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    METHOD AND SYSTEM FOR DEEP LEARNING BASED IMAGE FEATURE EXTRACTION
    1.
    发明公开
    METHOD AND SYSTEM FOR DEEP LEARNING BASED IMAGE FEATURE EXTRACTION 审中-公开

    公开(公告)号：US20240013516A1

    公开(公告)日：2024-01-11

    申请号：US18218349

    申请日：2023-07-05

    Applicant: Tata Consultancy Services Limited

    Inventor： Bagya Lakshmi VASUDEVAN ,  Kalyan Prakash BAISHYA ,  Gaurav SHARMA

    IPC: G06V10/77 ,  G06V10/30 ,  G06V10/32 ,  G06V10/774 ,  G06V10/82

    CPC classification number: G06V10/7715 ,  G06V10/30 ,  G06V10/32 ,  G06V10/774 ,  G06V10/82

    Abstract: The present disclosure provides a model for deep learning based image feature extraction considering a range of useful negative features. Conventional methods are either considering only positive features or considering all negative features along with positive features which leads to bias in feature extraction. The present disclosure overcomes the problem of the conventional methods using a bounded Rectified Linear activation Unit (B-ReLU) activation function based Bounded-Rectifier Network (B-RectNet). Initially, the present disclosure receives an image pertaining to an object. Further, the received image is preprocessed to remove a plurality of anomalies associated with the image a preprocessing technique. Further, a plurality of image features are extracted based on the preprocessed image using a trained B-RectNet. The bounded ReLU activation function filters a plurality of negative features based on a lower negative bound value and an upper negative bound value before inputting a plurality of feature values to a subsequent layer.

    METHOD AND SYSTEM FOR PERSONALIZED OUTFIT COMPATIBILITY PREDICTION
    3.
    发明申请
    METHOD AND SYSTEM FOR PERSONALIZED OUTFIT COMPATIBILITY PREDICTION 有权

    公开(公告)号：US20240420215A1

    公开(公告)日：2024-12-19

    申请号：US18666920

    申请日：2024-05-17

    Applicant: Tata Consultancy Services Limited

    Inventor： Vivek Bangalore SAMPATHKUMAR ,  Jayavardhana Rama GUBBI LAKSHMINARASIMHA ,  Gaurab BHATTACHARYA ,  Bagya Lakshmi VASUDEVAN ,  Arpan PAL ,  Balamuralidhar PURUSHOTHAMAN

    IPC: G06Q30/0601

    Abstract: Unlike visual similarity, visual compatibility is a complex concept. Existing approaches for outfit compatibility prediction does not focus on methods with personalization. The present disclosure proposes a novel approach to model the user's preference for different styles. The outfit compatibility prediction module is a critical component of an outfit recommendation system. An outfit is said to be compatible if all the items are visually compatible and match the user's preferences. The present disclosure represents the outfit as a graph and uses Graph Neural Network (GNN) with attention mechanism to capture the inter-relationship between the items. A graph read-out layer generates the final outfit embedding. The proposed approach efficiently models the preferences of the users for different styles. Finally, the outfit compatibility score is generated by computing the similarity between the outfit embedding and the user embedding.

    METHOD AND SYSTEM TO OPTIMIZE A PLURALITY OF TOPICS BY EVALUATION METRICS
    4.
    发明公开
    METHOD AND SYSTEM TO OPTIMIZE A PLURALITY OF TOPICS BY EVALUATION METRICS 审中-公开

    公开(公告)号：US20230267136A1

    公开(公告)日：2023-08-24

    申请号：US18110946

    申请日：2023-02-17

    Applicant: Tata Consultancy Services Limited

    Inventor： Bagya Lakshmi VASUDEVAN ,  Lavanya GUNASEKAR ,  Ananya SRIVASTAVA

    IPC: G06F16/31

    CPC classification number: G06F16/313

    Abstract: Rapid development in digitization results in generation of huge amounts of unstructured text data by most organizations from several sources. A major drawback of using existing metrics in the data management is assessing quality of topics associated with context in a document. A processed dataset obtained from a raw dataset is mapped to obtain a relationship between one or more words from one or more topics. A word score is determined based on probability of number of constructs in sentences. At least one repetitive word from the one or more topics are determined. A topic score with degree of contextual association between the one or more words in each topic is determined by mean value of the word score. A contiguity score with degree of contextual association between the one or more topics is determined based on median value of the topic score to obtain one or more connected topics.

    METHOD AND SYSTEM FOR PRODUCT DATA CATEGORIZATION BASED ON TEXT AND ATTRIBUTE FACTORIZATION
    5.
    发明公开
    METHOD AND SYSTEM FOR PRODUCT DATA CATEGORIZATION BASED ON TEXT AND ATTRIBUTE FACTORIZATION 审中-公开

    公开(公告)号：US20230153880A1

    公开(公告)日：2023-05-18

    申请号：US17974763

    申请日：2022-10-27

    Applicant: Tata Consultancy Services Limited

    Inventor： Bagya Lakshmi VASUDEVAN ,  Sudesna BARUAH

    IPC: G06Q30/06 ,  G06F40/30

    CPC classification number: G06Q30/0627 ,  G06F40/30

    Abstract: This disclosure relates generally to method and system for product data categorization based on text and attribute factorization. The method includes acquiring an input describing a set of product data from an application data store for categorization. The set of product data by removing extraneous text based on a predefined template. Further a dictionary for the set of product data based on a set of attributes comprising a product key with its corresponding product value. Further, a multi-level contextual data for the set of product data are extracted by assigning a weight to each product data based on likelihood and creating a set of datapoints for each product data. The set of product data are categorized by feeding the set of data points to a set of predefined parameters to compute a minimum count, a total size, total number of epochs, a skip gram value and a hierarchical softmax.

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