Abstract: After a host client establishes a multimedia stream with a guest client, host data is received from a host application. A state machine is updated using the host data. The host application executes on the host client. Guest data is received from a guest application. The state machine is updated using the guest data. The guest application executes on the guest client. Transaction data is propagated between the host application and the guest application. The transaction data is presented with the multimedia stream. The transaction data includes the host data and the guest data. Provider data is generated responsive to updating the state machine with the host data and the guest data. The provider data is sent to the guest client. The provider data is presented with the multimedia stream by the guest application on the guest client.

Abstract: A method and system that proactively generate alerts for updating a scraping script to avoid scraping script errors. A predetermined number of webpages targeted by the scraping script are randomly sampled. The scraping script is appended to each webpage in the sample. A structured list of text fragments across the webpages with the appended script is generated. At predetermined time intervals, a fresh set of webpages is sampled, the scraping script is appended to the webpages, and a new structured list is generated. If the new structured list and the previous structured list do not match, the webpages may have been changed and the scraping script may have to be updated. An alert is generated indicating that such update is required and may include a location of the mismatch. Therefore, scraping script errors are proactively detected and can be rectified before an actual error occurs and propagates.

Abstract: Systems and methods are provided to structure event description data.

Abstract: Systems and methods for automated techniques that generate queryable database table ownership attribution information in real-time. In addition to generating ownership attribution information, system and methods provide a novel framework for creating bi-partite graphs and generating insightful graph data.

Abstract: In one or more embodiments, transaction data between multiple users and multiple merchants is retrieved. The retrieved transaction data is aggregated for each of the multiple users and each of the multiple merchants. The aggregated data may then be normalized. An example normalization process may include income normalization, where a user's total transaction amount at a particular merchant is normalized by the user's income. Other forms of normalization may also be employed. Using the normalized data, user-merchant affinity may be predicted based on collaborative filtering models, cascading tree models, and or cosine similarity models. A recommendation engine may provide personalized advertisements based on the predicted affinity. Because of the normalization of the data, the affinity and therefore the recommendation is less biased toward larger merchants.

Abstract: Certain aspects of the present disclosure provide techniques for providing sentiment analysis of content. In order to determine the overall sentiment of content, a request is received by a sentiment analyzer, which then identifies a content identification number and retrieves comments associated with the content identification number. The sentiment analyzer pre-processes the comments, which includes removing all personal identifying information from the comments. The sentiment analyzer sends the pre-processed comments to a natural language processing service, and in turn, receives sentiment indications corresponding to the comments provided. Based on the sentiment scores, the sentiment analyzer generates a sentiment analysis and displays the sentiment analysis in the graphical user interface generated by the sentiment analyzer.

Abstract: A method comprising generating, during multiple user sessions of a first user with a software application, first clickstream data from the multiple user sessions, and extracting, from the first clickstream data, a first plurality of task instances of the first user performing a first plurality of tasks. The method also includes decomposing, from the first clickstream data, each task instance of the first plurality of task instances into a first plurality of steps to obtain a first plurality of decomposed task instances. The first plurality of steps in the first plurality of decomposed task instances are each associated with a timestamp. The method further includes training a first user model with the first plurality of decomposed task instances to learn a user optimal order to perform the first plurality of tasks and presenting, to the first user, the user optimal order to perform the first plurality of tasks.

Abstract: A method may include executing a baseline classifier on unreviewed transaction features of an unreviewed transaction record to obtain a baseline account identifier, and executing a comparison model on (i) an unreviewed transaction vector of the unreviewed transaction record and (ii) reviewed transaction vectors to obtain comparison scores. The reviewed transaction vectors may correspond to reviewed transaction records each having a user-approved account identifier. The method may further include selecting, using the comparison scores, a reviewed transaction record. The reviewed transaction record may correspond to a comparison score. The comparison score may correspond to a user-approved account identifier of the reviewed transaction record.

Abstract: 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.

Abstract: Certain aspects of the present disclosure provide techniques for generating a unified knowledge graph. In one example, a method includes receiving entity data from a data source comprising a plurality of entities; forming a plurality of type-specific groups of entity data based on the received entity data; for each respective type-specific group of entity data of the plurality of type-specific groups of entity data; disambiguating the entity data within the respective type-specific group of entity data; creating a plurality of entity relationships based on the disambiguated entity data; and exporting the plurality of entity relationships to a type-specific subgraph; and forming a unified knowledge graph based on a plurality of type-specific subgraphs, wherein each type-specific subgraph of the plurality of type-specific subgraphs is associated with a single type-specific group of entity data of the plurality of type-specific groups of entity data.

Abstract: Systems and methods are described for automatically documenting queries and dynamically populating interactive graphical user interfaces with query recommendations. A computing system receives an initial query from an interactive graphical user interface and asynchronously parses the query for strings matching predetermined phrases. In response to determining that the initial query recites strings matching the predetermined phrases, the system extracts metadata and identifies a table name in the initial query, and modifies a table data in key-value data structure corresponding to the table. Subsequent queries related to the initial query cause the system to asynchronously populate the graphical user interface with query recommendations related to the initial query.

Abstract: Certain aspects of the present disclosure provide techniques for adjusting access control policies of access controlled systems, such as techniques for identifying a vulnerability or for identifying parameters and values achieving a specified result from a system whose access is controlled by the policy. Requests to the system can be made using a testing system that executes test scripts using avatars having various parameter types and values. The avatar information and results of the test scripts are provided as training data to a machine learning model training system to generate a model that provides recommendations for parameter types and values likely to achieve a particular result. The recommendations are used to execute the test script to determine results including a rate of success for the recommended parameters and/or values. Various actions, such as adjusting or adding a rule to an access control policy, can be performed based on the results.

Abstract: Certain aspects of the present disclosure provide techniques for medoid-based data compression. One example method generally includes receiving item data indicative of one or more items, determining one or more medoids based on the item data, determining, for each item of the one or more items, a corresponding medoid based on the one or more medoids, identifying, for each item of the one or more items, a difference between the item and the corresponding medoid for the item, storing the one or more medoids, and storing, for each item of the one or more items, the identified difference between the item and the corresponding medoid.

Abstract: Certain aspects of the present disclosure provide techniques for orchestrating a user experience using natural language input. A user experience is orchestrated within an ecosystem of features for which a plurality of corresponding tokens is defined. Natural language describing a desired user experience result is received by a user experience orchestrator. A sequence of tokens corresponding to operations belonging to an ecosystem of features which produce a correct result for the natural language input can be identified by a trained large language model and executed by the user experience orchestrator using a token operator. The output operations determined by the model to produce or be likely to produce the correct result based on the natural language input can be disambiguated, confirmed, and/or executed.

Abstract: Certain aspects of the disclosure pertain to inferring a candidate entity associated with a transaction with a machine learning model. An organization identifier and description associated with a transaction can be received as input. In response, an entity embedding, comprising a vector for each entity of an organization based on the organization identifier, can be retrieved from storage. A machine learning model can be invoked with the entity embedding and description. The machine learning model can be trained to infer a transaction embedding from the description and compute a similarity score between the transaction embedding and each vector of the entity embedding. A candidate entity with a similarity score satisfying a threshold can be identified and returned. The candidate entity with the highest similarity score can be identified in certain aspects.

Abstract: Certain aspects of the present disclosure provide techniques for managing the transmission of mixed-modality messages using machine learning models. An example method generally includes generating, using a first machine learning model, an embedding representation of a mixed-modality message. The mixed-modality message is classified as an effective message or an ineffective message using a second machine learning model and the embedding representation of the mixed-modality message. One or more actions are taken to manage transmission of the mixed-modality message based on the classifying the mixed-modality message as an effective message or an ineffective message.

Abstract: Certain aspects of the present disclosure provide techniques for entering user credentials through a proxy. One example method generally includes receiving, at a user device, a push request for user data from a cloud server and receiving a request file from an aggregation system. The method further includes injecting user credentials stored on the user device into the request file, wherein when injected the user credentials replace at least one dummy entry of the request file, and transmitting the request file to a data source associated with the request file. The method further includes receiving user data from the data source and transmitting the user data to the aggregation system.

Abstract: A method includes receiving event strings from source code repositories, creating, for the source code repositories, digests of keywords, receiving log strings, and aggregating the log strings into a log group. The method further includes comparing the digests to the log group to generate scores, whereby the scores correlate the digests to the log group. The method further includes selecting a source code repository from the source code repositories according to the scores, and associating the log group to a service corresponding to the source code repository, where the source code repository corresponds to the digest with a highest score.

Abstract: Certain embodiments of the present disclosure provide techniques training a user detection model to identify a user of a software application based on voice recognition. The method generally includes receiving a data set including a plurality of voice interactions with users of a software application. For each respective recording in the data set, a spectrogram representation is generated based on the respective recording. A plurality of voice recognition models are trained. Each of the plurality of voice recognition models is trained based on the spectrogram representation for each of the plurality of voice recordings in the data set. The plurality of voice recognition models are deployed to an interactive voice response system.

Abstract: A method implements anonymous uncensorable cryptographic chains. The method includes receiving, from a first application, verifiable data for a current record and unverified data for the current record. The unverified data for the current record was received by the first application from a second application. The method further includes verifying the verifiable data for the current record with unverified data from a previous record. The method further includes recording the verifiable data for the current record and the unverified data for the current record to the current record responsive to verifying the verifiable data for the current record. The method further includes presenting the current record to one or more of the first application and to the second application.