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Patsnap Featured Data

Patent Data Bio Chemical Data Financial Data
Global Patent Data
187,318,393
  • Jurisdiction

    170

  • Update Frequency

    Daily

  • Latest Update

    2024-02-20

Basic Data

  • Patent Biblio

  • Citation

  • Patent Abstract

  • Full Text Translation

  • Full Text

  • Patent PDF & Image

  • Patsnap Patent Value

  • Patsnap Family

  • Patsnap Standardized Assignee

  • NAICS

  • Application Domain Classification

  • Technology Topic Classification

Legal Data

  • Legal Status

  • Estimate Expiration Date

  • File Wrapper

  • Litigation

  • License

  • Transfer

  • Re-examination

  • Invalidation/Appeal/PTAB

  • Pledge

  • Customs

  • SEP

Global Patent Data

187.3M

Legal Data

2.7B

Literature Data

131.9M

Leading Algorithm Capabilities

Utilizing technologies such as computer vision, machine learning, natural language processing, neural networks, OCR recognition, knowledge graphs, and large-scale models to process and analyze a wide range of data, assisting in innovative decision-making.

Quality Assurance of Data and Algorithms

  • Big Data Quality

    Ensure quality from seven dimensions - accuracy, completeness, consistency, timeliness, compliance, comprehensiveness, and business expertise.Use Patsnap self-developed Big Data monitoring and alert systems to gain insights, control, and guarantee stability.

  • Algorithm Quality

    Utilize NLP and a uniformly sampled strategy to build high-quality datasets that cover a range of scenarios. Patsnap measure performance through recall rate, precision rate, ks value, and ROC curve. Additionally, model stability is monitored through PSI value and other indicators. Vertical, domain-specific datasets accurately reflect specific business scenarios.

Data Use Cases

    Intellectual Property

  • Industry Competitor Monitor

  • R&D Partnership Identification

  • R&D Direction Positioning

Industry Competitor Monitor

    Use Case

  • Traditional Excel data sorting is time-consuming and labor-intensive, and inefficient.
  • Internal database has high privacy and compliance requirements for listed company data.
  • Lack of algorithm team and data model building information extraction system.
  • Patsnap Solution

  • Provide structured data in the form of datafeed or API.
  • Identify competitors' R&D new directions, and monitor their technological new trends by extracting high-value information from patent data.
  • Analyze competitors' dynamics from multiple dimensions, such as enterprise operation status, technical strength, and industry value chain, combined with enterprise business information, etc.

    Pharmaceuticals R&D

  • AI Pharmaceutical

  • Competitive Landscape Analysis

  • Pharmaceutical R&D Intelligence Acquisition

AI Pharmaceutical

    Use Case

  • Limited data volume and uneven data quality in open-source or semi-open-source databases.
  • High requirement of data processing and lack of professional teams for data extraction and labeling in vertical fields.
  • Difficulties in connecting data and model due to special formats and field requirements.
  • Patsnap Solution

  • Integrated and structured multiple data sources and map them with internal data.
  • Create internal platforms for labeling and verifying data quality for extracting antibody sequences and target antigen relations from patents.
  • Unique data extraction and linking utilizing NLP and deep learning, extracting massive sequences and structures, and establishing correlations with patent literature to support drug structure optimization.

    Investment Intelligence

  • Predictive Research & Signal Finding

  • ESG Theme Investment

  • Fundamental Analysis
  • Stock Selection & Portfolio Building

  • Investment Target Negative Elimination & Positive Screening

Predictive Research & Signal Finding

    Use Case

  • Predictive research and signal finding involve complex and time-consuming data analysis, which can be overwhelming and error-prone due to the unstructured and noisy nature of the data.
  • The lack of clarity can also impact the accuracy of results, requiring significant resources to manage and analyze.
  • Patsnap Solution

  • Integrated and structured multiple data sources and map patent data to company level.
  • Generate alpha and process equity information from an innovation perspective, use patent data as an alternative data add on and get information advantage before anyone else.
  • Intellectual Property
  • Pharmaceuticals R&D
  • Investment Intelligence
  • Industry Competitor Monitor

      Use Case

    • Traditional Excel data sorting is time-consuming and labor-intensive, and inefficient.
    • Internal database has high privacy and compliance requirements for listed company data.
    • Lack of algorithm team and data model building information extraction system.
    • Patsnap Solution

    • Provide structured data in the form of datafeed or API.
    • Identify competitors' R&D new directions, and monitor their technological new trends by extracting high-value information from patent data.
    • Analyze competitors' dynamics from multiple dimensions, such as enterprise operation status, technical strength, and industry value chain, combined with enterprise business information, etc.
  • R&D Partnership Identification

      Use Case

    • It's time-consuming and inefficient to find high-quality R&D partners, which can lead to a waste of resources.
    • Patsnap Solution

    • Evaluate potential partners based on multiple dimensions and offer industry distribution to find companies through technology.
  • R&D Direction Positioning

      Use Case

    • Enterprises need to consider multiple factors when choosing innovation directions, such as market demand, R&D technology, production cost, etc., but traditional methods relying solely on subjective judgment and accumulated experience make it difficult to achieve accurate analysis and prediction.
    • Patsnap Solution

    • R&D direction positioning based on patent data, using big data analysis and machine learning algorithms to deeply mine patent data from multiple dimensions, including technical fields and technical layout, to help enterprises quickly find the most suitable R&D direction, thereby improving R&D efficiency and success rate.