Intuit, Inc.

Shimon Shahar

Yehezkel Shraga Resheff

Yair Horesh

Noah Eyal Altman

Shir Meir Lador

Yaron Sheffer

Alexander Zhicharevich

Oren Sar Shalom

Shlomi Medalion

Elik Sror

Sigalit Bechler

Liron Hayman

Ido Meir Mintz

Noa Haas

Tzvika Barenholz

Erez Katzenelson

Onn Bar

Rotem Rozenblum

Gleb Keselman

Margarita Vald

Yehezkal Shraga Resheff

Asaf Brill

Yanai Elazar

Yehezkel S Resheff

Certain aspects of the present disclosure provide techniques for detecting errors in account numbers. One example method generally includes receiving, from a user device, an entered number associated with a user and determining, based on a first portion of the entered number, an entity associated with the entered number. The method further includes obtaining, from an account number database, a plurality of account numbers associated with the entity and generating, from the plurality of account numbers, an account number matrix. The method further includes attempting to solve a multiplication equation of the account number matrix, wherein a solution of the multiplication equation is a vector of constants, upon determining a solution to the multiplication equation, determining whether the entered vector is a valid number for the entity and upon determining the entered vector is a valid number for the entity, storing the entered number in the account number database.

Identifying checksum mechanisms using linear equations

Transactions include text fields, such as description fields. Transactions are extracted from financial institutions using web-scraping extraction. In the process of extracting transactions, errors can be introduced into text fields, such as the inclusion of a space within a word or the removal of a space between words. A statistical approach is applied to the text fields. When two alternative text fields are presented, the alternative that statistically includes more common tokens, such as unigrams and bigrams, is chosen as the correct alternative. The incorrect alternative is replaced by the correct alternative in the text field.

System and method for correction of acquired transaction text fields

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

Certain aspects of the present disclosure provide techniques for adaptively reducing the bit size of features in a training data set used to train a machine learning model. An example method generally includes receiving a data set to be used in training a machine learning model and a definition of the machine learning model to be trained. A reduced number of bits to represent features in the data set is determined based on values of each feature in the data set and the definition of the machine learning model. A reduced bit-size data set is generated by reducing a bit size of each feature in the data set according to the reduced number of bits, and the reduced bit-size data set is encrypted using a homomorphic encryption scheme. A machine learning model is trained based on the encrypted reduced bit-size data set.

Method and system for adaptively reducing feature bit-size for homomorphically encrypted data sets used to train machine learning models

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.

A method includes generating recommendations and user structures by applying a recommender machine learning model to training user information and item information, and generating, from the user structures and by applying a demographic machine learning model, demographic predictions of users represented by the user structures. The method further includes generating a first accuracy measure of the demographic machine learning model based on a first comparison of the demographic predictions with demographics of the users. A recommender loss function is generated based on the first accuracy measure and a second comparison of the recommendations with selections of users, where the recommender loss function uses the first accuracy measure to suppress detectability by the demographic machine learning model. The method further includes updating the recommender machine learning model according to the recommender loss function.

Adversarial user representations in recommender machine learning models

Certain aspects of the present disclosure provide techniques for privacy-preserving execution of a workflow in a software application. Embodiments include generally includes receiving homomorphically encrypted inputs from a client device corresponding to user-provided data needed to calculate a result for a step of a workflow in the software application. A result is calculated for the step of the workflow using the received homomorphically encrypted inputs. The calculated result is returned to the client device. The calculated result is homomorphically encrypted as a result of calculating the result using the received homomorphically encrypted inputs.

Method and system for privacy-protected execution of a workflow in a software application using encrypted inputs

Certain aspects of the present disclosure provide techniques for performing computations on encrypted data. One example method generally includes obtaining, at a computing device, encrypted data, wherein the encrypted data is encrypted using fully homomorphic encryption and performing at least one computation on the encrypted data while the encrypted data remains encrypted. The method further includes identifying a clear data operation to perform on the encrypted data and transmitting, from the computing device to a server, a request to perform the clear data operation on the encrypted data, wherein the request includes the encrypted data. The method further includes receiving, at the computing device in response to the request, encrypted output from the server, wherein the encrypted output is of the same size and the same format for all encrypted data transmitted to the server.

Privacy preserving oracle

Systems and methods that may be used to generate and use a social graph generated by user financial transaction data (i.e., a financial transaction-based social graph). Connections and other data within the financial transaction-based social graph can be used for targeted product offerings, other offerings, and or advertisements via e.g., collaborative filtering and user segmentation and profiling.

Systems and methods using financial information to generate a social graph and determine product and other offerings based on connections within the social graph

Certain aspects of the present disclosure provide techniques for predicting a location based on transaction record data. An example technique includes obtaining a first set of transaction records and determining a merchant associated with each transaction record. The example further includes based on the merchant, determining and appending a branch identifier to each transaction record associated with the merchant to generate a first set of extended transaction records. The example further includes creating a consumption graph based on the first set of extended transaction records and determining an estimated location based on the consumption graph. The example further includes determining a precise point location based on the estimated location.

Predicting locations based on transaction records

A method and system identify characteristics of financial transaction description strings. The method and system trains an analysis model with a machine learning process to classify financial transaction description strings. The analysis model generates a table that indicates the portions of the financial transaction description strings that were relevant in classifying the financial transaction description strings and the portions that were not relevant.

Method and system for identifying characteristics of transaction strings with an attention based recurrent neural network

A method is disclosed. The method includes: obtaining, from a management application, a login history including timestamps and internet protocol (IP) addresses corresponding to logins by a user; obtaining coordinates for the IP addresses; determining clusters for the coordinates based on distances between the coordinates; and determining primary locations for the user based on the clusters and the timestamps.

Location detection based on IP addresses and transactions

A method for detecting an unauthorized activity on a computer system involves obtaining current time stamps for a first type of access event related to the computer system, determining a current count of the first type of access event using the current time stamps, and predicting an expected count of the first type of access event using a current count of time stamps and a predictive model. The method further involves obtaining an actual count of the first type of access event, executing a first comparison of the actual count with the expected count, determining, based on a test comprising the first comparison, that the unauthorized access to the computer system occurred, and issuing an alert indicating the unauthorized activity occurred.

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Rishon Letziyon, Israel

Shimon Shahar