Intuit, Inc.

Alexander S Ran

Wei W Wang

Wei Wang

Christopher Z Lesner

Marko Rukonic

Cynthia Joann Osmon

Ajay Tripathi

Eugene Krivopaltsev

William Taylor Laaser

Desiree D G Gosby

Daniel C McGloin

Komal Bhatia

Bennett R Blank

Troy D Otillio

Siddharth Ram

Zhicheng Xue

Brendan D McDonald

Eran Arbel

Trevor D Dryer

Eric King Loong Shiu

Mithun Madadevan

Douglas Lethin

Mithun Mahadevan

Christopher Lesner

Marko Sasa Rukonic

A method and system assist users of an electronic bookkeeping system to sort financial transactions into accounts representing bookkeeping categories. The method and system generate vectors representing the accounts and the merchants known to the bookkeeping system. The method and system generate compressed versions of the vectors by compressing the vectors. The method and system assist users to sort the financial transactions by analyzing the compressed vectors.

System and method for dimensionality reduction of vendor co-occurrence observations for improved transaction categorization

Certain aspects of the present disclosure provide techniques for determining similarities between businesses. One example method generally includes receiving a similarity query and receiving transaction data associated with a plurality of businesses for comparing the plurality of businesses. The method further includes generating a set of vectors representing the plurality of businesses based on the transaction data and generating a set of compact vectors based on the vectors by applying a dimensionality reduction technique. The method further includes generating based on the set of compact vectors, a similarity index and determining a response to the similarity query using the similarity index.

Responding to similarity queries using vector dimensionality reduction

Systems and methods for authenticating a user are disclosed. An example method may be performed by one or more processors of a system and include generating an encoded identification tag including identification information associated with a user's account, embedding the encoded identification tag in a transaction description associated with a payment from the user, receiving a request to access the user's account, retrieving an existing identification tag from the transaction description, generating a decoded identification tag from the existing identification tag, determining whether information retrieved from the decoded identification tag matches the identification information, and granting the request to access the user's account if at least a portion of the information retrieved from the decoded identification tag matches the identification information or denying the request to access the user's account if the information retrieved from the decoded identification tag does not match at least a portion of the identification information.

System and method for error correcting coding of billing transactions for data management system user identity verification

A method including training predictor machine learning models (MLMs) using a first data set. The trained predictor MLMs are trained to predict classifications of data items in the first data set. The method also includes training confidence MLMs using second classifications, output by the trained predictor MLMs. The method also includes generating an aggregated ranked list of classes based on third classifications output by the trained predictor MLMs and second confidences output by the trained confidence MLMs. The method also includes training an ensemble confidence MLM using the aggregated ranked list of classes to generate a trained ensemble confidence MLM. The trained ensemble confidence MLM is trained to predict a corresponding selected classification for each corresponding data item in a training data set containing second data items similar to the first data items.

Training an ensemble of machine learning models for classification prediction using probabilities and ensemble confidence

The disclosed embodiments provide a system that verifies user attributes. During operation, the system obtains financial data containing a set of strings representing a set of deposits into one or more financial accounts of one or more users. Next, the system generates one or more clusters of the deposits according to a similarity between pairs of strings in the set of strings. For each cluster from the one or more clusters, the system associates a subset of the strings in the cluster with an income source and extracts one or more employment attributes of a user from a subset of the deposits associated with the user in the cluster. Finally, the system uses the one or more employment attributes to provide an automatic verification of an income of the user.

Transaction-based verification of income and employment

Certain aspects of the present disclosure provide techniques for generating a human readable summary explanation to a user for an outcome generated by a complex machine learning model. In one embodiment, a risk assessment service can receive a request from a user in which a risk model of the risk assessment service performs a specific task (e.g., determining the level of risk associated with the user). Once the risk model determines the risk associated with the user, in order to comply with regulations from a compliance system, the risk model can provide a user with an explanation as to the outcome for transparency purposes.

Explainable complex model

The disclosed embodiments provide a system that facilitates electronic communication among users. During operation, the system obtains financial data as a set of financial transactions. Upon receiving a request to transmit a message, the system obtains one or more recipient attributes from the request. Next, the system uses the financial transactions to identify a set of recipients that matches the one or more recipient attributes. The system then transmits the message to the set of recipients without revealing the PII for the set of recipients to a creator of the message.

Ad hoc electronic messaging using financial transaction data

A method involves scanning a symbol presented on a point of sale (POS) system including a POS processor; decoding the symbol to obtain purchase data and bidirectional connection data, where the bidirectional connection data describes a bidirectional connection; generating payment data using the purchase data; establishing the bidirectional connection with the POS system; sending the payment data to the POS system over the bidirectional connection; and receiving payment confirmation from the POS system over the bidirectional connection in response to the sending the payment data.

System and method to enable payment using mark generation and mobile device

A method and system assists users of an electronic bookkeeping system to sort financial transactions into accounts representing bookkeeping categories. The method and system generates vectors representing the accounts and the merchants known to the bookkeeping system. The method and system generates compressed versions of the vectors by compressing the vectors. The method and system assists users to sort the financial transactions by analyzing the compressed vectors.

A method and system authenticates a user of a data management system. The method and system store an identification tag in the transaction description for a transaction between the user and the data management system. When the user attempts to access the data management system at a later date, the method and system requests that the user provide access to the transaction description. If the user is able to provide access to a transaction description that includes the identification tag, then the method and system can authenticate the user.

A method may include generating a source transaction description, encoding, by an encoder model of a machine translation model executing on a computer processor, the source transaction description to create a context vector, decoding, by a decoder model of the machine translation model, the context vector to predict a target entity description, generating a transaction including the target entity description, detecting an acceptance, by a user, of an action performed on the transaction, in response to detecting the acceptance, updating a translation accuracy metric for the target entity description, determining that the updated translation accuracy metric satisfies a translation accuracy criterion, and in response to determining that the updated translation accuracy metric satisfies the translation accuracy criterion, adding the target entity description to golden entity descriptions.

Translating transaction descriptions using machine learning

During a verification technique, claim information for a claim made by an entity (which includes an attribute characterizing an entity) is verified using verification information determined from at least an account of the entity. For example, an individual may pre-register with a provider of the verification technique and authorize the provider to access the account, such as a social network, a financial account, and/or an account associated with a financial application (e.g., an accounting application, an income-tax preparation application, etc.). Subsequently, when the individual makes or provides the claim information associated with the claim, verification information associated with the claim information is aggregated and used to verify that the claim is valid. By verifying the claim, the verification technique may make it easier for individuals to prove their reputable identity attributes without disclosing additional confidential information, thereby maintaining their privacy.

Secure verification of claims

Embodiments disclosed herein relate to a method of writing a transaction to a data store having one or more data tables. An application agent receives transaction data for a first transaction of a first type. Application agent attempts to write the first transaction of the first type to a data table in the data store, provided that a transaction of the first type has been committed to the data store. Application agent scans a plurality of additional data stores for previously committed transactions of the first type Application agent determines additional transactions of another type that are co-located in a data table. Application agent ranks the additional transactions of the other type. Application agent identifies a data table in the data store to which a top ranking additional transaction of the other type has been committed. Application agent writes the first transaction to identified data table in the data store.

Writing incoming items to a database based on location of similar items in a database

A method for presence based mobile payment comprises receiving, from a user, a request for registration with a mobile payment service, registering, by the mobile payment service, the user based on the request, broadcasting, by a point of sale (PoS) system and through an advertising channel, an identification of a transaction, wherein the transaction comprises an item, wherein the advertising channel is used for discovery of available devices, and wherein the user is purchasing the item at the PoS system, receiving, from a mobile device of the user, a payment authorization for the transaction, and facilitating, by the mobile payment service, payment for the transaction.

Growing community of inventors

Mountain View, CA, United States of America

Alexander S Ran