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    METHOD AND SYSTEM FOR PROVIDING ASSISTANCE TO A USER OF A MACHINE
    1.
    发明公开
    METHOD AND SYSTEM FOR PROVIDING ASSISTANCE TO A USER OF A MACHINE 审中-公开

    公开(公告)号：EP4443312A1

    公开(公告)日：2024-10-09

    申请号：EP24168575.9

    申请日：2024-04-04

    申请人： SCM Group S.p.A.

    发明人： MANDRELLI, Luca

    IPC分类号： G06F16/33 ,  G06F16/335

    CPC分类号： G06F16/3334 ,  G06F16/3344 ,  G06F16/3347 ,  G06F16/335

    摘要： A method for providing assistance for a user (2) of a processing machine (22) comprises the following steps, performed by a server computer (11): receiving a request text (24), representing the request for assistance from the user (2); processing the request text (24) through a natural language processing (NLP) engine, trained to extract values for a plurality of predetermined parameters, so as to generate an input vector containing the values thus extracted from the request text (24); accessing a database (12) containing a plurality of solution texts, each solution text constituting a predetermined reply to a possible request for assistance or to a type of request for assistance; selecting a solution text from the plurality of solution texts, dependently on the input vector, to make the selected solution text available to the user (2).

    PROCESSING A SEARCH QUERY
    2.
    发明公开
    PROCESSING A SEARCH QUERY 审中-公开

    公开(公告)号：EP4390722A1

    公开(公告)日：2024-06-26

    申请号：EP22315334.7

    申请日：2022-12-19

    申请人： Amadeus S.A.S.

    发明人： LE TOQUIN, Youri ,  IANNINO, Paolo

    IPC分类号： G06F16/33 ,  G06F16/953 ,  G06N3/08

    CPC分类号： G06F16/953 ,  G06F16/3347 ,  G06N3/08 ,  G06N3/006

    摘要： A computerized method of processing a search query using reinforcement learning is presented. The method comprises receiving a search query, determining a state vector representing a current state of processing the search query based on at least one query parameter included in the search query, determining a search response to the search query according to at least one action determined by a policy network based on the state vector, the at least one action impacting an amount of resources to be utilized for determining the search response, determining a score based on the search response, the score defining a reward given for the search response, and updating the policy network according to the score.

    TOPIC-BASED SEMANTIC SEARCH OF ELECTRONIC DOCUMENTS BASED ON MACHINE LEARNING MODELS FROM BAYESIAN BELIEF NETWORKS
    3.
    发明公开
    TOPIC-BASED SEMANTIC SEARCH OF ELECTRONIC DOCUMENTS BASED ON MACHINE LEARNING MODELS FROM BAYESIAN BELIEF NETWORKS 审中-公开

    公开(公告)号：EP4432121A1

    公开(公告)日：2024-09-18

    申请号：EP23170670.6

    申请日：2023-04-28

    申请人： Invoca, Inc.

    发明人： McCOURT, Jr., Michael Kingsley ,  GHODOUSSI, Kian ,  BORDA, Victor

    IPC分类号： G06F16/33 ,  G06F16/35 ,  G06N7/01

    CPC分类号： G06F16/35 ,  G06F16/3347 ,  G06N7/01 ,  G06N20/00

    摘要： A computer-implemented method executed using a computing device comprises digitally generating and storing a machine learning statistical topic model in computer memory, the topic model being programmed to model call transcript data representing words spoken on a call as a function of one or more topics of a set of topics, the set of topics being modeled to comprise a set of pre-seeded topics and a set of non-pre-seeded topics, and the one or more topics being modeled as a function of a probability distribution of topics; programmatically pre-seeding the topic model with a set of keyword groups, each keyword group associating a respective set of keywords with a topic of the set of pre-seeded topics; programmatically training the topic model using unlabeled training data; conjoining a classifier to the topic model to create a classifier model, the classifier defining a joint probability distribution over topic vectors and one or more observed labels; programmatically training the classifier model using labeled training data; receiving target call transcript data comprising an electronic digital representation of a verbal transcription of a target call; programmatically determining, using the classifier model, at least one of one or more topics of the target call or one or more classifications of the target call; digitally storing the target call transcript data with additional data indicating the determined one or more topics of the target call and/or the determined one or more classifications of the target call; accessing, in computer storage, a first digitally stored electronic document comprising a first text; receiving computer input specifying a search query comprising one or more search terms; processing the search query using the classifier model to output a query topic vector representing a thematic content of the search query; processing the first text using the classifier model to output and store in the computer memory a first plurality of topic vectors each representing a topic in the text; using the query topic vector and the first plurality of topic vectors, calculating a plurality of similarity values, each of the similarity values representing a similarity of the query topic vector to a particular topic vector among the first plurality of topic vectors; outputting a visual display that specifies one or more topic vectors among the first plurality of topic vectors having one or more corresponding similarity values that are greater than a specified threshold similarity value.

    ENHANCED SEARCH PERFORMANCE USING CONTEXTUAL ASPECT RELATEDNESS
    6.
    发明公开
    ENHANCED SEARCH PERFORMANCE USING CONTEXTUAL ASPECT RELATEDNESS 审中-实审

    公开(公告)号：EP4421659A1

    公开(公告)日：2024-08-28

    申请号：EP24158564.5

    申请日：2024-02-20

    申请人： eBay Inc.

    发明人： BELLAM, Praveen Kumar ,  WU, Chih-Liang ,  LESHCHUK, Irina ,  KHAN, Ishita Kamal ,  MANDAL, Aritra ,  DONLEY, Mitchell Reid ,  WU, Zhe

    IPC分类号： G06F16/9535 ,  G06F16/33

    CPC分类号： G06F16/9535 ,  G06F16/3347

    摘要： The technology disclosed herein relates to identifying an aspect from a search query based on using a multipartite graph generated using past user behavior and a node embedding algorithm for determining vector representations of nodes of the multipartite graph. For example, nodes of the multipartite graph can include nodes for prior search queries, items or item listings associated with the prior search queries, and one or more of an aspect or category of the items or item listings. In embodiments, the multipartite graph has dynamic edges between the nodes for the prior search queries and the items or item listings. In embodiments, a query expansion is performed based on identifying the aspect using the multipartite graph and node embedding algorithm. In embodiments, search results are provided based on identifying the aspect and performing the query expansion. For example, one or more identified aspects can be provided as selectable options.