IoT Device Profile Mapping for Faster Data Normalization

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Solution Overview

Problem

Automated industrial and commercial environments face platform fragmentation and lack of interoperability due to the vast number of IoT devices from different manufacturers, each using unique protocols, requiring manual mapping and provisioning, which is time-consuming and labor-intensive, slowing down adoption and productivity.

Innovation Solution

A platform that automatically discovers, extracts, maps, and enriches data from IoT devices by generating device profiles, using data source discovery mechanisms, extraction systems, mapping mechanisms, and storage systems to provide normalized data through an API, employing machine learning models and automated document processing to handle protocol differences.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual mapping and provisioning is used for each IoT device, then data accuracy and interoperability can be achieved, but the process becomes time-consuming and labor-intensive

Engineering Contradiction:

Improvedata accuracyVSAvoidmapping time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables self-service through automated device profiling, where the platform automatically discovers devices, extracts their data models, maps them to a common ontology, and provisions them without human intervention. This eliminates the need for manual mapping while maintaining data accuracy through automated validation processes.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual process of reading documentation, mapping fields, and validating mappings with an automated software system that uses machine learning models, document processing algorithms, and ontology mapping engines to perform these tasks automatically, significantly reducing time while preserving accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If manual mapping is performed for each device, then proper data integration can be achieved, but productivity is lowered due to the extensive time required

Engineering Contradiction:

Improvedata integration qualityVSAvoiddevice adoption rate

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system performs preliminary actions by pre-defining a common ontology and data models for various device types. When a new device is discovered, the system automatically matches it against predefined templates and profiles, enabling rapid integration without starting from scratch. This preliminary preparation maintains integration quality while dramatically speeding up the process.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements a universal data model and common ontology that can accommodate multiple device types and protocols. This universal framework allows the same automated mapping process to handle diverse IoT devices from different manufacturers, maintaining data integration quality across the board while enabling rapid scalability and higher productivity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Productivity

If automated discovery and mapping is implemented, then time and labor are reduced, but handling protocol differences and device heterogeneity becomes more complex

Engineering Contradiction:

Improveintegration speedVSAvoidprotocol handling complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system introduces an intermediary layer consisting of a common ontology and standardized data models that sit between diverse IoT devices and the analytics platform. This intermediary translates various device protocols and data formats into a unified structure, enabling automated processing while managing protocol complexity without requiring manual intervention for each device type.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Manufacturing precision

If comprehensive device profiling is performed, then data normalization is improved, but the initial setup and processing overhead increases

Engineering Contradiction:

Improvedata normalization accuracyVSAvoidprocessing overhead

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system applies partial profiling by focusing on the most critical data elements and commonly used device attributes first, rather than attempting to profile every possible device parameter. This selective approach achieves sufficient data normalization accuracy for most use cases while reducing processing overhead and setup time, with the option to perform more comprehensive profiling only when needed.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11561522B2Data mapping based on device profiles

Publication Date: 2023.01.24 MAPPED INC

AI summary

Described are platforms, systems, and methods for mapping data found in connected equipment from a manufacturer's selected schema, format, and protocol to a normalized data model. The platforms, systems, and methods identify a plurality of data sources associated with an automation environment; retrieve data from at least one of the identified data sources; generate a plurality of data source mapping profiles, each data source mapping profile specific to a particular data source configuration; maintain a data store comprising the plurality of data source mapping profiles; select a data source mapping profile specific to the at least one identified data source configuration; and apply an algorithm to map the retrieved data to a predetermined ontology based on the selected data source mapping profile for the at least one identified data source.