Oracle Financial Services Software Limited

Samsung Electronics Co., Ltd.

Oracle International Corporation

Wipro Limited

Rahul Yadav

Veresh Jain

Shubham Negi

Gopichand Agnihotram

Arihant Jain

Phanindra Choda

Saroj Jasmine Merwana

Ajay Sharma

Sharoon Saxena

Nidhi Rajput

Rahul Kiran Gaddam

Amit Omprakash Rathi

Krishna Sameera Ellendula

Aashima Sethi

Kaushalendra Mishra

Krishnapriya Jaya Radhakrishnan

Utkarsh Hemant Kumar Sharma

A method includes: obtaining a record including text narration; inputting the text narration into an NLP model; generating, by the NLP model, encoded narration based on the text narration; determining similarity index between the encoded narration and each of encoded reference topics; comparing similarity index between the encoded narration and each encoded reference topic to similarity threshold; and determining whether similarity index between the encoded narration and each encoded reference topic is equal to or greater than similarity threshold. When the similarity index is equal to or greater than the similarity threshold, a corresponding reference topic is added to a result group, and, when the similarity index is less than the similarity threshold, the corresponding reference topic is left out of the result group. The record is classified based on the reference topic included in the result group and corresponding to the encoded reference topic having greatest similarity index.

Reference driven NLP-based topic categorization

Systems, methods, and other embodiments associated with identifying a document type of an unidentified document based on image features are described. In one embodiment, image pairs are formed by pairing the unidentified document with anchor images from a plurality of anchor images, wherein each anchor image is a known document type. For each image pair, first visual features are extracted from the unidentified document and second visual features are extracted from the paired anchor image. A similarity function is applied to compare the first visual features and the second visual features, and a similarity score is generated for each image pair based on the comparing. The most similar anchor image from the image pairs, which has a greatest similarity score, is identified. The document type of the unidentified document is then predicted as the known document type associated with the most similar anchor image.

Framework for identifying documents

A bill verification system for verifying bill records associated with an entity account is disclosed. The system receives a request to verify a bill record associated with an entity account. The system identifies a bill level model to be used for verifying the bill record and detects based on the bill level model, bill level anomaly information for the bill record. The system additionally identifies a bill line level model to be used for verifying one or more bill lines in the bill record and detects based on the bill line level model, bill line level anomaly information for the bill record. The system then aggregates the bill level anomaly information and the bill line level anomaly information to generate a bill verification report for the bill record. The system provides the bill verification report as a response to the request received to verify the bill record.

Anomaly detection for bill generation

According to an aspect, a technology system maintains an account data specifying the details of multiple accounts including a specific set of accounts as being delinquent. Upon receiving a feature set to be used, the system clusters the multiple accounts into groups based on the feature set. The system generates a multi-classification model for assignment of agents to the accounts based on the group qualifiers associated with the groups and the feature set. The system then identifies a target group likely to present problems with debt collection, assigns, using the multi-classification model, a corresponding agent for each account of the group and determines a recommended recovery strategy for each assignment. The system provides the details of the group, the corresponding agents assigned and the recommended recovery strategy for each assignment.

Technology system for assisting financial institutions in debt collection

The present invention discloses method and system for multimodal analysis based emotion recognition. The method comprising segmenting video data of a user into a plurality of video segments. A plurality of visual features, voice features and text features from the plurality of video segments is extracted. Autocorrelation values among each of the plurality of visual features, the voice features, and the text features is determined. Each of the plurality of visual features, the voice features and the text features is aligned based on video segment identifier and the autocorrelation values to obtain a plurality of aligned multimodal features. One of two classes of emotions is determined for each of the plurality of aligned multimodal features. The determined emotion for each of the plurality of aligned multimodal features is compared with historic multimodal features from a database, and emotion of the user is determined at real time based on comparison.

Method and system for multimodal analysis based emotion recognition

A method of authenticating a user includes obtaining a user authentication request for access to at least one application executed on an electronic device, identifying an actor and a task for authentication based on one or more context parameters associated with at least one of the electronic device or a user, providing a live challenge generated based on the identification, and identifying whether to access the at least one application based on whether the provided live challenge has been successfully performed.

Method and apparatus for performing user authentication

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Alwar, India

Rahul Yadav