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公开(公告)号：US11860949B2
公开(公告)日：2024-01-02
申请号：US17568573
申请日：2022-01-04
申请人： INTUIT INC.
IPC分类号： G06F17/00 , G06F16/903 , G06F16/93 , G06N20/00
CPC分类号： G06F16/90344 , G06F16/93 , G06N20/00
摘要： 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.
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公开(公告)号：US11257486B2
公开(公告)日：2022-02-22
申请号：US16805660
申请日：2020-02-28
申请人： Intuit Inc.
发明人： Shlomi Medalion , Alexander Zhicharevich , Yair Horesh , Oren Sar Shalom , Elik Sror , Adi Shalev
摘要： 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.
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3.发明授权公开(公告)号：US11210358B2
公开(公告)日：2021-12-28
申请号：US16699545
申请日：2019-11-29
申请人： Intuit Inc.
发明人： Elik Sror , Oren Sar Shalom , Rami Cohen
IPC分类号： G06F16/957 , G06F17/16 , G06N3/08 , G06F12/06 , G06F12/02 , G06F12/0895
摘要： A method for mitigating cold starts in recommendations includes receiving a request that identifies a requested page and identifying a content vector of the requested page. The content vector is generated based on providing text of the requested page to a neural network text encoder. The method further includes selecting, based on a rank engine and the content vector, a link to a cold start page that does not satisfy a threshold level of interaction data. The rank engine ranks the selected link above a second link to a warm page that does satisfy the threshold level of the interaction data. The method further includes presenting the requested page with the selected link.
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公开(公告)号：US11170765B2
公开(公告)日：2021-11-09
申请号：US16751867
申请日：2020-01-24
申请人： Intuit Inc.
发明人： Oren Sar Shalom , Yair Horesh , Alexander Zhicharevich , Elik Sror , Adi Shalev , Yehezkel Shraga Resheff
摘要： 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.
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公开(公告)号：US11893351B2
公开(公告)日：2024-02-06
申请号：US17893153
申请日：2022-08-22
申请人： Intuit Inc.
IPC分类号： G06F40/289 , G06F17/18 , G06N20/00
CPC分类号： G06F40/289 , G06F17/18 , G06N20/00
摘要： A method including receiving, in a machine learning model (MLM), a corpus including words. The MLM includes layers configured to extract keywords from the corpus, plus a retrospective layer. A first keyword and a second keyword from the corpus are identified in the layers. The first and second keywords are assigned first and second probabilities. Each probability is a likelihood that a keyword is to be included in a key phrase. A determination is made, in the retrospective layer, of a first probability modifier that modifies the first probability based on a first dependence relationship between the second keyword being placed after the first keyword. The first probability is modified using the first probability modifier. The first modified probability is used to determine whether the first keyword and the second keyword together form the key phrase. The key phrase is stored in a non-transitory computer readable storage medium.
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公开(公告)号：US20220405476A1
公开(公告)日：2022-12-22
申请号：US17893153
申请日：2022-08-22
申请人： Intuit Inc.
IPC分类号： G06F40/289 , G06F17/18 , G06N20/00
摘要： A method including receiving, in a machine learning model (MLM), a corpus including words. The MLM includes layers configured to extract keywords from the corpus, plus a retrospective layer. A first keyword and a second keyword from the corpus are identified in the layers. The first and second keywords are assigned first and second probabilities. Each probability is a likelihood that a keyword is to be included in a key phrase. A determination is made, in the retrospective layer, of a first probability modifier that modifies the first probability based on a first dependence relationship between the second keyword being placed after the first keyword. The first probability is modified using the first probability modifier. The first modified probability is used to determine whether the first keyword and the second keyword together form the key phrase. The key phrase is stored in a non-transitory computer readable storage medium.
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公开(公告)号：US11244009B2
公开(公告)日：2022-02-08
申请号：US16779701
申请日：2020-02-03
申请人： Intuit Inc.
IPC分类号： G06F17/00 , G06F16/903 , G06F16/93 , G06N20/00
摘要： 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.
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公开(公告)号：US20210304747A1
公开(公告)日：2021-09-30
申请号：US16836886
申请日：2020-03-31
申请人： Intuit Inc.
发明人： Noa Haas , Alexander Zicharevich , Oren Sar Shalom , Adi Shalev
摘要： Systems and methods for automatically identifying problem-relevant sentences in a transcript are disclosed. In an example method, a transcript may be received of a first support call. A region of the first support call transcript may be identified, and first customer utterances may be detected in the region using a trained classification model. A trained regression model may estimate a relevancy to the problem statement of each of the first customer utterances, and one or more most problem-relevant statements may be selected from the first customer utterances, based on the estimated relevancies.
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公开(公告)号：US10984193B1
公开(公告)日：2021-04-20
申请号：US16736874
申请日：2020-01-08
申请人： Intuit Inc.
IPC分类号： G06F40/279 , G06F17/18 , G06N20/10
摘要： 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.
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公开(公告)号：US11893608B2
公开(公告)日：2024-02-06
申请号：US16818268
申请日：2020-03-13
申请人： Intuit Inc.
发明人： Shlomi Medalion , Yair Horesh , Yehezkel Shraga Resheff , Sigalit Bechler , Oren Sar Shalom , Daniel Ben David
IPC分类号： G06Q30/0282 , G06Q30/0202 , G06F40/30 , G06F18/23 , G06F17/16 , G06N3/084 , G06F18/214
CPC分类号： G06Q30/0282 , G06F17/16 , G06F18/2148 , G06F18/23 , G06F40/30 , G06N3/084 , G06Q30/0202
摘要： A method may be used to predict a business' category by analyzing the business' vendors. A neural network architecture may be trained via supervised learning to predict categories for businesses based on listed vendors. The neural network may be used to classify uncategorized businesses within an accounting software database. A list of factors associated with a business' success may be generated by analyzing, aggregating and ranking factors determined to be relevant to a business based on its categorization. The factors associated with the business' success may be related to the products and/or services offered by the business and the format of which those products and/or services are offered by the business. The factors may also be related to the products and/or services purchased by the business from a vendor and the format of which those products and/or services are purchased from the vendor.
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