Aspects of the disclosure provide a computer-implemented method and system for the assignment of roadside assistance service providers such as tow trucks to distressed vehicles/drivers requiring roadside assistance. The methods and systems may include a roadside assistance service provider system with a collection module, an assignment module, and a feedback module. The collection module collects roadside assistance service provider information and historical statistics from real-world information and stores the information in a database that may then be analyzed using particular rules and formulas. The assignment module assigns particular roadside assistance service providers to particular distressed vehicles/drivers based on one or more characteristics. The feedback module may provide near real-time cues to the tow truck driver's mobile device, such as alerting when the amount of time spent on a task exceeds a predefined threshold, flagging high priority tasks/assignments, providing a step-by-step checklist for the repair.

Example aspects include techniques for accurate and expeditious cloud service health communication to customers. These techniques may include determining that a service health incident has customer impact, the service health incident corresponding to an outage of one or more services of a cloud computing platform, identifying a plurality of customers impacted by the service health incident, and predicting, based on the service health incident and one or more other service health incidents, aggregated incident information identifying a plurality of service health incidents associated with the outage of the one or more services. In addition, the techniques may include identifying the one or more services associated with the service health incident, and transmitting, based at least in part on the aggregated incident information and the one or more services, a health notification to the plurality of customers.

A management system operates in conjunction with entities to provide component recommendations for objects. The management system trains a machine learning model used to generate the component recommendations. The machine learning model is trained based on historical component entries describing components previously provided and identifiers of the components. The management system generates training data by classifying the historical component entries into predetermined component classifications. After the machine learning model is trained, the management system generates a customized recommendation of components for an object based on likelihoods of selection of the predetermined component classifications.

Techniques and systems for automated social synchrony measurements which can identify behaviorally relevant social synchrony are provided. A method for automated social synchrony measurements can include receiving a recording of a social interaction between a first participant and a second participant; for each feature, extracting, from the recording, a feature time series pair comprising a first time series of the first participant and a second time series of the second participant; for each feature time series pair, determining an individual social synchrony level between the feature time series pair using characteristics of the derivative dynamic time warping path of the feature time series pair; analyzing the determined individual social synchrony level of every feature time series pair to identify a set of the features related to the prediction target; and generating a notification for at least one feature based on the determined individual social synchrony level.

Methods and systems to provide a form of probabilistic labeling to associate an outage with a disturbance, which could itself be either known based on the available data or unknown. In the latter case, labeling is especially challenging, as it necessitates the discovery of the disturbance. One approach incorporates a statistical change-point analysis to time-series events that correspond to service tickets in the relevant geographic sub-regions. The method is calibrated to separate the regular periods from the environmental disturbance periods, under the assumption that disturbances significantly increase the rate of loss-causing events. To obtain the probability that a given loss-causing event is related to an environmental disturbance, the method leverages the difference between the rate of events expected in the absence of any disturbances (baseline) and the rate of actually observed events. In the analysis, the local disturbances are identified and estimators of their duration and magnitude are provided.

A system and method for predicting behavior and/or outcomes related to a consumer's experience with an organization are implemented. Household data for households that are associated with a customer service interaction as of a certain date is collected, the household data having been created over a first pre-determined period of time preceding the certain date. The household data is analyzed to identify positive household data sets and negative household data sets. The positive household data sets relate to customer service interactions which preceded a high level customer service interaction within a subsequent period of time and the negative household data sets relate to customer service transactions which did not precede a high level customer service interaction with the subsequent period of time. The positive household data sets and the negative household data sets are processed in the aggregate, using a trained support vector machine model, to determine cumulative differences between data contained within the positive household data sets and the negative household data sets. Each day, daily household data is collected. The daily household data describes individual customer service transactions occurring during a previous calendar day. The daily household data is processed using the model to determine whether each individual customer service transaction occurring during the previous calendar day is more similar to the positive household data sets or to the negative household data sets. The individual customer service transactions that are more similar to the positive household data sets are flagged for proactive intervention.

A system and method for predicting behavior and/or outcomes related to a consumer's experience with an organization are implemented. Household data for households that are associated with a customer service interaction as of a certain date is collected, the household data having been created over a first pre-determined period of time preceding the certain date. The household data is analyzed to identify positive household data sets and negative household data sets. The positive household data sets relate to customer service interactions which preceded a high level customer service interaction within a subsequent period of time and the negative household data sets relate to customer service transactions which did not precede a high level customer service interaction with the subsequent period of time. The positive household data sets and the negative household data sets are processed in the aggregate, using a trained support vector machine model, to determine cumulative differences between data contained within the positive household data sets and the negative household data sets. Each day, daily household data is collected. The daily household data describes individual customer service transactions occurring during a previous calendar day. The daily household data is processed using the model to determine whether each individual customer service transaction occurring during the previous calendar day is more similar to the positive household data sets or to the negative household data sets. The individual customer service transactions that are more similar to the positive household data sets are flagged for proactive intervention.

Disclosed embodiments may include a system that may receive an interaction message associated with an interaction a user has with an application or website, the interaction message may include an error message or a repeated action message. The system may identify, using a first machine learning model, one or more issues associated with the interaction message, retrieve one or more troubleshooting steps mapped to the one or more issues, and generate a first message comprising the one or more troubleshooting steps and a feedback request on an effectiveness of the one or more troubleshooting steps. The system may transmit the first message to the user, receive feedback from the user in response to the feedback request, and determine whether the feedback is negative. When the feedback is negative, the system may transmit a second message to a representative requesting the representative call the user.

A device may receive historical risk data identifying historical risks associated with entities, and historical compliance data identifying historical compliance actions performed by the entities. The device may train a machine learning model with the historical risk data and the historical compliance data to generate a structured semantic model, and may receive entity risk data identifying new and existing risks associated with an entity. The device may receive entity compliance data identifying new and existing compliance actions performed by the entity, and may process the entity risk data and the entity compliance data, with the structured semantic model, to determine risk and compliance insights for the entity. The risk and compliance insights may include insights associated with a key performance indicator, a compliance issue, a regulatory issue, an operational risk, a compliance risk, or a qualification of controls. The device may perform actions based on the risk and compliance insights.

A device may receive historical risk data identifying historical risks associated with entities, and historical compliance data identifying historical compliance actions performed by the entities. The device may train a machine learning model with the historical risk data and the historical compliance data to generate a structured semantic model, and may receive entity risk data identifying new and existing risks associated with an entity. The device may receive entity compliance data identifying new and existing compliance actions performed by the entity, and may process the entity risk data and the entity compliance data, with the structured semantic model, to determine risk and compliance insights for the entity. The risk and compliance insights may include insights associated with a key performance indicator, a compliance issue, a regulatory issue, an operational risk, a compliance risk, or a qualification of controls. The device may perform actions based on the risk and compliance insights.