USPTO Patent Search — US Prior Art via Google Patents Index

Data source: this actor queries the Google Patents index (patents.google.com) filtered to US publications. It does not call USPTO's Patent Public Search or PatentsView APIs.

Search USPTO patents for prior-art and patent research. Built for IP counsel, brand teams, researchers and AI agents.

📊 Sample Output

publication_number	assignee	inventor	priority_date	filing_date	grant_date
US12438891B1	Splunk Inc.	Sudhakar Muddu	2015-08-31	2022-02-18	2025-10-07
US12518214B2	Nantomics, Llc	Christopher W. Szeto	2016-07-18	2023-04-21	2026-01-06
US11606373B2	Darktrace Holdings Limited	Matthew Dunn	2018-02-20	2019-02-19	2023-03-14
US11768636B2	Pure Storage, Inc.	Brian Gold	2017-10-19	2022-12-27	2023-09-26
US12373428B2	Pure Storage, Inc.	Brian Gold	2017-10-19	2022-04-26	2025-07-29
US12547647B2	Bank Of America Corporation	Ronnie J. Morris	2018-06-12	2023-06-09	2026-02-10

Real rows from run vUdPYeLjhTknM2hfS on build 0.1.4 (2026-09-25), unedited apart from masked emails/phones and shortened long text; fields the source does not publish are empty.

🔧 Input reference

Field	Type	Default	What it does
query	string	"machine learning"	Keywords or Google Patents query syntax (e.g. 'solid state battery', 'machine learning').
assignee	string		Filter by patent owner/company (e.g. Apple, Tesla).
inventor	string		Filter by inventor name.
country	string		Patent office country code. Defaults to US (USPTO). Set EP, CN, JP, etc. for other offices, or empty string for worldwide.
status	string		GRANT or APPLICATION. Empty = both.
after	string		Only patents with priority date on/after this.
before	string		Only patents with priority date on/before this.
fetchDetails	boolean	true	Fetch each patent's detail page to add full abstract, claims text, CPC classifications, backward citations (prior art), cited-by count, and patent family size. Adds time per resul…
maxResults	integer	50	Maximum patents to return.
include_raw	boolean	false	Attach the raw Google Patents search record to each item.

🧾 JSON sample record

One real record from run vUdPYeLjhTknM2hfS (emails/phones masked, long text shortened):

{
  "publication_number": "US12438891B1",
  "title": "Anomaly detection based on ensemble machine learning model",
  "abstract": "A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.",
  "abstract_snippet": "accessing the entity profile to read the plurality of features scores for the entity; processing the plurality of feature scores for the entity, accessed from the entity profile, by using a plurality of machine-learning models; generating a plurality of intermediate anomaly scores for the entity, …",
  "claims_text": "What is claimed is: 1. A method comprising: receiving, by a computer system, event data associated with an entity on a computer network; analyzing, by the computer system, the event data; generating, by the computer system, a plurality of feature scores for the entity based on a result of analyzing the event data; creating, by the computer system, an entity profile uniquely associated with the entity, the entity profile including the plurality of feature scores for the entity; accessing the entity profile to read the plurality of features scores for the entity; processing the plurality of feature scores for the entity, accessed from the entity profile, by using a plurality of machine-learning models; generating a plurality of intermediate anomaly scores for the entity, each based on processing of a respective one of the plurality of feature scores of the entity using a respective one of the plurality of machine-learning models; processing the plurality of intermediate anomaly scores for the entity according to an ensemble learning model; generating an anomaly score for the entity based on processing the plurality of intermediate anomaly scores for the entity according to the ensemble learning model; and detecting an anomaly associated with the entity in response to determining that the anomaly score for the entity satisfies a specified criterion. 2. The method of claim 1 , wherein the detected anomaly is indicative of a malware communication. 3. The method of claim 1 , wherein each of the plurality of feature scores is representative of a quantified evaluation of risk associated with the entity. 4. The method of claim 1 , wherein detecting the anomaly includes assigning the anomaly score based on a weighted combination of the plurality of feature scores. 5. The method of claim 1 , wherein generating a feature score of the plurality of feature scores includes: processing the event data using a machine learning model, the machine learning model including: model processing logic defining a process for assigning the feature score based on the event data; and a model state defining a set of parameters for applying the model processing logic; wherein the anomaly is detected in response to determining that the anomaly score satisfies the specified criterion. 6. The method of claim 1 , wherein detecting the anomaly includes: determining a volume of event data associated with a communication between the entity and another entity; using the ensemble-learning model if the volume of event data is determined to be at or above a threshold volume. 7. The method of claim 1 , wherein the event data is associated with a communication between an internal entity within a computer network and an external entity outside the computer network. 8. The method of claim 1 , wherein the event data includes an identifier associated with the entity, and wherein at least one feature score of the plurality of feature scores is indicative of a level of confidence that the identifier is machine generated. 9. The method of claim 1 , further comprising: annotating, by the computer system, the detected anomaly with data from an external data source external to the computer network. 10. The method of claim 1 , further comprising: outputting, by the computer system, via a user interface, an indication of the detected anomaly to a user. 11. The method of claim 1 , wherein the event data is timestamped machine data. 12. The method of claim 1 , wherein the event data include one or more of: domain name system (DNS) generated log data, firewall generated log data, or proxy generated log data. 13. The method of claim 1 , wherein detecting the anomaly includes processing the entity profile using an anomaly model. 14. A system comprising: a processor; and a memory having instructions stored therein, execution of which by the processor causes the system to: receive event data associated with an entity on a computer network; analyze the event data; generate a plurality of feature scores for the entity based on a result of analyzing the event data; creating, by the computer system, an entity profile uniquely associated with the entity, the entity profile including the plurality of feature scores for the entity; accessing the entity profile to read the plurality of features scores for the entity; processing the plurality of feature scores for the entity, accessed from the entity profile, by using a plurality of machine-learning models; generating a plurality of intermediate anomaly scores for the entity, each based on processing of a respective one of the plurality of feature scores for the entity using a respective one of the plurality of machine-learning models; processing the plurality of intermediate anomaly scores for the entity according to an ensemble-learning model; generating an anomaly score for the entity based on processing the plurality of intermediate anomaly scores for the entity according to the ensemble-learning model; and detecting an anomaly associated with the entity in response to determining that the anomaly score for the entity satisfies a specified criterion. 15. The system of claim 14 , wherein the detected anomaly is indicative of a malware communication. 16. The system of claim 14 , wherein each of the plurality of feature scores is representative of a quantified evaluation of risk associated with the particular entity. 17. A non-transitory machine-readable storage medium containing instructions, execution of which by a computer system causes the computer system to perform operations comprising: receiving event data associated with an entity on a computer network; analyzing the event data; generating a plurality of feature scores for the entity based on a result of analyzing the event data; creating, by the computer system, an entity profile uniquely associated with the entity, the entity profile including the plurality of feature scores for the entity; accessing the entity profile to read the plurality of feat",
  "claims_count": 18,
  "cpc_classifications": [
    "G06F16/00",
    "G06F16/20",
    "G06F16/24",
    "G06F16/245",
    "G06F16/2457",
    "G06F16/24578",
    "G06F16/25",
    "G06F16/254",
    "…"
  ],
  "cited_patents": [
    "US20110202391A1",
    "US20150040231A1",
    "US7555523B1",
    "US20050278703A1",
    "US20060288415A1",
    "US20100241828A1",
    "US20110055921A1",
    "US9055012B2",
    "…"
  ],
  "cited_patents_count": 102,
  "cited_by_count": null,
  "family_size": 53,
  "assignee": "Splunk Inc.",
  "inventor": "Sudhakar Muddu",
  "priority_date": "2015-08-31",
  "filing_date": "2022-02-18",
  "grant_date": "2025-10-07",
  "publication_date": "2025-10-07",
  "language": "en",
  "pdf_url": null,
  "google_patents_url": "https://patents.google.com/patent/US12438891B1/en",
  "source": "Google Patents (USPTO)",
  "detected_at": "2026-09-25T00:05:52.581184+00:00"
}

💰 Pricing

Event	Price (USD)	When it is charged
Actor Start (apify-actor-start)	$5e-05	Charged when the Actor starts running. Number of events charged depends on Actor memory (one event per GB, minimum one event).
Patent record (apify-default-dataset-item)	$0.1	Single result in the default dataset.

Pay-per-event: you pay only for what the run delivers. A run that delivers nothing bills no result events (only the actor-start event, when the actor defines one). Example: a run that delivers 100 results costs 100 × $0.1 = $10.00 plus the start fee.

🔗 Related Actors

More from the NexGenData Patents & trademarks family:

• TTAB Opposition Tracker — Trademark Disputes API
• USPTO Patent Grants — Weekly Grant Data API
• USPTO Patent Grants Tracker — New Grant Alerts API
• PCT / WO Patent Search — via Google Patents Index

6 more in this family on the NexGenData Store page.

⏰ Run this as a prior-art watch (recommended)

Every IP registry actor is a monitoring subscription, not a one-shot lookup. Schedule a weekly sweep and catch new filings as they land:

{
  "query": "artificial intelligence",
  "limit": 100
}

Weekly cron (0 8 * * 1) turns this into an always-on prior-art watch.

📊 What you get

Clean JSON, one record per patent (full output has 22 fields):

• abstract — Abstract
• abstract_snippet — Abstract snippet
• assignee — Assignee
• cited_by_count — Cited by count
• cited_patents — Cited patents
• cited_patents_count — Cited patents count
• claims_count — Claims count
• claims_text — Claims text
• cpc_classifications — Cpc classifications
• detected_at — Detected at

Pricing: $0.10 per patent (Pay-Per-Event) — about 10 patents per $1.

🤖 Use with AI agents

Point Claude, the OpenAI Agents SDK, an n8n flow or any MCP-aware client at it and run USPTO prior-art and patent searches on demand.

Sample agent prompt:

Run a prior-art search for a technology and return the closest patents.

Agentic payments (x402): Supports agentic payment via x402 — agents can call this actor with USDC, no API key required.

🔗 Related IP actors

Prior-art & patent web: USPTO Patent Grants, USPTO Grants Tracker, WIPO PatentScope, PatentsView Inventors, FDA Orange Book

IP agent front door: Patents & Trademarks IP MCP

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Data from public intellectual-property registries.

Search globally first: Google Patents Scraper — one worldwide prior-art search across every jurisdiction.