract of: US2021409197A1

Techniques regarding privacy preservation in a federated learning environment are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a plurality of machine learning components that can execute a machine learning algorithm to generate a plurality of model parameters. The computer executable components can also comprise an aggregator component that can synthesize a machine learning model based on an aggregate of the plurality of model parameters. The aggregator component can communicate with the plurality of machine learning components via a data privacy scheme that comprises a privacy process and a homomorphic encryption process in a federated learning environment.

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Secure Machine Learning Analytics Using Homomorphic Encryption

Publication No.: US2021409191A1 30/12/2021

Applicant:

ENVEIL INC [US]

US_11196541_B2

Absstract of: US2021409191A1

Provided are methods and systems for performing a secure machine learning analysis over an instance of data. An example method includes acquiring, by a client, a homomorphic encryption scheme, and at least one machine learning model data structure. The method further includes generating, using the encryption scheme, at least one homomorphically encrypted data structure, and sending the encrypted data structure to at least one server. The method includes executing a machine learning model, by the at least one server based on the encrypted data structure to obtain an encrypted result. The method further includes sending, by the server, the encrypted result to the client where the encrypted result is decrypted. The machine learning model includes neural networks and decision trees.

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HANDHELD MONITORING AND EARLY WARNING DEVICE FOR FUSARIUM HEAD BLIGHT OF IN-FIELD WHEAT AND EARLY WARNING METHOD THEREOF

Publication No.: US2021407282A1 30/12/2021

Applicant:

UNIV NANJING AGRICULTURAL [CN]

CN_111611983_A

Absstract of: US2021407282A1

A handheld monitoring and early warning device for Fusarium head blight of in-field wheat includes an acquisition card, a processor, a camera, a touchscreen, a power supply, and a 4G network card. The acquisition card is configured to acquire data. The processor is configured to analyze the acquired data, to obtain the growth of wheat based on a deep learning algorithm. The camera is configured to acquire root, stem, and ear information of in-field wheat. The touchscreen is a medium configured to perform human-computer interaction. The power supply is configured to supply power to the monitoring and early warning device. The 4G network card is configured to perform data communication and at the same time communicate with an external cloud server. Further disclosed is an early warning method of a handheld monitoring and early warning device for Fusarium head blight of in-field wheat.

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Formation Flight of Unmanned Aerial Vehicles

Publication No.: US2021403159A1 30/12/2021

Applicant:

ERICSSON TELEFON AB L M [SE]

WO_2020079702_PA

Absstract of: US2021403159A1

A method (100) for managing a group of Unmanned Aerial Vehicles (UAVs) operable to fly in a formation is disclosed, each UAV being programmed with a task to be performed by the UAV. The method is performed in a controller UAV of the group and comprises receiving UAV status information from UAVs in the group (110), obtaining information on a current formation of the group (120) and combining the received UAV status information with the information on current group formation to form a representation of a first state of the group (130). The method further comprises using a machine learning model to predict, on the basis of the first state of the group, an optimal formation transition to a new formation (140) and to instruct the UAVs in the group to perform the predicted optimal formation transition (150). An optimal formation transition is a transition to a formation that will minimise predicted total energy consumption for all UAVs in the group to complete their tasks.

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BUILDING AND MANAGING COHESIVE INTERACTION FOR VIRTUAL ASSISTANTS

Publication No.: US2021406048A1 30/12/2021

Applicant:

KASISTO INC [US]

Absstract of: US2021406048A1

A method includes receiving data comprising a plurality of requests and a plurality of responses to the requests. The requests and the responses are associated with a virtual assistant programmed to address the plurality of requests. In the method, a machine learning (ML) classifier is used to partition the requests into a plurality of partitions corresponding to a plurality of request types. An interface for a user is generated to display a subset of the requests corresponding to at least one partition of the plurality of partitions and to display a response corresponding to the subset of the plurality of requests, wherein the response is based on one or more of the plurality of responses. The interface is configured to permit editing of the response by the user. The method also includes processing the response edited by the user, and transmitting the edited response to the virtual assistant.

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PREDICTING SYSTEM IN ADDITIVE MANUFACTURING PROCESS BY MACHINE LEARNING ALGORITHMS

Publication No.: US2021405613A1 30/12/2021

Applicant:

ATOS SPAIN SA [ES]

Absstract of: US2021405613A1

It is disclosed a method and a predicting system for automatic prediction of porosity appearance generated during Laser Powder Bed Fusion (L-PBF), performed by an additive manufacturing system from at least one material. The method comprises steps for training a neural network comprising: generating labels of pore in every pixel using a porosity simulator; pre-training, comprising a first sub-step and a second sub-step, the second sub-step comprises using the data set created from the first sub-step to generate a pre-trained ML model; and training, comprising a first sub-step and a second sub-step, the second sub-step comprises using the data set created from the first sub-step to train the pre-trained ML model to generate a trained ML model.

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SYSTEMS AND METHODS FOR PART TRACKING USING MACHINE LEARNING TECHNIQUES

Publication No.: US2021405620A1 30/12/2021

Applicant:

ILLINOIS TOOL WORKS [US]

Absstract of: US2021405620A1

Systems and methods for part tracking using machine learning techniques are described. In some examples a part tracking system analyzes feature characteristics related to one or more welds to identify, determine characteristics of, and/or label one or more parts repeatedly assembled by the welds. Identifying parts assembled from the welds may make it possible to do part based analytics (e.g., related to part quality, cost, production efficiency, etc.), as opposed to just weld based analytics, on past welding data. Additionally, identifying a part assembled from several welds results in an ordering of those several welds used to create the part, which can make it easier to compare/contrast similar welds across parts. Further, determining the characteristics of the parts can assist in configuring certain part tracking systems, thereby reducing the expertise, time, and personnel required.

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VISUAL PRODUCT IDENTIFICATION

Nº publicación: US2021407109A1 30/12/2021

Applicant:

MAUI JIM INC [US]

Absstract of: US2021407109A1

A model for visual product identification uses object detection with machine learning. Image acquisition with augmented reality can improve the model's identification, which may include classifying those images that is further verified with machine learning. Usage of the visual product identification data can further improve the model.

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