Systems and methods for generating a contextually and conversationally correct response to a query

Patent metadata

Field	Value
Publication number	US-2026003860-A1
Application number	US-202519322260-A
Country	US
Kind code	A1
Filing date	Sep 8, 2025
Priority date	May 3, 2018
Publication date	Jan 1, 2026
Grant date	—

Abstract

The present disclosure relates to systems and methods for generating contextually, grammatically, and conversationally correct answers to input questions. Embodiments provide for linguistic and syntactic structure analysis of a submitted question in order to determine whether the submitted question may be answered by at least one headnote. The question is then further analyzed to determine more details about the intent and context of the question. A federated search process, based on the linguistic and syntactic structure analysis, and the additional analysis of the question is used to identify candidate question-answer pairs from a corpus of previously created headnotes. Machine learning models are used to analyze the candidate question-answer pairs, additional rules are applied to rank the candidate answers, and dynamic thresholds are applied to identify the best potential answers to provide to a user as a response to the submitted question.

First claim

1 . A method performed by a computing system, the method comprising:
analyzing, by a question/answer processor of the computing system, a linguistic and syntactic structure of a question in a query; obtaining, by a query generator of the computing system, a plurality of candidate answers to the question based on the linguistic and syntactic structure of the question; pairing the question with individual candidate answers of the plurality of candidate answers to form a plurality of question-answer pairs; scoring, by a ranking model of a candidate ranker of the computing system, a question-answer pair of the plurality of question-answer pairs, wherein scoring the question-answer pair includes:
extracting at least one feature for the question-answer pair of the plurality of question-answer pairs, the at least one feature includes a verb tense of an answer portion of the question-answer pair; and
providing the extracted at least one feature to the ranking model to generate a score for the question-answer pair, the score represents a probability that the candidate answer of the question-answer pair is a correct answer to the question; and
providing, by an answer detector of the computing system, at least one of the plurality of candidate answers as an answer to the question based on the score of the at least one of the plurality of candidate answers.

2 . The method of claim 1 , wherein:
the ranking model associates:
a past tense of the answer portion with facts or an application of a law to a case; or
a present tense of the answer portion with a rule or a legal concept.

3 . The method of claim 1 , further comprising:
pre-processing the question, by the question/answer processor and prior to the analyzing, the pre-processing includes determining whether the question is of a type that can be answered by a headnote by performing a matching operation using a lookup table which defines static questions to be excluded.

4 . The method of claim 1 , wherein:
the verb tense of the answer portion is used to determine whether the question is a factoid-based question.

5 . The method of claim 1 , wherein:
a candidate answer of the plurality of candidate answers is obtained from a database of pre-generated summaries.

6 . The method of claim 5 , wherein:
the pre-generated summaries include editorially-created summaries of laws addressed in court opinions.

7 . The method of claim 1 , wherein:
the analyzing of the linguistic and syntactic structure of the question includes identifying a plurality of linguistic components and a sentence structure of the linguistic components.

8 . The method of claim 7 , wherein:
the plurality of linguistic components include a subject, a verb, an objects, what is done to whom, a subordinate clause, a main clause, a main verb, or a main verb tense.

9 . The method of claim 7 , wherein:
the analyzing of the linguistic and syntactic structure of the question includes identifying syntactic dependencies between the plurality of linguistic components.

10 . The method of claim 1 , wherein:
the extracting of the at least one feature includes using a statistical tagger trained to recognize specific entities, the specific entities include: a source of law, an evidence type, a cause of action, and a tolling condition.

11 . The method of claim 1 , further comprising:
determining, using a frame classifier, to classify the question into a particular frame category, the particular frame category being one or more of:
an admissibility of evidence frame,
an availability of damages or remedy frame,
a burden of proof frame,
a court authority frame,
a standard of review frame, or
an enforceability of contracts frame.

12 . The method of claim 11 , wherein:
the particular frame category is the burden of proof frame and defines a plurality of frame elements to be included in the individual candidate answers, the plurality of frame elements being a party element, a claim or remedy element, and a standard element.

13 . The method of claim 12 , wherein:
the claim or remedy element is default judgment, the party element is movant, and the standard element is clear, strong, and satisfactory proof.

14 . The method of claim 1 , wherein:
the score represents a probability that the answer is:
grammatically correct; and
contextually accurate.

15 . The method of claim 1 , wherein:
the ranking model prioritizes candidate answers with the verb tense being a present tense over candidate answers with the verb tense being a past tense.

16 . The method of claim 1 , further comprising:
applying a constraint rule to the plurality of candidate answers; and eliminating at least one of the plurality of candidate answers based on the constraint rule not being met.

17 . The method of claim 16 , wherein:
the constraint rule includes a presence, in the plurality of candidate answers, of a source of law entity and a cause of action entity that is present in the question.

18 . The method of claim 16 , further comprising:
classifying, using a frame classifier, the question into a statute of limitation duration frame, and the constraint rule includes a requirement, for the plurality of candidate answers, of a time duration.

19 . A computing system comprising:
a question/answer processor of the computing system, the question/answer processor configured to analyze a linguistic and syntactic structure of a question in a query; a query generator of the computing system configured to obtain a plurality of candidate answers to the question based on a search query, wherein the question is paired with individual candidate answers of the plurality of candidate answers to form a plurality of question-answer pairs; a feature extractor of the computing system configured to extract at least one feature for a question-answer pair of the plurality of question-answer pairs, the at least one feature includes at least a classification feature indicating a verb tense of an answer portion of the question-answer pair; a candidate ranker of the computing system configured to score, by a ranking model, a question-answer pair of the plurality of question-answer pairs based at least in part on the classification feature; and an answer detector of the computing system configured to provide at least one candidate answer of the plurality of candidate answers as an answer to the question based on the score of the at least one candidate answer.

20 . A computer-based tool including non-transitory computer readable media having stored thereon computer code which, when executed by a processor, causes a computing device to perform operations comprising:
analyzing, by a question/answer processor, a linguistic and syntactic structure of a question in a query; obtaining, by a query generator, a plurality of candidate answers to the question based on the linguistic and syntactic structure of the question; pairing the question with individual candidate answers of the plurality of candidate answers to form a plurality of question-answer pairs; scoring, by a ranking model of a candidate ranker, a question-answer pair of the plurality of question-answer pairs, wherein scoring the question-answer pair includes:
extracting at least one feature for the question-answer pair of the plurality of question-answer pairs, the at least one feature includes a verb tense of an answer portion of the question-answer pair; and
providing the extracted at least one feature to the ranking model to generate

Assignees

• Thomson Reuters Entpr Centre Gmbh

Inventors

• Mcelvain Gayle
• Custis Tonya
• Surprenant Matthew A
• Lindberg Erik

Classifications

• G06N3/08

  Learning methods · CPC title

• G06F16/248

  Presentation of query results · CPC title

• G06N5/046

  Forward inferencing; Production systems · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title