Most of us know how Intelligent Automation is already changing the way workflows are accomplished. And as an overall perspective, AI has permanently changed several things already. From a simple example of content scheduling on social media sourced from GenAI (queried through Intelligent Automation); to the complex designs, schemas, structures, methods, protocols etc which belong to the pre-AI era - the harsh truth is that everything is vulnerable to change. An IPv4 related patent may sound obsolete if an AI layer is embedded into the architecture. So where's the goldmine?

First things first - startups and corporates are in a favourable position to define KPIs like 'Total Patents filed in FY25' for at least 5 or more employees regardless of your overall size. My analysis says, as per the current conditions; globally there is a scope for 1000s of AI-linked patents in the upcoming years. That might help you to set the right targets for the KPIs. Filing patents ought to be a compulsive KPI for your R&D teams in this molten but already-solidifying AI era. Several top notch companies have already been GRANTED patents in 2024. The race is still on - and thankfully the AI-related patent landscape is still green. 

A tweaked up existing-patent might as well qualify for a new patent if at all the existing-patent is fulfilled without any AI-intervention but you think that AI-intervention is mandatory / makes it better.

A quick and short compilation on areas where patents could be fading:

1. Data Processing and Analysis: Patents related to traditional methods of data processing and analysis could become less valuable as AI algorithms become more efficient and accurate in handling large datasets.
2. Predictive Analytics and Forecasting: Patents covering manual or rule-based predictive analytics and forecasting methods may become obsolete as AI models can often outperform traditional approaches by analyzing vast amounts of data and detecting complex patterns.
3. Image and Video Recognition: Patents related to image and video recognition technologies might become outdated as AI-powered computer vision systems surpass older methods in accuracy and speed.
4. Natural Language Processing (NLP): Patents for linguistic analysis and natural language processing techniques might become less relevant as AI-driven NLP models improve in understanding and generating human language.
5. Medical Diagnosis and Healthcare Technologies: Patents for medical diagnosis methods and healthcare technologies could be affected as AI systems demonstrate superior performance in interpreting medical images, diagnosing diseases, and suggesting treatments.
6. Financial Analysis and Trading: Patents covering traditional financial analysis and trading strategies may become less valuable as AI algorithms increasingly dominate in high-frequency trading and investment decision-making.
7. Manufacturing and Robotics: Patents for conventional manufacturing processes and robotics technologies might become outdated as AI-driven automation systems optimize production processes and enhance efficiency.
8. Search Engines and Information Retrieval: Patents related to traditional search engine algorithms and information retrieval techniques may lose relevance as AI-powered search engines improve in understanding user intent and delivering more accurate search results.
9. Speech Recognition and Synthesis: Patents covering older speech recognition and synthesis technologies could become less significant as AI-powered speech recognition systems achieve higher accuracy rates and better understand natural language nuances.
10. Transportation and Autonomous Vehicles: Patents for conventional transportation systems and vehicle control methods may become obsolete as AI advances enable the widespread adoption of autonomous vehicles and intelligent transportation systems.

Wishing all intellects the best of outcomes as you wildly foray into the grazing zones.