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

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


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.


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


Artificial Intelligence


Computer-Implemented Inventions


European Patent Convention


European Patent Office


General Data Protection Regulation


Intellectual Property


United States Patent and Trademark Office



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.


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 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.


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.


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.


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.


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.


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.


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.


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.


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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

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