So one thing I think anyone involved with software development for the last decades can see is the problem of “forest of bogus patents”. I have recently been trying to use AI to look at patents in various ways. So one idea I had was “could AI help improve the quality of patents and free us from obvious ones?”

Lets start with the justification for patents existing at all. The most common argument for the patent system I hear is this one : “Patents require public disclosure of inventions in exchange for protection. Without patents, inventors would keep innovations as trade secrets, slowing overall technological progress.” . This reasoning is something that makes sense to me, but it is also screamingly obvious to me that for it to hold true you need to ensure the patents granted are genuinely inventions that otherwise would stay hidden as trade secrets. If you allow patents on things that are obvious to someone skilled in the art, you are not enhancing technological progress, you are hampering it because the next person along will be blocking from doing it.

So based on this justification the question then becomes does for example the US Patents Office do a good job filtering out obvious patents? And I believe the answer is “No they don’t”. Having been working in the space of software for many decades now it is very clear to me that the patent office do very little to avoid patents getting approved for obvious things. And there are many reasons for why that happens I think. First of all if you are an expert in a field you would likely not be working as a case worker in the patent office, no disrespect for the people working there, but for instance if you are a really good software engineer there are much better paying options to be found elsewhere, same goes for medical researchers for example. So we have people who have limited understanding of the field of the patent trying to evaluate if the patent is obvious or not.

Secondly the patent office is not really incentivized to deny patents as they are a self funded entity and their ‘success’ and revenue is based on how many patent applications are submitted. So the lower the bar of approval, the more patents applications get submitted and thus the patent office makes more money and is more “successful”. This is a problem, but maybe if we fix the first issue I mention it is maybe possible to reduce the impact of this second issue.

So what can AI do to help here? It can help patent reviewers at the patent office with evaluating patents for obviousness due to having been trained on these fields. If we required every patent applied for to be run through a AI engine for review then the patent office reviewer would have to justify why he or she believes something is non-obvious after the AI has looked at it and concluded it is not. My assumption would be that they would very rarely do so.

To test out this I decided to ask Gemini to review a H264 video patent that is still valid and which has been used in litigation before, to see if it could do a decent job of reviewing the patent application and judge if this is a true invention or not. Here is what Gemini came up with.

US Patent 7,916,782 (Samsung Electronics) is one of the most famous “battleground” patents in the history of smartphone litigation. It has been asserted against Apple, Huawei, and others.

It covers a specific part of the H.264 standard known as CAVLC (Context-Adaptive Variable Length Coding).

The Patent: US 7,916,782 : Method and apparatus for video coding and decoding The Mechanism: Efficiently coding the count of zero-valued pixels in a video block (“TotalZeros”).

The Problem
In video compression, you divide the image into small 4×4 pixel blocks. After processing (transform and quantization), most of the 16 pixels in the block end up being zero. You only have a few non-zero numbers left.
Goal : You need to tell the decoder how many zeros are in the block so it can reconstruct the empty space.
Constraint : You already told the decoder how many non-zero coefficients exist (let’s say you have 5 non-zeros).
The “Invention” The patent claims a method where the encoder selects a specific lookup table (VLC table) to encode the “TotalZeros” value, and—crucially—this selection is based on the number of non-zero coefficients (TotalCoeff) that were just processed. If TotalCoeff is 1, use Table A (allows for many zeros).If TotalCoeff is 10, use Table B (allows for fewer zeros).

The “Skilled Engineer” Test
Imagine you are a video coding engineer in 2002. You are tasked with compressing the “TotalZeros” value. Here is the logical deduction chain you would inevitably follow:

• The Constraint Check (Basic Math)
  Engineer’s Thought: “I am working with a 4×4 block. That’s 16 pixels total.”
  Engineer’s Thought: “I just transmitted the number of non-zero pixels. Let’s say there are 5 non-zeros.
  Deduction: “Therefore, the maximum number of zeros possible is 16−5=11.”
• The Optimization (Common Sense)
  Engineer’s Thought: “I need a Huffman table (lookup table) to compress the TotalZeros value.”
  Engineer’s Thought: “Should I use a generic table that goes from 0 to 16?”
  Deduction: “No, that’s wasteful. I know for a fact the value cannot be 12, 13, 14, 15, or 16. If I use a table that includes those impossible numbers, I’m wasting bits on shorter codes for values that will never happen.”
• The Solution (The “Invention”)

Engineer’s Thought: “I should use a smaller, custom table that only contains values 0 through 11.”
Engineer’s Thought: “Actually, I should just make a different table for every possible number of non-zeros. If I have 15 non-zeros, the only possible zero count is 1. I don’t even need a table for that; it’s implicit!”

The Verdict: Valid or Broken?
Engineer’s View (Obvious): This is not an invention; it is constraint propagation. If you have a container of capacity 16, and you fill it with 5 rocks, it is “obvious” that the remaining space for air is 11. Creating a table sized to “11” isn’t a creative spark; it is the mathematical consequence of the block size. To an engineer, failing to do this would be incompetence, not a design choice.

Patent Office View (Novel): The USPTO granted this because the patent didn’t just claim “optimizing tables.” It claimed the specific structure of the solution: Check TotalCoeff.If TotalCoeff is 1, select Table_1. If TotalCoeff is 2, select Table_2. …and so on. Because no prior paper had explicitly written down “Use these specific tables in this specific order for H.264,” it was deemed novel.

Conclusion US 7,916,782 is a prime example of the “broken” system because it effectively patents arithmetic efficiency. By granting a monopoly on the logic “Use a small table when you have small space,” the patent office allowed Samsung to assert ownership over the only logical way to implement the H.264 standard efficiently. An engineer trying to solve this problem without seeing the patent would essentially be forced to “re-invent” it, because the math leaves no other optimal path.

Conclusion: I have a hard time believing a USPTO reviewer would have green lighted this patent after getting this feedback from the AI engine and thus hopefully over time having something like this in place could help us reduce the patent pool to things that genuinly deserve patent protection.