Abstract: Methods and systems are provided for facilitating generation and utilization of a commonsense contextualizing machine learning (ML) model, in accordance with embodiments described herein. In embodiments, a commonsense contextual ML model is trained by fine-tuning a pre-trained language model using a set of training path-sentence pairs. Each training path-sentence pair includes a commonsense path, identified via a commonsense knowledge graph, and a natural language sentence identified as contextually related to the commonsense path. The trained commonsense contextualizing ML model can then be used to generate a commonsense inference path for a text input. Such a commonsense inference path can include a sequence of entities and relations that provide commonsense context to the text input. Thereafter, the commonsense inference path can be provided to a natural language processing system for use in performing a natural language processing task.

Abstract: System and methods for generating, validating, and augmenting question-answer pairs using generative AI are provided. An online interaction server accesses a set of digital content available at a set of designated network locations. The online interaction server further trains a pre-trained large language model (LLM) using the set of digital content to obtain a customized LLM. The online interaction server generates a set of question-answer pairs based on the set of digital content using the customized LLM and validates the set of question-answer pairs by determining if an answer in a question-answer pair is derived from the set of digital content. The online interaction server also selects a digital asset to augment an answer in a validated question-answer pair based on a semantic similarity between the validated question-answer pair and the digital asset.

Abstract: A method for managing a supply chain which provides for mapping each demand and its respective source of supply. The method entails attaching a reservation between a demand and a supply. The reservation is updated as the status of the source of supply changes. As a result, the user has complete visibility for modeling the entire supply chain as material moves from one location to another.

Abstract: A novel rules engine which automates many warehouse processes, and provides for efficient management of a warehouse. The rules engine provides a flexible and customizable structure for modeling the many different types of processes governing the function of a warehouse. The rules engine translates business level logic into code for manipulating the warehouse database. The rules engine allows a user to create their own user logic for working with the database, which operates at a level of abstraction more feasible for the user. The rules engine understands the user logic by the way the user decides how to string various restrictions together. The rules engine implements the translation via its internal understanding of the structure of the particular application. Thus, the rules engine allows the user to define logic without understanding how to manipulate a database.

Abstract: A novel rules engine which automates many warehouse processes, and provides for efficient management of a warehouse. The rules engine provides a flexible and customizable structure for modeling the many different types of processes governing the function of a warehouse. The rules engine translates business level logic into code for manipulating the warehouse database. The rules engine allows a user to create their own user logic for working with the database, which operates at a level of abstraction more feasible for the user. The rules engine understands the user logic by the way the user decides how to string various restrictions together. The rules engine implements the translation via its internal understanding of the structure of the particular application. Thus, the rules engine allows the user to define logic without understanding how to manipulate a database.

Abstract: A novel rules engine which automates many warehouse processes, and provides for efficient management of a warehouse. The rules engine provides a flexible and customizable structure for modeling the many different types of processes governing the function of a warehouse. The rules engine translates business level logic into code for manipulating the warehouse database. The rules engine allows a user to create their own user logic for working with the database, which operates at a level of abstraction more feasible for the user. The rules engine understands the user logic by the way the user decides how to string various restrictions together. The rules engine implements the translation via its internal understanding of the structure of the particular application. Thus, the rules engine allows the user to define logic without understanding how to manipulate a database.

Abstract: A method for managing a supply chain which provides for mapping each demand and its respective source of supply. The method entails attaching a reservation between a demand and a supply. The reservation is updated as the status of the source of supply changes. As a result, the user has complete visibility for modeling the entire supply chain as material moves from one location to another.