Lowest Price Guaranteed From USD 1,299
Published
February 2022
Slides
93
View Count
3630
Example Insights
.PNG) |
 |
 |
 |
 |
 |
 |
 |
.PNG) |
 |
 |
.PNG) |
 |
 |
Report Description
The global healthcare sector has been overtly reluctant to embrace technology. This may partially be due to the failure of early digitization efforts, which were fraught with challenges and turned out to be more of a liability rather than a path forward. In fact, according to a study, clinicians ended up spending more than twice as much time on administrative work (49%), such as updating electronic medical records, than on seeing patients (27%). The delivery of modern healthcare services is, therefore, still in many ways obsolete, relying on analog systems and manual execution on simple, repetitive operations. This situation is challenging for both healthcare providers (resulting in long and arduous work hours, causing medical staff to burn themselves out) and consumers (which is reflected in hurried, inappropriate diagnosis, lack of proper care and delays in treatment administration). Further, experts have predicted that if things don’t change significantly, there is likely to be a severe shortage of healthcare workers in the mid to long term. Similar inefficiencies exist in the pharmaceutical development segment as well, with over USD 800 million being spent on drug discovery alone; a typical product development cycle in this domain lasts close to a decade and costs USD 2.5 billion, on average. However, advances in automation and intuitive software have made it possible for stakeholders to markedly improve operational efficiencies and cut-down on both operational and administrative expenses.
Today, self-learning algorithms are being used to develop AI that can not only help automate various simple and complex tasks but can also assist clinicians in making critical diagnosis / treatment related decisions. It is estimated that over 33% of the tasks that are performed manually by clinicians can be automated, and there are artificially intelligent solutions that are capable of carrying out specialized functions, such as patient triage, without human supervision. Over the last couple of decades, computer scientists and medical researchers have successfully demonstrated the applications of AI in several diverse aspects of healthcare delivery, including (but not limited to) surgery, drug discovery and hospital / patient data management. The use of self-improving algorithms also guarantees cost savings; experts estimate that savings worth over USD 150 billion can be achieved by 2026, through the adoption of AI-enabled technologies, in the US alone. As a result, a lot of capital and effort are being invested by innovators across the world in this burgeoning field of research. This report attempts to identify key trends that describe the pace and focus of innovation related to healthcare focused AI technologies and solutions.
The “Artificial Intelligence in Healthcare: Intellectual Property Landscape” report features an extensive study of the historical and current collection of granted patents, patent applications and affiliated documents associated with the upcoming suite of intuitive software and automation enabling solutions, which are designed for use within the healthcare industry. The information in this report has been presented across two deliverables, namely an Excel sheet, featuring an interactive dashboard, and a PowerPoint presentation, summarizing the ongoing activity in this domain, and key insights drawn from the available data. The report features the following details:
An in-depth review of the various patents and affiliated IP documents that have been published related to technologies and methods associated with the healthcare-related applications of AI, featuring key insights on historical and recent trends.
An examination of IP literature, identifying key words and phrases that are used to describe innovations involving the use of AI and other intuitive algorithms for healthcare-focused applications, including information on historical usage in IP filings, key affiliated terms (which can be used to further identify similar innovations), and other related trends.
A competitive benchmarking and valuation analysis of the IP documents published in this field of innovation, taking into account important parameters, such as type of IP document, year of application, time to expiry, number of citations and jurisdiction (factoring in regional GDP).
A systematic approach to identifying relevant areas of innovation by analyzing published IP documents, defining the uniqueness patented / patent pending innovations, understanding the scope of patentability in this domain, and pinpointing jurisdictions where new and / or modified claims may be filed without infringing on existing IP.
A detailed summary of the patent applications that were filed across different jurisdictions and their relative value in the IP ecosystem. The analysis segregates the intellectual capital in terms of area of innovation and intended applications, thereby, offering the means to understand key areas of research and identify innovation-specific IP filing trends.
An elaborate summary of the granted patents across different jurisdictions and their relative value in the IP ecosystem. The analysis uses a slightly more specific segregation criteria, based on type of product / solution and intended applications; this offers the means to identify unique innovations that presently have marketing exclusivity and explore future opportunities to enter into promising product markets, once their patents expire.
An insightful analysis of the various CPC symbols mentioned in the published IP literature used and their affiliated families, in order to identify historical and existing pockets of innovation (based on the functional area / industry described by the elaborate and systematic system of classifying IP); the analysis also features a discussion on the prevalent white spaces (based on CPC symbols) in this area of research.
One of the key objectives of the report was to analyze and summarize key inferences from the independent claims mentioned in the granted, active patents in the entire dataset. Using a systematic segregation approach, we have analyzed trends associated with [A] the preamble, [B] type of patent (technology patent or method patent), [C] type of claim (open ended claim or closed ended claim) and [D] key elements of a claim (individual aspects of an innovation that are covered in a singular claim).
Excel Deliverables
Sheet 1 of the spreadsheet features details on how the input data for this project was collated, including the search strings used to query a popular patent database, data segregation guidelines and IP category definition, and noise removal criteria.
Sheet 2 is a summary MS Excel dashboard, offering a detailed graphical perspective of the intellectual property landscape of AI-enabled technologies and solutions for use in the healthcare industry. It includes pictorial representations of the [A] overall patent landscape, [B] IP valuation-related insights, [C] insights on patentability and freedom to operate, [D] key trends related to patent applications, [E] key trends related to granted patents, and [F] impact of the COVID-19 pandemic on IP filing / grant.
Sheet 3 is an elaborate tabular representation of the overall IP landscape, featuring information on the various patent application- and granted patent-related documents that have been published since 1995.
Sheet 4 includes a tabular representation of key words and phrases that are used to describe innovations involving the use of AI and other intuitive algorithms for healthcare-focused applications.
Sheet 5 is a subset of sheet 3, featuring all the patent applications, covering innovation related to AI-enabled software / technologies for healthcare applications.
Sheet 6 is a subset of sheet 3, featuring all the granted patents, covering innovation related to AI-enabled software / technologies for healthcare applications.
Sheet 7 is an insightful summary of key inferences from the independent claims mentioned in the granted, active patents in the dataset. We have used a systematic segregation approach, to analyze trends associated with the preamble, type of patent (technology patent or method patent), type of claim (open ended claim or closed ended claim) and key elements of a claim (individual aspects of an innovation that are covered in a singular claim).
Sheet 8 features insights related to patentability and freedom to operate dataset in the contemporary IP landscape, related to AI enabled solutions for healthcare use.
Sheet 9 is also a subset of sheet 3, which includes a tabulated representation of all IP documents that were published during the COVID-19 pandemic.
Sheet 10 is an appendix which includes pivot tables that drive the interactive elements in sheet 2.
Sheet 11 is an appendix, featuring country codes corresponding to the jurisdictions mentioned in the dataset.
PowerPoint Deliverable
Section I features an executive summary of the key insights generated from analyzing the intellectual property landscape of AI technologies and solutions designed for healthcare-related applications.
Section II provides important details related to the healthcare applications of AI and affiliated intuitive data processing algorithms, including key innovation related definitions, contemporary and promising future application areas, and detailed profiles of some the popular AI solutions developed by established players in the field, such as IBM and Google (DeepMind Technologies).
This section includes a review of the various patents and IP documents that have been published related to technologies and methods associated with the healthcare-related applications of AI, featuring key insights on historical and recent trends.
It includes an insightful examination of IP literature, identifying key words and phrases that are used to describe innovations involving the use of AI and other intuitive algorithms for healthcare-focused applications, including information on historical usage in IP filings, key affiliated terms (which can be used to further identify similar innovations), and other related trends.
In addition, it features a competitive benchmarking and valuation analysis of the IP documents published in this field of innovation, taking into account important parameters, such as type of IP document, year of application, time to expiry, number of citations and jurisdiction (factoring in regional GDP).
Section III describes a systematic approach to identifying relevant areas of innovation by analyzing published IP documents, defining the uniqueness patented / patent pending innovations, understanding the scope of patentability in this domain, and pinpointing jurisdictions where new and / or modified claims may be filed without infringing on existing IP.
Section IV provides a detailed summary of the patent applications that were filed across different jurisdictions and their relative value in the IP ecosystem. The analysis segregates the intellectual capital in terms of area of innovation and intended applications, thereby, offering the means to understand key areas of research and identify innovation-specific IP filing trends.
Section V is an elaborate summary of the granted patents across different jurisdictions and their relative value in the IP ecosystem. The analysis uses a slightly more specific segregation criteria, based on type of product / solution and intended applications; this offers the means to identify unique innovations that presently have marketing exclusivity and explore future opportunities to enter into promising product markets, once their patents expire.
It includes an insightful analysis of the various CPC symbols mentioned in the published IP literature used and their affiliated families, in order to identify historical and existing pockets of innovation (based on the functional area / industry described by the elaborate and systematic system of classifying IP); the analysis also features a discussion on the prevalent white spaces (based on CPC symbols) in this area of research.
Section VI offers an informed perspective on the IP filing and grant trends during the COVID-19 pandemic, when the demand for automating healthcare services was at its peak. Further, it provides insights on anticipated developments and trends that are likely to shape the future of the AI in healthcare market.
Contents
Excel Deliverable
1. Data Collection Guide
2. Summary Dashboard
A. Overall Patent Landscape
B. Key Prior Art Search Expressions
C. Overall Valuation Analysis
D. Patentability and Freedom to Operate
E. Key Trends related to Patent Applications
F. Key Trends related to Granted Patents
G. Impact on COVID-19 Pandemic on IP Filing / Grant
3. Overall Intellectual Property Landscape Dataset
4. Prior Art Search Expressions
5. Patent Applications Dataset
6. Granted Patents Dataset
7. Claims Analysis
8. Patentability and Freedom to Operate Dataset
9. Impact of COVID-19 Pandemic Dataset
10. Appendix I: Pivot Tables
11. Appendix II: Country / Geography Codes
PowerPoint Deliverable
1. Context
2. Project Approach
3. Project Objectives
Section I
4. Executive Summary
Section II
5. Healthcare Applications of Artificial Intelligence and Affiliated Solutions
5.1. Overview
5.2. Popular Technologies and Solutions
5.2.1. Watson (IBM)
5.2.2. DeepMind (DeepMind Technologies, a Subsidiary of Alphabet)
5.2.3. Other Intuitive Software / Technologies
5.3. Concluding Remarks
6. Overall Intellectual Property Landscape
7. Analysis of Prior Art Search Expressions
8. Intellectual Property Valuation Analysis
Section III
9. Patentability and Freedom to Operate
Section IV
10. Analysis of Patent Applications
10.1. Overview
10.2. Relative Valuation of Patent Applications
10.3. Analysis of Patent Applications
10.3.1. Analysis by Central and Peripheral IP Documents
10.3.2. Analysis by Legal Status
10.3.3. Analysis by Key Innovation Areas and Applications
10.4. Concluding Remarks
Section V
11. Analysis of Granted Patents
11.1. Overview
11.2. Relative Valuation of Granted Patents
11.3. Analysis of Granted Patents
11.3.1. Analysis by Central and Peripheral IP Documents
11.3.2. Analysis by Legal Status
11.3.3. Analysis by Key Innovation Areas and Applications
11.4. Key Applicants & Innovator Profiles
11.5. Claims Analysis
11.6. Concluding Remarks
12. Pockets of Innovation and White Spaces
Section VI
13. Impact of COVID-19 Pandemic
14. Conclusion and Future Outlook
15. Appendices
The following companies and organizations have been mentioned in the report.
PRICING DETAILS
Discounts available for multiple report purchases
sales@rootsanalysis.com
YOU MAY ALSO LIKE
SUBSCRIBE TO INDUSTRY UPDATES