Broadridge Financial Solutions, Inc.

Joseph Lo

James Kwiatkowski

Luca Marchesotti

Richard Bryce

Fitim Kryeziu

Nicholas Aronow

Jaiden Fallo

The present disclosure includes gathering a tagged document of a type, collecting a repository of tags pertaining to the predetermined type of document, providing the tagged document and the repository of tags to train a first pre-trained LLM, identifying a first tag in the gathered document, pairing one text with the first tag, identifying one value associated with the paired first tag and the text, formatting the paired first tag and the text and the associated value to form a training message to train a second pre-trained LLM, providing an unseen document of the type to the first trained LLM, generating, via executing the first trained LLM, a second tag from the unseen document, providing the second tag and the unseen document to the second trained LLM, and identifying, via executing the second trained LLM, an unseen text paired with the second tag and an associated value.

Systems and methods for classifying strings of arbitrary length in a large number of classes

Systems and methods of the present disclosure may receive, from a user computing device, a user-provided data record query including a natural language request for information associated with one or more data sources. User persona attributes of the user may be determined, such as a user role or security parameters or both. Based on the user persona attributes a context query may be generated to obtain context attributes associated with the user-provided query. The natural language request and the context attributes are input into the model orchestration large language model (LLM) to output instructions to machine learning (ML) agents based on the context attributes. The ML agents output responses associated with the user-provided data record query based on the instructions, and the responses are input into the model orchestration LLM to output to the user computing device a natural language response based on the context attributes.

Systems and methods of large language model driven orchestration of task-specific machine learning software agents

A machine-learning based method includes receiving an instruction to model an engagement predicting score for a user. User-specific, activity-specific data is obtained from digital resources that include a user-specific activity performance data regarding performance of at least one activity by the user, an object data for an object that allows the user to perform the at least one activity, and user-specific personal data of the user. A user-specific activity engagement labeling data for the at least one activity is predicted by utilizing a first-type data pipeline on the at least one user-specific activity performance data. User-specific, activity-specific data features are predicted by utilizing a second-type data pipeline on the user-specific, activity-specific data. The engagement predicting score is predicted from the user-specific, activity-specific data features and the user-specific activity engagement labeling data. A computing device is instructed to present at least one user-specific activity-related action instruction.

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Newark, NJ, United States of America

Joseph Lo