Predictive Quality Control Patent Landscape 2026
Predictive Quality Control Patent Landscape in 2026
Predictive quality control IP is heavily concentrated, with a single incumbent — Fisher-Rosemount Systems — holding a commanding share of a 617-family corpus that peaked in 2021 and has since eased. Steel and automation incumbents form a distant second tier, leaving meaningful white space in AI-model integration, robotics-linked quality assurance, and image-based inspection.
Fisher-Rosemount dominates a highly concentrated field
Fisher-Rosemount Systems holds the leading position by a wide margin, followed at a substantial distance by JFE Steel, Nippon Steel, and Siemens AG. The top five filers account for 57% of the combined output of the hundred largest filers, signalling an unusually tight concentration for an industrial-AI adjacent field.
The gap between the leader and the second-ranked applicant is striking: Fisher-Rosemount’s count is more than six times that of JFE Steel. Below the top five, scores fall sharply into a long tail of single-digit filers, confirming a pronounced two-tier structure with little mid-tier competition.
| # | Applicant | Patent families | Share |
|---|---|---|---|
| 1 | Fisher-Rosemount Systems Inc. | 290 | |
| 2 | JFE Steel Corporation | 43 | |
| 3 | Nippon Steel Corporation | 29 | |
| 4 | Siemens AG | 28 | |
| 5 | Daicel Corporation | 12 | |
| 6 | ABB (Switzerland) AG | 12 | |
| 7 | Nanotronics Imaging Inc. | 11 | |
| 8 | Omron Corporation | 11 | |
| 9 | Evonik Operations GmbH | 11 | |
| 10 | Mitsubishi Electric Corporation | 10 |
| # | Applicant | Patent families | Share |
|---|---|---|---|
| 11 | Dogtooth Technologies Ltd. | 8 | |
| 12 | Fujifilm Corporation | 8 | |
| 13 | Bristol Inc. | 8 | |
| 14 | Inter X Co., Ltd. | 7 | |
| 15 | Hitachi, Ltd. | 7 | |
| 16 | Applied Materials Inc. | 7 | |
| 17 | Robert Bosch GmbH | 6 | |
| 18 | Primetals Technologies Austria GmbH | 6 | |
| 19 | Primetals Technologies Germany GmbH | 6 | |
| 20 | CHINA UNIV OF MINING & TECH | 5 |
Fisher-Rosemount’s dominant position implies deep entrenchment in process-control quality applications; challengers entering the space must either target verticals underserved by the leader — such as robotics-integrated quality or image-based inspection — or pursue collaborative routes with academic or niche players.
Patent data for the most recent 18–24 months is subject to publication lag; apparent softening in 2025–2026 filings should not be read as a structural decline without further monitoring. Longer-window growth, applicant concentration, and technology-route coverage are therefore more reliable signals than the latest-year bar alone.
Activity peaked in 2021; control systems dominate the technology mix
Annual filings grew from 52 in 2017 to a peak of 96 in 2021 before easing; the technology mix is overwhelmingly concentrated in control and regulating systems, with AI and digital-processing branches a secondary but growing presence.
Annual filing trend
Filings climbed steadily from 2017 through 2021 (reaching 96), then moderated. The 2022 dip and subsequent partial recovery through 2023–2025 suggest the field has moved past its steepest growth phase, though the most recent data points (2025–2026) remain under-counted due to publication lag and should not be interpreted as a continued retreat.
↗ Hover for values · click a bar to ask EurekaTechnology composition
G05B (Control & regulating systems) dominates the branch mix by a large margin, reflecting the process-automation roots of the field. G06F (digital data processing), G06N (AI models), and G06Q (business/admin data processing) form a secondary cluster, with robotics (B25J), digital transmission (H04L), and image processing (G06T) appearing as smaller but distinct branches — the latter three representing areas where coverage remains sparse relative to their technological relevance.
↗ Hover for values · click a bar to ask EurekaHighly cited patent families surfaced by the query
Citation-heavy patent families returned by the query. Use this section as citation context, not as a curated list of the most topic-specific patents.
Quality prediction model creation system, quality …
A quality prediction model for predicting quality of an elongated product is created. A quality prediction model creation system includes: a first logging data acquisition unit acquiring already-known first logging data respectively detected at every predetermined interval by a plurality of detectors and stored at time of manufacture of an elongated… (excerpt from the patent abstract)
| # | Patent | Citations |
|---|---|---|
| 1 | Method and apparatus for controlling a process pla… | 469 |
| 2 | Quality prognostics system and method for manufact… | 289 |
| 3 | Data analytic services for distributed industrial … | 271 |
| 4 | Distributed industrial performance monitoring and … | 246 |
| 5 | Data pipeline for process control system anaytics | 237 |
| 6 | Determining associations and alignments of process… | 147 |
| 7 | Distributed industrial performance monitoring and … | 134 |
| 8 | Managing Big Data In Process Control Systems | 134 |
Ranked by total forward citations. Citation counts favour older and broadly cited patent families, and broad or adjacent patents may appear when they match the search scope. Treat this section as citation context, not as a curated list of the most topic-specific patents. Some patent titles may be shown in their original, non-English language where an accurate translation could not be guaranteed.
What the patent structure means for R&D strategy
The combination of a single dominant incumbent, a post-peak filing trend, and a sparse AI-integration layer creates a specific set of strategic implications for teams assessing where to invest.
Field is past its 2021 peak and entering a consolidation phase
Annual filing volume peaked in 2021 and has eased since, consistent with a field transitioning from rapid expansion to selective deepening. The multi-year base remains substantial at 617 patent families, but the fastest growth window has passed. R&D teams should expect incumbent portfolios to harden around core process-control claims while adjacent branches — AI models, image processing — remain more open.
Post-peakSingle-incumbent dominance creates both a moat and a gap
The top five filers account for 57% of the hundred largest filers’ combined total, and the leader alone (Fisher-Rosemount Systems, 290 patent families) dwarfs all others. This concentration signals high IP risk for direct competitors in core process-control quality but also a strategic gap: the long tail of single-digit filers suggests that many verticals — semiconductors, food and beverage, additive manufacturing — are underserved. New entrants may find more room in application-specific quality control than in platform-level approaches.
High concentrationSiemens AG is the only identified co-filer in the corpus
The collaboration evidence shows a single co-filing relationship: Siemens AG filing jointly with Siemens Corp, with two co-filed patent families. This is a notably sparse collaboration network for a field that spans process industries, academia, and automation. The absence of broad cross-sector alliances suggests that most IP is developed in-house, leaving open the possibility that academic-industry partnerships — particularly around AI and sensor integration — represent an underexplored route to building differentiated positions.
Thin ecosystemUS leads filings; China and UK are significant secondary jurisdictions
The United States is the leading jurisdiction by patent records, followed by China and the United Kingdom. Japan and the EPO form a strong secondary cluster, while WIPO PCT filings indicate a meaningful share of applicants seeking broad international protection. India and South Korea are emerging filing destinations. Teams building freedom-to-operate or filing strategies should treat the US, China, UK, and Japan as must-cover jurisdictions, with EPO as the primary European route.
US-China-UK triadGo beyond the landscape: Eureka’s TRIZ Solution agent breaks down an R&D problem and returns patented concept solutions, each with a technical approach and cited patent & literature evidence.
| Applicant | Collaborator | Co-filings |
|---|---|---|
| Siemens AG | SIEMENS CORP | 2 |
Co-filing pairs, ranked by the number of jointly-filed patent families.
Fisher-Rosemount anchors the field; steel and automation players form the challenger tier
Fisher-Rosemount Systems is the unambiguous leader in predictive quality control IP, with a portfolio more than six times larger than the next-ranked applicant. The challenger tier is led by Japanese steel producers and global automation incumbents, all of whom show declining recent momentum.
Fisher-Rosemount Systems
Fisher-Rosemount Systems holds 290 patent families, anchored overwhelmingly in G05B (Control & regulating systems), with a secondary presence in G06F (digital data processing). Its recent filing trend shows a sharp deceleration (down 87% vs. the prior period), suggesting the core portfolio is maturing rather than actively expanding — a signal that the leader may be defending existing IP rather than staking new technical ground.
families: 290JFE Steel
JFE Steel ranks second with 43 patent families, focused on G05B (control systems) alongside meaningful G06N (AI models) and G06Q (business data processing) activity — indicating a more digitally diversified approach than the leader. Recent filing momentum has declined (down 67% vs. the prior period), consistent with the broader post-2021 field trend. Siemens AG (28 patent families, down 40%) rounds out the active challenger set, with robotics (B25J) as a distinguishing technical emphasis. Nanotronics Imaging, the only applicant flagged as a new entrant, represents an early-stage image-inspection angle worth monitoring.
families: 43| Applicant | Recent (3 yrs) | Trend |
|---|---|---|
| Fisher-Rosemount Systems Inc. | 5 | ▼ -87% |
| JFE Steel Corporation | 8 | ▼ -67% |
| Siemens AG | 9 | ▼ -40% |
| Nanotronics Imaging Inc. | 2 | ▲ new entrant |
Under-served branches adjacent to the dominant control-systems core
Several IPC branches appear alongside the dominant G05B core but at markedly lower coverage levels; two stand out as technically plausible areas where incremental investment could differentiate a portfolio.
G06N · AI-model integration for quality prediction
G06N (Computing based on AI models) accounts for a relatively small share of records despite its clear relevance to predictive quality tasks — machine-learning-based defect prediction, anomaly detection, and process optimization are all natural extensions of the field. The branch is present but sparse relative to G05B. An entry path exists through applying established ML frameworks (reinforcement learning, neural process models) to verticals — such as semiconductor manufacturing or continuous casting — where the leader’s coverage is thinner. JFE Steel’s dual presence in G05B and G06N illustrates that the combination is technically tractable.
Search this in Eureka →B25J · Robotics-linked quality inspection
B25J (Manipulators & robots) appears in the corpus at a low share, yet robotic inline inspection and adaptive quality-gating are increasingly deployed in automotive and electronics manufacturing. ABB (Switzerland) and Evonik Operations are the primary filers in this branch, but coverage remains thin. Teams with existing robotics or computer-vision IP may find a realistic entry path by bridging G06T (image processing) and B25J with quality-outcome prediction logic — a combination that has very limited current coverage and aligns with the broader push toward autonomous quality assurance.
Search this in Eureka →How leaders differ by technology route
Strength of each leader across the main technology routes.
| Player | G05B 19 · Control & regulating systems | G05B 23 · Control & regulating systems | G06Q 50 · Business, commerce & admin data processing | G05B 13 · Control & regulating systems | G06N 20 · Computing based on AI models |
|---|---|---|---|---|---|
| Fisher-Rosemount Systems Inc. | Strong · 261 | Moderate · 61 | Absent | Emerging · 11 | Emerging · 8 |
| JFE Steel Corporation | Strong · 40 | Emerging · 4 | Moderate · 17 | Emerging · 4 | Moderate · 17 |
| Nippon Steel Corporation | Strong · 28 | Emerging · 5 | Strong · 25 | Absent | Absent |
| Siemens AG | Strong · 17 | Absent | Absent | Moderate · 4 | Absent |
| Nanotronics Imaging Inc. | Strong · 11 | Absent | Absent | Absent | Strong · 8 |
| Omron Corporation | Strong · 11 | Absent | Moderate · 5 | Absent | Absent |
Frequently asked questions
The corpus covers 617 patent families identified as relevant to predictive quality control.
Fisher-Rosemount Systems holds the leading position with 290 patent families — more than six times the count of the second-ranked applicant, JFE Steel (43 patent families).
Annual filing volume peaked in 2021 at 96 families and has eased since. The field is in a post-peak consolidation phase. Note that the most recent 18–24 months of data are subject to publication lag, so the apparent softening in 2025–2026 may be partly an artifact of that lag.
The United States leads by patent records, followed by China, the United Kingdom, Japan, and Europe (EPO). WIPO PCT filings indicate that a meaningful share of applicants seek broad international protection. India and South Korea are emerging secondary filing destinations.
G06N (AI models), B25J (robotics and manipulators), H04L (digital information transmission), and G06T (image data processing) all appear at relatively low record counts compared to the dominant G05B control-systems branch, making them the most observable areas of relative sparsity adjacent to the field’s core.
The evidence shows a single identified co-filing relationship — between Siemens AG and Siemens Corp, with 2 co-filed patent families — indicating that the predictive quality control IP ecosystem is largely developed in-house by individual applicants rather than through cross-organizational alliances.
Ready to map your own predictive quality control patent landscape?
Join 18,000+ innovators using PatSnap Eureka to map any technology landscape: search 2B+ patents and papers, surface key assignees, and generate a report like this in minutes.
Disclaimer. This page is generated from PatSnap Eureka data drawn from a limited snapshot of global patent and scientific-literature records, and is provided for general information and reference only.
Patent data carries inherent limitations: recent filings (typically the most recent 18–24 months) are under-counted due to standard publication lag; counts may be reported at either a patent-family or a patent-record basis and are not always directly comparable; classification, applicant-name, and citation data may contain errors, duplicates, or omissions; and the underlying search query defines and constrains the scope shown. As a result, the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
Nothing on this page constitutes an exhaustive prior-art, novelty, freedom-to-operate, or validity search, nor does it constitute legal, financial, investment, or professional advice, and it should not be relied upon as such. Any patent, commercial, or strategic decision should be verified independently and reviewed with qualified patent, legal, and domain professionals. PatSnap makes no warranties, express or implied, as to the accuracy, completeness, or fitness for any particular purpose of the information presented.