Intuit, Inc.

Natalie Bar Eliyahu

Sigalit Bechler

Omer Wosner

Yair Horesh

Alexander Zhicharevich

Shlomi Medalion

Adi Shalev

Hadas Baumer

Ido Farhi

Hadar Lackritz

Linoy Cohen

Noga Noff

Shon Mendelson

Shirbi Ish-shalom

Tom Klein

Dmitry Burshtein

Oren Dar

Gilad Uziely

Yaakov Tayeb

A method of executing a matching language model to perform a many-to-one matching task. The matching task uses a number of tokens that exceeds a token limit of the matching language model. The number of tokens is reduced to a reduced number of tokens by executing a rule-based application on a target entry and a dataset of entries to output a data subset including fewer entries. The reduced number of tokens are reduced within the token limit by executing a sorting language model on the data subset and the target entry to output candidate matching sets. The candidate matching sets are evaluated as to whether a potential match exists with the target entry. The matching language model is executed on the number of candidate matching sets and the target entry to output a selected matching set matching the target entry. The selected matching set is returned.

Large language model ensemble for combinatorial retrieval in one-to-many matching tasks

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.

Computer prediction of relevant data from multiple disparate sources

Certain aspects of the disclosure pertain to predicting a candidate entity match for a transaction with a machine learning model. A description of a transaction associated with an organization is received as input. In response, at least one machine learning model can be invoked to infer a transaction embedding based on the description, a first score that captures similarity between the transaction embedding entity embeddings associated with a global list of entities and organizations, a second score that captures a probability of interaction between the first organization and the entities based on organization and entity embeddings that capture profile data associated with the organization and the entities, and at least one candidate entity based on the first score and the second score. Finally, the inferred candidate entity can be output for use by an automated data entry or other process or system.

Transaction entity prediction with a global list

A method and computer system are provided for generating a text summary. An input text is processed by a model that comprises a set of attention heads. The model is trained to generate abstractive summaries of text documents. A subset of the attention heads are identified. For each attention head in the subset, a portion is identified from the input text that is used to generate the abstractive summary. For each sentence of the input text, a fractional size is calculated for an intersection of the portion and the respective sentence relative to the respective sentence. A subset of the sentences is then determined, where the respective fractional size of each sentence in the subset meets a first threshold. An extractive summary of the input text is then generated from the subset of sentences.

Extractive summary generation by abstractive trained model

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.

Transaction entity prediction through learned embeddings

Systems and methods for matching entities to target objects using an ensemble model are disclosed. The ensemble model includes a general trained machine learning (ML) model (which is trained using the entirety of a training dataset) and a subarea trained ML model (which is trained using a subset of the training dataset corresponding to a specific, defined subarea) that provides potential matches to a meta-model of the ensemble model to generate a final match. The ensemble model may also include a general trained natural language processing (NLP) model and a subarea trained NLP model that provides potential matches to the meta-model. The meta-model of a quad-ensemble ML model combines the four potential matches (such as probabilities and similarities of matching specific pairs of targets objects and entities) to generate a final match (such as a final probability used to identify the final match).

Ensemble model for entity resolution in matching problems using classification, subarea, NLP and subarea NLP machine learning models

Aspects of the present disclosure provide techniques for categorical anomaly detection. Embodiments include receiving values for a plurality of data categories for an entity of a plurality of entities. Embodiments include generating a feature vector for the entity based on the values, the feature vector excluding a first value for a first data category of the plurality of data categories. Embodiments include providing one or more inputs to a machine learning model based on the feature vector and determining, based on one or more outputs received from the machine learning model, one or more other entities of the plurality of entities that are grouped with the entity. Embodiments include determining that the first value is anomalous based on respective values for the first data category for the one or more other entities. Embodiments include performing one or more actions based on the determining that the first value is anomalous.

Growing community of inventors

Azor, Israel

Natalie Bar Eliyahu