中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

2335 records (took 0.031 seconds)

    Transaction entity prediction through learned embeddings
    62.
    发明授权
    Transaction entity prediction through learned embeddings 有权

    公开(公告)号：US11928423B1

    公开(公告)日：2024-03-12

    申请号：US18198777

    申请日：2023-05-17

    Applicant: Intuit, Inc.

    Inventor： Natalie Bar Eliyahu ,  Shirbi Ish-Shalom ,  Omer Wosner ,  Dmitry Burshtein

    IPC: G06F17/00 ,  G06F40/174 ,  G06F40/20

    CPC classification number: G06F40/174 ,  G06F40/20

    Abstract: Certain aspects of the disclosure pertain to inferring a candidate entity associated with a transaction with a machine learning model. An organization identifier and description associated with a transaction can be received as input. In response, an entity embedding, comprising a vector for each entity of an organization based on the organization identifier, can be retrieved from storage. A machine learning model can be invoked with the entity embedding and description. The machine learning model can be trained to infer a transaction embedding from the description and compute a similarity score between the transaction embedding and each vector of the entity embedding. A candidate entity with a similarity score satisfying a threshold can be identified and returned. The candidate entity with the highest similarity score can be identified in certain aspects.

    Medoid-based data compression
    63.
    发明授权
    Medoid-based data compression 有权

    公开(公告)号：US11928134B1

    公开(公告)日：2024-03-12

    申请号：US17823925

    申请日：2022-08-31

    Applicant: INTUIT INC.

    Inventor： Itay Margolin

    IPC: G06F16/30 ,  G06F16/28

    CPC classification number: G06F16/285

    Abstract: Certain aspects of the present disclosure provide techniques for medoid-based data compression. One example method generally includes receiving item data indicative of one or more items, determining one or more medoids based on the item data, determining, for each item of the one or more items, a corresponding medoid based on the one or more medoids, identifying, for each item of the one or more items, a difference between the item and the corresponding medoid for the item, storing the one or more medoids, and storing, for each item of the one or more items, the identified difference between the item and the corresponding medoid.

    Hybrid model for time series data processing
    64.
    发明授权
    Hybrid model for time series data processing 有权

    公开(公告)号：US11922208B1

    公开(公告)日：2024-03-05

    申请号：US18326255

    申请日：2023-05-31

    Applicant: Intuit Inc.

    Inventor： Immanuel David Buder ,  Shashank Shashikant Rao

    IPC: G06F9/48

    CPC classification number: G06F9/48

    Abstract: Systems and methods are disclosed for switching between batch processing and real-time processing of time series data, with a system being configured to switch between a batch processing module and a real-time processing module to process time series data. The system includes an orchestration service to indicate when to switch, which may be based on a switching event identified by the orchestration service. In some implementations, the orchestration service identifies a switching event in incoming time series data to be processed. When a batch processing module is to be used to batch process time series data, the real-time processing module may be disabled, with the real-time processing module being enabled when it is used to process the time series data. In some implementations, the real-time processing module includes the same processing models as the batch processing module such that the two modules' outputs have a similar accuracy.

    ANONYMOUS UNCENSORABLE CRYPTOGRAPHIC CHAINS
    66.
    发明公开
    ANONYMOUS UNCENSORABLE CRYPTOGRAPHIC CHAINS 审中-公开

    公开(公告)号：US20240039741A1

    公开(公告)日：2024-02-01

    申请号：US17877544

    申请日：2022-07-29

    Applicant: INTUIT INC.

    Inventor： Glenn Carter SCOTT

    IPC: H04L9/00 ,  H04L9/08 ,  H04L9/32

    CPC classification number: H04L9/50 ,  H04L9/0819 ,  H04L9/3213

    Abstract: A method implements anonymous uncensorable cryptographic chains. The method includes receiving, from a first application, verifiable data for a current record and unverified data for the current record. The unverified data for the current record was received by the first application from a second application. The method further includes verifying the verifiable data for the current record with unverified data from a previous record. The method further includes recording the verifiable data for the current record and the unverified data for the current record to the current record responsive to verifying the verifiable data for the current record. The method further includes presenting the current record to one or more of the first application and to the second application.

    Synthetic utterance generation
    67.
    发明授权
    Synthetic utterance generation 有权

    公开(公告)号：US11887579B1

    公开(公告)日：2024-01-30

    申请号：US17955412

    申请日：2022-09-28

    Applicant: Intuit Inc.

    Inventor： Jianxiang Chang ,  Sayan Paul

    IPC: G10L13/02

    CPC classification number: G10L13/02

    Abstract: This disclosure relates to generating a comprehensive set of synthetic utterances. An example system is configured to provide an input utterance to a plurality of synthetic utterance generation pipelines in parallel. Each of the plurality of synthetic utterance generation pipelines include one or more utterance synthesizers. For example, one or more pipelines may use a synthesizer chain that includes a plurality of synthesizers in parallel. The plurality of synthetic utterance generation pipelines generates synthetic utterances, which may be stored in a database after evaluating the similarity between the original input utterance and each resulting synthetic utterance. For example, a synthetic utterance may be retained if the cosine similarity between the input and synthetic utterances is less than a predetermined threshold. Additionally, the synthetic utterances may be fed back at input utterances based on the similarity evaluation and the feedback loop repeated until a desired number of utterances are generated.

    CROSS-HIERARCHICAL MACHINE LEARNING PREDICTION
    69.
    发明公开
    CROSS-HIERARCHICAL MACHINE LEARNING PREDICTION 审中-公开

    公开(公告)号：US20240028973A1

    公开(公告)日：2024-01-25

    申请号：US17869780

    申请日：2022-07-20

    Applicant: INTUIT INC.

    Inventor： Sheer DANGOOR ,  Daniel BEN DAVID ,  Ido Meir MINTZ ,  Alexander ZHICHAREVICH ,  Lior TABORI

    IPC: G06Q30/04 ,  G06N20/20

    CPC classification number: G06Q30/04 ,  G06N20/20

    Abstract: A method including training, using training data including a first ontological hierarchical level, trained machine learning models (MLMs) to predict a first output type including a second ontological hierarchical level different than the first ontological hierarchical level. The method also includes generating instances of the first output type by executing the trained MLMs on unknown data including the first ontological hierarchical level. Outputs of the trained MLMs include the instances at the second ontological hierarchical level. The method also includes training, using the instances, a voting classifier MLM to predict a selected instance from the instances. The voting classifier MLM is trained to predict the selected instance to satisfy a criterion including a third ontological hierarchical level different than the first ontological hierarchal level and the second ontological hierarchical level.

    Advice generation system
    70.
    发明授权
    Advice generation system 有权

    公开(公告)号：US11875123B1

    公开(公告)日：2024-01-16

    申请号：US18362890

    申请日：2023-07-31

    Applicant: Intuit Inc.

    Inventor： Daniel Ben David ,  Kenneth Grant Yocum

    IPC: G06F40/30 ,  G06N20/00 ,  G06F40/40 ,  G06F40/103

    CPC classification number: G06F40/30 ,  G06F40/103 ,  G06F40/40 ,  G06N20/00

    Abstract: The one or more embodiments provide for a method, system, and computer program product, an intent, generated by a large language model from a text, is received from a user device as a first input to an advice planner. A state of an account is received as a second input to the advice planner. The advice planner classifieds the intent into a domain corresponding to the intent, and generates, as output, a plan comprising a first set of action logic associated with the domain. Each action logic is a discrete step in an ordered sequence for achieving a desired state of the account. The advice planner forwards the plan to the large language model (LLM). The large language model receives the plan as input and generates advice in a natural language format as output. The advice is then forwarded to the user device.

Patent Agency Ranking