CVS Pharmacy, Inc.

A method is provided. The method includes: obtaining a new marketing promotion for a particular product; determining, based on the particular product, a first product segment from a plurality of product segments; determining, by the PFCS, one or more promotional forecasting machine learning-artificial intelligence (ML-AI) models from a plurality of promotional forecasting ML-AI models to use for the new marketing promotion based on the first product segment; inputting promotional information associated with the new marketing promotion into the one or more determined promotional forecasting ML-AI models to forecast an amount of the particular product to provide to one or more storefronts; and providing product information indicating the amount of the particular product to one or more facility computing systems associated with the one or more storefronts.

A system includes a product label, vision capturing devices, sensor system computing devices, and a user device. The product label includes a first variable ink and a second variable ink configured to alter a respective sensory indicator based on experiencing a respective environmental condition. The vision capturing devices are configured to obtain images of the first and second variable ink. The sensor system computing devices are configured to determine condition information indicating a status of the product label based on the images of first and second variable ink and a determination of an environmental condition the product label was exposed to from a plurality of potential environmental conditions including the respective environmental conditions; and provide an indicator indicating the status of the label based on the condition information. The user device is configured to receive the indicator; and cause display of a prompt indicating a condition of the product.

In some instances, a method is provided. The method comprises: deploying a software application within an application deployment computing system using operational code that manages and provisions resources of the application deployment computing system to execute the software application, wherein architectural code is embedded within the operational code and comprises semantic deployments domain-specific language (SDDSL) embeddings that specify cloud resource tags; extracting, from the operational code, the SDDSL embeddings and implicit operational code; rendering the extracted SDDSL embeddings and the implicit operational code to generate an architectural diagram; and displaying the architectural diagram.

A system, method, and apparatus are provided that include a system having a scheduling machine with: a plurality of configuration data sets; and a rules engine that generates a response to a timeslot request by applying a plurality of rules to the timeslot request, where the plurality of rules utilize the plurality of configuration data sets as inputs thereto, and where the rules engine resolves one or more conflicts and eliminates timeslots that would otherwise be available if a booking of such a timeslot would result in a violation of a rule in the plurality of rules.

A system can include one or more memory devices storing instructions thereon, that, when executed by one or more processors, cause the one or more processors to receive a selection of at least one type of private information of a plurality of types of private information of a health provider from a computing system of the health provider. The instructions can cause the one or more processors to receive private information of the type from the computing system. The instructions can cause the one or more processors to issue a credential that guarantees an identify of the health provider based on the type selected and a verification of the private information. The instructions can cause the one or more processors to add the credential to a credential store of the computing system, the credential store to verify the identity of the health provider with a plurality of applications.

A system can include one or more memory devices that can store instructions thereon that, when executed by one or more processors, cause the one or more processors to receive a natural language query for information associated with a first entity, generate a vector embedding based on the natural language query, search a vector database having a plurality of vector embeddings to identify a set of vector embeddings of the plurality of vector embeddings that are associated with the vector embedding, identify metadata that corresponds to respective vector embeddings of the set of vector embeddings, select a first vector embedding that corresponds to the first entity, generate a prompt based on the natural language query, generate a response to the natural language query using a machine learning model, and provide an electronic message that includes the response to the natural language query.

A method and system provide for automated counting of prescription product and enables virtual verification of the dispensed prescription product. The method and system include capturing by a camera at a first site an image of the prescription product to be dispensed according to a prescription to a patient, electronically displaying the image on a display at a second site remote from the first site, and electronically transmitting a verification from the second site to the first site in response to the image of the prescription product being determined at the second site to be consistent with the prescription.

A system can include one or more memory devices storing instructions thereon that, when executed by one or more processors, can cause the one or more processors to receive a selection of a product, determine a status of the product, retrieve a plurality of descriptions of a plurality of products, the plurality of products having the first category, provide the plurality of descriptions and the description of the product to cause a machine learning model to generate one or more outputs that represent correlations between the product and one or more products of the plurality of products, identify the one or more products of the plurality of products, and provide a recommendation to replace the product with the one or more products of the plurality of products.

A system can include one or more memory devices storing instructions thereon that, when executed by one or more processors, can cause the one or more processors to retrieve information for a product of an entity, determine that the information includes proprietary information associated with the product or the entity, modify the information to remove the proprietary information from the information, input the modified information into a machine learning model, and generate a second description of the product using the modified information.

A system includes a heads up display (HUD) device for a vehicle, a cloud provisioned computing system, and one or more service provider devices is provided. The HUD device is configured to obtain biometric feedback data of a user; input the biometric feedback data to a remote photoplethysmography (rPPG) model to determine biometric condition data of the user; and generate an output for the user. The cloud provisioned computing system is configured to input a prompt to a machine learning-artificial intelligence (ML-AI) language model to determine a service provider system and provide to a common interface a request for service provider data; receive the service provider data; and provide the control signal to the HUD. The service provider device is configured to receive the request; determine the service provider data associated with the request; and provide the service provider data.