Hyperlink Messages for Dynamic M2M Semantic Adaptation
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Solution Overview
Problem
Current M2M communication systems require inflexible and costly re-engineering for semantic changes, as they rely on user-defined and standard-imposed semantics, which are not dynamic and cannot generate new submodels, and lack the ability to utilize user-generated metadata tags like social media platforms.
Innovation Solution
A system and method for machines to generate executable hyperlink messages with self-validated semantic content, allowing them to subscribe to topics and engage in discussions without human intervention, using a machine social media platform that supports dynamic generation of submodels and flexible metadata tags.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user-defined semantics and standard-imposed semantics are used in M2M communication, then data exchange meaning is established, but any semantic change requires inflexible and costly re-engineering
Solution Approach 1:
The patent applies dynamics by enabling machines to dynamically generate and modify their own submodels and semantic definitions at runtime without requiring system re-engineering. Machines can adapt their data models, communication protocols, and semantic interpretations dynamically based on operational needs, transforming the static semantic framework into a dynamic self-adapting system.
Solution Approach 2:
The patent implements self-service by allowing machines to autonomously generate their own submodels, define their own semantics, and perform self-validation of semantic content without human intervention. Each machine serves itself in creating and maintaining its semantic framework, eliminating the need for external re-engineering when semantic changes are needed.
2Reliability
If AAS provides standard-imposed semantics, then some semantic structure is established, but additional semantics must be encoded prior to asset operation and cannot be dynamically generated
Solution Approach 1:
The patent applies preliminary action by providing a framework where machines prepare and validate their semantic models in advance through self-validation mechanisms, but unlike AAS, this preparation is not fixed prior to operation. Machines can continuously update and regenerate their semantic structures dynamically while maintaining validation, allowing automation to extend throughout the asset's operational lifecycle rather than being confined to pre-deployment configuration.
Solution Approach 2:
The patent transforms the static AAS semantic structure into a dynamic system where submodels can be generated, modified, and updated at runtime. Machines autonomously create new submodels based on operational data and requirements, enabling the semantic structure to evolve dynamically rather than remaining fixed as in traditional AAS implementations.
3Adaptability or versatility
If hyperlink tags are applied to machine communications, then user-generated labels enable flexible content organization, but responding to tagged messages requires human-in-the-loop to understand tag semantics
Solution Approach 1:
The patent implements self-service by enabling machines to autonomously generate, interpret, and respond to hyperlink-tagged messages without human intervention. Machines validate the semantics of received messages against their own submodels and automatically generate appropriate responses, eliminating the need for human operators to understand or mediate the semantic meaning of tags while maintaining full automation of the communication workflow.
Solution Approach 2:
The patent applies feedback by creating a closed-loop communication system where machines receive tagged messages, validate their semantics through self-validation mechanisms, generate appropriate responses, and post them back to the platform. This automated feedback loop enables machines to continuously interact and coordinate based on hyperlinked messages without requiring human interpretation or intervention.
Data Source
AI summary
A hyperlink message for machine-to-machine (M2M) or machine-to-human (M2H) communication has a semantic metadata tag, a content field, an executable specification field. Executable specification instructs a machine to perform a task associated with the machine related data, and post to a machine social media platform results of the task as content for the hyperlink message. The hyperlink message posting is visible and available to other participating machines on the machine social media platform to read and contribute related content as a hyperlink discussion of M2M or M2H communication.