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Every year, over 250,000 patents are filed in the US alone. Companies, especially in the high-tech sector, fiercely protect their intellectual property. They employ teams of lawyers to ensure their ideas are safeguarded, even if these patents never materialize into actual products. The cost-benefit analysis of these patents would likely reveal some surprising insights for these companies.

The Fear of Missing Out and Patent Hoarding

The fear of being left behind drives this patent-hoarding mentality. Companies worry that if they introduce a feature like a 'Click' button, someone else might have already patented it. As a result, many patents end up like cash hidden under a mattress—depreciating assets with no returns. Although the patent office could address this issue, AI has the potential to help organizations unlock the untapped value of these dormant patents.

AI as a Key Player in Innovation Harvesting

AI models should become a crucial part of an innovation harvesting framework, identifying ideas worth patenting. Multi-variate hypothesis testing can assist in several ways:

• Creating Probabilistic Models: AI can predict the success of products, helping companies make informed decisions.
• Identifying Differentiators: It can pinpoint innovations that provide a competitive edge for existing products.
• Proposing New Products: By analyzing the company's knowledge and patent repository, AI can suggest new product ideas.

Structured vs. Unstructured Innovation

During my time at Intel, I managed various initiatives focused on knowledge and idea harvesting. My experience has shown that structured innovation yields significantly better results than unstructured approaches. Observing from the outside, this structured approach seems to be a key difference between how Amazon and Google handle innovation and new ideas.

By integrating AI into the innovation process, companies can unlock the hidden potential of their patents, drive meaningful differentiation, and propose successful new products.