SYSTEM AND METHOD FOR ANALYSIS AND DETERMINATION OF RELATIONSHIPS FROM A VARIETY OF DATA SOURCES
First Claim
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1. A computer-implemented method for analyzing data from a variety of data sources, the method comprising:
- receiving, as inputs, data from the variety of data sources;
converting the received data from each of the variety of data sources into a common data structure;
identifying keywords in the received data;
generating sentence or word embeddings based on the identified keywords;
receiving a selection of one or more labels based on the generated sentence or word embeddings;
adding the selected one or more labels to a model;
training the model over the common data structure based on a configuration file; and
generating a result in response to a user question based on the model, wherein the generating includes;
retrieving related documents from the received data;
determining which information should be reported from which of the retrieved related documents; and
providing the result based on the determination and a graph schema associated with the related documents.
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Abstract
The invention relates to computer-implemented systems and methods for analyzing data from a variety of data sources. Embodiments of the systems and the methods further provide for generating responses to specific questions based on the analyzed data, wherein the generating includes: retrieving related documents associated with the analyzed data; determining which information should be reported from which of the retrieved related documents; and providing a response based on the determination and a graph schema associated with the related documents.
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Citations
19 Claims
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1. A computer-implemented method for analyzing data from a variety of data sources, the method comprising:
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receiving, as inputs, data from the variety of data sources; converting the received data from each of the variety of data sources into a common data structure; identifying keywords in the received data; generating sentence or word embeddings based on the identified keywords; receiving a selection of one or more labels based on the generated sentence or word embeddings; adding the selected one or more labels to a model; training the model over the common data structure based on a configuration file; and generating a result in response to a user question based on the model, wherein the generating includes; retrieving related documents from the received data; determining which information should be reported from which of the retrieved related documents; and providing the result based on the determination and a graph schema associated with the related documents. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A computer-implemented system for analyzing data from a variety of data sources, the system comprising:
a processor, wherein the processor is configured to; receive, as inputs, data from the variety of data sources; convert the received data from each of the variety of data sources into a common data structure; identify keywords in the received data; generate word or sentence embeddings based on the identified keywords; receive a selection of one or more labels based on the generated word or sentence embeddings; add the selected one or more labels to a model; train the model over the common data structure based on a configuration file; and generate a result in response to a user question based on the model, wherein the generating includes; retrieving related documents from the received data; determining which information should be reported from which of the retrieved related documents; and providing the result based on the determination and a graph schema associated with the related documents. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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19. A computer-implemented system for analyzing data from a variety of data sources, the system comprising:
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an application programming interface; and a processor, wherein the processor is configured to; generate a result in response to a user question based on a machine learning model, wherein the generating includes; retrieving related documents from the received data; determining which information should be reported from which of the retrieved related documents; and providing the result based on the determination and a graph schema associated with the related documents; wherein the machine learning model is trained on annotation candidates provided by the application programming interface.
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Specification