Patent application EP20151531A (EP), filed 2020-01-13
Publication number: EP3680802A1
Title: SYSTEM AND METHOD FOR USING A QUESTION AND ANSWER ENGINE
Abstract: A system and method for using, training, building, and managing a question and answer engine to automatically generate responses to an end-user is disclosed. Specifically, the method and system make use of a topic builder that uses cluster predictions to generate and identify a list of topics and subtopics. A question and answer database may then be sorted by topic and subtopic using a similarity scorer. New user utterances may be analyzed to identify questions, with a cluster predictor identifying the corresponding topic and subtopics for each question, and a similarity scorer may identify the closest known question for the user's question to a recommender as an answer. Analytics of new user utterances are tracked to update the historical utterance database and question and answer database, thus allowing continuous improvement of the engine.
IPC codes: G06F  16/   332            A I
G06F  40/    30            A I
G06F  40/    35            A I
Claims: number: 1
text: A method of using a question and answer engine to automatically generate responses to an end-user, the method comprising:
number: 2
text: The method of claim 1, wherein the first user utterance is classified as a question, and the method further comprises generating a similarity score for the first user utterance in relation to the first question, and the similarity score is greater than a preselected threshold.
number: 3
text: The method of claim 1 or claim 2, wherein classifying each utterance under at least one topic and at least one subtopic further comprises:
number: 4
text: The method of any one of claims 1 to 3, further comprising:
number: 5
text: The method of claim 4, wherein the second user utterance is classified as an issue or request, and the method further comprises:
number: 6
text: The method of claim 4, wherein the second user utterance is classified as a question, and the method further comprises:
number: 7
text: The method of claim 4, wherein the second user utterance is classified as a question, and the method further comprises:
number: 8
text: The method of claim 6, wherein the first electronic resource is one of a document, URL, video, image, and tutorial.
number: 9
text: A system for using a question and answer engine to automatically generate responses to an end-user, the system comprising:
number: 10
text: The system of claim 9, wherein the first user utterance is classified as a question, and the method further comprises generating a similarity score for the first user utterance in relation to the first question, and the similarity score is greater than a preselected threshold.
number: 11
text: The system of claim 9 or claim 10, wherein the instructions further cause the processor to:
number: 12
text: The system of any one of claims 9 to 11, wherein the instructions further cause the processor to:
number: 13
text: The system of claim 12, wherein the second user utterance is classified as a question, and the instructions further cause the processor to:
number: 14
text: A non-transitory computer-readable medium storing software comprising instructions executable by one or more processors which, upon such execution, cause the one or more processors to:
number: 15
text: The non-transitory computer-readable medium storing software of claim 14, wherein the first user utterance is classified as a question, and the instructions further cause the one or more processors to generate a similarity score for the first user utterance in relation to the first question, and the similarity score is greater than a preselected threshold.
Office action: search_report, dated 2020-07-15
Rejection reasons: novelty_epc_54
Prior art cited: ref_id: WO2018077655A1
ref_type: patent
severity: background
ref_id: US2010153094A1
ref_type: patent
severity: background
ref_id: - ANONYMOUS: "Latent Semantic Analysis - Wikipedia", 6 November 2018 (2018-11-06), XP055699464, Retrieved from the Internet <URL:https://de.wikipedia.org/w/index.php?title=Latent_Semantic_Analysis&oldid=182518896> [retrieved on 20200528]
ref_type: npl
severity: background
ref_id: - BERNARDO MAGNINI ET AL: "Integrating Subject Field Codes into WordNet", LREC 2000, 2000, XP055699449
ref_type: npl
severity: background
ref_id: - TOM CHAO ZHOU ET AL: "A classification-based approach to question routing in community question answering", PROCEEDINGS OF THE 21ST INTERNATIONAL CONFERENCE COMPANION ON WORLD WIDE WEB, WWW '12 COMPANION, 2012, New York, New York, USA, pages 783, XP055699492, ISBN: 978-1-4503-1230-1, DOI: 10.1145/2187980.2188201
ref_type: npl
severity: background
ref_id: US2010153094
ref_type: patent
severity: novelty_destroying
ref_id: WO2018077655
ref_type: patent
severity: novelty_destroying
ref_id: - Anonymous, "Latent Semantic Analysis - Wikipedia", (20181106), URL: https://de.wikipedia.org/w/index.php?title=Latent_Semantic_Analysis&oldid=182518896, (20200528), XP055699464 [T] 1-15 * the whole document *
ref_type: npl
severity: background
ref_id: - BERNARDO MAGNINI ET AL, "Integrating Subject Field Codes into WordNet", LREC 2000, (2000), XP055699449 [A] 1-15 * the whole document *
ref_type: npl
severity: background
ref_id: - TOM CHAO ZHOU ET AL, "A classification-based approach to question routing in community question answering", PROCEEDINGS OF THE 21ST INTERNATIONAL CONFERENCE COMPANION ON WORLD WIDE WEB, WWW '12 COMPANION, New York, New York, USA, (2012), doi:10.1145/2187980.2188201, ISBN 978-1-4503-1230-1, page 783, XP055699492 [A] 4,5,7,12 * abstract *
ref_type: npl
severity: background
Final disposition: pending (2020-07-15)