Knowledge Graph for Manufacturing SOP — PatSnap Eureka
Knowledge Graph for Manufacturing SOP Technology Landscape 2026
Knowledge graphs applied to manufacturing SOPs are converging NLP, semantic web technologies, and industrial process formalization. This dataset spans filings from 2000 to 2026 across major jurisdictions including US, CN, EP, and DE.
From Paper SOPs to Semantic Knowledge Graphs in Manufacturing
Within this dataset, the technology field spans three interlocking layers: knowledge graph construction for manufacturing domains, SOP and workflow formalization through NLP and semantic modeling, and integration of structured procedural knowledge into Manufacturing Execution Systems (MES), cyber-physical systems, and ontology-based engineering tools.
The innovation arc across approximately 25 years runs from Ford Global Technologies’ 2000 JP patent on logic-model-driven knowledge networks, through IBM’s 2022 NLP-to-workflow diagram generation patents, to the 2026 CN filing on OPC UA auto-construction via LLMs and knowledge graphs — reflecting a clear maturation trajectory within this dataset.
Key technology clusters include NLP-driven workflow extraction, manufacturing knowledge graph construction and completion, ontology-based engineering program and SOP generation, and MES integration with process standards. Each cluster addresses a distinct barrier in converting unstructured SOP documentation into machine-actionable semantic structures.
In this dataset, US filings account for approximately 60% of patent records, followed by CN at roughly 20% and EP at approximately 8%. Siemens holds 6 retrieved patents across the broadest jurisdictional spread, while Istari Digital accounts for 9 individual filings in retrieved records, and Chinese institutional filers contribute 4 CN filings between 2021 and 2026.
Filing Trends and Technology Cluster Distribution
Patent activity in this dataset clusters around four technology areas and shows a pronounced acceleration post-2022, coinciding with the convergence of LLMs with knowledge graph and ontology methods applied to manufacturing SOPs.
Patent Count by Technology Cluster — Knowledge Graph Manufacturing SOP (Dataset Snapshot)
In this dataset, the MES–SOP Integration cluster and NLP-Driven Workflow Extraction cluster each hold significant representation, with Ontology-Based Engineering Program Generation and Manufacturing Knowledge Graph Construction completing the four main clusters.
↗ Click bars to exploreFiling Activity by Era — Knowledge Graph for Manufacturing SOP (Dataset Snapshot)
In this dataset, filing activity shows a clear step-up pattern across three eras: Early Foundations (2000–2013) established core MES–SOP and knowledge network concepts, Mid-Stage Development (2013–2021) introduced semantic plant models, and Recent Acceleration (2022–2026) brought LLM-augmented knowledge graph filings.
↗ Click bars to exploreKey Application Domains for Knowledge Graph SOP Technology
Within this dataset, knowledge graph and SOP formalization technologies are being applied across four major manufacturing domains: discrete assembly, continuous process industries, aerospace, and smart manufacturing and IIoT environments.
Discrete Manufacturing & Assembly
The MES–SOP integration cluster focuses on discrete manufacturing, particularly electronics assembly and automotive lines. Shin Giant Enterprise’s 2007 TW patent connects an ESOP database to the MES layer, integrating material handling, quality control, and warehouse management. Ford Motor Company’s 2014 US patent applies standardized technology selection to automotive part manufacturing.
MES IntegrationProcess Industry & Industrial Plants
Yokogawa’s 2013 US apparatus for task procedure presentation and VDEh’s 2020 ES semantic procedure model are oriented toward continuous process industries including chemical plants, power generation, and oil and gas. VDEh’s filing uses semantic plant models to link procedural steps to analytics applications, enabling data-driven SOP validation. Siemens’ 2021 US process system planning patent generates secondary technology solutions from flow-diagram-based primary technology descriptions.
In-situ NetworkAerospace & Defense Manufacturing
Boeing’s 2012 US patent on authoritative manufacturing work instructions is explicitly aerospace-oriented, addressing regulatory compliance and engineering authority traceability through reusable dynamic metadata fragments. Literature on lean workflow implementation in global aerospace manufacturing companies (2018) and digital workflow challenges in aerospace manufacturing engineering (2017) reinforce aerospace as a primary SOP formalization domain.
Regulatory ComplianceSmart Manufacturing & IIoT
The 2020 literature paper on Knowledge Graph for Industry 4.0 and the 2021 literature on MES integration through cyber-physical production systems position knowledge graphs and semantic SOP models as foundational infrastructure for smart factory architectures. The 2026 CN patent from Chongqing University of Posts and Telecommunications Industrial Internet Research Institute integrates LLMs with knowledge graphs to auto-construct OPC UA node-set XML models from unstructured industrial text.
AI AssessmentLeading Assignees in Knowledge Graph for Manufacturing SOP — Dataset Snapshot
In this dataset, Siemens Aktiengesellschaft holds the broadest jurisdictional portfolio with 6 retrieved patents spanning US, EP, CN, and MX, while Istari Digital accounts for 9 individual filings in retrieved records directed at digital engineering and certification ecosystems filed across US and WO jurisdictions between 2023 and 2025.
Top Assignees by Filing Count — Knowledge Graph Manufacturing SOP in Retrieved Records
↗ Click bars to exploreSiemens Aktiengesellschaft
Siemens holds 6 retrieved patents in this dataset spanning US, EP, CN, and MX jurisdictions, making it the assignee with the broadest jurisdictional coverage in this dataset. Their portfolio covers ontology-based engineering program generation (2022 US and EP, 2025 US), process system planning (2021 US, 2023 US), technical data interlinking (2007 MX), and requirement-to-knowledge-graph pipelines (2025 EP). This range represents the most comprehensive coverage of the SOP knowledge graph intersection among retrieved records.
Germany — DEIstari Digital, Inc.
Istari Digital accounts for 9 individual filings in this dataset — the largest single-assignee volume in retrieved records — all filed across US and WO jurisdictions between 2023 and 2025. Their filings address an interconnected digital engineering and certification ecosystem encompassing manufacturing models, product lifecycle management, and model-based systems engineering tools relevant to SOP formalization. While their focus is broader engineering data interoperability rather than SOP-specific knowledge graphs, their claims intersect manufacturing knowledge graph infrastructure.
United StatesFour Emerging Directions in Manufacturing SOP Knowledge Graphs (2023–2026)
The most recent filings in this dataset (2023–2026) signal four distinct emerging directions, all reflecting convergence of large language models, knowledge graphs, and industrial interoperability standards applied to manufacturing SOP formalization.
LLM-Augmented Construction of OPC UA Industrial Standards
The 2026 CN patent from Chongqing University of Posts and Telecommunications Industrial Internet Research Institute integrates LLM sequence reasoning with knowledge graph augmentation to convert unstructured industrial text — including SOP-adjacent documentation — into standardized OPC UA node-set XML models. This represents a direct path from natural language SOPs to machine-actionable industrial standards. It is the most forward-looking filing in this dataset.
Automated Knowledge Graph Completion for Sparse Manufacturing Ontologies
Robert Bosch’s 2025 DE patent addresses a practical barrier: manufacturing knowledge graphs are typically incomplete. Using entity, attribute, and relationship embeddings derived from both ontology data and the knowledge graph itself, the method automatically fills missing nodes and relations. This approach is critical for achieving SOP coverage completeness across complex product lines with heterogeneous data sources.
NLP-Driven Workflow Extraction vs. Ontology-Based Program Generation
Click any row to explore further.
| Dimension | NLP-Driven Workflow Extraction | Ontology-Based Program Generation |
|---|---|---|
| Primary Assignee | IBM (International Business Machines Corporation) | Siemens Aktiengesellschaft |
| Filing Jurisdiction | US (2022) | US, EP, MX (2007–2025) |
| Core Method | NLP extracts semantics from technical documents; associates components to symbol database; generates workflow diagrams with node-vector representations | Ontology schemas generated from programming blocks capturing variable relationships and KPIs; requirements generate knowledge graph analyzed for completeness |
| SOP Input Type | Unstructured or semi-structured technical literature and procedural documentation | Structured engineering requirements, programming blocks, and domain ontologies |
| Output Format | Workflow diagrams with node-vector architecture | Engineering programs for industrial installations; ontology schemas; knowledge graphs cleared for manufacturing use |
| Application Domain | Automated SOP knowledge graph construction from procedural text | Industrial automation, process plant programming, and regulated manufacturing design |
| Key Patent Example | Computer Automated Generation of Work-Flow Diagram from Technology Specific Literature (IBM, US, 2022) | Method and System for Generating Engineering Programs for an Industrial Domain (Siemens, US/EP, 2022) |
| Patent Count in Dataset | 2 US patents (2022) | 6 patents across US, EP, MX (2007–2025) |
Frequently Asked Questions — Knowledge Graph for Manufacturing SOP
The earliest patent in this dataset is Ford Global Technologies’ ‘Method for Simultaneously Realizing Production and Product Technology by Using Network of Knowledge’ filed in JP in 2000, which established the concept of logic-model-driven knowledge networks for simultaneous production and product engineering.
Siemens Aktiengesellschaft holds the broadest jurisdictional portfolio in this dataset, with 6 retrieved patents spanning US, EP, CN, and MX. Their filings cover ontology-based engineering program generation, process system planning, technical data interlinking, and requirement-to-knowledge-graph pipelines.
The most recent filing in this dataset is a 2026 CN patent from Chongqing University of Posts and Telecommunications Industrial Internet Research Institute, titled ‘OPC UA Information Model Automatic Construction Method Based on Knowledge Graph and Large Language Model.’ It integrates LLM sequence reasoning with knowledge graph augmentation to convert unstructured industrial text into standardized OPC UA node-set XML models.
According to this dataset, OPC UA is emerging as the interoperability backbone for SOP knowledge graphs. Multiple filings converge on OPC UA as the standard through which knowledge graphs interface with manufacturing execution layers. The 2026 CN patent specifically auto-builds OPC UA node-set XML models from knowledge graph and LLM outputs, enabling machine-actionable SOP standards.
According to the strategic analysis in this dataset, MES–SOP integration is foundationally patented but largely lapsed or inactive. The core MES–SOP linking architecture (Shin Giant Enterprise, TW, 2007) and field operator task procedure presentation (Yokogawa Electric Corporation, US, 2013) are described as either inactive or expired, opening design-around opportunities for new entrants.
Chinese institutional filers — including Xi’an Jiaotong University, Chongqing University, and Chongqing University of Posts and Telecommunications Industrial Internet Research Institute — contribute 4 CN filings between 2021 and 2026 in this dataset. These are described as reflecting an accelerating Chinese research-to-patent pipeline, with the most technically current work at the LLM–knowledge graph–manufacturing SOP intersection.
Data and insights on this page are based on a limited patent and literature dataset and are for reference only. Figures may not represent the complete technology landscape.