Abstract: Various embodiments of a system and associated method for a smart handoff integrated platform for handoff of items to customers in the retail space are disclosed herein.

Abstract: Processing circuitry of a learning platform may be configured to maintain a graph database describing student learners. Processing circuitry may obtain new student learner data and load the data into the graph database. Processing circuitry may receive an engagement or interaction from the new student learner and responsively extract new learnings about the new student learner which are loaded into the graph database. Processing circuitry may receive an inquiry from the new student learner and in response, extract the new student learner data and the new learnings from the graph database and contextualize, using a large language model, a learning unit from the educational content provided by the learning platform as a response to the inquiry using the new student learner data and the new learnings. Processing circuitry may further return the learning unit contextualized by the large language model to the new student learner.

Abstract: An artificial intelligence (AI) language model is trained using input data from one or more original data sources and a selected training algorithm that includes data extraction and condensing techniques. The trained AI model generates self-written code to create an executable query script for extracting data and an executable load script for loading the extracted data into a graph database. The extracted data is stored as newly created nodes within the graph database. The AI model transforms the loaded data into a condensed data structure that represents the full architecture of the data in a natural language format. The AI model is further trained to respond to user input in natural human language using the condensed data structure. Upon receiving a user query, the AI model processes the input, generates a structured data query, retrieves relevant information from the graph database, and outputs a response in natural language.

Abstract: Processing circuitry of a learning platform may be configured to maintain a graph database describing student learners. Processing circuitry may obtain new student learner data and load the data into the graph database. Processing circuitry may receive an engagement or interaction from the new student learner and responsively extract new learnings about the new student learner which are loaded into the graph database. Processing circuitry may receive an inquiry from the new student learner and in response, extract the new student learner data and the new learnings from the graph database and contextualize, using a large language model, a learning unit from the educational content provided by the learning platform as a response to the inquiry using the new student learner data and the new learnings. Processing circuitry may further return the learning unit contextualized by the large language model to the new student learner.

Abstract: A system with graph-based Natural Language Processing (NLP) for querying, analyzing, and visualizing complex data structures is described. Such a system executes a generalized AI language model; defines and migrates a training dataset into a graph database by exposing data sources to an executing AI language model that self-defines a structure and self-writes an executable script to query the original data sources and self-writes code to load the extracted data into a graph database in the form of new nodes and new relationships with directionality between the nodes. The system further includes means for loading the extracted data into the graph database; condensing the information stored within the graph database into a condensed data structure representing the full architecture of the data in a natural language format; and responding to human language inquiries with responsive text, speech, and visualizations using the data loaded into the graph database.

Abstract: Processing circuitry of a learning platform may be configured to maintain a graph database describing student learners. Processing circuitry may obtain new student learner data and load the data into the graph database. Processing circuitry may receive an engagement or interaction from the new student learner and responsively extract new learnings about the new student learner which are loaded into the graph database. Processing circuitry may receive an inquiry from the new student learner and in response, extract the new student learner data and the new learnings from the graph database and contextualize, using a large language model, a learning unit from the educational content provided by the learning platform as a response to the inquiry using the new student learner data and the new learnings. Processing circuitry may further return the learning unit contextualized by the large language model to the new student learner.

Abstract: A system with graph-based Natural Language Processing (NLP) for querying, analyzing, and visualizing complex data structures is described. Such a system executes a generalized AI language model; defines and migrates a training dataset into a graph database by exposing data sources to an executing AI language model that self-defines a structure and self-writes an executable script to query the original data sources and self-writes code to load the extracted data into a graph database in the form of new nodes and new relationships with directionality between the nodes. The system further includes means for loading the extracted data into the graph database; condensing the information stored within the graph database into a condensed data structure representing the full architecture of the data in a natural language format; and responding to human language inquiries with responsive text, speech, and visualizations using the data loaded into the graph database.

Abstract: A urine collection assembly includes a container; a funnel removably coupled to the container, the funnel in fluid communication with the container; a first collection chamber in selective communication with the funnel; a valve configured to allow selective communication between the first collection chamber and the funnel; a second collection chamber in selective communication with the funnel; and a lid removably coupleable to the funnel. A saddle may be removably coupleable to the funnel and configured to guide urine into the container.

Abstract: A urine collection assembly includes a container; a funnel removably coupled to the container, the funnel in fluid communication with the container; a first collection chamber in selective communication with the funnel; a valve configured to allow selective communication between the first collection chamber and the funnel; a second collection chamber in selective communication with the funnel; and a lid removably coupleable to the funnel. A saddle may be removably coupleable to the funnel and configured to guide urine into the container.

Abstract: Various embodiments of a system and associated method for a smart handoff integrated platform for handoff of items to customers in the retail space are disclosed herein.

Abstract: A urine collection assembly includes a container; a funnel removably coupled to the container, the funnel in fluid communication with the container; a first collection chamber in selective communication with the funnel; a valve configured to allow selective communication between the first collection chamber and the funnel; a second collection chamber in selective communication with the funnel; and a lid removably coupleable to the funnel. A saddle may be removably coupleable to the funnel and configured to guide urine into the container.