Perovskite AI/ML Composition Discovery Patent Snapshot
The patent corpus for AI/ML-driven perovskite composition discovery is at a nascent stage, with only 1 patent family in scope, filed solely in India in 2024. All activity is concentrated among individual academic inventors, signalling that this intersection of machine learning and perovskite materials design remains largely uncharted in formal IP.
A single-family corpus with no established corporate leaders
The current evidence snapshot contains 1 patent family, with E. Ramesh holding the top-ranked position among eleven individual inventors, each contributing 1 patent record. The visible assignee structure is entirely flat: no single entity has established a meaningful IP lead.
The top five filers account for 45% of the ranked applicants visible in this query’ combined total, but given the extremely small corpus size, this concentration figure reflects a filing coincidence rather than a strategic moat. There is no discernible tier gap between a leader and challengers.
| # | Applicant | Patent records | Share |
|---|---|---|---|
| 1 | E. Ramesh | 1 | |
| 2 | Dr. Alla Srivani | 1 | |
| 3 | N. Seshagiri Rao | 1 | |
| 4 | R. Nagaraju | 1 | |
| 5 | Dr. A. Anitha Ezhil Mangaiyar Karasi | 1 | |
| 6 | Dr. A. Angelin Prema | 1 |
| # | Applicant | Patent records | Share |
|---|---|---|---|
| 7 | Dr. Sajja Ravi Babu | 1 | |
| 8 | Kandukuri Venkateswara Rao | 1 | |
| 9 | Dr. Ashes Maji | 1 | |
| 10 | Pravat Kumar Swain | 1 | |
| 11 | R. Shanthi | 1 |
The absence of corporate or institutional assignees among the ranked filers implies that formal R&D programs at major companies or universities have not yet translated into published IP in this specific intersection of AI/ML and perovskite composition discovery.
The most recent filing period is subject to publication lag; the single record dated to 2024 may therefore underrepresent actual activity from that year onward. Longer-window growth, applicant concentration, and technology-route coverage are therefore more reliable signals than the latest-year bar alone.
Activity emerged only in 2024; technology mix spans AI models and semiconductor devices
The filing trend and technology composition charts together reveal a corpus that has only just begun to take shape, with all recorded activity concentrated in a single year and split evenly across two IPC branches.
Annual filing trend
Zero filings were recorded from 2017 through 2023; the single patent record appeared in 2024, with no records yet visible for 2025–2026. Given standard publication lag of 18–24 months, it is too early to determine whether 2024 represents a one-off or the leading edge of a broader wave.
↗ Hover for values · click a bar to ask EurekaTechnology composition
The corpus splits equally between G06N (computing based on AI models) and H01L (semiconductor devices), reflecting the dual nature of this research area — algorithmic methods on one side and photovoltaic or optoelectronic device embodiments on the other. Neither branch currently is visible in.
↗ 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.
Deep learning approaches for bandgap and efficienc…
The invention relates to a deep learning-based system and method for the rapid and accurate estimation of bandgap and efficiency in perovskite solar cells. By training convolutional or recurrent neural network models on comprehensive datasets encompassing perovskite properties and corresponding performance metrics, the invention enables precise prediction… (excerpt from the patent abstract)
Open this patent in Eureka →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.
Assignee snapshot from the current evidence set
The applicants below are visible in this query result. Because the evidence set is relatively small, read this section as a directional snapshot rather than a full competitive ranking.
E Ramesh
E. Ramesh holds the top-ranked position with 1 patent record, entering as a new entrant with no prior filings in the dataset. Technology focus spans G06N 3 (AI models), H01L 31, and H01L 51 (semiconductor devices), reflecting an integrated approach to both the algorithmic and device dimensions of perovskite composition discovery.
patent records: 1Dr. Sajja Ravi Babu
Dr. Sajja Ravi Babu holds 1 patent record and is also a new entrant. Technology emphasis mirrors the corpus pattern — G06N 3, H01L 31, and H01L 51 — indicating that all filers in this corpus share the same technical scope rather than occupying differentiated niches.
patent records: 1Frequently asked questions
The evidence identifies 1 patent family in scope. This is an extremely small corpus, indicating that the specific intersection of AI/ML methods and perovskite composition discovery has not yet attracted substantial formal IP filings globally.
All eleven ranked filers are individual inventors, with E. Ramesh holding the top-ranked position at 1 patent record. No corporate, university, or government entities appear in the applicant ranking based on current evidence.
India is the only jurisdiction represented in the evidence, with 1 patent record filed there. No filings have been identified in the United States, Europe, China, Japan, or South Korea for this specific sub-field.
The single patent record in the corpus is dated 2024, with no prior activity recorded from 2017 through 2023. Because patent publication typically lags filing by 18 to 24 months, additional 2024 and 2025 filings may not yet be visible in the data.
The corpus spans two IPC classes: G06N (computing based on AI models) and H01L (semiconductor devices). At the sub-class level, activity touches G06N 3, H01L 31, and H01L 51, reflecting both the algorithmic methodology and the photovoltaic or optoelectronic device context.
Based on the evidence, IP risk from third-party blocking patents is currently very low, given only 1 family in scope, no corporate assignees, and no highly-cited prior art identified. However, the publication lag means that undisclosed filings from 2024 onward could change this picture; a freedom-to-operate analysis should account for unpublished applications.
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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.