Abstract: A computing system identifies an incoming voice call from a user device to an agent device associated with the computing system. The computing system generates a transcription of the incoming voice call using one or more natural language processing techniques. The computing system extracts a problem description from the transcription. The problem description indicates a topic for the incoming voice call. A first machine learning model estimates a situation vector from the problem description. A second machine learning model identifies a pre-existing situation vector that closely matches the estimated situation vector. The computing system retrieves a situation description that corresponds to the identified pre-existing situation vector.

Abstract: A computing system identifies an incoming voice call from a user device to an agent device associated with the computing system. The computing system generates a transcription of the incoming voice call using one or more natural language processing techniques. The computing system extracts a problem description from the transcription. The problem description indicates a topic for the incoming voice call. A first machine learning model estimates a situation vector from the problem description. A second machine learning model identifies a pre-existing situation vector that closely matches the estimated situation vector. The computing system retrieves a situation description that corresponds to the identified pre-existing situation vector.

Abstract: A computing system identifies an incoming voice call from a user device to an agent device associated with the computing system. The computing system generates a transcription of the incoming voice call using one or more natural language processing techniques. The computing system extracts a problem description from the transcription. The problem description indicates a topic for the incoming voice call. A first machine learning model estimates a situation vector from the problem description. A second machine learning model identifies a pre-existing situation vector that closely matches the estimated situation vector. The computing system retrieves a situation description that corresponds to the identified pre-existing situation vector.

Abstract: A computing system identifies an incoming voice call from a user device to an agent device associated with the computing system. The computing system generates a transcription of the incoming voice call using one or more natural language processing techniques. The computing system extracts a problem description from the transcription. The problem description indicates a topic for the incoming voice call. A first machine learning model estimates a situation vector from the problem description. A second machine learning model identifies a pre-existing situation vector that closely matches the estimated situation vector. The computing system retrieves a situation description that corresponds to the identified pre-existing situation vector.

Abstract: A computing system identifies an incoming voice call from a user device to an agent device associated with the computing system. The computing system generates a transcription of the incoming voice call using one or more natural language processing techniques. The computing system extracts a problem description from the transcription. The problem description indicates a topic for the incoming voice call. A first machine learning model estimates a situation vector from the problem description. A second machine learning model identifies a pre-existing situation vector that closely matches the estimated situation vector. The computing system retrieves a situation description that corresponds to the identified pre-existing situation vector.

Abstract: Systems and methods for automatically identifying problem-relevant sentences in a transcript are disclosed. In an example method, a transcript may be received of a first support call. A region of the first support call transcript may be identified, and first customer utterances may be detected in the region using a trained classification model. A trained regression model may estimate a relevancy to the problem statement of each of the first customer utterances, and one or more most problem-relevant statements may be selected from the first customer utterances, based on the estimated relevancies.

Abstract: Operating an encoder with double decoder machine learning models include executing, on a transcript, an encoder machine learning model to generate an encoder output, and executing a situation decoder machine learning model on the encoder output to obtain a situation model output having a situation identifier, and executing a trouble decoder machine learning model using the encoder output to obtain a trouble identifier. The method further includes outputting the situation identifier and the trouble identifier.

Abstract: Operating an encoder with double decoder machine learning models include executing, on a transcript, an encoder machine learning model to generate an encoder output, and executing a situation decoder machine learning model on the encoder output to obtain a situation model output having a situation identifier, and executing a trouble decoder machine learning model using the encoder output to obtain a trouble identifier. The method further includes outputting the situation identifier and the trouble identifier.

Abstract: 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.

Abstract: Systems and methods for automatically identifying problem-relevant sentences in a transcript are disclosed. In an example method, a transcript may be received of a first support call. A region of the first support call transcript may be identified, and first customer utterances may be detected in the region using a trained classification model. A trained regression model may estimate a relevancy to the problem statement of each of the first customer utterances, and one or more most problem-relevant statements may be selected from the first customer utterances, based on the estimated relevancies.

Abstract: A method including establishing, using electronic transactions of a user, a geo-temporal trajectory of the user. The method also includes forming a first data structure by sub-dividing the geo-temporal trajectory into segments including subsets of the electronic transactions along the geo-temporal trajectory. Sub-dividing is performed with respect to a selected feature. The method also includes gathering, for a subset of the segments, a corresponding labeled dataset of transactions within the electronic transactions to generate a second data structure. The method also includes applying, as input, the second data structure to a machine learning classifier. The method also includes receiving, from the machine learning classifier, an assignment of disambiguated labels to the electronic transactions. The method also includes storing, automatically in a financial management application, the disambiguated labels as assigned to the electronic transactions.

Abstract: A method that involves receiving a set of first log records. The set of first log records are duplicated using key operation pairs to generate a set of second log records from the set of first log records. The set of second log records are duplicated using a second set of intervals to generate a set of third log records from the set of second log records. The set of third log records are aggregated using the second set of intervals to generate a set of aggregated log records. The set of aggregated log records includes an aggregated log record comprising a number indicating a number of events from the set of first log records that have the key, that have the operation, and that occurred during an interval of the second set of intervals. The operation is identified by comparing the aggregated log record to a server log record.

Abstract: 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.