Intuit, Inc.

Sigalit Bechler

Shlomi Medalion

Elik Sror

Yehezkel Shraga Resheff

Alexander Zhicharevich

Shir Meir Lador

Erez Katzenelson

Onn Bar

Yair Horesh

Oren Sar Shalom

Shimon Shahar

Natalie Bar Eliyahu

Adi Shalev

Daniel Ben David

Omer Wosner

Miriam Hanna Manevitz

Hadas Baumer

Hadar Lackritz

Ariel Simhon

Linoy Cohen

Guy Maman

Gal Keinan

Gilad Uziely

Shon Mendelson

Tom Klein

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

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 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.

System and method providing business insights based on cluster success analytics for products based businesses

A method for classifying organizations involves obtaining, for an unknown organization, transactional data representing a multitude of transactions. The transactional data comprises a descriptive text for each of the multitude of transactions. The method further involves processing the descriptive text for each of the multitude of transactions to obtain one vector representing the unknown organization, categorizing the unknown organization using a classifier applied to the vector, and identifying a software service for the unknown organization, according to the categorization.

Classifying organizations based on transactional data associated with the organizations

A method may include receiving, for a package, shipment details including attributes, obtaining, for a subset of the attributes, logistic preferences, applying the logistic preferences to the shipment details to obtain modified shipment details, training a classifier using shipment transactions each including values for the attributes and labeled with a vendor logistic service, generating, by applying the classifier to the modified shipment details, scores for vendor logistic services, and recommending a vendor logistic service from the vendor logistic services using the scores.

Logistic recommendation engine

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.

Machine learning technique with targeted feature sets for categorical anomaly detection

In one aspect, the present disclosure relates to a method of generating business descriptions performed by a server, said method may include: receiving a plurality of invoices, each invoice being associated with a business of a plurality of businesses; extracting a plurality of texts from the plurality of invoices; embedding the plurality of texts to a vector space to obtain a plurality of invoice vectors; generating a plurality of clusters in the vector space, each cluster of the plurality of clusters comprising at least one invoice vector of the plurality of invoice vectors; generating a description for a cluster, the description for the cluster representing all invoice vectors assigned to the cluster; for each business of the plurality of businesses that has at least one invoice vector assigned to the cluster, associating the business with the description; and indexing the plurality of businesses within a database by the generated descriptions.

Systems and methods of business categorization and service recommendation

This disclosure provides systems, methods and apparatuses for recommending items to users of a recommendation system. In some implementations, the recommendation system determines a plurality of contribution values based on interactions between a plurality of users and a plurality of items. Each of the plurality of contribution values represents a confidence level that a respective user prefers a respective item. The recommendation system further determines a capacity of each of the plurality of items. The capacity of each item represents a maximum number of users to which the item can be recommended. The recommendation system recommends one or more items of the plurality of items to each of the plurality of users based at least in part on the plurality of contribution values and the capacities of the plurality of items.

System, method, and computer-readable medium for capacity-constrained recommendation

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.

Invoice data classification and clustering

A method for recommending offerings to a business may include: receiving a request for recommended business offerings from a device; receiving business data associated with a business from the device, the business data comprising invoice data associated with the business; embedding the business data to a vector space to obtain a business vector, the vector space comprising a plurality of other vectors associated with other businesses; calculating a relation metric between the business vector and a vector of the plurality of other vectors, the vector being associated with a second business, the relation metric representing a degree of relation between the business and the second business; determining that the relation metric is above a pre-defined threshold value; and responsive to the determining, sending business data associated with the second business to the device, the business data associated with the second business comprising invoice data associated with the second business.

Growing community of inventors

Hod Hasharon, Israel

Sigalit Bechler