CDW LLC

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.

A computing system may normalize messages by parsing, extracting attachments, masking data, and storing queued records. A non-transitory computer-readable medium may store instructions that normalize messages by parsing, masking, and queueing. A computer-implemented method may normalize messages by parsing content, extracting records, rewriting links, and masking data.

A machine learning task automation method includes receiving a message, preprocessing the message to generate extracted data, analyzing the extracted data to generate classification data, analyzing the extracted data to generate information extraction; and generating and transmitting a response. A computing system includes a processor, and a memory including instructions that, when executed, cause the computing system to receive a message, preprocess the message to generate extracted data, analyze the extracted data to generate classification data, analyze the extracted data to generate information extraction data; and generate and transmit a response. A non-transitory computer readable medium containing program instructions that when executed, cause a computer to receive a message, preprocess the message to generate extracted data, analyze the extracted data to generate classification data, analyze the extracted data to generate information extraction data; and generate and transmit a response.

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.

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. The example method further includes causing a user device of the user to convey the matching appointment to the user.

A computer system for evaluating information technology (IT) documentation including machine learning models, vector databases, processors, and memories to generate responses for queries associated with one or more of: IT problems, IT solutions, or IT devices. A computer-implemented method involving receiving input data including one or more user queries; generating at least one prompt by interpolating the user queries into a template prompt; processing, via an embedding model, the prompt to generate a retrieval vector corresponding to the prompt; retrieving, by querying a vector database using the retrieval vector as an input parameter, one or more retrieval results; processing, using a trained language model, the prompt, the retrieval results and an assistant prompt including a set of assistant instructions for restricting the output of the trained language model; and displaying, via a graphical user interface, one or more responses corresponding to the user queries from the trained language model.

A computing system and method are disclosed for converting network configurations across different platforms using a language model. This system preprocesses a source network configuration from a first network platform, generates a prompt based on the preprocessed source network configuration and additional network data for input to the language model. In response to the prompt, the language model converts the source network configuration into a target network configuration for a second platform, ensuring that common configuration elements are preserved while unsupported protocols are adapted to supported alternatives on the second network platform. The system outputs the converted configuration, facilitating seamless network migration with minimal, or no, manual intervention. Additional features include the ability to receive and process active device states and network diagrams, suggesting alternative solutions for unsupported protocols, validating the target configuration against the second platform's specifications, and interacting with users to refine the conversion based on their input.

Techniques for improving data reliability, security, and monitoring performance are disclosed herein. An example device includes a networking interface providing access to (i) a centralized record including entity data and (ii) sensors configured to sense phenomena associated with the entity; processors; and memories communicatively coupled with the networking interface. The sensors and processors store (i) a local record of cached data and (ii) computer-executable instructions thereon that, when executed, cause the device to: receive a set of measurements of the one or more phenomena sensed by the one or more sensors, determine an event corresponding to the entity based on the set of measurements, update the centralized record with event data associated with the event, and cache at least a portion of the event data in the local record.

An account manager virtual assistant computing system includes a processor and a memory storing instructions that when executed cause the system to receive a message, process the message using a quote classification machine learning model, generate a response including price information, and process the message using an information extraction machine deep learning model; and transmit the response. A non-transitory computer readable medium includes program instructions that when executed cause a computer to receive a message, process the message using a quote classification machine learning model, generate a response including price information, and process the message using an information extraction machine deep learning model; and transmit the response. An account manager virtual assistant method includes receiving a message, processing the message using a quote classification machine learning model, generating a response including price information, and processing the message using an information extraction machine deep learning model; and transmitting the response.

A computing system for cross-platform support and log summarization includes a language model, APIs, processors, and memories to generate queries and troubleshooting steps from log files and IR system results. A method involves obtaining log files, generating IR system queries, and creating troubleshooting steps using a language model. A non-transitory computer readable medium contains instructions for obtaining log files, generating queries, and creating troubleshooting steps based on log files and IR results.