Big Data Patent Informatics for Strategic Decision Making
Lead Research Organisation:
University of Cambridge
Department Name: Engineering
Abstract
Big data is increasingly available in all areas of manufacturing and operations. Data as such presents value for enabling a competitive, data-driven economy (EPSRC's Delivery Plan 2016 "Connectedness"), which is at the heart of the Internet of things and Industry 4.0. Increased data availability presents potential value for better decision making and strategy development, to introduce the next generation of innovative and disruptive technologies and drive business innovation through digital transformation (EPSRC's Area of "Connected Nation").
Over the last two decades, there has been a large development in the field of IP analytics. With the digitization of patent data, the world's largest repository of technical information has become accessible for rapidly decreasing costs. Several analytical techniques for analysing this data have been developed (e.g. citation networks, landscape maps and recently semantic analyses). Integrating IP data from different sources, obviously provides more reliable insights, while democratizing IP data making it accessible to a broader range of stakeholder, such as start-ups, SMEs, university technology transfer offices, but also individual researchers.
While IP data is abundantly available and tools have been developed to run the analytics, for many manufacturing firms it still remains a problem how they can create value from IP data (Cambridge Big Data Initiative - "Making Big Data Work" theme). Firms struggle to decide which tools and techniques to use for supporting which decisions in the different stages of technology and innovation development projects. This project aims to contribute to solving this problem: How can UK manufacturing firms better create value from IP data, and help them in making decisions about emerging technologies?
The research project contributes to solving this problem in three ways:
1. Firstly, we will develop a framework that helps manufacturing firms to understand how they can benefit from IP data based decision making in innovation and technology development projects (EPSRC's Area of "Manufacturing the Future").
2. Secondly, we will provide guidance on how manufacturing firms can integrate the use of IP data in the decision making process of innovation and technology development projects (EPSRC's Delivery Plan 2016 of "Productivity", for the successful development of world-leading technology based processes on the discovery and innovation). Through the digital transformation (use of IP analytics), to guide the decision making process for development of disruptive technologies, quantifying uncertainty and value creation/ generation (EPSRC's Area of "Productive Nation", and specifically "data driven economy").
3. Thirdly, we will develop a decision making framework that links the decision needs of manufacturing firms with the availability of IP data, analytical techniques, indicators and tools (EPSRC's Delivery Plan 2016 of "Resilience"). The project aims at helping manufacturing firms to increase their knowledge on IP informatics, and feed IP data into decision making processes along technology and innovation project for increased value creation and capture.
The project will deliver both academic and practical outcomes. With conference and journals publications (e.g. in R&D Management, Creativity and Innovation Management, World Patent Information) the project will contribute to both the discussion on effective decision making in technology and innovation development projects and the exploration of using IP analytics to help shape the firm landscape. The project will further create industrial impact by helping manufacturing firms to use IP data, contributing to patent analytics by higher R&D productivity and data-driven economical decisions, for better strategic decision making in technology and innovation development projects. This project will help UK based firms to strengthen their IP management and IP analytics capabilities, hence to improve p
Over the last two decades, there has been a large development in the field of IP analytics. With the digitization of patent data, the world's largest repository of technical information has become accessible for rapidly decreasing costs. Several analytical techniques for analysing this data have been developed (e.g. citation networks, landscape maps and recently semantic analyses). Integrating IP data from different sources, obviously provides more reliable insights, while democratizing IP data making it accessible to a broader range of stakeholder, such as start-ups, SMEs, university technology transfer offices, but also individual researchers.
While IP data is abundantly available and tools have been developed to run the analytics, for many manufacturing firms it still remains a problem how they can create value from IP data (Cambridge Big Data Initiative - "Making Big Data Work" theme). Firms struggle to decide which tools and techniques to use for supporting which decisions in the different stages of technology and innovation development projects. This project aims to contribute to solving this problem: How can UK manufacturing firms better create value from IP data, and help them in making decisions about emerging technologies?
The research project contributes to solving this problem in three ways:
1. Firstly, we will develop a framework that helps manufacturing firms to understand how they can benefit from IP data based decision making in innovation and technology development projects (EPSRC's Area of "Manufacturing the Future").
2. Secondly, we will provide guidance on how manufacturing firms can integrate the use of IP data in the decision making process of innovation and technology development projects (EPSRC's Delivery Plan 2016 of "Productivity", for the successful development of world-leading technology based processes on the discovery and innovation). Through the digital transformation (use of IP analytics), to guide the decision making process for development of disruptive technologies, quantifying uncertainty and value creation/ generation (EPSRC's Area of "Productive Nation", and specifically "data driven economy").
3. Thirdly, we will develop a decision making framework that links the decision needs of manufacturing firms with the availability of IP data, analytical techniques, indicators and tools (EPSRC's Delivery Plan 2016 of "Resilience"). The project aims at helping manufacturing firms to increase their knowledge on IP informatics, and feed IP data into decision making processes along technology and innovation project for increased value creation and capture.
The project will deliver both academic and practical outcomes. With conference and journals publications (e.g. in R&D Management, Creativity and Innovation Management, World Patent Information) the project will contribute to both the discussion on effective decision making in technology and innovation development projects and the exploration of using IP analytics to help shape the firm landscape. The project will further create industrial impact by helping manufacturing firms to use IP data, contributing to patent analytics by higher R&D productivity and data-driven economical decisions, for better strategic decision making in technology and innovation development projects. This project will help UK based firms to strengthen their IP management and IP analytics capabilities, hence to improve p
Organisations
People |
ORCID iD |
| Frank Tietze (Primary Supervisor) | |
| Leonidas Aristodemou (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509620/1 | 01/10/2016 | 30/09/2022 | |||
| 1732290 | Studentship | EP/N509620/1 | 01/10/2016 | 30/09/2020 | Leonidas Aristodemou |
| Description | In this research, we contribute in the growing literature on structuring the fuzzy front end of innovation and enhancing the technology development stage gate processes to include IP data. By using applied machine learning to classify technology projects for strategic decision making, and increase the transfer of beneficial and high impact innovations to the society. This methodology also improves the quality and validity of patents that are granted, as it benchmarks a potential application before applying for a patent with valid granted patents. This is also a contribution to the growing literature on standardising and improving the patent data quality for the patent offices and firms. We predict technological value for patents at the early stage technology development process, which improves the innovation output. We have also identified that there is the possibility that the technical language within the patent text itself can be used to predict technological value and subsequently economic value of patents (which is the purest disclosure of early stage technologies). In addition, the work on the future of patent analytics contributes to expanding the field of patent analytics for more effective exploitation of the largest worldwide repository of technological information to enable new use cases, supporting better decision-making and partnerships of R&D pursuing organisations. We have developed a public technology roadmap to facilitate the collaboration and coordinated action of actors in the patent analytics community to further develop the capabilities for analysing patent data. We have identified 16 technologies that can contribute to the field, together with 15 complementary technologies. Moreover, 21 enablers have been identified, which play an important and equal role in the domain, and are classified under the themes of technology development cycle/methodologies, legislation, training/continuous professional development and cooperation. The research findings are still under development since the award is active. This particular research stream of applications of data science to technology management, and the implications this has on the intellectual property domain has sparked a large interest. The trained deep learning models predict with a high accuracy the technological value of technologies (modelled as patents), in 3 time domains (year 4, 8 and 12), and over a large number of outputs (citations, generality, renewals, grant lag). |
| Exploitation Route | - The technology roadmap can be used to facilitate discussions on the policy level on how to improve the international patent and IP systems. - The valuation methodologies can be used to evaluate early stage technology projects and thus improving the innovation output that goes to society, and strategic decision making within firms. - The valuation methodologies can also be used to evaluate the patent quality, as well as in the future predict the monetary value of patents, contributing in the intangible asset valuation. The models can further be developed to take other types of data, which will allow for holistic valuation of intellectual property, and discovery of valuable patents early. This could prove very beneficial for governments, which can tailor public policy to involve those valuable technologies, and aspects surrounding them (ethic, accessibility etc.) |
| Sectors | Aerospace, Defence and Marine,Agriculture, Food and Drink,Chemicals,Communities and Social Services/Policy,Construction,Creative Economy,Digital/Communication/Information Technologies (including Software),Electronics,Energy,Environment,Financial Services, and Management Consultancy,Healthcare,Government, Democracy and Justice,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology,Security and Diplomacy |
| Description | The initial findings on the report of the Future of Patent analytics have sparked an interest in the wider policy community (WIPO, EPO), to put forward some of the suggestions. There is a large stream of work under way to improve the data quality, data interconnectedness, data analysis for patent data. In addition, the further development of the early technology valuation models can be used to tailor the strategic decision making of firms to tailored high valued innovation outputs to the society. The findings are still underway as the award is in progress, but some of the methodologies have sparked a large number of interest through the Strategic Technology and Innovation Management (STIM) consortium. Also, I have been invited to give high profile presentations on the potential impact of the research at the European Patent Office (EPO), World Intellectual Property Organisation (WIPO), European Industrial Research Management Association (EIRMA). I have attended a large number of conferences, R&D Management, EPIP, Data for Policy, where the particular research has sparked a large number of discussions. |
| Sector | Aerospace, Defence and Marine,Chemicals,Construction,Digital/Communication/Information Technologies (including Software),Electronics,Energy,Environment,Financial Services, and Management Consultancy,Healthcare,Government, Democracy and Justice,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology |
| Impact Types | Economic,Policy & public services |
| Description | EIRMA 2017 |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | I have been invited as a speaker to the special interest group on Big Data Analytics and Artificial Intelligence at the European Industrial Research Management Association (EIRMA), where it is attended by high calibre professionals, namely CTOs and executive managers. I have presented my research and we discuss future expansions of my models and case study applications |
| Year(s) Of Engagement Activity | 2017 |
| URL | https://www.eirma.org/content/big-data-analytics-and-artificial-intelligence |
| Description | EPO 2018 |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Policymakers/politicians |
| Results and Impact | I have been invited to attend the European Patent Office in Austria, to present and discuss my research. This interest and subsequent talks have emerged after the publication of the report on the Future of Patent Analytics. Discussions in data structures and data management were very important for shaping my analysis points and influencing some of the policy and discussions the EPO has on the global level. |
| Year(s) Of Engagement Activity | 2018 |
| Description | Emerson 2018 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Invited as a speaker for Emerson Technology to give a global webinar on my research |
| Year(s) Of Engagement Activity | 2018 |
| Description | Pat-Tech Exchange 2017 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | I have attended as a speaker at the Pat-Tech Exchange, and presented research on Building IP awareness from technology strategic management perspective, and preliminary research findings |
| Year(s) Of Engagement Activity | 2017 |
| Description | STIM 2018 |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | Presented my research at the Strategic Technology and Innovation Management consortium 2018, throughout the year and got feedback from industry and opportunity to have focus groups/ interviews/ case studies with the industry. |
| Year(s) Of Engagement Activity | 2018 |
| URL | https://www.ifm.eng.cam.ac.uk/research/ctm/stim-2018/ |
| Description | STIM 2019 |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | Presented my research at the Strategic Technology and Innovation Management consortium 2019, throughout the year and got feedback from industry and opportunity to have focus groups/ interviews/ case studies with the industry. |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://www.ifm.eng.cam.ac.uk/research/ctm/stim/ |
| Description | The Alan Turing Institute 2018 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Postgraduate students |
| Results and Impact | Invited to present my research on the Enrichment day at the Alan Turing Institute in London. |
| Year(s) Of Engagement Activity | 2018 |
| Description | WIPO 2019 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Policymakers/politicians |
| Results and Impact | Invited to attend the release of the World Intellectual Property Organisation technology trends report on Artificial Intelligence. We have done work in the past with the knowledge division team (Future of Patent Analytics) and this invite was a result of that |
| Year(s) Of Engagement Activity | 2019 |