Visa International Service Association

Microsoft Technology Licensing, LLC

Mahesh Subhash Joshi

Gungor Polatkan

Fares Hedayati

Sathis Kumar Appukutty

Jeffrey Gee

Sukalyan Chakraborty

Prithwiraj Mitra

Shivani Rao

Abhijith Ramesh Kashyap

Urjit Anand Khadilkar

Konstantin Salomatin

Sneha S Chaudhari

Nitin Singhal

Deepak Kumar

Basudeb Ghosh

Martin D Izenson

Deepak Kumar Dileep Kumar

Young Jin Yun

Linda McGee

Tatyana Dubinsky

Ankur Raina

Teresa Ho

Todd Winners

Gautam Borooah

Nelson Inas Dsouza

Christopher Wright Lloyd, Ii

Bonnie Bills

Yulia Astakhova

Alexei Fomitchev

Vladislav Tcheprasov

Xiaoxu Lu

Aleksander Epelman

Nelli Kayton

Suresh Pulikara

John Wu

Provided is an emergency management system including at least one processor in communication with an electronic payment processing network, the electronic payment processing network including a transaction processing system, a plurality of merchant systems associated with merchant locations, and a plurality of issuer systems. The at least one processor is programmed or configured to retrieve, from the electronic payment processing network, transaction data associated with a plurality of transactions and determine an operational status of at least one first entity associated with the plurality of transactions based on the difference between a first transaction count and a second transaction count, and in response to determining the operation status of the at least one first entity, automatically communicate a message to at least one second entity based on the operational status of the at least one first entity.

Emergency management system

A system and method may retrieve transaction data for a plurality of electronic payment system transactions. The transaction data may describe a plurality of electronic payment transactions between a payment network system and a plurality of merchants. The transaction data may include a transaction location and a merchant store number. The system and method may parse the transaction data to identify the transaction location and the merchant store number and analyze the retrieved and parsed transaction data to identify one or more centroids for at least a portion of the retrieved and parsed transaction data. The one or more centroids may correspond to a merchant location for each transaction of the retrieved and parsed transaction data. The system and method may then clean the retrieved and parsed transaction data having a transaction location outside a threshold distance from the one or more centroids to include accurate merchant store number data.

System and method for determining merchant store number

Efficient tagging of content items using content embeddings are provided. In one technique, multiple content items are stored a content embedding for content item is stored. Entity names are also stored along with an entity name embedding for each entity name. For each content item, (1) multiple content embeddings that are associated with the content item are identified; (2) a subset of the entity names is identified; and (3) for each entity name in the subset, (i) an embedding of the entity name is identified, (ii) similarity measures are generated based on the entity name embedding and the multiple content embeddings, (iii), a distribution of the similarity measures is generated, (iv) feature values are generated based on the distribution, (v) the feature values are input into a machine-learned classifier, and (vi) based on output from the classifier, it is determined whether to associate the entity name with the content item.

Efficient tagging of content items using multi-granular embeddings

Provided is a method for managing source identifiers of clustered records, which may include receiving a plurality of records, each having identification data associated with a source thereof. The records may be sorted into a first plurality of clusters. A first source identifier may be generated for each cluster. Update data associated with an update to the records may be received. At least some of the records may be sorted into a second plurality of clusters. A first cluster of the second plurality of clusters may have first identification data different from at least one cluster of the first plurality of clusters, and a second cluster of the second plurality of clusters may have identification data that substantially matches the at least one cluster. Whether to assign the first source identifier to the first or second cluster may be determined. A system and computer program product are also disclosed.

Method, system, and computer program product for managing source identifiers of clustered records

In an embodiment, the disclosed technologies include determining a digital identifier, computing, using aggregate digital event data obtained from at least one computing device, digital feature data relating to the digital identifier, inputting the digital feature data relating to the digital identifier into a digital model that has machine-learned correlations between digital feature data and digital propensity prediction values, receiving, from the digital model, a predicted propensity value associated with the digital identifier, determining a propensity score based on the predicted propensity value, causing a digital content item to be displayed on a user interface of a network-based software application associated with the digital identifier if the propensity score satisfies a propensity criterion.

Automatic modeling of online learning propensity for target identification

Techniques for efficiently matching two sets of video items are provided. In on technique, an embedding is generated for each video item in each set. For the first set of video items, multiple groups are generated. The first set of video items may have a relatively little amount of metadata information for them. Each video item in the first set is assigned to one of the groups. Then, for each video item in the second set, one of the groups is selected based on embedding similarity. For each video item in the selected group, an embedding similarity is determined between that video item in the selected group and the video item in the second set. If the embedding similarity is above a certain threshold, then an association is generated for that pair of video items.

Grouping a first set of video items to efficiently match against a second set of video items using embedding similarity

Techniques for implementing a feature generation pipeline for machine learning are provided. In one technique, multiple jobs are executed, each of which computes a different set of feature values for a different feature of multiple features associated with videos. A feature registry is stored that lists each of the multiple features. After the jobs are executed and the feature registry is stored, a model specification is received that indicates a set of features for a model. For each feature in a subset of the set of features, a location is identified in storage where a value for said each feature is found and the value for that feature is retrieved from the location. A feature vector is created that comprises, for each feature in the set of features, the value that corresponds to that feature. The feature vector is used to train the model or as input to the model.

Feature generation pipeline for machine learning

A system for identifying and associating merchant attributes data of a merchant with transaction data may be integrated with existing payment processing systems to provide for detailed merchant attributes data (e.g., merchant name, merchant ownership data, and merchant address data). The system may receive and modify merchant attributes data retrieved from an external source (e.g., an acquirer computer or third party to a transaction) and store the merchant attributes data in a database. The merchant attributes data may be retrieved and correlated with transaction data and provided to entities (e.g., issuer computers) involved with the transaction for authorization decisioning, fraud analysis, and data analytics.

Enhanced merchant identification using transaction data

The video recommendation system provided with an on-line connection system generates on-line video recommendations using collaborative filtering for clusters of member profiles. The recommendation system clusters member profiles using member profile information as clustering criteria. The video recommendations are then generated for a given cluster, based on aggregation of video viewing history recorded for the member profiles that are in the given cluster, using the video similarity matrix. In order to produce video recommendations for a particular member profile, the recommendation system first determines cluster membership for the member profile, retrieves recommendations generated for that cluster, and provides recommendations to the associated member. A user interface including references to one or more recommended videos is rendered on a display device of a viewer.

Cluster-based collaborative filtering

Provided is a system for determining a status of at least one merchant during or after an emergency event including at least one processor to retrieve historical transaction data for at least one merchant associated with a merchant category code (MCC), determine a first transaction count for the at least one merchant based on the historical transaction data for the at least one merchant, retrieve current transaction data for the at least one merchant, determine a second transaction count for the at least one merchant based on the current transaction data for the at least one merchant, classify the at least one merchant based at least partially on a comparison between the first transaction count and the second transaction count, and display a classification of the at least one merchant based on classifying the at least one merchant. A method and computer program product are also disclosed.

Techniques for classifying videos as standalone or non-standalone are provided. Feature (or attribute) values associated with a particular video are identified. Feature values are extracted from metadata associated with the particular video and/or from within a transcript of the particular video. The extracted feature values of the particular video are input to a rule-based or a machine-learned model and the model scores the particular video. Once a determination pertaining to whether the particular video is standalone is made, information about the particular video being a standalone video is presented to one or more users within the network.

Standalone video classification

The recommendation system provided with an on-line connection system identifies on-line recommendations of videos and generates a user interface (UI) by including into the resulting presentation selected recommendations of videos. The recommendations of videos presented in the UI are organized into groups that are topically coherent, where each group is decorated with a context annotation&#x2014;an explanation of why the recommendations in a given carousel are relevant for a member. Each video that is being evaluated by the recommendation system with respect to a subject member profile is assigned an annotation that is selected from a plurality of potentially applicable annotations. The technical problem of optimizing an order of presentation of recommendations grouped by context annotations, in a UI where annotations drive the layout, is addressed by deriving ranks for different context annotations based on global and personalized click through rates and using these values assigned to respective context annotations in constructing the UI.

Ranking carousels of on-line recommendations of videos

In one embodiment, the location or zip code of purchasers in a card present electronic transaction may be reviewed and compared to the reported location or zip code of a merchant. If the difference or variance from the customer zip code or location to the customer location or zip code is over a threshold, the merchant is indicating as requiring additional attention.

Growing community of inventors

Belmont, CA, United States of America

Mahesh Subhash Joshi