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    SYSTEMS AND METHODS FOR IDENTIFICATION AND REPLENISHMENT OF TARGETED ITEMS ON SHELVES OF STORES
    3.
    发明公开
    SYSTEMS AND METHODS FOR IDENTIFICATION AND REPLENISHMENT OF TARGETED ITEMS ON SHELVES OF STORES 审中-公开

    公开(公告)号：US20240346438A1

    公开(公告)日：2024-10-17

    申请号：US18603014

    申请日：2024-03-12

    申请人： Tata Consultancy Services Limited

    发明人： SAI RAVIKANTH PARVATAM LAKSHMI ,  MOHAN KRISHNAN ,  SAMEER VASANT AMBALKAR

    IPC分类号： G06Q10/087 ,  G06Q30/0202

    CPC分类号： G06Q10/087 ,  G06Q30/0202

    摘要： Retail stores have limited visibility of on shelf inventory. Conventional approaches for targeted replenishment are reactive in nature and are also infrastructure and labor heavy. Present disclosure provides systems and methods for identification and replenishment of targeted items on shelves of stores wherein input data pertaining to sales of items is pre-processed and stock keeping unit (SKU) wise optimal bucket size is determined for predicting sales events for individual SKU based on historical events. Top-up requests are generated for each SKU for the planning bucket sizes and further a pick-up list using smart batching of the top-up requests is created based on SKU priorities. The pick-up list and top-up requests are executed to ensure items are topped up at the right time. Further, rate of sales or forecast the rate of sales are continually monitored throughout the day to ensure items are identified for targeted replenishment in retail stores.

    SYSTEMS AND METHODS FOR MONITORING AND CONTROLLING A MANUFACTURING PROCESS USING CONTEXTUAL HYBRID DIGITAL TWIN
    4.
    发明公开
    SYSTEMS AND METHODS FOR MONITORING AND CONTROLLING A MANUFACTURING PROCESS USING CONTEXTUAL HYBRID DIGITAL TWIN 审中-公开

    公开(公告)号：US20240345570A1

    公开(公告)日：2024-10-17

    申请号：US18426152

    申请日：2024-01-29

    申请人： Tata Consultancy Services Limited

    发明人： ANKUR KRISHNA ,  PURUSHOTTHAM GAUTHAM BASAVARSU ,  SREEHARI KUMAR BHOGINENI

    IPC分类号： G05B19/418

    CPC分类号： G05B19/41885

    摘要： This disclosure relates generally to systems and methods for monitoring and controlling a manufacturing process using contextual hybrid digital twin. Data pertaining to the manufacturing process is obtained from a plurality of data generation sources which is further inputted to one or more physics based models and train machine learning models such that simulated data and real time tata is obtained. Further, a gap between the simulated data and the real time data is determined and learnt. The learnt gap is further minimized and an augmented set of models are obtained. The augmented set of models along with a set of soft-sensing data is used to create the contextual hybrid digital twin for the manufacturing process. The performance of the manufacturing process is monitored and controlled using a performance analytics and decision making enablers of the contextual hybrid digital twin respectively in real time.

    Multi-agent deep reinforcement learning for dynamically controlling electrical equipment in buildings
    5.
    发明授权
    Multi-agent deep reinforcement learning for dynamically controlling electrical equipment in buildings 有权

    公开(公告)号：US12111620B2

    公开(公告)日：2024-10-08

    申请号：US17029788

    申请日：2020-09-23

    申请人： Tata Consultancy Services Limited

    发明人： Srinarayana Nagarathinam ,  Avinash Achar ,  Arunchandar Vasan

    IPC分类号： G05B13/02 ,  F24F11/62 ,  G05B15/02 ,  G06N3/088

    CPC分类号： G05B13/027 ,  F24F11/62 ,  G05B15/02 ,  G06N3/088

    摘要： Reinforcement Learning agent interacting with a real-world building to determine optimal policy may not be viable due to comfort constraints. Embodiments of the present disclosure provide multi-deep agent RL for dynamically controlling electrical equipment in buildings, wherein a simulation model is generated using design specification of (i) controllable electrical equipment (or subsystem) and (ii) building. Each RL agent is trained using simulation model and deployed in the subsystem. Reward function for each subsystem includes some portion of reward from other subsystem(s). Based on reward function of each RL agent, each RL agent learns an optimal control parameter during execution of RL agent in subsystem. Further, a global optimal control parameter list is generated using the optimal control parameter. The control parameters in the global optimal control parameters list are fine-tuned to improve subsystem's performance. Information on fine-tuning parameters of the subsystem and reward function are used for training RL agents.

    METHOD AND SYSTEM OF TRAINING OF CHAINED NEURAL NETWORKS FOR DELAY PREDICTION IN TRANSIT NETWORKS
    6.
    发明公开
    METHOD AND SYSTEM OF TRAINING OF CHAINED NEURAL NETWORKS FOR DELAY PREDICTION IN TRANSIT NETWORKS 审中-公开

    公开(公告)号：US20240330787A1

    公开(公告)日：2024-10-03

    申请号：US18493639

    申请日：2023-10-24

    申请人： Tata Consultancy Services Limited

    发明人： Rohith REGIKUMAR ,  Priyanga KASTHURIRAJAN ,  Arvind RAMANUJAM ,  Rajesh JAYAPRAKASH

    IPC分类号： G06Q10/04 ,  G06Q50/30

    CPC分类号： G06Q10/04 ,  G06Q50/40

    摘要： State of the art approaches for training chained neural network models for delay prediction train the data models using only real data and not predicted data. Such models when used in a chained way leads to worse results as they are not exposed to predicted data during training. This leads to the model prediction errors showing sharp increase as the models tries to predict for subsequent stations past the immediate station. Embodiments disclosed herein provide a method and system for training of chained neural networks for delay prediction in transit networks. In this approach, a chained neural network model used by the system is trained such that data containing a mix of real data and predicted data is used for training each data model in a sequence of data models in the chained neural network model.

    ROBOTIC NAVIGATION WITH SIMULTANEOUS LOCAL PATH PLANNING AND LEARNING
    9.
    发明公开
    ROBOTIC NAVIGATION WITH SIMULTANEOUS LOCAL PATH PLANNING AND LEARNING 审中-公开

    公开(公告)号：US20240319735A1

    公开(公告)日：2024-09-26

    申请号：US18417504

    申请日：2024-01-19

    申请人： Tata Consultancy Services Limited

    发明人： Arup Kumar SADHU ,  Lokesh KUMAR ,  Ranjan DASGUPTA ,  Mohit LUDHIYANI ,  Titas BERA

    IPC分类号： G05D1/229 ,  G05D1/246

    CPC分类号： G05D1/229 ,  G05D1/2469 ,  G05D2101/15

    摘要： In conventional robot navigation techniques learning and planning algorithms act independently without guiding each other simultaneously. A method and system for robotic navigation with simultaneous local path planning and learning is disclosed. The method discloses an approach to learn and plan simultaneously by assisting each other and improve the overall system performance. The planner acts as an actuator and helps to balance exploration and exploitation in the learning algorithm. The synergy between dynamic window approach (DWA) as a planning algorithm and a disclosed Next best Q-learning (NBQ) as a learning algorithm offers an efficient local planning algorithm. Unlike the traditional Q-learning, dimension of Q-tree in the NBQ is dynamic and does not require to define a priori.