Google Patents Details API
Google Patents Details API allows you to scrape patent details or scholar details from Google Patents. The API is accessed through the following endpoint: /search?engine=google_patents_details. A user may query the following: https://serpapi.com/search?engine=google_patents_details utilizing a GET request. Head to the playground for a live and interactive demo.
API Parameters
Search Query
patent_id
Required
Parameter defines the ID of the patent you want to retrieve. You can get it from the results of our Google Patents API.
It can be:
- A patent ID: patent/<publication number>/<two-letter country code>, the country code can be omitted (e.g. patent/US11734097B1/en or patent/US11734097B1)
- A scholar ID: scholar/<scholar ID> (e.g. scholar/6497879044063343659)
Serpapi Parameters
engine
Required
Set parameter to google_patents_details to use the Google Patents Details API engine.
no_cache
Optional
Parameter will force SerpApi to fetch the Google Patents Details results even if a cached version is already present. A cache is served only if the query and all parameters are exactly the same. Cache expires after 1h. Cached searches are free, and are not counted towards your searches per month. It can be set to false (default) to allow results from the cache, or true to disallow results from the cache. no_cache and async parameters should not be used together.
async
Optional
Parameter defines the way you want to submit your search to SerpApi. It can be set to false (default) to open an HTTP connection and keep it open until you got your search results, or true to just submit your search to SerpApi and retrieve them later. In this case, you'll need to use our Searches Archive API to retrieve your results. async and no_cache parameters should not be used together. async should not be used on accounts with Ludicrous Speed enabled.
zero_trace
Optional
Enterprise only. Parameter enables ZeroTrace mode. It can be set to false (default) or true. Enable this mode to skip storing search parameters, search files, and search metadata on our servers. This may make debugging more difficult.
API Results
JSON Results
JSON output includes structured data for patents results.
A search status is accessible through search_metadata.status. It flows this way: Processing -> Success || Error. If a search has failed, the error will contain an error message. search_metadata.id is the search ID inside SerpApi.
HTML Results
HTML output is useful to debug JSON results or support features not supported yet by SerpApi.
HTML output gives you the raw HTML results from Google Patents Details.
API Examples
JSON structure overview
{
...
"title": "String - Patent / scholar document title",
"type": "String - Type of the document. The value can either be a 'patent' or a 'scholar'",
"header": "String - Document header",
"pdf": "String - URL to the PDF",
"publication_number": "String - The publication number of the patent",
"publication_year": "String - The publication year of the scholar document",
"publication_venue": "String - The publication venue of the scholar document",
"full_view_url": "String - URL to the full view",
"institution_url": "String - URL to the institution",
"cited_by_url": "String - URL to whom the document is cited by",
"main_url": "String - Main URL to the document",
"other_versions_url": "String - URL to the other versions of the document",
"country": "String - Country of the patent",
"prior_art_keywords": "Array - Keywords of prior art",
"prior_art_date": "String - Date of prior art",
"application_number": "String - Patent's application number",
"inventors": [
{
"name": "String - Inventor's name",
"link": "String - Search link of the inventor",
"serpapi_link": "String - URL to the SerpApi search",
}
],
"assignees": "Array - List of assignees",
"authors": "Array - List of authors",
"priority_year": "String - Patent's priority year",
"priority_date": "String - Patent's priority date",
"filing_date": "String - Patent's filing date",
"publication_date": "String - Patent's publication date",
"worldwide_applications": {
"<year>": [
{
"filing_date": "String - Filing date of the application",
"country_code": "String - Country code of the application",
"application_number": "String - Application number",
"document_id": "String - Document ID",
"legal_status_cat": "String - Legal status category",
"legal_status": "String - Legal status",
"this_app": "Boolean - True if it's the current application",
}
]
},
"events": [
{
"date": "String - Date of the event",
"title": "String - Title of the event",
"type": "String - Event type",
"critical": "Boolean - True if the event is critical",
"assignee_search": "String - Associated assignee",
}
],
"external_links": [
{
"text": "String - Link text",
"link": "String - Link URL",
}
],
"images": "Array - List of document's images",
"classifications": [
{
"code": "String - Classification code",
"description": "String - The description",
"leaf": "Boolean - True if it's a leaf classification",
"is_cpc": "Boolean - True if it's a CPC",
}
],
"abstract": "String - Abstract of the patent",
"abstract_original": "String - Abstract in the original language",
"snippet": "String - Snippet of the scholar document",
"description_link": "String - URL to the HTML content of the patent description",
"claims": "Array - List of patent claims",
"child_applications": [
{
"application_number": "String - Application number",
"relation_type": "String - Relation type",
"representative_publication": "String - Representative publication number",
"primary_language": "String - Primary language",
"priority_date": "String - Priority date of the application",
"filing_date": "String - Filing date of the application",
"title": "String - Application title",
}
]
"parent_applications": "Array - List of parent applications, with the same structure of `child_applications`",
"priority_applications": "Array - List of priority applications, with the same structure of `child_applications`",
"family_id": "String - Family ID",
"applications_claiming_priority": "Array - List of applications claiming priority, with the same structure of `child_applications`",
"patent_citations": {
"original": [
{
"patent_id": "String - ID of the cited patent",
"serpapi_link": "String - URL to the SerpApi search",
"publication_number": "String - Publication number of the cited patent",
"primary_language": "String - Primary language of the cited patent",
"priority_date": "String - Priority date of the cited patent",
"publication_date": "String - Publication date of the cited patent",
"assignee_original": "String - Original assignee",
"title": "String - Title of the cited patent"
}
],
"family_to_family": "Array - List of family to family citations, with the same structure of `original`",
},
"non_patent_citations": "Array - List of non-patent citations",
"cited_by": {
"original": "Array - List of original citations, with the same structure of `patent_citations.original`",
"family_to_family": "Array - List of family to family citations, with the same structure of `patent_citations.original`",
},
"similar_documents": [
{
"is_patent": "Boolean - True if it's a patent",
"is_scholar": "Boolean - True if it's a scholar document",
"patent_id": "String - ID of the patent / scholar",
"serpapi_link": "String - URL to the SerpApi search",
"publication_number": "String - Publication number of the patent",
"primary_language": "String - Primary language of the patent",
"publication_date": "String - Publication date of the patent / scholar",
"title": "String - Title of the patent / scholar",
"scholar_id": "String - ID of the scholar document",
"scholar_authors": "String - Authors of the scholar document",
}
],
"legal_events": [
{
"date": "String - Date of the event",
"code": "String - Event code",
"title": "String - Event title",
"attributes": [
{
"label": "String - Attribute label",
"value": "String - Attribute value",
}
]
}
],
"concepts": {
"match": [
{
"id": "String - Concept ID",
"name": "String - Concept name",
"domain": "String - Concept domain",
"similarity": "Float - Concept similarity",
"sections": "Array - List of sections",
"count": "Integer - Count of the concept",
}
]
},
...
}
Patent example
{
"search_metadata": {
"id": "6582737f15afff70f8cce55a",
"status": "Success",
"json_endpoint": "https://serpapi.com/searches/4e27b55e78dbf3d3/6582737f15afff70f8cce55a.json",
"created_at": "2023-12-20 04:54:23 UTC",
"processed_at": "2023-12-20 04:54:23 UTC",
"google_patents_details_url": "https://patents.google.com/patent/US11734097B1/en",
"raw_html_file": "https://serpapi.com/searches/4e27b55e78dbf3d3/6582737f15afff70f8cce55a.html",
"total_time_taken": 2.29
},
"search_parameters": {
"engine": "google_patents_details",
"patent_id": "patent/US11734097B1/en"
},
"title": "Machine learning-based hardware component monitoring",
"type": "patent",
"pdf": "https://patentimages.storage.googleapis.com/00/a7/59/6750cd74efdf32/US11734097.pdf",
"publication_number": "US11734097B1",
"country": "United States",
"prior_art_keywords": [
"storage",
"data",
"hardware component",
"anomaly",
"monitoring system"
],
"prior_art_date": "2018-01-18",
"application_number": "US17/160,053",
"inventors": [
{
"name": "Christopher Golden",
"link": "https://patents.google.com/?inventor=Christopher+Golden",
"serpapi_link": "https://serpapi.com/search.json?engine=google_patents&inventor=Christopher+Golden",
},
{
"name": "Emily Watkins",
"link": "https://patents.google.com/?inventor=Emily+Watkins",
"serpapi_link": "https://serpapi.com/search.json?engine=google_patents&inventor=Emily+Watkins",
}
],
"assignees": [
"Pure Storage Inc"
],
"priority_date": "2018-01-18",
"filing_date": "2021-01-27",
"publication_date": "2023-08-22",
"worldwide_applications": {
"2019": [
{
"filing_date": "2019-01-16",
"country_code": "US",
"application_number": "US16/249,534",
"document_id": "patent/US11010233B1/en",
"legal_status_cat": "active",
"legal_status": "Active"
}
],
"2021": [
{
"filing_date": "2021-01-27",
"country_code": "US",
"application_number": "US17/160,053",
"document_id": "patent/US11734097B1/en",
"legal_status_cat": "active",
"legal_status": "Active",
"this_app": true
}
],
"2023": [
{
"filing_date": "2023-06-14",
"country_code": "US",
"application_number": "US18/209,789",
"document_id": "patent/US20230325272A1/en",
"legal_status_cat": "active",
"legal_status": "Pending"
}
]
},
"events": [
{
"date": "2018-01-18",
"title": "Priority claimed from US15/874,719",
"type": "external-priority",
"document_id": "patent/US11144638B1/en"
},
{
"date": "2018-10-23",
"title": "Priority claimed from US16/168,224",
"type": "external-priority",
"document_id": "patent/US10970395B1/en"
},
...
{
"date": "2021-01-27",
"title": "Assigned to PURE STORAGE, INC., A DELAWARE CORPORATION",
"type": "reassignment",
"assignee_search": "PURE STORAGE, INC., A DELAWARE CORPORATION",
"description": [
"ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS).",
"Assignors: WATKINS, Emily, GOLDEN, CHRISTOPHER"
]
},
...
],
"external_links": [
{
"text": "USPTO",
"link": "https://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&p=1&u=/netahtml/PTO/srchnum.html&r=1&f=G&l=50&d=PALL&s1=11734097.PN."
},
{
"text": "USPTO PatentCenter",
"link": "https://patentcenter.uspto.gov/applications/17160053"
},
...
],
"images": [
"https://patentimages.storage.googleapis.com/70/46/9d/c9498a668efa1b/US11734097-20230822-D00000.png",
"https://patentimages.storage.googleapis.com/4b/8e/f0/ed9d34a6b3808c/US11734097-20230822-D00001.png",
"https://patentimages.storage.googleapis.com/ee/17/e9/7c96c61f8141c1/US11734097-20230822-D00002.png",
...
],
"classifications": [
{
"code": "G06F11/079",
"description": "Root cause analysis, i.e. error or fault diagnosis",
"leaf": true,
"first_code": true,
"is_cpc": true
},
{
"code": "G06F21/554",
"description": "Detecting local intrusion or implementing counter-measures involving event detection and direct action",
"leaf": true,
"first_code": true,
"is_cpc": true
},
...
],
"abstract": "An illustrative method includes identifying, based on an output of a machine learning model that receives data associated with an operation of a hardware component as an input, an anomaly in the data, determining that the anomaly is representative of an issue associated with the hardware component, and performing, based on the determining that the anomaly is representative of the issue associated with the hardware component, a remedial action that affects a performance of the operation of the hardware component.",
"description_link": "https://serpapi.com/searches/6582737f15afff70f8cce55a/google_patents_details/description.html",
"claims": [
"1. A method comprising:\nidentifying, by a monitoring system based on an output of a machine learning model that receives data associated with an operation of a hardware component as an input, an anomaly in the data;\ndetermining, by the monitoring system, that the anomaly is representative of an issue associated with the hardware component; and\nperforming, by the monitoring system based on the determining that the anomaly is representative of the issue associated with the hardware component, a remedial action that affects a performance of the operation of the hardware component.",
"2. The method of claim 1, wherein the performing of the remedial action comprises slowing down a performance of the operation of the hardware component.",
"3. The method of claim 1, wherein the performing of the remedial action comprises preventing the operation of the hardware component from being performed.",
...
],
"child_applications": [
{
"application_number": "US18/209,789",
"relation_type": "Continuation",
"representative_publication": "US20230325272A1",
"primary_language": "en",
"priority_date": "2018-01-18",
"filing_date": "2023-06-14",
"title": "Hardware Component Monitoring-based Performance of a Remedial Action"
}
],
"parent_applications": [
{
"application_number": "US16/249,534",
"relation_type": "Continuation",
"representative_publication": "US11010233B1",
"primary_language": "en",
"priority_date": "2018-01-18",
"filing_date": "2019-01-16",
"title": "Hardware-based system monitoring"
}
],
"priority_applications": [
{
"application_number": "US17/160,053",
"representative_publication": "US11734097B1",
"primary_language": "en",
"priority_date": "2018-01-18",
"filing_date": "2021-01-27",
"title": "Machine learning-based hardware component monitoring"
},
{
"application_number": "US18/209,789",
"representative_publication": "US20230325272A1",
"primary_language": "en",
"priority_date": "2018-01-18",
"filing_date": "2023-06-14",
"title": "Hardware Component Monitoring-based Performance of a Remedial Action"
}
],
"applications_claiming_priority": [
{
"application_number": "US15/874,719",
"representative_publication": "US11144638B1",
"primary_language": "en",
"priority_date": "2018-01-18",
"filing_date": "2018-01-18",
"title": "Method for storage system detection and alerting on potential malicious action"
},
{
"application_number": "US16/168,224",
"representative_publication": "US10970395B1",
"primary_language": "en",
"priority_date": "2018-01-18",
"filing_date": "2018-10-23",
"title": "Security threat monitoring for a storage system"
},
...
],
"family_id": "75910323",
"patent_citations": {
"original": [
{
"patent_id": "patent/US5208813A/en",
"serpapi_link": "https://serpapi.com/search.json?engine=google_patents_details&patent_id=patent%2FUS5208813A%2Fen",
"publication_number": "US5208813A",
"primary_language": "en",
"priority_date": "1990-10-23",
"publication_date": "1993-05-04",
"assignee_original": "Array Technology Corporation",
"title": "On-line reconstruction of a failed redundant array system"
},
{
"patent_id": "patent/WO1995002349A1/en",
"serpapi_link": "https://serpapi.com/search.json?engine=google_patents_details&patent_id=patent%2FWO1995002349A1%2Fen",
"publication_number": "WO1995002349A1",
"primary_language": "en",
"priority_date": "1993-07-15",
"publication_date": "1995-01-26",
"assignee_original": "Paul Hettich Gmbh & Co.",
"title": "Locking device for drawers and the like"
},
...
],
"family_to_family": [
{
"patent_id": "patent/US9098566B2/en",
"serpapi_link": "https://serpapi.com/search.json?engine=google_patents_details&patent_id=patent%2FUS9098566B2%2Fen",
"publication_number": "US9098566B2",
"primary_language": "en",
"priority_date": "2011-05-24",
"publication_date": "2015-08-04",
"assignee_original": "Oracle International Corporation",
"title": "Method and system for presenting RDF data as a set of relational views"
}
]
},
"non_patent_citations": [
{
"title": "\"International Search Report and Written Opinion received in International Application No. PCT/US2022/047039 dated Jan. 30, 2023\"."
},
{
"title": "Final Office Action received in U.S. Appl. No. 16/249,534 dated Dec. 4, 2020."
},
...
],
"cited_by": {
"original": [
{
"patent_id": "patent/US20220253350A1/en",
"serpapi_link": "https://serpapi.com/search.json?engine=google_patents_details&patent_id=patent%2FUS20220253350A1%2Fen",
"publication_number": "US20220253350A1",
"primary_language": "en",
"examiner_cited": "*",
"priority_date": "2021-02-10",
"publication_date": "2022-08-11",
"assignee_original": "Electronics And Telecommunications Research Institrute",
"title": "Apparatus and method for predicting remaining lifetime of environment data collection sensor in livestock house"
}
],
"family_to_family": [
{
"patent_id": "patent/US11010233B1/en",
"serpapi_link": "https://serpapi.com/search.json?engine=google_patents_details&patent_id=patent%2FUS11010233B1%2Fen",
"publication_number": "US11010233B1",
"primary_language": "en",
"examiner_cited": "*",
"priority_date": "2018-01-18",
"publication_date": "2021-05-18",
"assignee_original": "Pure Storage, Inc",
"title": "Hardware-based system monitoring"
},
{
"patent_id": "patent/US20210166548A1/en",
"serpapi_link": "https://serpapi.com/search.json?engine=google_patents_details&patent_id=patent%2FUS20210166548A1%2Fen",
"publication_number": "US20210166548A1",
"primary_language": "en",
"examiner_cited": "*",
"priority_date": "2018-07-23",
"publication_date": "2021-06-03",
"assignee_original": "Hewlett-Packard Development Company, L.P.",
"title": "Adjusting an alert threshold"
},
...
]
},
"similar_documents": [
{
"is_patent": true,
"patent_id": "patent/US11734097B1/en",
"serpapi_link": "https://serpapi.com/search.json?engine=google_patents_details&patent_id=patent%2FUS11734097B1%2Fen",
"publication_number": "US11734097B1",
"primary_language": "en",
"publication_date": "2023-08-22",
"title": "Machine learning-based hardware component monitoring"
},
{
"is_patent": true,
"patent_id": "patent/US11520936B1/en",
"serpapi_link": "https://serpapi.com/search.json?engine=google_patents_details&patent_id=patent%2FUS11520936B1%2Fen",
"publication_number": "US11520936B1",
"primary_language": "en",
"publication_date": "2022-12-06",
"title": "Reducing metadata for volumes"
},
...
],
"legal_events": [
{
"date": "2021-01-27",
"code": "FEPP",
"title": "Fee payment procedure",
"attributes": [
{
"label": "Free format text",
"value": "ENTITY STATUS SET TO UNDISCOUNTED (ORIGINAL EVENT CODE: BIG.); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY"
}
]
},
{
"date": "2023-08-02",
"code": "STCF",
"title": "Information on status: patent grant",
"attributes": [
{
"label": "Free format text",
"value": "PATENTED CASE"
}
]
}
]
}
Scholar example
{
"search_metadata": {
"id": "658134a415afff453dea8abd",
"status": "Success",
"json_endpoint": "https://serpapi.com/searches/d2d0941f19a02e9d/658134a415afff453dea8abd.json",
"created_at": "2023-12-19 06:13:56 UTC",
"processed_at": "2023-12-19 06:13:56 UTC",
"google_patents_details_url": "https://patents.google.com/scholar/6497879044063343659",
"raw_html_file": "https://serpapi.com/searches/d2d0941f19a02e9d/658134a415afff453dea8abd.html",
"total_time_taken": 1.11
},
"search_parameters": {
"engine": "google_patents_details",
"patent_id": "scholar/6497879044063343659"
},
"title": "Coffee, tea, and cocoa and risk of stroke",
"type": "scholar",
"header": "Larsson, 2014",
"publication_year": "2014",
"publication_venue": "Stroke",
"full_view_url": "https://www.ahajournals.org/doi/full/10.1161/strokeaha.113.003131",
"institution_url": "https://www.ahajournals.org/doi/pdf/10.1161/STROKEAHA.113.003131",
"cited_by_url": "https://scholar.google.com/scholar?cites=6497879044063343659&hl=en",
"main_url": "https://www.ahajournals.org/doi/full/10.1161/strokeaha.113.003131",
"other_versions_url": "https://scholar.google.com/scholar?cluster=6497879044063343659&hl=en",
"authors": [
"Larsson S"
],
"classifications": [
{
"code": "A61K36/185",
"description": "Magnoliopsida (dicotyledons)",
"leaf": true,
"is_cpc": true
},
{
"code": "A23F3/163",
"description": "Liquid or semi-liquid tea extract preparations, e.g. gels, liquid extracts in solid capsules",
"leaf": true,
"is_cpc": true
},
...
],
"snippet": "Methods References for this review were identified through a literature search of the PubMed database through October 2013 by using the following search terms: coffee, tea, cocoa, chocolate, prospective study, cohort study, randomized trial, meta-analysis, review … ",
"similar_documents": [
{
"is_scholar": true,
"patent_id": "scholar/6497879044063343659",
"serpapi_link": "https://serpapi.com/search.json?engine=google_patents_details&patent_id=scholar%2F6497879044063343659",
"scholar_id": "6497879044063343659",
"scholar_authors": "Larsson",
"publication_date": "2014",
"title": "Coffee, tea, and cocoa and risk of stroke"
},
{
"is_scholar": true,
"patent_id": "scholar/17560065614648074459",
"serpapi_link": "https://serpapi.com/search.json?engine=google_patents_details&patent_id=scholar%2F17560065614648074459",
"scholar_id": "17560065614648074459",
"scholar_authors": "O'Keefe et al.",
"publication_date": "2018",
"title": "Coffee for cardioprotection and longevity"
},
{
"is_scholar": true,
"patent_id": "scholar/2545051365713692887",
"serpapi_link": "https://serpapi.com/search.json?engine=google_patents_details&patent_id=scholar%2F2545051365713692887",
"scholar_id": "2545051365713692887",
"scholar_authors": "Arab et al.",
"publication_date": "2009",
"title": "Green and black tea consumption and risk of stroke: a meta-analysis"
},
...
]
}
Patent example with concepts
{
"concepts": {
"match": [
{
"id": "239000002096",
"name": "quantum dot",
"domain": "Substances",
"similarity": 0.0,
"sections": [
"title",
"claims",
"abstract",
"description"
],
"count": 2915
},
{
"id": "238000000034",
"name": "method",
"domain": "Methods",
"similarity": 0.0,
"sections": [
"title",
"claims",
"abstract",
"description"
],
"count": 283
},
{
"id": "238000012545",
"name": "processing",
"domain": "Methods",
"similarity": 0.0,
"sections": [
"title",
"description"
],
"count": 6
},
...
]
}
}