Intuit, Inc.

Microsoft Technology Licensing, LLC

Yehezkel Shraga Resheff

Yair Horesh

Shimon Shahar

Oren Sar Shalom

Daniel Ben David

Alexander Zhicharevich

Shlomi Medalion

Adi Shalev

Noah Eyal Altman

Yaron Sheffer

Elik Sror

Margarita Vald

Sigalit Bechler

Ido Meir Mintz

Tzvika Barenholz

Liron Hayman

Elhanan Mishraky

Boaz Sapir

Laetitia Kahn

Adi Eldar

Talia Tron

Shiomi Medalion

Or Basson

Roy Ben Ofer

Rotem Rozenblum

Sheer Dangoor

Noa Haas

Shir Meir Lador

Nirmala Ranganathan

Miriam Hanna Manevitz

Yonatan Ben-Simhon

Yaron Moshe

Daniel Vaisman

Alexander Zicharevich

Yonantan Ben-Simhon

Nir Keret

This disclosure relates to systems and methods for identifying fraudulent email addresses associated with an electronic payment service. In some implementations, a computing device receives an email with a prefix having a number of characters and characterized by a prefix length indicative of the number of characters in the prefix. The computing device identifies each of a number of bigrams is identified within the prefix, and determines a row and column distance for each bigram between two consecutive characters of the bigram as positioned on a keyboard. The computing device calculates a Euclidean distance between the two consecutive characters of the bigram based on the row and column distances, and determines a normalized distance based on the prefix length and an average of the Euclidean distances calculated for the number of bigrams in the prefix. The normalized distance is compared with a value to classify the email as suspicious or as not suspicious.

Detecting fraud by calculating email address prefix mean keyboard distances using machine learning optimization

An entry validation system executed by a processor, may compare an entry into a user interface (UI) field with at least one range of valid entries. The at least one range of valid entries may be generated by a machine learning (ML) system by clustering known valid data using a clustering algorithm producing a lowest number of clusters that absorbs all of the known valid data within the clusters. In response to the comparing, the entry validation system may provide a determination of validity or invalidity to an application displaying the UI field. The determination of validity may cause the entry to be processed by the application, and the determination of invalidity may cause the application to reject the entry.

Automatic entry validation using density based clustering

Systems and methods that implement a paired-consistency-based process for evaluating and or regulating fairness in machine learning models.

Paired-consistency-based model-agnostic approach to fairness in machine learning models

A method implements private categorization using shared keys. The method includes selecting an encryption key, encrypting a transaction vector, generated from a transaction record, with the encryption key to generate an encrypted transaction vector, and receiving an encrypted category vector generated by a classifier model, corresponding to the encryption key, from the encrypted transaction vector. The method further includes decrypting a category from the encrypted category vector with a decryption key corresponding to the encryption key and presenting the category.

Private categorization using shared keys

Methods, computer systems and computer program product are provided for retrieving contextually relevant documents in near real time. When text data it's received from an application, the text data is processed through a text segmentation model to generate a set of documents. Each document corresponds to a segment of the text data. A first vector representation is generated for a first document of the set of documents. A machine learning process compares the first vector representation and a set of vector representations for a set of documents within a data repository to determine a subset of the documents. A composite rank is generated for each respective document of the subset. The subset of documents is then presented through an interface, sorted according to the respective composite ranks.

Similar cases retrieval in real time for call center agents

A method including receiving, in a machine learning model (MLM), a corpus including words. The MLM includes layers configured to extract keywords from the corpus, plus a retrospective layer. A first keyword and a second keyword from the corpus are identified in the layers. The first and second keywords are assigned first and second probabilities. Each probability is a likelihood that a keyword is to be included in a key phrase. A determination is made, in the retrospective layer, of a first probability modifier that modifies the first probability based on a first dependence relationship between the second keyword being placed after the first keyword. The first probability is modified using the first probability modifier. The first modified probability is used to determine whether the first keyword and the second keyword together form the key phrase. The key phrase is stored in a non-transitory computer readable storage medium.

Modified machine learning model and method for coherent key phrase extraction

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 including inputting, into a phrase recognition model comprising a neural network, a vector comprising a plurality of ngrams of text. The method also includes applying, using the phrase recognition model, a filter to the plurality of ngrams during execution. The filter has a skip word setting of at least one. The method also includes determining, based on the skip word setting, at least one ngram in the vector to be skipped to form at least one skip word. The method also includes outputting an intermediate score for a set of ngrams that match the filter. The method also includes calculating a scalar number representing a semantic meaning of the at least one skip word. The method also includes generating based on the scalar number and the intermediate score, a final score for the set of ngrams. A computer action is performed using the final score.

Machine learning models with improved semantic awareness

Automatic keyphrase labeling and machine learning training may include a processor extracting a plurality of keywords from at least one search query that resulted in a selection of a document appearing in a search result. For each of the plurality of keywords, the processor may determine a probability that the keyword describes the document. The processor may generate one or more keyphrases by performing processing including selecting each of the plurality of keywords having a probability greater than a predetermined threshold value for insertion into at least one of the one or more keyphrases and assembling the one or more keyphrases from the selected plurality of keywords. The processor may label the document with the keyphrase.

Automatic keyphrase labeling using search queries

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 computer-implemented method and system are provided to utilize machine learning technology to process user financial transaction data to predict a personalized payment screen architecture. A plurality of feature datasets associated with transaction data of a plurality of electronic invoices are obtained by a computing device. Each feature dataset comprises a plurality of features, a payment screen and a payment method configured to be presented on at least one payment screen. The computing device is configured to train a machine learning model with the feature datasets to produce a probability matrix with probabilities of each payment method used to pay the invoices through each payment screen. The computing device may weigh the probability matrix to generate a recommendation matrix and determine a prediction of a payment screen based on the recommendation matrix.

System and method for predicting personalized payment screen architecture

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

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

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

A method collects word-based data corresponding to a first identifier. A first phrase vector is generated for the first identifier by extracting frequency data from the word-based data. A similarity metric is generated corresponding to the first identifier and a second identifier by comparing the first phrase vector of the first identifier to a second phrase vector of the second identifier. A tuple is generated that includes the first identifier and the second identifier using the similarity metric. A machine learning model is trained with the tuple to generate an embedded vector corresponding to the first identifier.

Growing community of inventors

Tel Aviv, Israel

Yehezkel Shraga Resheff