Intuit, Inc.

Empow Cyber Security Ltd.

Cybereason Inc.

Shlomi Medalion

Alexander Zhicharevich

Elik Sror

Sigalit Bechler

Yair Horesh

Yehezkel Shraga Resheff

Avi Chesla

Oren Sar Shalom

Adi Shalev

Liron Hayman

Shir Meir Lador

Erez Katzenelson

Onn Bar

Rami Cohen

Shimon Shahar

Ido Meir Mintz

Ron Katz

Daniel Ben David

Natalie Bar Eliyahu

Miriam Hanna Manevitz

Guy Maman

Alexander Zicharevich

Ariel Simhon

Liat Ben Porat Roda

Idan Bellayev

Gal Keinan

Tom Jacobe

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

A method for privacy preserving synthetic string generation using recurrent neural networks includes receiving input data that includes a plurality of strings with private information. A neural network model is trained using the plurality of strings. The neural network model includes a recurrent neural network (RNN). An anonymous string is generated with the neural network model after training the neural network model with the plurality of strings from the input data. The anonymous string is validated to preclude the private information from the anonymous string. Anonymous data is transmitted that includes the anonymous string and precludes the private information in response to a request for the anonymous data.

Privacy preserving synthetic string generation using recurrent neural networks

A method may include extracting, from a transaction, a routing transit number (RTN) and an account number, embedding the RTN to obtain an RTN vector, embedding the account number to obtain an account number matrix, combining, using a trained machine learning model, the RTN vector and the account number matrix to obtain a combined matrix, and classifying the account number as invalid. The classifying may include applying the trained machine learning model to the combined matrix.

Automated validation of digit sequences in transactions

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 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

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

A method including embedding, by a trained issue MLM (machine learning model), a new natural language issue statement into an issue vector. An inner product of the issue vector with an actions matrix is calculated. The actions matrix includes centroid-vectors calculated using a clustering method from a second output of a trained action MLM which embedded prior actions expressed in natural language action statements taken as a result of prior natural issue statements. Calculating the inner product results in probabilities associated with the prior actions. Each of the probabilities represents a corresponding estimate that a corresponding prior action is relevant to the issue vector. A list of proposed actions relevant to the issue vector is generated by comparing the probabilities to a threshold value and selecting a subset of the prior actions with corresponding probabilities above the threshold. The list of proposed actions is transmitted to a user device.

Machine learning to propose actions in response to natural language questions

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

Systems and methods for validating credentials are disclosed. One example method, performed by one or more processors of a computing device associated with a neural network, includes training the neural network to infer validity information for encrypted credentials received from a credential source, wherein the validity information is inferred without decrypting the encrypted credentials, receiving a first encrypted credential from the credential source, generating an encrypted validity indicator for the first encrypted credential based on the validity information inferred by the neural network, and providing the encrypted validity indicator to the credential source.

Validation as a service for third parties using homomorphic encryption

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 of training machine learning models (MLMs). An issue vector is generated using an issue MLM to generate a first output including first embedded natural language issue statements. An action vector is generated using an action MLM to generate a second output including related embedded natural language action statements. The issue and action MLMs are of a same type. An inner product of the first and second output is calculated, forming a third output. The third output is processed according to a sigmoid gate process to predict whether a given issue statement and corresponding action statement relate to a same call, resulting in a fourth output. A loss function is calculated from the fourth output by comparing the fourth output to a known result. The issue MLM and the action MLM are modified using the loss function to obtain a trained issue MLM and a trained action MLM.

A method and system for classification of cyber-threats is provided. The method includes receiving a request for classifying a cyber-threat detected by a cyber-security system, wherein the request includes initial information about the detected cyber-threat; enriching the initial information about the detected cyber-threat to provide textual information about at least one perceived threat related to the detected cyber-threat; and classifying each of the at least one perceived threat into a security service, wherein the classification is performed based on the respective textual information.

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Lod, Israel

Shlomi Medalion