New Methods, New Value: IP Analyses in M&A Processes

Nvidia, Microsoft, Apple, Alphabet, Amazon, Meta, Broadcom, Saudi Aramco, Tesla, TSMC—nine out of ten of the world’s most valuable companies are tech giants. This list makes it clear: Innovation is the key driver of value for companies in the 21st century. And this applies not only to large corporations in the U.S. or Asia, but also to small and medium-sized enterprises in the DACH region.

But how can innovative strength be measured and compared in the first place? Patents have clear advantages over other indicators in this regard:

• Patent information is compiled by independent third parties (patent examiners and patent offices) and maintained in public databases

• Patent information is available to private companies as well, regardless of disclosure requirements or company size

• Unlike R&D expenditures, patent applications represent not the input but the preliminary output of an innovation process—closer to products and revenue, and subject to a quality filter by the company, which bears the costs of the patent application

• In addition to the sheer quantity of applications or grants, patent databases offer insight into the technological direction a company or industry is taking

• Transparent quality indicators are now available

Patent documents are rich in information. At the corporate level, they reveal how much and how dynamically a company is investing in existing or new technologies. Aggregated statistics provide insight into which industry and technology trends are emerging regionally or globally.

For a long time, this type of innovation analysis was accessible only to IP professionals who were skilled at searching patent databases and understood the legal and technical language of patent documents. New statistical methods and AI applications now make patent databases usable for other professional groups as well, such as M&A advisors.

Application: Deal Sourcing

Innovation is also crucial for small and medium-sized enterprises. Family-owned businesses and SMEs seek to keep pace with—or expand their lead over—global competition through technology-driven acquisitions.

For technology-oriented mandates, it is often necessary to bring in specialists. However, specialized industry and technology knowledge is expensive and often not readily available. Patent-based innovation research offers a good alternative here. Modern tools use AI to translate the description of the sought-after technology into formal search terms for patent databases. This makes it easy to generate lists of companies that are actively filing patent applications in the relevant technology area. The advantages over traditional company databases with industry classifications are obvious:

1. The search becomes more targeted—the more specialized the technology, the better the results compared to traditional industry classifications.

2. The search identifies companies with relevant technology, even if they operate in industries that would not have been queried at all using a traditional search. The search scope expands beyond the well-trodden paths of suppliers and competitors.

3. In addition to standard filters for region and company size, a few indicators allow for an initial assessment of a company’s innovation strength.

Tech DD Application

Not all patents are created equal. While many inventions turn out to be insignificant over time, a select few contribute significantly to a company’s competitiveness. In fact, assessing the significance of a patented invention for a product, future products, or the value creation of the entire company is often a time-consuming, expensive process and frequently a source of debate between buyers and sellers.

However, with new methods of data analysis, the time-consuming manual process—which is usually carried out by IP specialists—can be made significantly more efficient and enriched with new insights. The quality of patented inventions can be assessed using indicators. These help identify particularly important and particularly weak patents in the portfolio. Algorithms generate relevant peer groups for benchmarking. The result:

• Time savings by focusing on relevant parts of the patent portfolio

• Risk alerts for particularly weak portfolios

• Risk alerts regarding a competitive situation that has deteriorated over time

The following examples illustrate the specific insights that even IP novices can derive from this:

Example 1: ParTec AG

ParTec AG, listed in the Scale segment of the stock exchange, attracted attention in early 2024. The company, which operates in the field of quantum computing, transferred its patents to a subsidiary and had them valued. The result: The approximately 150 patents were valued at 767 million euros. In the weeks that followed, the company’s market capitalization rose from 360 million to just under 1.2 billion euros.

The hard numbers behind the valuation paid by ParTec: The 150 patents were spread across just 11 inventions, only 4 of which were made in the previous 10 years. These relevant inventions, in turn, achieved an average Quant IP Quality Score of just 50 out of 100 points*. As of mid-September, ParTec AG’s market capitalization stands at less than 300 million euros—approximately 80 percent below the high reached following the patent valuation.

ParTec AG’s Patent Portfolio (as of January 2024)

Example 2: SoftBank Group

The Japanese tech holding company owned by billionaire Masayoshi Son is known for its aggressive investments in startups—with some spectacular successes and some write-offs amounting to several billion per investment. But this past spring, the company drew attention with a different figure: Son had employees’ ideas translated into patent applications with the help of AI. In a single month, SoftBank published 10,000 new patented inventions in one fell swoop, thereby multiplying the size of its patent portfolio.

However, the strategy of having an AI write thousands of AI-related patents is open to question. This is because, as the quantity increased, the quality of the patent portfolio declined:

Most recently, SoftBank announced plans to replace 20% of its employees with AI. Son seems confident that he has resolved the quality issue with the output of AI agents.

Conclusion: Patents are good indicators of innovation, the key driver of corporate value. Using new statistical methods and AI tools, M&A professionals can leverage information from patent databases to streamline processes and improve outcomes. Whether identifying relevant and new targets during deal sourcing or flagging risks during tech due diligence, IP analyses can transform from a cost center into a competitive advantage.

*The Quant IP Quality Score quantifies the quality of patented inventions across three dimensions: legal strength, technical quality, and product relevance. Quant IP’s proprietary methodology for measuring quality is technology-agnostic.