International Business Machines Corporation

Liu Yao He

Lu Yu

Wen Jie Wang

Yi Chen Zhong

Ying Li

Yi Ming Wang

A present invention embodiment analyzes documents. A first document is received comprising a plurality of sentences that each include one or more words. A matrix is populated with the plurality of sentences, wherein each of the one or more words of each sentence in the matrix is encoded as a numerical value. The matrix is processed using a machine learning model to generate a first feature map. The first feature map is compared to a second feature map of a corresponding second document to identify a shared document element between the first document and the second document based on a common feature in the first feature map and the second feature map. The shared document element is indicated via a user interface.

Method implemented in computer system for analyzing document versions to identify shared document elements using machine learning, and non-transitory computer-readable storage medium

A computer retrieves data from a database. The computer retrieves a Machine Learning (ML) model trained to generate database queries. The computer applies the ML model to generate a primary database query based, at least in part, on a user inquiry available to the computer. The computer retrieves the primary database query, an initial set of data from a database available to the computer. The computer, in response to retrieving the initial set of data, receives feedback assessing the initial set of data. The computer, in response to receiving the feedback, applies a Natural Language Processing (NLP) model to identify query adjustment content within the feedback. The computer revises the primary database query based, at least in part, on the model adjustment content, to generate a secondary database query. The computer retrieves using the secondary database query, a secondary set of data from the database.

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Beijing, China

Liu Yao He