US-20260127453-A1 - SYSTEMS AND METHODS FOR ACQUIRING KNOWLEDGE BASED IN AI-GUIDED INTERVIEWS

US20260127453A1US 20260127453 A1US20260127453 A1US 20260127453A1US-20260127453-A1

Abstract

A method for acquiring knowledge based on an interview is provided. The method includes obtaining, through an interface displayed on a screen, an objective of an interview from a user; deriving one or more topics from the objective of the interview using a large language model (LLM) and displaying, on the interface, the one or more topics; generating a plurality of questions using the LLM based on the objective of the interview and the derived one or more topics; providing the plurality of questions to a target user; obtaining answers to the plurality of questions from the target user; gathering the plurality of questions and the answers as knowledge units; and providing an answer to a question input by another user based on the knowledge units.

Inventors

Melody Ayeli
Paul Greenberg
Stephen Ellis
Brian Kursar

Assignees

Toyota Motor North America, Inc.

Dates

Publication Date: 20260507
Application Date: 20250502

Claims (20)

1 . A method for acquiring knowledge based on an interview, the method comprising: obtaining, through an interface displayed on a screen, an objective of an interview from a user; deriving one or more topics from the objective of the interview using a large language model (LLM) and displaying, on the interface, the one or more topics; generating a plurality of questions using the LLM based on the objective of the interview and the derived one or more topics; providing the plurality of questions to a target user; obtaining answers to the plurality of questions from the target user; gathering the plurality of questions and the answers as knowledge units; and providing an answer to a question input by another user based on the knowledge units.
2 . The method of claim 1 , further comprising: updating a knowledge graph based on the knowledge unit; and training a machine learning model using the knowledge graph.
3 . The method of claim 1 , further comprising: filtering the one or more topics based on an input by the user to the interface; and generating the plurality of questions using the LLM based on the objective of the interview and the filtered topics.
4 . The method of claim 1 , wherein: two or more topics are derived from the objective of the interview; and each of the plurality of questions is assigned to one of the two or more topics.
5 . The method of claim 1 , wherein providing the answer to the question input by another user based on the knowledge unit comprises: displaying, on the interface, a virtual persona of the target user in response to selection of the target user by the another user; and displaying, on the interface, the virtual persona providing the answer to the question input by the another user.
6 . The method of claim 5 , further comprising: comparing the answers provided by the virtual persona of the target user to one or more previous answers provided by the target user; and validating accuracy of the knowledge unit based on the comparison.
7 . The method of claim 1 , wherein: the plurality of questions is provided to a target subject in a first order; the method further comprises: obtaining information that the plurality of questions is reordered in a second order by the target subject; and gathering the reordered plurality of questions and answers to the reordered plurality of questions along with information about the reorder of the plurality of questions as the knowledge units.
8 . The method of claim 1 , further comprising: obtaining voice input from the target subject; transcribing the voice to text; interpreting the text using an model trained based on acronyms or jargons related to a predetermined technical field; and storing the interpreted text as the answers.
9 . The method of claim 1 , further comprising generating additional questions for the target subject using the LLM based on the answers obtained from the target user.
10 . The method of claim 1 , wherein the target user may provide feedback to the LLM, wherein the feedback comprises: ranking the plurality of questions; answering the plurality questions; or deleting the plurality questions.
11 . The method of claim 10 , wherein the LLM prioritizes the plurality of questions based on the feedback provided from the target user.
12 . The method of claim 10 , further comprising: receiving edits of the plurality of questions from the user; and training the LLM based on the edits of the plurality of questions.
13 . A system for acquiring knowledge based on an interview, the system comprising: one or more processors; and one or more memories for storing and encoding computer executable instructions that, when executed by the one or more processors is operative to: obtain, through an interface displayed on a screen, an objective of an interview from a user; derive one or more topics from the objective of the interview using a large language model (LLM) and displaying, on the interface, the one or more topics; generate a plurality of questions using the LLM based on the objective of the interview and the derived one or more topics; provide the plurality of questions to a target user; obtain answers to the plurality of questions from the target user; gather the plurality of questions and the answers as knowledge units; and provide an answer to a question input by another user based on the knowledge unit.
14 . The system of claim 13 , wherein the computer executable instructions, when executed by the one or more processors, are operative to: update a knowledge graph based on the knowledge units; and train the LLM using the knowledge graph.
15 . The system of claim 13 , wherein the computer executable instructions, when executed by the one or more processors, are further operative to: filter the one or more topics based on an input by the user to the interface; and generate the plurality of questions using the LLM based on the objective of the interview and the filtered topics.
16 . The system of claim 14 , wherein the computer executable instructions, when executed by the one or more processors, are further operative to: derive two or more topics from the objective of the interview; and assign each of the plurality of questions to one of the two or more topics.
17 . The system of claim 14 , wherein the computer executable instructions, when executed by the one or more processors, are further operative to: display, on the interface, a virtual persona of the target user in response to selection of the target user by the another user; and display, on the interface, the virtual persona providing the answer to the question input by the another user.
18 . The system of claim 17 , wherein the computer executable instructions, when executed by the one or more processors, are further operative to: compare the answers provided by the virtual persona of the target user to one or more previous answers provided by the target user; and validate accuracy of the knowledge unit based on the comparison.
19 . The system of claim 14 , wherein the computer executable instructions, when executed by the one or more processors, are further operative to: provide the plurality of questions is to a target subject in a first order; obtain information that the plurality of questions is reordered in a second order by the target subject; and gather the reordered plurality of questions and answers to the reordered plurality of questions along with information about the reorder of the plurality of questions as the knowledge units.
20 . The system of claim 19 , wherein the computer executable instructions, when executed by the one or more processors, are further operative to: generate additional questions for the target subject using the LLM based on the answers obtained from the target user.

Description

CROSS-REFERENCE TO RELATED APPLICATION This application claims the benefit of U.S. Provisional Application No. 63/715,223 filed on Nov. 1, 2024, the entire contents of which are herein incorporated by reference. TECHNICAL FIELD The present specification generally relates to acquiring knowledge based in AI-guided interviews and, more specifically, to systems and methods for acquiring knowledge through streamlined interviews that conduct employee interviews based on AI generated questions and gather valuable insights and information from long-standing employees. BACKGROUND Current large language models focus on knowledge retrieval. Specifically, a user requests information about a certain topic or question from a large language model (LLM), and the LLM provides the requested information to the user. However, LLMs mainly focus on knowledge retrieval from already known data, and does not implement knowledge acquisition. SUMMARY The present disclosure provides an effective method of knowledge acquisition using a streamlined interview process between an interviewer and a target subject, i.e., an interviewee. In one embodiment, a method for acquiring knowledge based on an interview is provided. The method includes obtaining, through an interface displayed on a screen, an objective of an interview from a user; deriving one or more topics from the objective of the interview using a large language model (LLM) and displaying, on the interface, the one or more topics; generating a plurality of questions using the LLM based on the objective of the interview and the derived one or more topics; providing the plurality of questions to a target user; obtaining answers to the plurality of questions from the target user; gathering the plurality of questions and the answers as knowledge units; and providing an answer to a question input by another user based on the knowledge units. In another embodiment, a system for acquiring knowledge based on an interview is provided. The system includes one or more processors; and one or more memories for storing and encoding computer executable instructions that, when executed by the one or more processors is operative to: obtain, through an interface displayed on a screen, an objective of an interview from a user; derive one or more topics from the objective of the interview using a large language model (LLM) and displaying, on the interface, the one or more topics; generate a plurality of questions using the LLM based on the objective of the interview and the derived one or more topics; provide the plurality of questions to a target user; obtain answers to the plurality of questions from the target user; gather the plurality of questions and the answers as knowledge units; and provide an answer to a question input by another user based on the knowledge unit. These and additional features provided by the embodiments of the present disclosure will be more fully understood in view of the following detailed description, in conjunction with the drawings. BRIEF DESCRIPTION OF THE DRAWINGS The embodiments set forth in the drawings are illustrative and exemplary in nature and not intended to limit the disclosure. The following detailed description of the illustrative embodiments can be understood when read in conjunction with the following drawings, where like structure is indicated with like reference numerals and in which: FIG. 1A schematically depicts an overview of an interview process, according to one or more embodiments shown and described herein; FIG. 1B depicts schematic diagram of the present system, according to one or more embodiments shown and described herein; FIG. 2 depicts a user interface for conducting an interview for knowledge acquisition, according to one or more embodiments shown and described herein; FIG. 3 depicts a schematic diagram illustrating a flow chart of the process utilized by the interview system, according to one or more embodiments shown and described herein; FIG. 4A depicts a schematic diagram of step 310. Wherein the knowledge acquisition system obtains, through an interface displayed on a screen, an objective of an interview from a user, according to one or more embodiments shown and described herein; FIG. 4B schematically depicts the process of step 330. Wherein the knowledge acquisition system generates a plurality of questions using the LLM based on the objective of the interview and the derived one or more topics, according to one or more embodiments shown and described herein; FIG. 5 depicts a user interface for when the knowledge acquisition system provides a plurality of questions to a target user and for when the target user answers a question, according to one or more embodiments shown and described herein; FIG. 6 depicts a user interface for saved interview templates, according to one or more embodiments shown and described herein; FIG. 7 depicts a knowledge graph compiled from multiple knowledge units, according to one or more embodiments shown