Machine Learning for Grid Operations Patents: Who Leads, Gaps 2026
Machine Learning for Grid Operations Patents: Who Is Filing and Where the Field Is Still Open
Small but concentrated field. 17 records in scope, with the top 5 assignees accounting for 64.7% of all filings. Filings were at zero in 2017 and climbed unevenly to a peak of 10 records in 2025, the last complete year in scope. Three records have already published in 2026 ahead of the mid-year cut-off, but the true 2025-2026 total will read higher once publication lag closes.
- 1VELLORE INSITUTE OF TECH3
- 2MANIPAL UNIVERSITY JAIPUR2
- 3UNIVERSITY OF TENNESSEE RESEARCH FOUNDATION2
- 4ONMED LLC2
- 5ARRIS ENTERPRISES LLC2
See the full machine learning for grid operations analysis in Eureka
- The complete ranking, not just the top five
- Every IPC branch with its share of the corpus
- The most-cited records, and where claim space is still thin
Common questions
Who are the leading patent filers in machine learning for grid operations?
The dataset ranks 12 companies and institutions, with the top-ranked assignee holding 3 of the 17 records in scope and the top 5 combined accounting for 64.7% of all records. The field mixes academic institutions, a research foundation, individually named inventors and one large commercial platform filer, so leadership is spread thinly rather than concentrated in one organisation. Anyone assessing competitive risk should look at the full ranked list rather than assume a single dominant player, since even the leader holds only a small fraction of total filings.
How many patents exist for machine learning in grid operations?
This landscape identifies 17 published records matching the search scope, covering filings from 2015 through the 2026-07-31 data cut-off. That is a narrow, filtered count built around deployment-specific language such as training data scarcity, explainability and operator trust, not a count of all machine-learning-for-grids patents broadly. Filing peaked at 10 records in 2025, and 2026 already shows 3 more before the cut-off, though publication lag means recent-year counts will rise as more filings publish.
What technology areas do these patents cover?
G06N, covering AI-model computing, appears on 70.6% of the 17 records, making it by far the dominant classification. G06F (digital data processing) follows at 41.2%, H04L (digital transmission) at 23.5%, and G05B (control and regulating systems) at only 17.6%. Because a single record can carry multiple IPC classes, these percentages add up to well over 100%, and the low G05B share suggests most filings claim the modelling layer rather than direct grid control integration.
Disclaimer. This analysis is based on Patsnap Eureka data drawn from a limited snapshot of global patent records and is provided for general information and reference only. Patent data carries inherent limitations — recent filings are under-counted because of publication lag, counts may be on a record or family basis, classification and applicant-name data may contain errors or duplicates, and the underlying search query defines the scope shown — so the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
Nothing here is an exhaustive prior-art, novelty, freedom-to-operate or validity search, nor does it constitute legal, financial or professional advice, and it should not be relied upon as such. Verify independently and review with qualified patent and legal professionals before acting on it.
Method: Filing trend and technology composition. Derived from a Patsnap search on Machine Learning for Grid Operations covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish. Every share divides by all records in scope. Data: Patsnap Eureka. See the full landscape report.