Abstract: An artificial intelligence (AI) system for guiding a user interaction in a phone call or chat session. The system includes a computer running an AI algorithm, such as a machine learning algorithm, which is trained to recognize patterns in user interaction dialog which lead to satisfactory outcomes for the user. The system may operate in a completely autonomous mode, or the system connect a human agent in the loop. The algorithm adaptively guides the dialog to achieve a favorable outcome based on the current status of the dialog—including identifying a next question to ask, information to provide, or an action to take. The algorithm is trained via supervised learning using real dialog transcriptions from past user interactions which have been supplemented with decision points and outcomes. After deployment, update training may be performed on the algorithm using data captured by the system after the user interactions.

Abstract: A system for monitoring quantized signals in a bidirectional data stream and alerting a networked device of metric divergence by georegion includes a computing system that automatically, for each of multiple user entities, receives input signals and stores corresponding records, associated with the user entity, each representing a quantized input event. The computing system increments a resource of the user entity by a respective input quantity. Georegion-specific attributes associated with a respective georegion are discriminated, and the input records are associated with georegion. The computing system measures user-specific georegion-specific metrics for each georegion and determines whether the metrics diverge from a georegion-specific index. An alert is sent across a network for display on a user device upon determining a user-specific georegion-specific metric diverges from a georegion-specific index.

Abstract: An artificial intelligence (AI) system for guiding a user interaction in a phone call or chat session. The system includes a computer running an AI algorithm, such as a machine learning algorithm, which is trained to recognize patterns in user interaction dialog which lead to satisfactory outcomes for the user. The system may operate in a completely autonomous mode, or the system connect a human agent in the loop. The algorithm adaptively guides the dialog to achieve a favorable outcome based on the current status of the dialog—including identifying a next question to ask, information to provide, or an action to take. The algorithm is trained via supervised learning using real dialog transcriptions from past user interactions which have been supplemented with decision points and outcomes. After deployment, update training may be performed on the algorithm using data captured by the system after the user interactions.

Abstract: A system monitors signals in a service context specific channel (SCS) among multiple SCS channels and sends an advisory message when discharges are disproportionate with respect to a peer-representative index (PRI). The system monitors signals in multiple bidirectional SCS channels between multiple system devices and at least one user device, each SCS channel conveying signals to and from a respective system device of the multiple system devices. The system identifies discharges to a third party from an account associated with the user, determines the discharges are disproportionate relative to the PRI, and generates an advisory message regarding disproportionate discharges for at least one SCS channel. The advisory message is sent via the at least one SCS channel to at least one of the user device and the system device of the at least one SCS channel. Advisory messaging may be sent directly to client device, or indirectly via system agents.

Abstract: A system determines a priority service context specific (SCS) channel among multiple SCS channels, according to a priority status metric (PSM), and sends an advisory message to the priority channel. The system includes at least one processor, a communication interface communicatively coupled to the at least one processor, and a memory device storing executable code. The system monitors signals in multiple bidirectional SCS channels between multiple system devices and at least one user device, each SCS channel conveying signals to and from a respective system device of the multiple system devices, and identify a respective PSM for each SCS channel. The system further determines a priority SCS channel having a PSM higher than at least some of the other SCS channels, generates an advisory message for the priority SCS channel, and sends, the advisory message to the respective system device of the priority SCS channel.

Abstract: A system for monitoring quantized signals in a bidirectional data stream and alerting a networked device of metric divergence by georegion includes a computing system that automatically, for each of multiple user entities, receives input signals and stores corresponding records, associated with the user entity, each representing a quantized input event. The computing system increments a resource of the user entity by a respective input quantity. Georegion-specific attributes associated with a respective georegion are discriminated, and the input records are associated with georegion. The computing system measures user-specific georegion-specific metrics for each georegion and determines whether the metrics diverge from a georegion-specific index. An alert is sent across a network for display on a user device upon determining a user-specific georegion-specific metric diverges from a georegion-specific index.

Abstract: A system for monitoring quantized signals in a bidirectional data stream and alerting a networked device of metric divergence by georegion includes a computing system that automatically, for each of multiple user entities, receives input signals and stores corresponding records, associated with the user entity, each representing a quantized input event. The computing system increments a resource of the user entity by a respective input quantity. Georegion-specific attributes associated with a respective georegion are discriminated, and the input records are associated with georegion. The computing system measures user-specific georegion-specific metrics for each georegion and determines whether the metrics diverge from a georegion-specific index. An alert is sent across a network for display on a user device upon determining a user-specific georegion-specific metric diverges from a georegion-specific index.

Abstract: An artificial intelligence (AI) system for guiding a user interaction in a phone call or chat session. The system includes a computer running an AI algorithm, such as a machine learning algorithm, which is trained to recognize patterns in user interaction dialog which lead to satisfactory outcomes for the user. The system may operate in a completely autonomous mode, or the system connect a human agent in the loop. The algorithm adaptively guides the dialog to achieve a favorable outcome based on the current status of the dialog—including identifying a next question to ask, information to provide, or an action to take. The algorithm is trained via supervised learning using real dialog transcriptions from past user interactions which have been supplemented with decision points and outcomes. After deployment, update training may be performed on the algorithm using data captured by the system after the user interactions.

Abstract: A number of variations may include a mobile user device and a back-end server system for remotely authorizing a user of a user mobile device proximate a point of interaction system for real-time transmission of contemporaneous interaction information from the point of interaction system to the user mobile device prior to its periodic deletion by the point of interaction system, the back-end server system non-collocated with both the user mobile device and the point of interaction system.

Abstract: An artificial intelligence (AI) system for guiding a user interaction in a phone call or chat session. The system includes a computer running an AI algorithm, such as a machine learning algorithm, which is trained to recognize patterns in user interaction dialog which lead to satisfactory outcomes for the user. The system may operate in a completely autonomous mode, or the system connect a human agent in the loop. The algorithm adaptively guides the dialog to achieve a favorable outcome based on the current status of the dialog—including identifying a next question to ask, information to provide, or an action to take. The algorithm is trained via supervised learning using real dialog transcriptions from past user interactions which have been supplemented with decision points and outcomes. After deployment, update training may be performed on the algorithm using data captured by the system after the user interactions.

Abstract: A number of variations may include a mobile user device and back-end server system for remotely authorizing a user of a user mobile device proximate a point of interaction system for real-time transmission of contemporaneous interaction information from the point of interaction system to the user mobile device prior to its periodic deletion by the point of interaction system, the back-end server system non-collocated with both the user mobile device and the point of interaction system.

Abstract: A system for identifying life events for individuals based on relationships found in data. The system includes a database containing data records and fields and identifying individuals involved in each record. A supplemental source of data including location and social media data is also included. The database and the supplemental source of data are provided to a computer which executes a machine learning algorithm configured to identify life events for the individuals based on clusters in the data, where the machine learning algorithm provides output data identifying the life events, and rating values for each of the life events for each of the individuals. A communication system algorithm sends actionable communications to particular ones of the individuals based on the output data. Unsupervised learning may be used for initial system training, and supervised learning for ongoing training.

Abstract: A monitoring system for detecting anomalous discharges stores input and output records, and fetches and discharges corresponding quantities to and from user resources. The output event records are discriminated for category attributes, and user-specific output category-specific metrics are aggregated for comparison to peer-representative output-category indexes. An alert is sent across a network connection for display on a user device upon determining the user-specific output category-specific metric diverges from the peer-representative output-category index. An alert, in some examples, indicates that the user-specific output category metric exceeds the peer-representative output-category index. Exposure of user-entity resources to outputs that are divergent from peer-representative levels is mitigated. The alert is advantageous to user entities and user devices, enabling early action to be taken by the user entity.

Abstract: A system for improving distributed network data flow efficiency by using a machine learning model to engage a reduced set of unknown objects is disclosed. The system includes at least one processor, a communication interface communicatively coupled to the at least one processor, and a memory device storing executable code that, when executed causes the at least one processor to receive interaction data corresponding to interactions between known objects and unknown objects, develop an interaction map, develop a profile for each unknown object, process each unknown object using a machine learning algorithm to generate a reduced set of unknown objects most likely to reciprocate and identify properties of each unknown object, and trigger a communication to each of the reduced set of unknown objects.

Abstract: A system for employing artificial intelligence to initiate a concurrent reaction according to predicted behavior is disclosed. The system includes at least one processor, a communication interface communicatively coupled to the at least one processor, and a memory device storing executable code that, when executed, causes the at least one processor to receive labeled interaction data for a group of users, receive user information for the group of users, correlate the labeled interaction data with the user information, apply a first algorithm to the correlated data to segment the users, based on the user’s segment apply a second algorithm to make a prediction about the user’s future behavior, and based on the predicted behavior trigger a concurrent communication to the user. Additionally, the system may update a user dashboard.

Abstract: A system for using a machine learning model to introduce new solutions, based on data gathering and processing, to a user based on user requirements and current solutions. The system includes a back-end server having a processor for processing data and information, a communications interface communicatively coupled to the processor, and a memory device storing data and executable code. The executable code causes the processor to collect data and information from multiple interaction channels, where the data corresponds to interactions between the user and multiple nodes indicating user requirements, store the collected data and information in the memory device, process the stored data and information through a machine learning model to determine which of a set of available solutions are currently being implemented, receive a result from the machine learning model, where the result includes identifying new solutions, and transmit a communication to the user to propose the new solutions.

Abstract: A system including a back-end coupled to an interaction database and a master database. The back-end server includes a processor, a communications interface communicatively coupled to the processor, and a memory device storing executable code that, when executed, causes the processor to collect interaction data and information from multiple interaction channels between all users and nodes, store the collected interaction data and information in the interaction database, collect user data and information corresponding to all of the users, store the collected user data and information in the master database, access both the interaction database and the master database to access the stored interaction data and user data, process the accessed interaction data and user data through a machine learning model, and receive a result from the machine learning model, where the result corresponds to a probability of user attrition that minimizes trash data stored in system databases.

Abstract: A system including a back-end coupled to an interaction database and a master database. The back-end server includes a processor, a communications interface communicatively coupled to the processor, and a memory device storing executable code that, when executed, causes the processor to collect interaction data and information from multiple interaction channels between all users and nodes, store the collected interaction data and information in the interaction database, collect user data and information corresponding to all of the users, store the collected user data and information in the master database, access both the interaction database and the master database to access the stored interaction data and user data, process the accessed interaction data and user data through a machine learning model, receive an attrition result from the machine learning model, and trigger a retention action corresponding to the user, where retention of the users minimizes trash data stored in system databases.

Abstract: A system and a method for providing mobile services uses an autonomous automated teller machine vehicle including a controller operating a drive unit to move the autonomous vehicle and execute a machine learning algorithm configured to guide the movement along a selected travel path according to travel path information. Sensors on the autonomous vehicle detect fixed objects, new objects and persons along the travel path. The machine learning algorithm performs steps for guiding the movement along the travel path including: determining whether each of the detected fixed objects is included in the travel path information for use in guiding the vehicle; determining whether each of the detected new objects is to be included in the travel path information; and determining whether each of the detected persons interferes with the travel path and requires the vehicle to stop moving until the interfering person moves away from the travel path.

Abstract: A system and a method for providing mobile services uses an autonomous automated teller machine vehicle including a controller operating a drive unit to move the autonomous vehicle and execute a machine learning algorithm configured to guide the movement along a selected travel path according to travel path information. Sensors on the autonomous vehicle detect fixed objects, new objects and persons along the travel path. The machine learning algorithm performs steps for guiding the movement along the travel path including: determining whether each of the detected fixed objects is included in the travel path information for use in guiding the vehicle; determining whether each of the detected new objects is to be included in the travel path information; and determining whether each of the detected persons interferes with the travel path and requires the vehicle to stop moving until the interfering person moves away from the travel path.