Advertisement

The Role of Innovation and IP in AI-Based Business Models

  • Martin A. BaderEmail author
  • Christian Stummeyer
Chapter
Part of the FGF Studies in Small Business and Entrepreneurship book series (FGFS)

Abstract

We give insights into proprietary and open innovation approaches that are applied in artificial intelligence (AI)-based business models. Starting with the historical emergence of AI, we present the state of the art of innovation structures in AI applications and AI-based business models. Finally, we elaborate on the role of intellectual property (IP) with a special focus on patents, analyzing patenting data, and the top AI patentees: corporations, research organizations, and top patenting AI start-ups. We conclude with our own model of formal and informal protection strategies applied in AI-based business models and how to balance open and proprietary innovation with a focus on entrepreneurship and start-ups.

Keywords

Artificial intelligence Business models Innovation management Intellectual property Patents Open innovation Proprietary innovation Bought control model Free public commons model Startups Entrepreneurship 

List of Abbreviations

AI

Artificial Intelligence

CII

Computer-Implemented Inventions

EPC

European Patent Convention

EPO

European Patent Office

GDPR

General Data Protection Regulation

IP

Intellectual Property

USPTO

United States Patent and Trademark Office

Notes

Acknowledgements

As the basis for the AI intellectual property rights and patent data, as well as for some AI technology trend statements, we have relied on data of the just published report of the World Intellectual Property Organization “WIPO Technology Trends 2019: Artificial Intelligence.” We have clearly indicated if changes were made to the original content.

We also acknowledge the support given by Ms. Karin Klöti, consultant at BGW AG, St. Gallen.

Glossary4

Banking and finance

Machine learning is already deeply integrated into many aspects of financial systems, from the approval of loans to the management of assets and the assessment of risks. Automated trading systems involve the use of complex AI algorithms to make extremely fast trading decisions. Modern fraud detection systems actively learn new potential security threats. AI is predicted to have an impact on financial customer services in the future, with specialized chatbots and voice assistants and recommendation systems for financial products and for improving safety by exploiting advances in biometric systems.

Computer vision

An interdisciplinary field that deals with how computers see and understand digital images and videos. Computer vision spans all tasks performed by biological vision systems, including “seeing” or sensing a visual stimulus, understanding what is being seen, and extracting complex information into a form that can be used in other processes.

Computer-implemented inventions (CII)

Computer-implemented inventions are treated differently by patent offices in different regions of the world. In Europe, Article 52 of the European Patent Convention (EPC) excludes computer programs “as such” from patent protection. This exclusion does not mean that all inventions involving software are excluded from patenting; what it does mean is that tighter scrutiny of the technical character of these inventions is required. Over the years, the case law of the EPO boards of appeal has clarified the implications of Article 52 EPC, establishing a stable and predictable framework for the patentability of computer-implemented inventions. Like all other inventions, in order to be patentable, computer-implemented inventions must meet the fundamental legal requirements of novelty, inventive step, and industrial application. In addition, it must be established that they have a technical character that distinguishes them from computer programs “as such.” In other words, they must solve a technical problem in a novel and non-obvious manner. The normal physical effects of the execution of a program, e.g., electrical currents, are not in themselves sufficient to lend a computer program technical character, and a further technical effect is needed. The further technical effect may result, for example, from the control of an industrial process or the working of a piece of machinery, or from the internal functioning of the computer itself (e.g., memory organization, program execution control) under the influence of the computer program. The EPC thus enables the EPO to grant patents for inventions in many fields of technology in which computer programs make a technical contribution. Such fields include medical devices, the automotive sector, aerospace, industrial control, communication/media technology, including automated natural language translation, voice recognition and video compression, and also the computer/processor itself.

Copyright

Copyright laws grant authors, artists, and other creators protection for their literary and artistic creations, generally referred to as “works.” A closely associated field is “related rights” or rights related to copyright that encompass rights similar or identical to those of copyright, although sometimes more limited and of shorter duration. The beneficiaries of related rights are (a) performers (such as actors and musicians) in their performances, (b) producers of phonograms (for example, compact discs) in their sound recordings, and (c) broadcasting organizations in their radio and television programs. Works covered by copyright include, but are not limited to, novels, poems, plays, reference works, newspapers, advertisements, computer programs, databases, films, musical compositions, choreography, paintings, drawings, photographs, sculpture, architecture, maps, and technical drawings.

Deep learning

A machine learning approach that tries to understand the world in terms of a hierarchy of concepts. Most deep learning models are implemented by increasing the number of layers in a neural network.

Distributed AI

Systems consisting of distributed, multiple autonomous learning agents which process independently data and provide partial solutions which are then integrated, through communication nodes connecting the individual agents. Distributed AI systems can by design aim at solving complex learning and decision-making tasks, involving large datasets and requiring high computational power.

Document management and publishing

Over the past two decades, AI has been continuously improving automatic data extraction, structuring, and conversion of documents (including automatic translation). Improved document clustering and advanced data analytics are expected to better exploit the huge volume of documents that exist. AI-powered document management systems could also enhance security and protect customer data.

Expert system

A computer system that solves complex problems within a specialized domain, usually requiring a high level of human intelligence and expertise. This expertise is expressed manually by human experts in the form of a set of rules which are simple logical tests.

Fuzzy logic

A decision-making approach which is not based on the usual “true or false” assessment, but rather on “degrees of truth” (where the “true” value ranges between completely true and completely false). Fuzzy logic relies on the principle that people make decisions based on imprecise and non-numerical information.

Industrial Design Right

Refers to the ornamental or aesthetic aspects of an article. A design may consist of three-dimensional features, such as the shape or surface of an article, or two-dimensional features, such as patterns, lines, or color. Industrial designs are applied to a wide variety of industrial products and handicrafts: from technical and medical instruments to watches, jewelry, and other luxury items; from house wares and electrical appliances to vehicles and architectural structures; from textile designs to leisure goods. To be protected under most national laws, an industrial design must be new or original and nonfunctional. This means that an industrial design is primarily of an aesthetic nature, and any technical features of the article to which it is applied are not protected by the design registration. However, those features could be protected by a patent.

Industry and manufacturing

AI is likely to have major impact on industry and manufacturing. Predictive maintenance is expected to limit costs related to unplanned downtime and malfunction. AI algorithms should also help companies to cope with the increasing complexity of products, engineering processes, and quality regulations. Improved robots are expected to handle more cognitive tasks and make autonomous decisions. Generative design systems are able to quickly generate, explore, and optimize design alternatives from a set of high-level design goals. Continuous monitoring of the market by AI tools could help proactively to optimize staffing, inventory, energy consumption, and the supply of raw materials.

Knowledge representation and reasoning

The field dedicated to representing information about the world usable by a computer to solve complex tasks. These representations are usually based on the way humans represent knowledge, reason (for instance, through rules and building relations of sets and subsets), and solve problems.

Life and medical sciences

Automatic diagnostic systems are a very promising application of new machine learning techniques. Recent results have shown that it is possible to surpass human expert accuracy for several narrow tasks, such as detection of melanoma or risks of atherosclerosis in arteries. Drug personalization is also frequently cited as a key marker of progress driven by AI. The availability of large amounts of clinical data mean AI is predicted to improve drug discovery and reduce development costs by helping select the most promising hypotheses and focus on more targeted research.

Logic programming

Uses facts and rules to make decisions, without specifying additional intermediary steps, in order to achieve a particular goal.

Machine learning

An AI process that uses algorithms and statistical models to allow computers to make decisions without having to explicitly program it to perform the task. Machine learning algorithms build a model on sample data used as training data in order to identify and extract patterns from data and therefore acquire their own knowledge. A typical example is a program that identifies and filters spam email.

Natural language processing

Use of algorithms to analyze human (natural) language data so that computers can understand what humans have written or said and further interact with them.

Neural network

A learning process inspired by the neural structures of the brain. The network is a connected framework of many functions (neurons) working together to process multiple data inputs. The network is generally organized in successive layers of functions, each layer using the output of the previous one as an input.

Object tracking

The process of locating one or more moving objects over time in a video.

Ontology engineering

A set of tasks related to the methodologies for building ontologies, namely the way concepts and their relationship in a particular domain are formally represented.

Patent

An exclusive right granted for an invention—a product or process that provides a new way of doing something, or that offers a new technical solution to a problem. A patent provides patent owners with protection for their inventions. Protection is granted for a limited period, generally 20 years.

Planning/scheduling

The realization of strategies or action sequences for execution by intelligent agents, such as autonomous robots and unmanned vehicles.

Predictive analytics

The process of making predictions about future or otherwise unknown events using a variety of statistical techniques to analyze current and historical facts.

Probabilistic reasoning

An AI approach which combines deductive logic and probability theory to model logical relations under uncertainty in data.

Robotics

The design, construction, and operation of machines able to follow step-by-step instructions or perform complex actions automatically and with a certain level of autonomy. Robotics combines hardware with the implementation of AI techniques to perform these tasks. Agents which process independently data and provide partial solutions which are then integrated, through communication nodes connecting the individual agents. Distributed AI systems can by design aim at solving complex learning and decision-making tasks, involving large datasets and requiring high computational power.

Security

Cyber-security (spam filtering, intrusion-detection) has benefited from machine learning since the 1990s. Automated surveillance is developing quickly, sometimes in conjunction with smart city technologies. AI techniques such as face detection, behavior, and crowd analysis are mature enough to make surveillance cameras more “active” without the need for human supervision. Predictive policing technology has started to be used in several US states and the UK and AI techniques are also integrated in mass surveillance programs. AI is also considered as a new enabler for a vast range of military requirements, including intelligence, surveillance, reconnaissance, logistics, battlefield planning, weapons systems, and defense/offense decisions.

Speech processing

Systems involving analysis of speech signals, including speech recognition, natural language processing, and speech synthesis.

Speech recognition

The process of identifying words in spoken language and of translating them into text.

Speech synthesis

The artificial production of human speech.

Supervised learning

The most widely adopted form of machine learning. In supervised learning, the expected grouping of the information in certain categories (output) is provided to the computer through examples of data (input) which have been manually categorized correctly and form the training dataset. Based on these examples of input–output, the AI system can categorize new, unseen data into the predefined categories.

Telecommunications

AI is expected to drive new opportunities in telecoms by helping to improve network performance, thanks to anomaly detection and prediction of service degradations, and also by optimizing customer services.

Trade Secret

Any confidential business information which provides an enterprise a competitive edge may be considered a trade secret. Trade secrets encompass manufacturing or industrial secrets and commercial secrets. The unauthorized use of such information by persons other than the holder is regarded as an unfair practice and a violation of the trade secret. Depending on the legal system, the protection of trade secrets forms part of the general concept of protection against unfair competition or is based on specific provisions or case law on the protection of confidential information. The subject matter of trade secrets is usually defined in broad terms and includes sales methods, distribution methods, consumer profiles, advertising strategies, lists of suppliers and clients, and manufacturing processes. While a final determination of what information constitutes a trade secret will depend on the circumstances of each individual case, clearly unfair practices in respect of secret information include industrial or commercial espionage, breach of contract, and breach of confidence.

Trademark

A distinctive sign that identifies certain goods or services produced or provided by an individual or a company. Its origin dates back to ancient times when craftsmen reproduced their signatures, or “marks,” on their artistic works or products of a functional or practical nature. Over the years, these marks have evolved into today’s system of trademark registration and protection. The system helps consumers to identify and purchase a product or service based on whether its specific characteristics and quality—as indicated by its unique trademark—meet their needs.

Transportation

Fuzzy logic and other AI approaches have been used in transportation since the 1980s. It is widely predicted that autonomous vehicles will save costs, lower emissions, and enhance road safety and that AI will improve traffic management by reducing traffic jams and make possible crewless cargo ships and fully automated package delivery.

Unsupervised learning

A type of machine learning algorithm that finds and analyzes hidden patterns or commonalities in data that has not been labeled or classified. Unlike supervised learning, the system has not been provided with a predefined set of classes, but rather identifies patterns and creates labels/groups in which it classifies the data.

References

  1. Amara, N., Landry, R., & Traoré, N. (2008). Managing the protection of innovations in knowledge intensive business services. Research Policy, 37(9), 1530–1547.CrossRefGoogle Scholar
  2. Amit, R., & Zott, C. (2001). Value creation in e-business. Strategic Management Journal, 22(6/7), 493.CrossRefGoogle Scholar
  3. Arora, A., & Ceccagnoli, M. (2006). Patent protection, complementary assets, and firms incentives for technology licensing. Management Science, 52(2), 293–308.CrossRefGoogle Scholar
  4. Bader, M. A. (2007). Managing intellectual property in a collaborative environment: Learning from IBM. International Journal Intellectual Property Management, 1(3), 206–225.CrossRefGoogle Scholar
  5. Bader, M. A. (2008). Managing intellectual property in the financial services industry sector: Learning from Swiss Re. Technovation, 28, 196–207.CrossRefGoogle Scholar
  6. Bonakdar, A., Frankenberger, K., Bader, M. A., & Gassmann, O. (2017). Capturing value from business models: The role of formal and informal protection strategies. International Journal Technology Management, 73(4), 151–175.Google Scholar
  7. Brem, A., Maier, M., & Wimschneider, C. (2016). Competitive advantage through innovation: The case of Nespresso. European Journal of Innovation Management, 19(1), 133–148.CrossRefGoogle Scholar
  8. Buxmann, P., & Schmidt, H. (2019). Grundlagen der Künstlichen Intelligenz und des Maschinellen Lernens. In P. Buxmann & H. Schmidt (Eds.), Künstliche Intelligenz – Mit Algorithmen zum wirtschaftlichen Erfolg. Berlin: Springer.CrossRefGoogle Scholar
  9. Chen, F. (2019). The investors’ view. In WIPO (Ed.), WIPO technology trends 2019: Artificial intelligence (p. 105). Geneva: World Intellectual Property Organization.Google Scholar
  10. Chesbrough, H. (2007). Business model innovation: It’s not just about technology anymore. Strategy & Leadership, 35(6), 12–17.CrossRefGoogle Scholar
  11. Chesbrough, H., & Rosenbloom, R. S. (2002). The role of the business model in capturing value from innovation: Evidence from Xerox Corporation’s technology spin-off companies. Industrial and Corporate Change, 11(3), 529–555.CrossRefGoogle Scholar
  12. Chesbrough, H., Birkinshaw, J., & Teubal, M. (2006). Introduction to the research policy 20th anniversary special issue of the publication of ‘profiting from innovation by David J. Teece’. Research Policy, 35(8), 1091–1099.CrossRefGoogle Scholar
  13. Cohen, W. M., Nelson, R. R., & Walsh, J. P. (2000) Protecting their intellectual assets: Appropriability conditions and why US manufacturing firms patent (or not), No. w7552. National Bureau of Economic Research.Google Scholar
  14. Corea, F. (2017). Artificial intelligence and exponential technologies: Business models evolution and new investment opportunities. Cham: Springer.CrossRefGoogle Scholar
  15. Desyllas, P., & Sako, M. (2013). Profiting from business model innovation: Evidence from pay-as-you-drive auto insurance. Research Policy, 42(1), 101–116.CrossRefGoogle Scholar
  16. Dosi, G., Marengo, L., & Pasquali, C. (2006). How much should society fuel the greed of innovators? On the relations between appropriability, opportunities and rates of innovation. Research Policy, 35(8), 1110–1121.CrossRefGoogle Scholar
  17. EC. (2018). Artificial Intelligence: European strategy. Accessed March 01, 2019, from https://ec.europa.eu/jrc/sites/jrcsh/files/23112018-artificial_intelligence-huet_en.pdf
  18. EconSight. (2019). Künstliche Intelligenz. Globale Entwicklungen, Anwendungsgebiete, Innovationstreiber und Weltklasseforschung. Basel: EconSight.Google Scholar
  19. EPO. (2013). Patents for software? European law and practice. Munich: European Patent Office.Google Scholar
  20. EPO. (2017). Patents and the fourth industrial revolution. The inventions behind digital transformation. Munich: European Patent Office.Google Scholar
  21. EPO. (2018a). Patenting artificial intelligence. Conference summary. Munich: European Patent Office.Google Scholar
  22. EPO. (2018b). Guidelines for examination: Artificial intelligence and machine learning (G-II 3.3.1). Munich: European Patent Office.Google Scholar
  23. Ernst, H., & Omland, N. (2011). The patent asset index – A new approach to benchmark patent portfolios. World Patent Information, 33, 34–41.CrossRefGoogle Scholar
  24. Flaim, J. G., & Chae, Y. (2019). Subject-matter eligibility in the United States, Europe, Japan, China and Korea. In WIPO Technology Trends (Ed.), Artificial intelligence (p. 96). Geneva: World Intellectual Property Organization.Google Scholar
  25. Gallié, E. P., & Legros, D. (2012). French firms’ strategies for protecting their intellectual property. Research Policy, 41(4), 780–794.CrossRefGoogle Scholar
  26. Gassmann, O., & Bader, M. A. (2017). Patentmanagement: Innovationen erfolgreich nutzen und schützen (4th ed.). Berlin: Springer.CrossRefGoogle Scholar
  27. Gassmann, O., Frankenberger, K., & Csik, M. (2015). The business model navigator: 55 models that will revolutionise your business. Harlow: Pearson.Google Scholar
  28. Getsch, P. (2018). Künstliche Intelligenz für Sales, Marketing und Service. Wiesbaden: Springer.CrossRefGoogle Scholar
  29. Grosz, B. J., & Stone, P. (2018, December). A century long commitment to assessing artificial intelligence and its impact on society. Communications of the ACM (CACM).Google Scholar
  30. Hall, B. H., & Ziedonis, R. H. (2001). The patent paradox revisited: An empirical study of patenting in the US semiconductor industry 1979–1995. RAND Journal of Economics, 32(1), 101–128.CrossRefGoogle Scholar
  31. Harabi, N. (1995). Appropriability of technical innovations an empirical analysis. Research Policy, 24(6), 981–992.CrossRefGoogle Scholar
  32. Kerns, J. (2017). What’s the difference between weak and strong AI? Accessed March 01, 2019, from https://www.machinedesign.com/robotics/what-s-difference-between-weak-and-strong-ai
  33. Kumar, G. N. C. (2018). Artificial intelligence: Definition, types, examples, technologies. Accessed March 01, 2019, from https://medium.com/@chethankumargn/artificial-intelligence-definition-types-examples-technologies-962ea75c7b9b
  34. Lepak, D. P., Smith, K. G., & Taylor, M. S. (2007). Value creation and value capture: A multilevel perspective. Academy of Management Review, 32(1), 180–194.CrossRefGoogle Scholar
  35. Litzel, N. (2016). Was ist machine learning. Accessed March 01, 2019, from https://www.bigdata-insider.de/was-ist-machine-learning-a-592092/
  36. Mainzer, K. (2019). Künstliche Intelligenz – Wann übernehmen die Maschinen. Berlin: Springer.CrossRefGoogle Scholar
  37. McGahan, A. M., & Silverman, B. S. (2006). Profiting from technological innovation by others: The effect of competitor patenting on firm value. Research Policy, 35(8), 1222–1242.CrossRefGoogle Scholar
  38. Mills, M. (2016). Artificial intelligence in law: The state of play 2016 (Part 1). Accessed March 01, 2019, from http://www.legalexecutiveinstitute.com/artificial-intelligence-in-law-the-state-of-play-2016-part-1/
  39. Nguyen-Huu, D. (2018). Choose your own adventure: Which AI business model is right for you? Accessed March 01, 2019, from https://www.forbes.com/sites/valleyvoices/2018/12/06/which-ai-business-model-is-right-for-you/#31cc43906af7
  40. OECD. (2017). Key issues for digital transformation in the G20. Report prepared for a joint G20 German Presidency/OECD conference. Paris: Organisation for Economic Co-operation and Development.Google Scholar
  41. Pisano, G. (2006). Profiting from innovation and the intellectual property revolution. Research Policy, 35(8), 1122–1130.CrossRefGoogle Scholar
  42. Priem, R. L. (2007). A consumer perspective on value creation. Academy of Management Review, 32(1), 219–235.CrossRefGoogle Scholar
  43. Rich, E., Knight, K., & Shivashankar, B. (2009). Artificial intelligence (3rd ed.). New York: Tata McGraw-Hill Education.Google Scholar
  44. Rivette, K. G., & Kline, D. (2000). Discovering new value in intellectual property. Harvard Business Review, 79(1), 54–66.Google Scholar
  45. Šrámek, P. (2019). AI startups in Europe. In WIPO (Ed.), WIPO technology trends 2019: Artificial intelligence (p. 108). Geneva: World Intellectual Property Organization.Google Scholar
  46. Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., Hager, G., et al. (2016). Artificial intelligence and life in 2030. One hundred year study on artificial intelligence: Report of the 2015–2016 study panel, September 2016. Stanford, CA: Stanford University. Accessed September 6, 2016, from http://ai100.stanford.edu/2016-report
  47. techopedia. (2019). Weak artificial intelligence. Accessed March 01, 2019, from https://www.techopedia.com/definition/31621/weak-artificial-intelligence-weak-ai
  48. Teece, D. J. (1986). Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy. Research Policy, 15(6), 285–305.CrossRefGoogle Scholar
  49. Teece, D. J. (2006). Reflections on “profiting from innovation”. Research Policy, 35(8), 1131–1146.CrossRefGoogle Scholar
  50. Teece, D. J. (2010). Business models, business strategy and innovation. Long Range Planning, 43(2), 172–194.CrossRefGoogle Scholar
  51. USPTO. (2019). 2019 Revised patent subject matter eligibility guidance. Alexandria, VA: United States Patent and Trademark Office.Google Scholar
  52. WIPO. (2004). What is intellectual property? WIPO Publication No. 450(E). Geneva: World Intellectual Property Organization.Google Scholar
  53. WIPO. (2019a). WIPO technology trends 2019: Artificial intelligence. Geneva: World Intellectual Property Organization. The user is allowed to reproduce, distribute, adapt, translate and publicly perform this publication, including for commercial purposes, without explicit permission, provided that the content is accompanied by an acknowledgement that WIPO is the source and that it is clearly indicated if changes were made to the original content. “The Secretariat of WIPO assumes no liability or responsibility with regard to the transformation or translation of the original content.”Google Scholar
  54. WIPO. (2019b). What is a trade secret? Accessed March 01, 2019, from https://www.wipo.int/sme/en/ip_business/trade_secrets/trade_secrets.htm
  55. Zott, C., Amit, R., & Massa, L. (2011). The business model: Recent developments and future research. Journal of Management, 37(4), 1019–1042.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.THI Business SchoolTechnical University of IngolstadtIngolstadtGermany
  2. 2.BGW AG Management Advisory GroupSt. GallenSwitzerland
  3. 3.Stummeyer ConsultingMunichGermany

Personalised recommendations