公开(公告)号：US11501075B1

公开(公告)日：2022-11-15

申请号：US17365084

申请日：2021-07-01

申请人： FMR LLC

发明人： Abhinav Pandey ,  Praveen Narayanasamy ,  Kaustubh Ajgaonkar ,  Gunjan Narulkar ,  Ankur Jaiswal

IPC分类号： G06F40/10 ,  G06F40/295 ,  G06K9/62 ,  G06F16/33 ,  G06F40/216

摘要： Systems and methods for extracting data from unstructured data sources based on proximity co-reference resolution model. The method includes receiving an electronic document from an unstructured data source and extracting entities from the electronic document. The method also includes receiving fields to be extracted from the electronic document and generating keywords based on the fields. Each of the entities is associated with at least one of the fields. The method further includes identifying keywords in the electronic document based on the generated keywords and calculating, for each of the fields, proximity scores based on a proximity co-reference resolution model. The method also includes, for each of the fields, identifying a field-entity pair based on the calculated proximity scores and generating for display on a user device the field-entity pair.

公开(公告)号：US20240212684A1

公开(公告)日：2024-06-27

申请号：US18086742

申请日：2022-12-22

申请人： FMR LLC

发明人： Vaishnavi Mysore Sridhar ,  Anupriya Gupta ,  Gunjan Narulkar ,  Ankur Jaiswal ,  Suwetha Selvakumar ,  Mari Pavithra

IPC分类号： G10L15/26 ,  G10L15/02 ,  H04M3/51

CPC分类号： G10L15/26 ,  G10L15/02 ,  H04M3/5175 ,  G10L2015/025

摘要： A computer-implemented method is provided for recommending at least one pertinent electronic document for supporting a call between a customer and an agent. The method includes converting in real time content of the call between the customer and the agent from speech to digitized text, isolating a predefined number of words in the digitized text of the converted call content as the call is in progress and converting the predefined number of words in text to a phoneme sequence. The method also includes identifying at least one probable business category associated with the phoneme sequence and detecting sections of one or more documents associated with the probable business category that are similar to the content of the call.

公开(公告)号：US20240095549A1

公开(公告)日：2024-03-21

申请号：US18367563

申请日：2023-09-13

申请人： FMR LLC

发明人： Xiang Song ,  Abhinav Malhotra ,  Dennis Robert Bowden ,  Gunjan Narulkar ,  Alain Wilkinson ,  Manish Worlikar ,  Nicholas Luc Steenhaut

IPC分类号： G06N5/022

CPC分类号： G06N5/022

摘要： Methods and apparatuses are described for predictive analysis of transaction data using machine learning. A server computing device trains a plurality of machine learning models using historical transaction data for a set of entities as input to predict a likelihood of future transaction activity for each of the entities, each machine learning model trained on a different target transaction variable. The server computing device executes each of the plurality of machine learning models to generate, for each entity, a predicted likelihood value for a future transaction associated with the entity and each of the target transaction variables. The server computing device transmits the predicted likelihood values for each entity to a remote computing device for display.

公开(公告)号：US20220019738A1

公开(公告)日：2022-01-20

申请号：US16930026

申请日：2020-07-15

申请人： FMR LLC

发明人： Gunjan Narulkar ,  Shreyash Kumar Sharma ,  Greeshma Girish

IPC分类号： G06F40/279 ,  G06F40/232 ,  G06F40/10 ,  G06F16/93 ,  G06F16/33

摘要： Systems and methods for identifying data strings in electronic documents using pattern recognition. The method includes receiving a first data string corresponding to an electronic reference document from a first database and a second data string corresponding to an electronic legal document from a second database. The method also includes processing the first data string into a first processed data string and processing the second data string into a second processed data string. The method also includes calculating a cosine similarity between the first processed data string and the second processed data string. The method also includes receiving a feedback score from a user which corresponds to an accuracy of the calculated cosine similarity. The method also includes calculating an adjusted cosine similarity between the first processed data string and the second processed data string based on the calculated cosine similarity and the feedback score.