IP data-as-a-service

Patsnap Featured Data

Patent Data Bio Chemical Data Financial Data

Global Patent Data

190,963,764

Jurisdiction

170
Update Frequency

Daily
Latest Update

2024-07-01

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

191.0M

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.

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.

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.

Competitive Landscape Analysis

Use Case

A wide range of data dimensions including target drugs, clinical progress, patent literature, and news information.
Poor data accuracy and insufficient data processing capabilities, making it difficult to obtain necessary data direct.
Difficulties in integrating different information systems required by various business modules, resulting in a lack of compatibility and integration.

Patsnap Solution

Deep machine learning on a large amount of selected medical data, integrating 73,000 global new drug competitive landscapes.
Pharmaceutical experts analyze and extract values from domain data, providing data indexing and review to ensure accuracy.
Establish a target-disease-drug competitive landscape analysis through knowledge fusion and correlation to seek low competition pathways.

Pharmaceutical R&D Intelligence Acquisition

Use Case

High manpower costs in R&D intelligence tracking and monitoring, as well as complicated data processing.
Lack of algorithm teams and data model construction information extraction system for deep analysis.
High demands on data security and privacy protection, with a lack of management on data sharing and openness.

Patsnap Solution

Provide target enterprise R&D pipeline tracking, real-time monitoring of competitors' R&D dynamics, and information support for project decision-making.
Discover unsatisfied clinical demands based on global new drug R&D progress and segmented indications.
Establish information security management system and certification to ensure clear data inflow and outflow.
Real-time automation ETL on global massive medical-related data, utilizing AI technology to construct a big data system from data monitoring, mining to application.

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.

ESG Theme Investment

Use Case

Insufficient standardized ESG metrics: A major challenge in ESG investments is the lack of a standardized set of reliable metrics that can measure the ESG performance and risk of companies.
This can make it challenging for investors to make informed decisions and compare different investments.
Limited access to ESG data: Investors require comprehensive, accurate, and trustworthy ESG data to make informed investments.
However, this data can be hard to obtain, especially for smaller companies and firms in emerging markets.

Patsnap Solution

Integrate ESG factors into financial analysis and valuation pricing in investment decisions.
Use patent data as measurable assessment metrics, focus on investing in sustainability-related themes or assets. For example, clean energy, green technology or sustainable agriculture.

Fundamental Analysis

Use Case

Data quality: Incomplete or inaccurate data can lead to errors in analysis and ultimately impact decision-making.
Data accessibility: Relevant data often stored across multiple systems and make it difficult for analysts to access and effectively use the data required for analysis.
Domain expertise: Domain technical expertise is necessary in terms of specific data analytics to gain in-depth insights.

Patsnap Solution

Track enterprise technology iteration and patent ownership disputes daily.
Track enterprise technology iteration and patent ownership disputes daily.
Aggregate patent data from around the world in one place, eliminating the need for additional data gathering.
Process and structure patent data into common business language, enabling analysts to conduct autonomous and comprehensive research.

Stock Selection & Portfolio Building

Use Case

Data Quality: Investors may struggle to find alternative datasets that are reliable, accurate and consistently updated.
Data Integration: Investors may find it challenging to combine and integrate alternative datasets with existing investment workflows and technologies.
Data Interpretation: Investors may struggle to make sense of the data they are receiving and may require specialized expertise to extract meaningful insights from alternative data sources.
Accessibility: Investors may find alternative data difficult to access or analyze, particularly if they lack specialized technical knowledge or if the dataset is not available in a user-friendly format.

Patsnap Solution

Filter patented enterprises with innovation technologies in emerging industries to build an innovative portfolio.
Integrate data in the format you needed, PatSanp offers data delivery through real-time APIs and offline datafeed.
Patents mapped to stock entity level as well as its private subsidiaries, investors can avoid challenges related to multiple data integration.

Investment Target Negative Elimination & Positive Screening

Use Case

Rapid Technological Change: Technological innovation is happening at an incredibly fast pace, and investors may struggle to keep up with emerging trends and assess their potential impact on investments.
Understanding of Technology: Without a thorough understanding of technological concepts and factors, investors may be unable to identify potential concerns in investment targets.
Inadequate Disclosure: Companies may not provide adequate disclosure of technology factors, leading to incomplete screening decisions.

Patsnap Solution

Filter investment targets based on technologies. For instance, exclude companies, industries, or investment targets that do not meet ESG criteria.
PatSnap's high quality and structured patent data reveals technology factors companies don't disclose in their financial statements. This helps investors make informed decisions and avoids incomplete screenings.

Intellectual Property
Pharmaceuticals R&D
Investment Intelligence

Industry Competitor Monitor
- 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.
- 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
- It's time-consuming and inefficient to find high-quality R&D partners, which can lead to a waste of resources.
- Evaluate potential partners based on multiple dimensions and offer industry distribution to find companies through technology.
R&D Direction Positioning
- 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.
- 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.

AI Pharmaceutical
- 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.
- 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.
Competitive Landscape Analysis
- A wide range of data dimensions including target drugs, clinical progress, patent literature, and news information.
- Poor data accuracy and insufficient data processing capabilities, making it difficult to obtain necessary data direct.
- Difficulties in integrating different information systems required by various business modules, resulting in a lack of compatibility and integration.
- Deep machine learning on a large amount of selected medical data, integrating 73,000 global new drug competitive landscapes.
- Pharmaceutical experts analyze and extract values from domain data, providing data indexing and review to ensure accuracy.
- Establish a target-disease-drug competitive landscape analysis through knowledge fusion and correlation to seek low competition pathways.
Pharmaceutical R&D Intelligence Acquisition
- High manpower costs in R&D intelligence tracking and monitoring, as well as complicated data processing.
- Lack of algorithm teams and data model construction information extraction system for deep analysis.
- High demands on data security and privacy protection, with a lack of management on data sharing and openness.
- Provide target enterprise R&D pipeline tracking, real-time monitoring of competitors' R&D dynamics, and information support for project decision-making.
- Discover unsatisfied clinical demands based on global new drug R&D progress and segmented indications.
- Establish information security management system and certification to ensure clear data inflow and outflow.
- Real-time automation ETL on global massive medical-related data, utilizing AI technology to construct a big data system from data monitoring, mining to application.

Predictive Research & Signal Finding
- 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.
- 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.
ESG Theme Investment
- Insufficient standardized ESG metrics: A major challenge in ESG investments is the lack of a standardized set of reliable metrics that can measure the ESG performance and risk of companies.
- This can make it challenging for investors to make informed decisions and compare different investments.
- Limited access to ESG data: Investors require comprehensive, accurate, and trustworthy ESG data to make informed investments.
- However, this data can be hard to obtain, especially for smaller companies and firms in emerging markets.
- Integrate ESG factors into financial analysis and valuation pricing in investment decisions.
- Use patent data as measurable assessment metrics, focus on investing in sustainability-related themes or assets. For example, clean energy, green technology or sustainable agriculture.
Fundamental Analysis
- Data quality: Incomplete or inaccurate data can lead to errors in analysis and ultimately impact decision-making.
- Data accessibility: Relevant data often stored across multiple systems and make it difficult for analysts to access and effectively use the data required for analysis.
- Domain expertise: Domain technical expertise is necessary in terms of specific data analytics to gain in-depth insights.
- Track enterprise technology iteration and patent ownership disputes daily.
- Track enterprise technology iteration and patent ownership disputes daily.
- Aggregate patent data from around the world in one place, eliminating the need for additional data gathering.
- Process and structure patent data into common business language, enabling analysts to conduct autonomous and comprehensive research.
Stock Selection & Portfolio Building
- Data Quality: Investors may struggle to find alternative datasets that are reliable, accurate and consistently updated.
- Data Integration: Investors may find it challenging to combine and integrate alternative datasets with existing investment workflows and technologies.
- Data Interpretation: Investors may struggle to make sense of the data they are receiving and may require specialized expertise to extract meaningful insights from alternative data sources.
- Accessibility: Investors may find alternative data difficult to access or analyze, particularly if they lack specialized technical knowledge or if the dataset is not available in a user-friendly format.
- Filter patented enterprises with innovation technologies in emerging industries to build an innovative portfolio.
- Integrate data in the format you needed, PatSanp offers data delivery through real-time APIs and offline datafeed.
- Patents mapped to stock entity level as well as its private subsidiaries, investors can avoid challenges related to multiple data integration.
Investment Target Negative Elimination & Positive Screening
- Rapid Technological Change: Technological innovation is happening at an incredibly fast pace, and investors may struggle to keep up with emerging trends and assess their potential impact on investments.
- Understanding of Technology: Without a thorough understanding of technological concepts and factors, investors may be unable to identify potential concerns in investment targets.
- Inadequate Disclosure: Companies may not provide adequate disclosure of technology factors, leading to incomplete screening decisions.
- Filter investment targets based on technologies. For instance, exclude companies, industries, or investment targets that do not meet ESG criteria.
- PatSnap's high quality and structured patent data reveals technology factors companies don't disclose in their financial statements. This helps investors make informed decisions and avoids incomplete screenings.

IP Intelligence

R&D Intelligence

Life Sciences Intelligence

Other Services

Al-Powered Innovation Intelligence

APIs & Data Feeds ConnectInnovation with Data

Patsnap Featured Data

170

Daily

2024-07-01

Basic Data

Legal Data

191.0M

2.7B

131.9M

Date Type

Sources

Count

Update Frequency

138.7M

139.4M

2,801.8K

4,149.6K

870.4K

833.6K

25.6M

192.2M

189.6M

643.8K

Leading Algorithm Capabilities

Quality Assurance of Data and Algorithms

Big Data Quality

Algorithm Quality

Data Use Cases

Intellectual Property

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Patsnap Solution

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Patsnap Solution

Pharmaceuticals R&D

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Investment Intelligence

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Patsnap Solution

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APIs & Data Feeds Connect
Innovation with Data

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