Abstract: Embodiments are generally directed to techniques for query-based binarized point decisions, such as with a system that can provide end-to-end decision making support. Some embodiments are particularly directed to a system that makes point decisions, with traceable reasoning, for queries while factoring in a variety of influences. In many embodiments, the traceable reasoning may refer to the generation and collection of metadata associated with various aspects of the decision making process. For example, the metadata may include, or be used to create, a decision lineage and/or reasoning summary that is generated for each point decision. Some embodiments may ensure various influences do not derail the query from the overall objective. In several such embodiments, the point decisioner may confirm or deny that a decision was made with aspect to a certain objective.

Abstract: Techniques are described for generating a set of emotion factor values using one or more machine learning models for customer communications. For example, a computing system includes one or more processors in communication with a memory. The one or more processors are configured to receive communication data of a current communication associated with a customer, apply the communication data to an emotion-based indexer as input wherein the emotion-based indexer includes a set of machine-learning models for a set of emotion factors, generate as output from the emotion-based indexer a set of emotion factor values for the current communication wherein each emotion factor value indicates the measure of a particular emotion factor in the current communication, apply the set of emotion factor values to an emotion classification model, and classify the current communication into an emotion state based on the set of emotion factor values.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for generating a predicted effect for a target cluster in response to a synthetic stimulus. An example method includes receiving a synthetic behavior prediction request and generating a plurality of clusters, wherein each cluster is associated with a corresponding centroid position. The method further includes selecting the target cluster from the plurality of clusters and generating the predicted effect for the target cluster in response to the synthetic stimulus based on the centroid position corresponding to the target cluster. The method further includes providing a predicted effect notification.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for predicting an emotion in real-time based on facial gestures and limb gestures derived from captured images. An example method includes receiving a series of images captured in real-time. The example method further includes causing generation of a face segmentation and one or more limb segmentations using the series of images. The example method further extracting one or more face segmentations vectors and one or more limb segmentation vectors. The example method further includes causing generation of weighted vectors using the one or more face segmentation vectors and the one or more limb segmentation vectors for each of the one or more the limb segmentations. The example method includes calculating a probability distribution based on the one or more probabilities corresponding to one or more emotions. The example method finally includes determining a predicted emotion based on the probability distribution.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for predicting an emotion in real-time based on a multimodal input including at least (i) an amount of keystrokes over a period of time and (ii) text. An example method may include receiving, by a communications circuitry, a multimodal input from a user including at least (i) an amount of keystrokes over a period of time and (ii) text. The example method may further include generating, by a trained machine learning model of an emotion prediction circuitry and using the multimodal input, an EmotionPrint for the user. The example method may finally include determining, by the emotion prediction circuitry and using the EmotionPrint, a next action.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for mitigating deterioration of modeling system performance. An example method includes detecting, by context analysis circuitry, occurrence of a triggering condition. The example method also includes determining, by context analysis circuitry and in response to detecting the occurrence of the triggering condition, a destination modeling solution based on a regime strategy for the modeling system. The example method also includes switching, by regime deployment circuitry and based on the regime strategy, from a source modeling solution to a destination modeling solution.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for predicting an entity and intent based on captured speech. An example method includes capturing speech and converting the speech to text. The example method further includes causing generation of one or more entities and one or more intents based on the speech and the text. The example method further includes determining a next action based on each of the one or more entities and each of the one or more intents.

Abstract: Techniques are described for personalizing customer interactions using one or more machine learning models for customer communications. For example, a computing system includes a memory and one or more processors in communication with the memory. The one or more processors are configured to: retrieve, from a database in memory, one or more sets of emotion factor values for communication data associated with a customer over time; classify, using an emotion propensity model running on the one or more processors, the customer into an emotional profile according to the customer's typical emotional response during customer communications based on the one or more sets of emotion factor values for the communication data associated with the customer over time; and determine a probability that the customer will respond positively to a particular type of customer engagement based on the emotional profile for the customer.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for generating a predicted effect for a target cluster in response to a synthetic stimulus. An example method includes receiving a synthetic behavior prediction request and generating a plurality of clusters, wherein each cluster is associated with a corresponding centroid position. The method further includes selecting the target cluster from the plurality of clusters and generating the predicted effect for the target cluster in response to the synthetic stimulus based on the centroid position corresponding to the target cluster. The method further includes providing a predicted effect notification.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for generating a predicted effect for a target user or a target cluster in response to a synthetic stimulus. An example method includes receiving a synthetic behavior prediction request and determining a cluster for a target user. The method further includes identifying a digital twin for a target user and generating a predicted effect for the target user in response to the synthetic stimulus and based on an inferred effect or predicted effect associated with the digital twin. The method further includes providing a predicted effect notification which includes the predicted effect generated for the target user.

Abstract: An example method includes detecting, by context analysis circuitry, occurrence of a triggering condition. The example method also includes scheduling, by context analysis circuitry and based on the occurrence of the triggering condition, retraining of a model. The example method also includes generating, by data grafting circuitry and in response to scheduling the retraining of the model, a context-relevant training data set based on a target context vector. The example method also includes retraining, by model training circuitry, the model using the context-relevant training data set to mitigate deterioration of performance of the model.

Abstract: An example method includes detecting, by context analysis circuitry, occurrence of a triggering condition. The example method also includes scheduling, by context analysis circuitry and based on the occurrence of the triggering condition, retraining of a model. The example method also includes generating, by data grafting circuitry and in response to scheduling the retraining of the model, a context-relevant training data set based on a target context vector. The example method also includes retraining, by model training circuitry, the model using the context-relevant training data set to mitigate deterioration of performance of the model.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for simultaneous multifactor authentication. An example method includes simultaneously receiving, by communications hardware and in response to a first authentication request, a plurality of authentication factors associated with an individual via a plurality of channels. The example method further includes performing, by an authentication engine, a verification operation that evaluates the plurality of authentication factors in parallel. The example method further includes authorizing, by the authentication engine, the one or more actions based on a successful outcome of the verification operation.

Abstract: An automated teller machine (“ATM”) receives, from a user computing device via a first wireless communication session, a unique transaction identifier (“UTN”). The ATM transmits the UTN to a bank computing system via a network. The ATM receives, from the bank computing system responsive to transmitting the UTN, transaction details and a first security code associated with the UTN. The ATM receives, from the user computing device via a second wireless communication session, a second security code. Responsive to determining that the first security code matches the second security code, the ATM processes a transaction in accordance with the transaction details.

Abstract: Embodiments are generally directed to techniques for query-based binarized point decisions, such as with a system that can provide end-to-end decision making support. Some embodiments are particularly directed to a system that makes point decisions, with traceable reasoning, for queries while factoring in a variety of influences. In many embodiments, the traceable reasoning may refer to the generation and collection of metadata associated with various aspects of the decision making process. For example, the metadata may include, or be used to create, a decision lineage and/or reasoning summary that is generated for each point decision. Some embodiments may ensure various influences do not derail the query from the overall objective. In several such embodiments, the point decisioner may confirm or deny that a decision was made with aspect to a certain objective.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for mitigating deterioration of modeling system performance. An example method includes detecting, by context analysis circuitry, occurrence of a triggering condition. The example method also includes determining, by context analysis circuitry and in response to detecting the occurrence of the triggering condition, a destination modeling solution based on a regime strategy for the modeling system. The example method also includes switching, by regime deployment circuitry and based on the regime strategy, from a source modeling solution to a destination modeling solution.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for measuring employee engagement. An example method includes receiving, by communications circuitry, an exogenous event report and identifying, by a frequency modulator and based on the exogenous event report, a frequency at which to deliver an employee survey to an employee. The example method further includes causing, by an engagement engine, delivery of the employee survey to the employee based on the identified frequency and receiving, by communications circuitry, a response to the employee survey. The example method further includes storing the received response in a memory as a measure of employee engagement.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for generating predictive outcomes for scenarios using generative artificial intelligence. An example method includes receiving first historical customer scenario data and training, by a scenario generator circuitry, a scenario generation model using the first historical customer scenario data and generating an artificial scenario where the artificial scenario includes a node and an edge and where the node is associated with an action and the edge is associated with a decision weight. The example method further includes receiving second historical customer scenario data and training, by a scenario discriminator circuitry, a scenario discrimination model using the second historical customer data. The example method further includes receiving the artificial scenario and determining a scenario discrimination score based on the artificial scenario.

Abstract: Techniques are described for personalizing customer interactions using one or more machine learning models for customer communications. For example, a computing system includes a memory and one or more processors in communication with the memory. The one or more processors are configured to: retrieve, from a database in memory, one or more sets of emotion factor values for communication data associated with a customer over time; classify, using an emotion propensity model running on the one or more processors, the customer into an emotional profile according to the customer's typical emotional response during customer communications based on the one or more sets of emotion factor values for the communication data associated with the customer over time; and determine a probability that the customer will respond positively to a particular type of customer engagement based on the emotional profile for the customer.

Abstract: Disclosed in some examples are methods, systems, and machine readable mediums which provide summaries of topics determined within a corpus of documents. These summaries may be used by customer service associates, analysts, or other users to quickly determine both topics discussed and contexts of those topics over a large corpus of text. For example, a corpus of documents may be related to customer complaints and the topics may be summarized to produce summaries such as “credit report update due to stolen identity.” These summarizations may be used to efficiently spot trends and issues.