OpenAPI Specification
openapi: 3.1.0
info:
title: Patsnap Open Platform AI Translation Technical Q&A API
version: 1.0.0
description: OpenAPI specification for Patsnap Open Platform APIs, including patent search, analytics, and Eureka AI capabilities.
servers:
- url: https://connect.patsnap.com
description: Patsnap Open Platform API gateway
tags:
- name: Technical Q&A
description: Technical Q&A APIs.
paths:
/ai/technical-qa/submit:
post:
operationId: ai36-1TechnicalQaSubmit
summary: AI36-1 Technical Q&A - Submit Task
description: Submit AI Q&A task based on technical question. The system will automatically perform question analysis, knowledge retrieval and answer generation. Task is executed asynchronously, returns task_id for subsequent polling to get results.
tags:
- Technical Q&A
security:
- bearerAuth: []
responses:
'200':
description: Successful ai36-1 technical q&a - submit task response.
content:
application/json:
schema:
$ref: '#/components/schemas/AI36-1TechnicalQaSubmitResponse'
'201':
description: Created.
'401':
description: Unauthorized.
'403':
description: Forbidden.
'404':
description: Not Found.
externalDocs:
description: API Reference documentation
url: https://open.patsnap.com/devportal/api-reference/ai/technical-qa/submit
requestBody:
required: true
content:
application/json:
schema:
$ref: '#/components/schemas/AI36-1TechnicalQaSubmitRequest'
/ai/technical-qa/report:
post:
operationId: ai36-2TechnicalQaReport
summary: AI36-2 Technical Q&A - Query Task
description: 'Based on the task_id returned by [AI36-1] submit task API, poll to get the execution result of technical Q&A task. Returns: task status, question analysis results, references and complete answer content.'
tags:
- Technical Q&A
security:
- bearerAuth: []
responses:
'200':
description: Successful ai36-2 technical q&a - query task response.
content:
application/json:
schema:
$ref: '#/components/schemas/AI36-2TechnicalQaReportResponse'
'201':
description: Created.
'401':
description: Unauthorized.
'403':
description: Forbidden.
'404':
description: Not Found.
externalDocs:
description: API Reference documentation
url: https://open.patsnap.com/devportal/api-reference/ai/technical-qa/report
requestBody:
required: true
content:
application/json:
schema:
$ref: '#/components/schemas/AI36-2TechnicalQaReportRequest'
components:
schemas:
AI36-2TechnicalQaReport_TechnicalQaResponse:
type: object
properties:
task_id:
type: string
example: a1b2c3d4-e5f6-7890-abcd-ef1234567890
description: Unique task identifier
split_query:
$ref: '#/components/schemas/AI36-2TechnicalQaReport_ValidationSplitQueryDTO'
task_status:
type: integer
format: int32
example: 2
description: 'Task Status (1: running, 2: success, 3: failed)'
message_response:
$ref: '#/components/schemas/AI36-2TechnicalQaReport_SeekStreamMessageResponseDTO'
required:
- task_id
- task_status
AI36-2TechnicalQaReport_OrgInfo:
type: object
properties:
id:
type: string
example: 37e3e5a882bc2bfd36fbc3754171e311
description: Organization ID
logo:
type: string
example: https://filecdn.shuidi.cn/img/upload/images_logo/b1/50/9d/b1509dfe5b2ac787dbe2d5e0753d6f00.png/0x0.png
description: Logo icon
name:
type: string
example: 武汉数博科技有限责任公司
description: Organization name
site:
type: string
example: www.qhhry.com
description: Site information
name_cn:
type: string
example: 武汉数博科技有限责任公司
description: Chinese name
name_en:
type: string
example: Dnect
description: English name
website:
type: string
example: http://www.qhhry.com
description: Organization website
state_id:
type: string
example: 80cd8682-4344-3436-88b9-cfba03d34b78
description: State/Province ID
entity_id:
type: string
example: 37e3e5a882bc2bfd36fbc3754171e311
description: Entity ID
country_id:
type: string
example: 5a365096-b2a6-31cb-acdf-1de1f5ab3abe
description: Country ID
state_name:
type: string
example: 湖北省
description: State/Province name
entity_type:
type: string
example: Company
description: Entity type
country_name:
type: string
example: 中国
description: Country name
display_name:
type: string
example: 武汉数博科技有限责任公司
description: Display name
founded_date:
type: integer
format: int32
example: 20160722
description: Founded date
normalized_id:
type: string
example: 8adef1df2dc299c10291a4a610a69068
description: Normalized ID
normalized_logo:
type: string
example: https://filecdn.shuidi.cn/img/upload/images_logo/c7/0c/34/c70c34f6c211298e8d563c49df7706f4.png/0x0.png
description: Normalized logo
normalized_name:
type: string
example: 武汉数博科技有限责任公司
description: Normalized organization name
normalized_display_name:
type: string
example: Chang'an University
description: Normalized display name
normalized_entity_type_en:
type: string
example: Company
description: Normalized entity type in English
AI36-1TechnicalQaSubmitResponse:
type: object
properties:
data:
$ref: '#/components/schemas/AI36-1TechnicalQaSubmit_AsyncTaskIdResponse'
status:
type: boolean
example: 'false'
description: Status
error_msg:
type: string
example: The request parameter format is incorrect！
description: Error Message
error_code:
type: integer
example: '0'
description: Error Code
required:
- status
- error_code
AI36-2TechnicalQaReport_PaperAuthor:
type: object
properties:
id:
type: string
example: author_123456
description: Author ID
name:
type: string
example: 张三
description: Author name
AI36-2TechnicalQaReport_SeekReferenceResponseDTO:
type: object
properties:
apd:
type: string
example: '2020-11-09'
description: Application date of the patent
pbd:
type: string
example: '2024-03-01'
description: Publication date of the patent or paper
link:
type: string
example: https://eureka.zhihuiya.com/view/#/fullText'figures/?patentId=a8935c31-bf83-461d-bc05-2fd7a110c80e
description: Link URL pointing to the detailed page of the patent or paper
title:
type: string
example: 用于命名实体识别的改进BERT训练模型及命名实体识别方法
description: Title of the patent or paper
authors:
type: array
example:
- id: author_123456
name: 张三
- id: author_789012
name: 李四
description: List of authors containing author information of the paper or patent
items:
$ref: '#/components/schemas/AI36-2TechnicalQaReport_PaperAuthor'
content:
type: string
example: 通过在BERT模型中增加强化位置编码层和分类层，增强位置编码信息，解决了BERT模型中位置编码信息弱化导致的实体标签预测错误问题，提高了命名实体识别的准确性和召回率。
description: Content summary containing main technical description
org_info:
type: array
example:
- id: 37e3e5a882bc2bfd36fbc3754171e311
logo: https://filecdn.shuidi.cn/img/upload/images_logo/b1/50/9d/b1509dfe5b2ac787dbe2d5e0753d6f00.png/0x0.png
name: 武汉数博科技有限责任公司
site: www.qhhry.com
name_cn: 武汉数博科技有限责任公司
name_en: Dnect
website: http://www.qhhry.com
entity_id: 37e3e5a882bc2bfd36fbc3754171e311
country_id: 5a365096-b2a6-31cb-acdf-1de1f5ab3abe
state_name: 湖北省
entity_type: Company
country_name: 中国
display_name: 武汉数博科技有限责任公司
founded_date: 20160722
normalized_name: 武汉数博科技有限责任公司
normalized_entity_type_en: Company
description: Organization information list containing detailed info about applicant or author affiliations
items:
$ref: '#/components/schemas/AI36-2TechnicalQaReport_OrgInfo'
pdf_image:
type: array
example:
- labels:
- '1'
image_id: HDA0002768365640000011
extracted: false
patent_id: a8935c31-bf83-461d-bc05-2fd7a110c80e
image_from: official
image_type: drawing
is_extracted: false
storage_path: https://data-fulltext-image.zhihuiya.com/CN/B/11/25/60/48/4/HDA0002768365640000011.png
official_size: 1000x886
fig_title_code: '1'
source_image_type: drawing
fulltext_image240_url: https://data-fulltext-image-thumbnail.zhihuiya.com/CN/B/11/25/60/48/4/HDA0002768365640000011.png
description: List of PDF images containing image information from the document
items:
$ref: '#/components/schemas/AI36-2TechnicalQaReport_PdfImage'
project_id:
type: string
example: proj_12345
description: Project ID, identifier of the associated project
solution_id:
type: string
example: a8935c31-bf83-461d-bc05-2fd7a110c80e
description: Unique solution identifier for identifying patents or papers
project_name:
type: string
example: BERT优化研究项目
description: Project name of the associated project
solution_type:
type: string
example: PATENT
description: Solution type (PATENT/PAPER/WEBSITE)
pdf_image_count:
type: integer
format: int32
example: 6
description: Count of PDF images, total number of images in the document
solution_sub_type:
type: string
example: Utility Patent
description: Solution sub-type providing more detailed classification
AI36-1TechnicalQaSubmitRequest:
type: object
properties:
input:
type: string
example: 如何提升多语言专利文献的语义相似度计算准确率
description: Technical question input
lang:
type: string
example: cn
description: Report language, cn for Chinese, en for English
model:
type: string
example: seekgpt-thinking
description: AI model to use(seekgpt-thinking/o3-mini/summary-gpt)
rag_source:
type: array
example:
- patent
- paper
- website
description: RAG data source list(patent/paper/website)
summary_check:
type: boolean
example: true
description: Whether to perform summary check
required:
- input
- lang
AI36-2TechnicalQaReportResponse:
type: object
properties:
data:
$ref: '#/components/schemas/AI36-2TechnicalQaReport_TechnicalQaResponse'
status:
type: boolean
example: 'false'
description: Status
error_msg:
type: string
example: The request parameter format is incorrect！
description: Error Message
error_code:
type: integer
example: '0'
description: Error Code
required:
- status
- error_code
AI36-2TechnicalQaReport_ValidationSplitQueryDTO:
type: object
properties:
query:
type: array
example:
- BERT模型在专利文献命名实体识别中的应用
- 专利文献命名实体识别的特点和挑战
- BERT模型用于命名实体识别的优化方法和改进策略
description: List of split query statements extracted from the original question
concept:
type: array
example:
- 专利文献
- 命名实体识别
- BERT模型
- 优化方法
description: List of key concepts extracted from the query, including core technical terms and domain concepts
required:
- query
- concept
AI36-2TechnicalQaReport_SeekStreamMessageResponseDTO:
type: object
properties:
title:
type: string
example: 专利文献中命名实体识别的BERT模型优化方法有哪些
description: Technical Q&A title summarizing the core content of the question
modules:
type: array
example:
- SUMMARY
- APPLICATION
- RECOMMEND
description: List of technical modules identifying the technology domains involved in the question
summary:
type: string
example: '### 命名实体识别中BERT模型的优化方法\n\n命名实体识别（NER）是自然语言处理的核心任务，旨在从文本中识别并分类实体（如人名、地名、机构名）。BERT（Bidirectional Encoder Representations from Transformers）模型通过预训练和微调，在NER任务中展现了卓越性能...'
description: Technical Q&A summary providing a comprehensive answer to the question
recommend:
type: array
example:
- 如何在专利文献中进一步优化BERT模型的位置注意力机制，以提高命名实体识别的效率和准确性？
- 在专利文献中，针对低资源环境，如何有效结合轻量级模型和知识图谱注入来提升命名实体识别的性能？
- 在专利文献中，如何评估和比较不同词典增强策略（如顺序词典增强）对BERT模型命名实体识别性能的影响？
- 在专利文献中，如何通过模型拆分与服务化架构优化BERT模型以适应移动端或低资源环境下的命名实体识别任务？
- 在专利文献中，如何结合多任务学习和对抗训练来增强BERT模型在命名实体识别任务中的鲁棒性和泛化能力？
description: List of recommended related questions to guide further exploration
references:
type: array
example:
- APD: ''
PBD: '2025-05-06'
LINK: https://eureka.zhihuiya.com/literature/#/?paperId=b2e8bd4d-a01b-48b5-b44e-83ff4eb544a1
TITLE: 高效基于BERT的命名实体识别的位置关注
CONTENT: 本文介绍了一个命名实体识别(NER)的框架,该框架利用自然语言处理(NLP)中变形金刚(BERT)模型的双向编码器表示...
SOLUTION_ID: b2e8bd4d-a01b-48b5-b44e-83ff4eb544a1
SOLUTION_TYPE: PAPER
PDF_IMAGE_COUNT: 0
- APD: '2020-11-09'
PBD: '2024-03-01'
LINK: https://eureka.zhihuiya.com/view/#/fullText'figures/?patentId=a8935c31-bf83-461d-bc05-2fd7a110c80e
TITLE: 用于命名实体识别的改进BERT训练模型及命名实体识别方法
CONTENT: 通过在BERT模型中增加强化位置编码层和分类层，增强位置编码信息，解决了BERT模型中位置编码信息弱化导致的实体标签预测错误问题，提高了命名实体识别的准确性和召回率。
SOLUTION_ID: a8935c31-bf83-461d-bc05-2fd7a110c80e
SOLUTION_TYPE: PATENT
PDF_IMAGE_COUNT: 6
description: List of references including patents, papers and other relevant materials
items:
$ref: '#/components/schemas/AI36-2TechnicalQaReport_SeekReferenceResponseDTO'
application:
type: array
example:
- - 产品/项目
- 技术成效
- 适用场景
- - 改进BERT命名实体识别模型<br/><span class='org-hit' org-id='37e3e5a882bc2bfd36fbc3754171e311'>武汉数博科技有限责任公司</span>
- 通过增加强化位置编码层和分类层，增强位置编码信息，提高了命名实体识别的准确性和召回率 <seek-ref-tip data-ref-id='9' >9</seek-ref-tip>
- 专利文献中的命名实体识别任务，需要精确识别实体标签的场景
- - 命名实体识别系统<br/><span class='org-hit' org-id='05fce7c74b3cf9f248fe5997053139a0'>北京沃东天骏信息技术有限公司</span>
- 通过拆分BERT子模型为BERT词向量生成服务和下游机器学习子模型，解决了BERT模型复杂性导致的高性能设备依赖问题，实现了在常规性能设备上高效准确的命名实体识别 <seek-ref-tip data-ref-id='10' >10</seek-ref-tip>
- 资源受限的常规性能设备环境，需要高效部署命名实体识别服务的场景
description: List of application scenarios, each containing title and description
check_summary:
type: string
example: '### 命名实体识别中BERT模型的优化方法\n\n命名实体识别（NER）是自然语言处理的核心任务，旨在从文本中识别并分类实体（如人名、地名、机构名）。<seek-check-reject-tip data-ref-id=''2dc0d0c9-251c-4eba-9d9c-01a99eec0226''>BERT模型通过预训练和微调，在NER任务中展现了卓越性能...</seek-check-reject-tip>'
description: Check summary providing validation assessment of the technical solution
summary_think:
type: string
example: 首先，用户的问题是："专利文献中命名实体识别的BERT模型优化方法有哪些"。用户是研发专家，所以我需要提供专业、深入的内容，参考我们平台的数据处理专业标签体系，比如从解决的问题、使用的手段、达到的效果（性能+数值）、应用领域等角度来组织。
description: Summary thinking process showing the AI's analytical reasoning chain
agent_suggestion:
type: string
example: 如何在保持BERT模型NER识别精度(F1>95%)的前提下,将计算资源消耗降低70%以上,同时实现跨领域迁移时无需大规模标注数据即可快速适配新场景?
description: Agent suggestion providing comprehensive advice from the AI system
tech_mind_suggestion:
type: string
example: 如何在保持BERT模型NER识别精度(F1>95%)的前提下,将计算资源消耗降低70%以上,同时实现跨领域迁移时无需大规模标注数据即可快速适配新场景?
description: Technical mind suggestion providing professional advice from a technical perspective
agent_suggestion_scene:
type: string
example: TECH_MIND
description: Agent suggestion scene describing specific scenarios where the suggestion applies
AI36-2TechnicalQaReportRequest:
type: object
properties:
task_id:
type: string
example: 80d440b7-80a5-4233-a75f-ab72b0885c88
description: Unique task identifier, returned by submit task API
required:
- task_id
AI36-2TechnicalQaReport_PdfImage:
type: object
properties:
image_id:
type: string
example: HDA0002768365640000011
description: Image ID
extracted:
type: boolean
example: false
description: Extracted flag
patent_id:
type: string
example: a8935c31-bf83-461d-bc05-2fd7a110c80e
description: Patent ID
image_from:
type: string
example: official
description: Image source
image_type:
type: string
example: drawing
description: Image type
is_extracted:
type: boolean
example: false
description: Whether extracted
storage_path:
type: string
example: https://data-fulltext-image.zhihuiya.com/CN/B/11/25/60/48/4/HDA0002768365640000011.png
description: Storage path
official_size:
type: string
example: 1000x886
description: Official image size
source_image_type:
type: string
example: drawing
description: Source image type
fulltext_image240_url:
type: string
example: https://data-fulltext-image-thumbnail.zhihuiya.com/CN/B/11/25/60/48/4/HDA0002768365640000011.png
description: Fulltext image 240 size URL
AI36-1TechnicalQaSubmit_AsyncTaskIdResponse:
type: object
properties:
task_id:
type: string
example: 80d440b7-80a5-4233-a75f-ab72b0885c88
description: Unique task identifier for subsequent polling to get task results
required:
- task_id
securitySchemes:
bearerAuth:
type: http
scheme: bearer