Ramesh M Nallapati

20210157845, May 27, 2021

Nov 27, 2019

16/697948

- Seattle WA, US
Zhiheng HUANG - Santa Clara CA, US
Xiaofei MA - New York NY, US
Ramesh M. NALLAPATI - New Canaan CT, US
Krishnakumar RAJAGOPALAN - Edison NJ, US
Milan SAINI - West New York NJ, US
Sudipta SENGUPTA - Sammamish WA, US
Saurabh Kumar SINGH - Seattle WA, US
Dimitrios SOULIOS - Seattle WA, US
Ankit SULTANIA - Seattle WA, US
Dong WANG - New York NY, US
Zhiguo WANG - Great Neck NY, US
Bing XIANG - Mount Kisco NY, US
Peng XU - Sunnyvale CA, US
Yong YUAN - Mercer Island WA, US

G06F 16/901
G06F 16/903
G06F 16/2457
G06N 3/04

Techniques for searching documents are described. An exemplary method includes receiving a document search query; querying at least one index based upon the document search query to identify matching data; fetching the identified matched data; determining one or more of a top ranked passage and top ranked documents from the set of documents based upon one or more invocations of one or more machine learning models based at least on the fetched identified matched data and the document search query; and returning one or more of the top ranked passage and the proper subset of documents.

20210157854, May 27, 2021

Nov 27, 2019

16/697979

- Seattle WA, US
Zhiheng HUANG - Santa Clara CA, US
Ramesh M. NALLAPATI - New Canaan CT, US
Bing XIANG - Mount Kisco NY, US

G06F 16/9038
G06N 20/00
G06F 16/93
G06F 16/908

Techniques for displaying a search are described. An exemplary method includes receiving a search query, performing the search query on a plurality of documents, the documents including text passages, to generate a search query result, determining an aspect of the search query result that has a confidence value that exceeds a first confidence threshold with respect to its relevance to the search query; and, displaying the search result including an emphasis on the aspect of the result exceeds the first confidence threshold.

20210157857, May 27, 2021

Nov 27, 2019

16/698080

- Seattle WA, US
Xiaofei MA - New York NY, US
Peng XU - Sunnyvale CA, US
Ramesh M. NALLAPATI - New Canaan CT, US
Bing XIANG - Mount Kisco NY, US
Sudipta SENGUPTA - Sammamish WA, US
Zhiguo WANG - Great Neck NY, US
Patrick NG - Jersey City NJ, US

G06F 16/9032
G06N 20/00
G06F 16/9038
G06K 9/62

Techniques for generation of synthetic queries from customer data for training of document querying machine learning (ML) models as a service are described. A service may receive one or more documents from a user, generate a set of question and answer pairs from the one or more documents from the user using a machine learning model trained to predict a question from an answer, and store the set of question and answer pairs generated from the one or more documents from the user. The question and answer pairs may be used to train another machine learning model, for example, a document ranking model, a passage ranking model, a question/answer model, or a frequently asked question (FAQ) model.

20210158209, May 27, 2021

Nov 27, 2019

16/698027

- Seattle WA, US
Jean-Pierre DODEL - Seattle WA, US
Ramesh M. NALLAPATI - New Canaan CT, US

G06N 20/00
G06F 16/93
G06F 16/9038
G06F 16/903

Techniques for active learning for document querying machine learning (ML) models as a service are described. A service may perform a search of data of a user, using a machine learning model, for a search query to generate a result, generate a confidence score for the result of the search, select a proper subset of the data to be provided to the user based on the confidence score, display the proper subset of the data to the user, receive an indication from the user of one or more sections of the proper subset of the data for use in a next training iteration of the machine learning model, and perform the next training iteration of the machine learning model with the one or more sections of the proper subset of the data.

20140180934, Jun 26, 2014

Jan 18, 2013

13/745117

Lex Machina, Inc. - , US
Ingrid Kaldre Foster - Redwood City CA, US
Carla L. Rydholm - Los Gatos CA, US
Ramesh Maruthi Nallapati - San Jose CA, US
Joshua H. Walker - Los Altos CA, US
George D. Gregory - Los Altos CA, US
Gavin Carothers - Sebastopol CA, US
Nicholas O. P. Pilon - Sebastopol CA, US

LEX MACHINA, INC. - Palo Alto CA

G06Q 50/18
G06F 17/30

705310

Aspects of the present invention comprise using non-textual information in analyses of patent matters. In embodiments, patent matter similarity may comprise a combination of two or more metrics: (a) a metric that measures the textual similarity between an input patent portfolio and patent matters; (b) a metric that measures the behavior between portfolio patents and other patent matters at issue (e.g., which patents are asserted in the same proceeding with portfolio patents); (c) a metric that measures the textual similarity between the textual description and patent matters; and (d) a metric that inspects which patent matters are placed at issue by peer companies. In embodiments, patent matter similarity may be determined using textual similarity in combination with non-textual information.