-
公开(公告)号：US12265794B2
公开(公告)日：2025-04-01
申请号：US18482783
申请日：2023-10-06
Applicant: INTUIT INC.
Inventor： Rami Cohen , Noa Haas , Oren Sar Shalom , Alexander Zhicharevich
Abstract: A computing system identifies an incoming voice call from a user device to an agent device associated with the computing system. The computing system generates a transcription of the incoming voice call using one or more natural language processing techniques. The computing system extracts a problem description from the transcription. The problem description indicates a topic for the incoming voice call. A first machine learning model estimates a situation vector from the problem description. A second machine learning model identifies a pre-existing situation vector that closely matches the estimated situation vector. The computing system retrieves a situation description that corresponds to the identified pre-existing situation vector.
-
公开(公告)号：US11860949B2
公开(公告)日：2024-01-02
申请号：US17568573
申请日：2022-01-04
Applicant: INTUIT INC.
Inventor： Yair Horesh , Yehezkel Shraga Resheff , Oren Sar Shalom , Alexander Zhicharevich
IPC: G06F17/00 , G06F16/903 , G06F16/93 , G06N20/00
CPC classification number: G06F16/90344 , G06F16/93 , G06N20/00
Abstract: Automatic keyphrase labeling and machine learning training may include a processor extracting a plurality of keywords from at least one search query that resulted in a selection of a document appearing in a search result. For each of the plurality of keywords, the processor may determine a probability that the keyword describes the document. The processor may generate one or more keyphrases by performing processing including selecting each of the plurality of keywords having a probability greater than a predetermined threshold value for insertion into at least one of the one or more keyphrases and assembling the one or more keyphrases from the selected plurality of keywords. The processor may label the document with the keyphrase.
-
公开(公告)号：US11257486B2
公开(公告)日：2022-02-22
申请号：US16805660
申请日：2020-02-28
Applicant: Intuit Inc.
Inventor： Shlomi Medalion , Alexander Zhicharevich , Yair Horesh , Oren Sar Shalom , Elik Sror , Adi Shalev
Abstract: A method of training machine learning models (MLMs). An issue vector is generated using an issue MLM to generate a first output including first embedded natural language issue statements. An action vector is generated using an action MLM to generate a second output including related embedded natural language action statements. The issue and action MLMs are of a same type. An inner product of the first and second output is calculated, forming a third output. The third output is processed according to a sigmoid gate process to predict whether a given issue statement and corresponding action statement relate to a same call, resulting in a fourth output. A loss function is calculated from the fourth output by comparing the fourth output to a known result. The issue MLM and the action MLM are modified using the loss function to obtain a trained issue MLM and a trained action MLM.
-
公开(公告)号：US11170765B2
公开(公告)日：2021-11-09
申请号：US16751867
申请日：2020-01-24
Applicant: Intuit Inc.
Inventor： Oren Sar Shalom , Yair Horesh , Alexander Zhicharevich , Elik Sror , Adi Shalev , Yehezkel Shraga Resheff
Abstract: A method for improving a transcription may include identifying, in the transcription, reliable channel tokens of an utterance of a reliable channel and an unreliable channel token of an utterance of an unreliable channel, and generating, using a machine learning model, a vector embedding for the unreliable channel token and vector embeddings for the reliable channel tokens. The method may further include calculating vector distances between the vector embedding and the vector embeddings, and generating, for the unreliable channel token and using the vector distances, a score corresponding to a reliable channel token. The method may further include determining that the score is within a threshold score, and in response to determining that the score is within the threshold score, replacing, in the transcription, the unreliable channel token with the reliable channel token.
-
公开(公告)号：US12271827B2
公开(公告)日：2025-04-08
申请号：US17086317
申请日：2020-10-30
Applicant: Intuit Inc.
Inventor： Yair Horesh , Alexander Zhicharevich , Shlomi Medalion , Natalie Bar Eliyahu
IPC: G06N5/022 , G06F18/214 , G06F40/30 , G06N5/045 , G06N20/00
Abstract: A method including extracting data from disparate data sources. The data includes data pairs including a corresponding data point and a corresponding time associated with the corresponding data point. The method also includes extracting insights from the data at least by identifying a trend in the data pairs. The method also includes forming a model vector including the insights and an additional attribute to the insights. The additional attribute characterizes the insights. The additional attribute includes at least user feedback including a user ranking of a ranked subset of the insights from a user. The method also includes inputting the model vector into a trained insight machine learning model to obtain a predicted ranking of the insights. The method also includes selecting, based on the predicted user ranking, a pre-determined number of insights to form predicted relevant insights. The method also includes reporting the predicted relevant insights.
-
Patent Agency Ranking
公开(公告)号：US11544555B1
公开(公告)日：2023-01-03
申请号：US16526767
申请日：2019-07-30
Applicant: Intuit Inc.
Inventor： Shir Meir Lador , Sigalit Bechler , Elik Sror , Shlomi Medalion , Onn Bar , Erez Katzenelson , Alexander Zhicharevich , Ariel Simhon , Gal Keinan
Abstract: Methods and systems classify and cluster invoice data. An invoice is obtained. A category vector is generated from an invoice string of the invoice with a dense layer of a machine learning model that includes an embedding layer, a neural network layer, and the dense layer. A suggestion is selected with a selection engine and in response to comparing the category vector to a set of clusters. The suggestion is presented.
-
公开(公告)号：US11244009B2
公开(公告)日：2022-02-08
申请号：US16779701
申请日：2020-02-03
Applicant: Intuit Inc.
Inventor： Yair Horesh , Yehezkel Shraga Resheff , Oren Sar Shalom , Alexander Zhicharevich
IPC: G06F17/00 , G06F16/903 , G06F16/93 , G06N20/00
Abstract: Automatic keyphrase labeling and machine learning training may include a processor extracting a plurality of keywords from at least one search query that resulted in a selection of a document appearing in a search result. For each of the plurality of keywords, the processor may determine a probability that the keyword describes the document. The processor may generate one or more keyphrases by performing processing including selecting each of the plurality of keywords having a probability greater than a predetermined threshold value for insertion into at least one of the one or more keyphrases and assembling the one or more keyphrases from the selected plurality of keywords. The processor may label the document with the keyphrase.
-
公开(公告)号：US10984193B1
公开(公告)日：2021-04-20
申请号：US16736874
申请日：2020-01-08
Applicant: Intuit Inc.
Inventor： Adi Shalev , Yair Horesh , Yehezkel Shraga Resheff , Oren Sar Shalom , Alexander Zhicharevich
IPC: G06F40/279 , G06F17/18 , G06N20/10
Abstract: A processor may generate a plurality of vectors from an original text by processing the original text with at least one unsupervised learning algorithm. Each of the plurality of vectors may correspond to a separate portion of a plurality of portions of the original text. The processor may determine respective segments to which respective vectors belong. The processor may minimize a distance between at least one vector belonging to the segment and a known vector from among one or more known vectors and applying a label of the known vector to the segment.
-
公开(公告)号：US12204587B2
公开(公告)日：2025-01-21
申请号：US16667189
申请日：2019-10-29
Applicant: INTUIT INC.
Inventor： Yonatan Ben-Simhon , Rami Cohen , Oren Sar Shalom , Alexander Zhicharevich
IPC: G06F16/9032 , G06F17/18 , G06F18/22 , G06N3/04
Abstract: Aspects of the present disclosure provide techniques for predicting content relevant to questions based on reference links. Embodiments include receiving a set of question and answer (Q/A) pairs and identifying a set of references in the set of Q/A pairs that link pairs of Q/A pairs of the set of Q/A pairs. Embodiments include identifying popular Q/A pairs of the set of Q/A pairs based on the set of references. The popular Q/A pairs may be referenced by a subset of the set of Q/A pairs and each respective Q/A pair of the subset of the set of Q/A pairs may comprise a respective question of a plurality of questions. Embodiments include training a model based on the plurality of questions, the popular Q/A pairs, and the set of references, to predict Q/A pairs of the set of Q/A pairs that are relevant to a given question.
-
公开(公告)号：US12067976B2
公开(公告)日：2024-08-20
申请号：US17489667
申请日：2021-09-29
Applicant: Intuit Inc.
Inventor： Byungkyu Kang , Alexander Zhicharevich , Kate Elizabeth Swift-Spong , Zhewen Fan , Elik Sror
IPC: G10L15/18 , G06F16/9538 , G06F40/284 , G06N20/00 , G10L15/06 , G10L15/16 , G10L15/22
CPC classification number: G10L15/18 , G06F16/9538 , G06F40/284 , G06N20/00 , G10L15/063 , G10L15/16 , G10L15/22
Abstract: A method including transcribing, into digital tokens, utterances from a conversation between an agent and a person. The method also includes embedding the digital tokens into an utterances tensor including sequences of the digital tokens. The method also includes obtaining a metadata tensor by encoding metadata related to the utterances into the metadata tensor. The method also includes executing a machine learning model which takes, as input, the utterances tensor and the metadata tensor, and which outputs a predicted source article predicted to be related to the utterances. The method also includes generating an interactive link to the predicted source article.
-
-
-
-
-
-
-
-
-
Search Results
39
records
(took 0.015 seconds)
