Abstract: Artificial intelligence (AI) based technologies for improving patient intake are disclosed herein. An example method includes receiving a patient intake request from a user; initiating, based on the patient intake request, a patient intake data stream including verbal responses from the user regarding patient intake of the user; applying, while simultaneously receiving the patient intake data stream, a natural language processing (NLP) model to the verbal responses from the user to output (i) textual transcriptions and (ii) intent interpretations; generating, by a care plan generation module, a recommended care plan based on the textual transcriptions and the intent interpretations; identifying, by an intent interpretation fulfillment module, one or more recipient entities to receive the textual transcriptions and the recommended care plan; and transferring the textual transcriptions and the recommended care plan to one or more recipient entity devices of the one or more recipient entities.

Abstract: Techniques for providing natural language understanding (NLU) services to contact centers are disclosed herein. An example method includes receiving, at a cloud NLU connector, a data stream from a contact center that includes an audio stream, extracting the audio stream from the data stream, and transmitting the audio stream through a secure network to a cloud-based NLU hub. The cloud-based NLU hub is communicatively coupled to a plurality of cloud-based NLU service providers and stores a plurality of user NLU profiles that each designate one or more cloud-based NLU service providers to provide NLU services. The example method further includes determining one or more designated cloud-based NLU service providers to provide at least one NLU service corresponding to the audio stream, and causing the one or more designated cloud-based NLU service providers to process the audio stream in accordance with the at least one NLU service.

Abstract: Artificial intelligence (AI) based technologies for improving patient intake are disclosed herein. An example method includes receiving a patient intake request from a user; initiating, based on the patient intake request, a patient intake data stream including verbal responses from the user regarding patient intake of the user; applying, while simultaneously receiving the patient intake data stream, a natural language processing (NLP) model to the verbal responses from the user to output (i) textual transcriptions and (ii) intent interpretations; generating, by a care plan generation module, a recommended care plan based on the textual transcriptions and the intent interpretations; identifying, by an intent interpretation fulfillment module, one or more recipient entities to receive the textual transcriptions and the recommended care plan; and transferring the textual transcriptions and the recommended care plan to one or more recipient entity devices of the one or more recipient entities.

Abstract: Artificial intelligence (AI) based technologies for improving patient appointment scheduling and inventory management are disclosed herein. An example method includes receiving, at a server including a natural language processing (NLP) model, an appointment request from a user. The example method further includes initiating, based on the appointment request, a patient appointment data stream including verbal responses from the user regarding an appointment of the user. The example method further includes applying, while simultaneously receiving the patient appointment data stream, the NLP model to the verbal responses from the user to output (i) textual transcriptions and (ii) intent interpretations. The example method further includes querying a scheduling database to determine a matching appointment that satisfies a distance threshold, a date threshold, a service threshold, and an inventory threshold.

Abstract: Artificial intelligence (AI) based technologies for improving patient appointment scheduling and inventory management are disclosed herein. An example method includes receiving, at a server including a natural language processing (NLP) model, an appointment request from a user. The example method further includes initiating, based on the appointment request, a patient appointment data stream including verbal responses from the user regarding an appointment of the user. The example method further includes applying, while simultaneously receiving the patient appointment data stream, the NLP model to the verbal responses from the user to output (i) textual transcriptions and (ii) intent interpretations. The example method further includes querying a scheduling database to determine a matching appointment that satisfies a distance threshold, a date threshold, a service threshold, and an inventory threshold.

Abstract: Techniques for providing natural language understanding (NLU) services to contact centers are disclosed herein. An example method includes receiving, at a cloud NLU connector, a data stream from a contact center that includes an audio stream, extracting the audio stream from the data stream, and transmitting the audio stream through a secure network to a cloud-based NLU hub. The cloud-based NLU hub is communicatively coupled to a plurality of cloud-based NLU service providers and stores a plurality of user NLU profiles that each designate one or more cloud-based NLU service providers to provide NLU services. The example method further includes determining one or more designated cloud-based NLU service providers to provide at least one NLU service corresponding to the audio stream, and causing the one or more designated cloud-based NLU service providers to process the audio stream in accordance with the at least one NLU service.

Abstract: Modular technologies for servicing telephony systems are disclosed herein. An example method includes receiving a user request to service a telephony system of a user, and transmitting a set of available session initiation protocol (SIP) providers to a user computing device for analysis by the user. Responsive to receiving a user input, the example method includes connecting the user computing device to an SIP trunk, connecting to a serviceable call location included in the user request through the SIP trunk, wherein connecting to the serviceable call location initiates a data stream, and executing a servicing task included in the user request. The example method includes recording a portion of the data stream during execution of the servicing task, storing the portion of the data stream, and causing the user computing device to display the portion of the data stream for viewing by the user.

Abstract: Artificial intelligence (Al) based technologies for improving patient appointment scheduling and inventory management are disclosed herein. An example method includes receiving, at a server including a natural language processing (NLP) model, an appointment request from a user. The example method further includes initiating, based on the appointment request, a patient appointment data stream including verbal responses from the user regarding an appointment of the user. The example method further includes applying, while simultaneously receiving the patient appointment data stream, the NLP model to the verbal responses from the user to output (i) textual transcriptions and (ii) intent interpretations. The example method further includes querying a scheduling database to determine a matching appointment that satisfies a distance threshold, a date threshold, a service threshold, and an inventory threshold.

Abstract: Artificial intelligence (AI) based technologies for improving patient intake are disclosed herein. An example method includes receiving a patient intake request from a user; initiating, based on the patient intake request, a patient intake data stream including verbal responses from the user regarding patient intake of the user; applying, while simultaneously receiving the patient intake data stream, a natural language processing (NLP) model to the verbal responses from the user to output (i) textual transcriptions and (ii) intent interpretations; generating, by a care plan generation module, a recommended care plan based on the textual transcriptions and the intent interpretations; identifying, by an intent interpretation fulfillment module, one or more recipient entities to receive the textual transcriptions and the recommended care plan; and transferring the textual transcriptions and the recommended care plan to one or more recipient entity devices of the one or more recipient entities.