Abstract: Method starts with a processor receiving configuration settings including an identified task, a relationship data, and a criticality value. Processor initializes a communication session with an agent client device. The communication session is between a virtual caller associated with the system and the agent client device. Processor then processes an audio signal of the communication session to generate an agent utterance and generates a transcribed agent utterance based on the agent utterance using a speech-to-text processor. Processor generates a virtual caller utterance using a task-specific virtual caller neural network associated with the identified task. The virtual caller utterance can be generated based on the transcribed agent utterance. Processor then causes the virtual caller utterance to be played back in the communication session to the agent client device. Other embodiments are disclosed herein.

Abstract: Method starts with a processor receiving configuration settings including an identified task, a relationship data, and a criticality value. Processor initializes a communication session with an agent client device. The communication session is between a virtual caller associated with the system and the agent client device. Processor then processes an audio signal of the communication session to generate an agent utterance and generates a transcribed agent utterance based on the agent utterance using a speech-to-text processor. Processor generates a virtual caller utterance using a task-specific virtual caller neural network associated with the identified task. The virtual caller utterance can be generated based on the transcribed agent utterance. Processor then causes the virtual caller utterance to be played back in the communication session to the agent client device. Other embodiments are disclosed herein.

Abstract: Method for generating user interface that indicates medication changes in medication starts with a processor detecting a medication change event. Processor retrieves medication information based on the medication change event including images of two medications. Processor generates color difference output using a color neural network, image of first medication and second medication. Color difference output comprises information on a difference in hue, saturation or color distribution. Processor generates medication appearance difference output using medication appearance neural network, image of first medication and second medication. Medication appearance difference output comprises information on a difference in shape, segmentation or form. Processor generates a differential record using the color difference output and medication appearance difference output.

Abstract: Method starts with processing, by a processor, audio signal to generate audio caller utterance and transcribed caller utterance. Processor generates identified task based on transcribed caller utterance. Processor samples audio caller utterance to generate samples of audio caller utterance. Processor generates loudness result based on loudness values of samples using loudness neural network associated with identified task. Processor generates pitch result based on pitch values of samples using pitch neural network associated with identified task. Processor generates tone result for each word in transcribed caller utterance using tone neural network associated with identified task. Using task completion probability neural network associated with identified task, processor generates task completion probability result that is based on at least one of: loudness result, pitch result, or tone result. Other embodiments are disclosed herein.

Abstract: Method starts with a processor receiving configuration settings including an identified task, a relationship data, and a criticality value. Processor initializes a communication session with an agent client device. The communication session is between a virtual caller associated with the system and the agent client device. Processor then processes an audio signal of the communication session to generate an agent utterance and generates a transcribed agent utterance based on the agent utterance using a speech-to-text processor. Processor generates a virtual caller utterance using a task-specific virtual caller neural network associated with the identified task. The virtual caller utterance can be generated based on the transcribed agent utterance. Processor then causes the virtual caller utterance to be played back in the communication session to the agent client device. Other embodiments are disclosed herein.

Abstract: Method starts with processing, by a processor, audio signal to generate audio caller utterance. Processor generates an agent action ranking score associated with the audio caller utterance and determines whether the agent action ranking score is below a minimum threshold. In response to determining that the agent action ranking score is below the minimum threshold, processor generates a transcribed caller utterance using a speech-to-text processor and generates an identified task based on the transcribed caller utterance. Using the transcribed caller utterance and a task-specific agent coaching neural network associated with the identified task, processor generates an ideal response. Processor generates a feedback result and causes the feedback result to be displayed on a display device of the agent client device. Other embodiments are disclosed herein.

Abstract: Method starts with processing, by a processor, audio signal to generate audio caller utterance and transcribed caller utterance. Processor generates identified tasks based on transcribed caller utterances. Processor obtains member context associated with member identification and obtains available tasks associated with member identification. Processor determines lengths of time associated with available tasks, respectively. Lengths of time associated with available tasks are lengths of time that the available tasks have been available. Processor determines messaging data associated with available tasks, respectively, that is based on dates of receipt of messages pertaining to available tasks. Processor, using a predictive analysis neural network, generates a predictive analysis result that is based on the available tasks, the lengths of time associated with the available tasks, or messaging data associated with the available tasks.

Abstract: A method starts with processor retrieving member's initial context data. Processor receives a string that is a transcribed utterance or an electronic message from the communication session established between member client device and agent client device. Processor determines potential relationships between the member and a patient that is the subject of the string by processing the string using Long Short-Term Memory (LSTM) neural networks to generate a plurality of relationship values. Relationship values are associated with relationship types. Processor generates weight values based on member's initial context data for each of the plurality of relationship types, and generates probability values for the relationship types based on the relationship values and the weight values. Processor narrows the potential relationships, generates relationship data that includes the narrowed potential relationships, and causes the relationship data to be displayed by the agent client device.

Abstract: Method starts with a processor receiving configuration settings including an identified task, a relationship data, and a criticality value. Processor initializes a communication session with an agent client device. The communication session is between a virtual caller associated with the system and the agent client device. Processor then processes an audio signal of the communication session to generate an agent utterance and generates a transcribed agent utterance based on the agent utterance using a speech-to-text processor. Processor generates a virtual caller utterance using a task-specific virtual caller neural network associated with the identified task. The virtual caller utterance can be generated based on the transcribed agent utterance. Processor then causes the virtual caller utterance to be played back in the communication session to the agent client device. Other embodiments are disclosed herein.

Abstract: Method starts with processing, by a processor, audio signal to generate audio caller utterance. Processor generates an agent action ranking score associated with the audio caller utterance and determines whether the agent action ranking score is below a minimum threshold. In response to determining that the agent action ranking score is below the minimum threshold, processor generates a transcribed caller utterance using a speech-to-text processor and generates an identified task based on the transcribed caller utterance. Using the transcribed caller utterance and a task-specific agent coaching neural network associated with the identified task, processor generates an ideal response. Processor generates a feedback result and causes the feedback result to be displayed on a display device of the agent client device. Other embodiments are disclosed herein.

Abstract: Method starts with processing, by a processor, audio signal to generate audio caller utterance. Processor generates an agent action ranking score associated with the audio caller utterance and determines whether the agent action ranking score is below a minimum threshold. In response to determining that the agent action ranking score is below the minimum threshold, processor generates a transcribed caller utterance using a speech-to-text processor and generates an identified task based on the transcribed caller utterance. Using the transcribed caller utterance and a task-specific agent coaching neural network associated with the identified task, processor generates an ideal response. Processor generates a feedback result and causes the feedback result to be displayed on a display device of the agent client device. Other embodiments are disclosed herein.

Abstract: Method starts with processing, by processor, receiving audio signal of communication session between member-related client device and agent client device. Processor processes audio signal to generate caller utterances, each including audio caller utterance and transcribed caller utterance. Caller utterances includes first and second caller utterances. For each of the plurality of caller utterances, processor generates relationship data based on transcribed caller utterance, generates identified task based on transcribed caller utterance, and generates task completion probability result based on audio caller utterance, relationship data, and identified task, and stores the task completion probability result in a database. Processor computes agent action ranking score that is based on difference between task completion probability result of first caller utterance and task completion probability result of second caller utterance that precedes first caller utterance.

Abstract: Method starts with processing, by a processor, audio signal to generate audio caller utterance and transcribed caller utterance. Processor generates identified tasks based on transcribed caller utterances. Processor obtains member context associated with member identification and obtains available tasks associated with member identification. Processor determines lengths of time associated with available tasks, respectively. Lengths of time associated with available tasks are lengths of time that the available tasks have been available. Processor determines messaging data associated with available tasks, respectively, that is based on dates of receipt of messages pertaining to available tasks. Processor, using a predictive analysis neural network, generates a predictive analysis result that is based on the available tasks, the lengths of time associated with the available tasks, or messaging data associated with the available tasks.

Abstract: Method starts with processing, by a processor, audio signal to generate audio caller utterance and transcribed caller utterance. Processor generates identified tasks based on transcribed caller utterances. Processor obtains member context associated with member identification and obtains available tasks associated with member identification. Processor determines lengths of time associated with available tasks, respectively. Lengths of time associated with available tasks are lengths of time that the available tasks have been available. Processor determines messaging data associated with available tasks, respectively, that is based on dates of receipt of messages pertaining to available tasks. Processor, using a predictive analysis neural network, generates a predictive analysis result that is based on the available tasks, the lengths of time associated with the available tasks, or messaging data associated with the available tasks.

Abstract: Method starts with processing, by a processor, audio signal to generate audio caller utterance and transcribed caller utterance. Processor generates identified task based on transcribed caller utterance. Processor samples audio caller utterance to generate samples of audio caller utterance. Processor generates loudness result based on loudness values of samples using loudness neural network associated with identified task. Processor generates pitch result based on pitch values of samples using pitch neural network associated with identified task. Processor generates tone result for each word in transcribed caller utterance using tone neural network associated with identified task. Using task completion probability neural network associated with identified task, processor generates task completion probability result that is based on at least one of: loudness result, pitch result, or tone result. Other embodiments are disclosed herein.

Abstract: A method starts with processor retrieving member's initial context data. Processor receives a string that is a transcribed utterance or an electronic message from the communication session established between member client device and agent client device. Processor determines potential relationships between the member and a patient that is the subject of the string by processing the string using Long Short-Term Memory (LSTM) neural networks to generate a plurality of relationship values. Relationship values are associated with relationship types. Processor generates weight values based on member's initial context data for each of the plurality of relationship types, and generates probability values for the relationship types based on the relationship values and the weight values. Processor narrows the potential relationships, generates relationship data that includes the narrowed potential relationships, and causes the relationship data to be displayed by the agent client device.

Abstract: Method starts with processing, by processor, receiving audio signal of communication session between member-related client device and agent client device. Processor processes audio signal to generate caller utterances, each including audio caller utterance and transcribed caller utterance. Caller utterances includes first and second caller utterances. For each of the plurality of caller utterances, processor generates relationship data based on transcribed caller utterance, generates identified task based on transcribed caller utterance, and generates task completion probability result based on audio caller utterance, relationship data, and identified task, and stores the task completion probability result in a database. Processor computes agent action ranking score that is based on difference between task completion probability result of first caller utterance and task completion probability result of second caller utterance that precedes first caller utterance.

Abstract: A method starts with processor retrieving member's initial context data. Processor receives a string that is a transcribed utterance or an electronic message from the communication session established between member client device and agent client device. Processor determines potential relationships between the member and a patient that is the subject of the string by processing the string using Long Short-Term Memory (LSTM) neural networks to generate a plurality of relationship values. Relationship values are associated with relationship types. Processor generates weight values based on member's initial context data for each of the plurality of relationship types, and generates probability values for the relationship types based on the relationship values and the weight values. Processor narrows the potential relationships, generates relationship data that includes the narrowed potential relationships, and causes the relationship data to be displayed by the agent client device.

Abstract: Method starts with processing, by a processor, audio signal to generate audio caller utterance and transcribed caller utterance. Processor generates identified task based on transcribed caller utterance. Processor samples audio caller utterance to generate samples of audio caller utterance. Processor generates loudness result based on loudness values of samples using loudness neural network associated with identified task. Processor generates pitch result based on pitch values of samples using pitch neural network associated with identified task. Processor generates tone result for each word in transcribed caller utterance using tone neural network associated with identified task. Using task completion probability neural network associated with identified task, processor generates task completion probability result that is based on at least one of: loudness result, pitch result, or tone result. Other embodiments are disclosed herein.

Abstract: Method starts with processing, by processor, receiving audio signal of communication session between member-related client device and agent client device. Processor processes audio signal to generate caller utterances, each including audio caller utterance and transcribed caller utterance. Caller utterances includes first and second caller utterances. For each of the plurality of caller utterances, processor generates relationship data based on transcribed caller utterance, generates identified task based on transcribed caller utterance, and generates task completion probability result based on audio caller utterance, relationship data, and identified task, and stores the task completion probability result in a database. Processor computes agent action ranking score that is based on difference between task completion probability result of first caller utterance and task completion probability result of second caller utterance that precedes first caller utterance.