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    METHOD FOR GENERALIZED AND ALIGNMENT MODEL FOR REPAIR RECOMMENDATION
    2.
    发明申请
    METHOD FOR GENERALIZED AND ALIGNMENT MODEL FOR REPAIR RECOMMENDATION 有权

    公开(公告)号：US20250053800A1

    公开(公告)日：2025-02-13

    申请号：US18232063

    申请日：2023-08-09

    Applicant: HITACHI, Ltd.

    Inventor： Dipanjan GHOSH ,  Ahmed FARAHAT ,  Xian Yeow LEE ,  Lasitha VIDYARATNE ,  Chetan GUPTA

    IPC: G06N3/08 ,  G06N3/0475

    Abstract: Systems and methods described herein can involve training a first generative artificial intelligence (AI) model for a general domain, the first generative AI model trained using standard information components of the general domain; training a second AI model for a specific domain from the first generative AI model, the training of the second AI model being based on the use of the standard information components, non-standard information components of the specific domain and available label data of the specific domain; and fine-tuning the second AI model to align with preferences of the specific domain to maximize reward and minimize error.

    MULTI-OBJECTIVE MULTI-POLICY REINFORCEMENT LEARNING SYSTEM
    3.
    发明申请
    MULTI-OBJECTIVE MULTI-POLICY REINFORCEMENT LEARNING SYSTEM 有权

    公开(公告)号：US20240403381A1

    公开(公告)日：2024-12-05

    申请号：US18205781

    申请日：2023-06-05

    Applicant: HITACHI, Ltd.

    Inventor： Takuya KANAZAWA ,  Haiyan WANG ,  Chetan GUPTA

    IPC: G06F17/11

    Abstract: Systems and methods described herein can involve obtaining Pareto optimal solutions through making sequential decisions in a system that has multi-dimensional rewards and a continuous state space, and is controllable through a finite discrete set of actions, involving learning a value function through reinforcement learning (RL), wherein the value function is configured to take in an input of a state and an action pair, and provides a set of vectors as output, each of the set of vectors representing an expected total sum of rewards corresponding to a sequence of future control decisions; receiving, at an initial stage of a control sequence, a request about a total sum of rewards to be achieved; and determining a sequence of actions iteratively based on the output of the value function, an observation of the current state, and the request.

    UNCERTAINTY-AWARE CONTINUOUS CONTROL SYSTEM BASED ON REINFORCEMENT LEARNING
    4.
    发明公开
    UNCERTAINTY-AWARE CONTINUOUS CONTROL SYSTEM BASED ON REINFORCEMENT LEARNING 审中-公开

    公开(公告)号：US20240013090A1

    公开(公告)日：2024-01-11

    申请号：US17862147

    申请日：2022-07-11

    Applicant: Hitachi, Ltd.

    Inventor： Takuya KANAZAWA ,  Haiyan WANG ,  Chetan GUPTA

    IPC: G06N20/00

    CPC classification number: G06N20/00

    Abstract: A method for reinforcement learning (RL) of continuous actions. The method may include receiving a state as input to at least one actor network to predict candidate actions based on the state, wherein the state is a current observation; outputting the candidate actions from the at least one actor network; receiving the state and the candidate actions as inputs to a plurality of distributional critic networks, wherein the plurality of distributional critic networks calculates quantiles of a return distribution associated with the candidate actions in relation to the state; outputting the quantiles from the plurality of distributional critic networks; and selecting an output action based on the candidate actions and the quantiles.

    VERSATILE ANOMALY DETECTION SYSTEM FOR INDUSTRIAL SYSTEMS
    5.
    发明公开
    VERSATILE ANOMALY DETECTION SYSTEM FOR INDUSTRIAL SYSTEMS 审中-公开

    公开(公告)号：US20230341832A1

    公开(公告)日：2023-10-26

    申请号：US17730007

    申请日：2022-04-26

    Applicant: Hitachi, Ltd.

    Inventor： Qiyao WANG ,  Wei HUANG ,  Ahmed FARAHAT ,  Haiyan WANG ,  Chetan GUPTA

    IPC: G05B19/4063

    CPC classification number: G05B19/4063 ,  G05B2219/14036

    Abstract: A method for detecting an anomaly in time series sensor data. The method may include identifying a noisiest cycle from the time series sensor data; for an evaluation of the noisiest cycle indicative of the anomaly being detected at a confidence level above a threshold, providing an output associated with the noisiest cycle as being the anomaly; and for the evaluation of the noisiest cycle indicative of the anomaly being detected at the confidence level not above the threshold: identifying a cycle from the time series sensor data having a most differing shape; and providing the output associated with the cycle having the most differing shape as being the anomaly.

    METHOD FOR CREATING A KNOWLEDGE BASE OF COMPONENTS AND THEIR PROBLEMS FROM SHORT TEXT UTTERANCES
    6.
    发明申请
    METHOD FOR CREATING A KNOWLEDGE BASE OF COMPONENTS AND THEIR PROBLEMS FROM SHORT TEXT UTTERANCES 审中-公开

    公开(公告)号：US20200327886A1

    公开(公告)日：2020-10-15

    申请号：US16380343

    申请日：2019-04-10

    Applicant: Hitachi, Ltd.

    Inventor： Walid SHALABY ,  Chetan GUPTA ,  Maria Teresa GONZALEZ DIAZ ,  Adriano ARANTES

    IPC: G10L15/22 ,  G10L15/18

    Abstract: Example implementations involve a framework for knowledge base construction of components and problems in short texts. The framework extracts domain-specific components and problems from textual corpora such as service manuals, repair records, and public Q/A forums using: 1) domain-specific syntactic rules leveraging part of speech tagging (POS), and 2) a neural attention-based seq2seq model which tags raw sentences end-to-end identifying components and their associated problems. Once acquired, this knowledge can be leveraged to accelerate the development and deployment of intelligent conversational assistants for various industrial AI scenarios (e.g., repair recommendation, operations, and so on) through better understanding of user utterances. The example implementations give better tagging accuracy on various datasets outperforming well known off-the-shelf systems.

    METHOD FOR COMBINING CLASSIFICATION AND FUNCTIONAL DATA ANALYSIS FOR ENERGY CONSUMPTION FORECASTING
    10.
    发明公开
    METHOD FOR COMBINING CLASSIFICATION AND FUNCTIONAL DATA ANALYSIS FOR ENERGY CONSUMPTION FORECASTING 审中-公开

    公开(公告)号：US20240249135A1

    公开(公告)日：2024-07-25

    申请号：US18100933

    申请日：2023-01-24

    Applicant: Hitachi, Ltd.

    Inventor： Aniruddha Rajendra RAO ,  Chandrasekar VENKATRAMAN ,  Chetan GUPTA

    IPC: G06N3/08

    CPC classification number: G06N3/08

    Abstract: Example implementations described herein involve systems and methods that can include, for receipt of time-series data indicative of energy consumption associated with a type of building of a plurality of different types of buildings and a climatic zone from a plurality of climatic zones, executing random convolutional kernel (RCK) on the time-series data to generate a classification group of the time-series data according to type of building and the climatic zone; and executing a trained functional neural network (FNN) on the time-series data of the classification group to provide a short-term energy consumption forecast.

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