NEW: AI chatbot front-end to this web site built with Google NotebookLM (NBLM):

• https://notebooklm.google.com/notebook/b659034d-b640-4385-a572-e27ce2df4962/preview
• The chatbot takes ridiculously long to answer your first question, I think reflecting RAG initial chunking/indexing of docs. (TODO: see if a ChatGPT Custom GPT has the same problem — but looks like they support max only 20 docs?)
• You need to be logged in with your Google account.
• If the chatbot’s answers are too lengthy to be useful, you can change response length to “Shorter” by clicking the  Chat Config button.
• It’s way too ready to make unspoken inferences. If you ask something like “Tell me about examining open-source code like Chromium,” it will tell you all kinds of useful things about examining “open-source code including Chromium,” without noting that there’s nothing here explicitly about Chromium specifically. If you follow up by asking it, “is there actually anything in the sources specifically about Chromium? Or did you infer that? What are some specific open-source projects that are explicitly referenced here?” it will answer something like, “… the mention of Chromium was an inference on my part to provide a concrete, well-known example of a large, complex open-source project. There is no explicit mention of Chromium in the provided sources…. The sources do explicitly reference several other specific open-source projects: • EMACS • OpenBSD • KDE • GNOME • DeepSeek-V3 … • Spongy Castle cryptography library (also referred to as Bouncy Castle) … • LDAP (specifically, a directory of about 50 open-source C++ files related to LDAP) …,” etc.
• I asked the chatbot, “What are 10 good questions that someone could ask here?”:
  Here are 10 good questions that could be asked, drawing upon the various topics discussed in the provided sources:
  1. What are the primary purposes of claim charts in patent litigation, and how do they relate to the shift towards “plausibility” standards and mandatory disclosures?
  2. How do patent claims and their “limitations” define the boundaries of a patented invention within litigation, and what are the well-established tests for determining literal infringement versus infringement under the “Doctrine of Equivalents” (DoE)?
  3. What are the requirements for “plausibility” and “reasonable pre-filing investigation” when a patent owner initiates an infringement lawsuit, and why is reverse engineering often considered “required” for this purpose in software patent cases?
  4. What role do expert witnesses and non-testifying consultants play in software patent litigation, particularly concerning technical facts and infringement/invalidity analysis, and how is the reliability of their methodology assessed under Daubert criteria? [FRE 702]
  5. What is the gap between being able to interpret a neural network’s processing (e.g., using techniques like SHAP or LIME) and getting explanations that align with human mathematical thinking, and do hybrid AI approaches offer a promising way to bridge this gap?
  6. How can AI chatbots be effectively used and prompted to assist in the reverse engineering of binary executable files, and what challenges exist in getting them to proactively identify key features without human nudging, especially given tendencies to miss main points or “hallucinate”?
  7. What are the considerations and methodologies for analyzing source code or reverse-engineered code using AI models in a local, offline environment, particularly to ensure data confidentiality and compliance with court protective orders in litigation?
  8. Beyond traditional keyword-based searching, how can AI, particularly through the use of sentence embeddings and semantic similarity, assist in patent claim analysis for purposes such as identifying holes in technology landscapes or uncovering prior art?
  9. To what extent do AI systems genuinely “reason” or “introspect” into their own processes, as opposed to merely performing next-token prediction, and how should “chain of thought” outputs be interpreted in light of these underlying mechanisms and potential inaccuracies?
  10. What legal issues arise from the use of reverse-engineered software products as prior art in patent litigation, especially when the original source code was proprietary or licensing agreements prohibited reverse engineering?
• Not enough about source code in those proposed questions, so I asked “How about 5 more questions, relating to source code examination?”. The chatbot got a bit over-enthusiastic, and gave me answers as well as questions; here are the proposed questions:
  Here are 5 additional questions related to source code examination, drawing on the provided sources:
  1. Given the stringent protective orders (POs) typically imposed in software litigation, what specific restrictions do source code examiners commonly face, and how do these restrictions impact the methodologies and tools they can use for analysis, particularly regarding internet connectivity, note-taking, and comparisons with external codebases? …
  2. Beyond mere keyword searching, what are the sophisticated methodologies and “gotchas” involved in “close reading” and “tracing” source code in litigation, and how do these techniques address issues like implicit function calls, data flow, and the potential inaccuracy of programmer comments or naming conventions? …
  3. How do experts ensure the completeness and responsiveness of a source code production in discovery, and what methods are employed to correlate the produced source code with the actual commercial product to identify “dead code,” “latent code,” or missing files? …
  4. In what specific ways does source code examination for litigation differ from standard software development practices like code maintenance or security auditing, particularly concerning its focus on comparison, narrowly-framed technical questions, and external (outsider) perspective? …
  5. What role can Artificial Intelligence (AI) and Large Language Models (LLMs) play in assisting source code examination, including summarization, comparison, and identifying patterns, while addressing the critical challenges of data confidentiality and adherence to protective orders in a litigation setting? …
• I asked “What are some AI-specific questions I can ask here,” and it generated a truly enormous list of about 70 (!) proposed questions. (Umm, a little less enthusiasm, please!) A few selected ones:
  • How can AI models like Claude analyze numeric data, and is this ability a natural extension of next-token prediction or something new layered on top?
  • What unique skills do AI systems possess, particularly in associative pattern matching, that go beyond what a human or even traditional computational methods can achieve?
  • How can AI be used for “Code Mining” or “Big Code” to extract useful information from publicly accessible commercial software files, such as indexing contents for prior art searches or finding security vulnerabilities? …
  • How can AI help create checklists of issues to consider when comparing two pieces of similar code, or when analyzing binary executables? …
  • How can AI facilitate patent “landscaping” using patent claim sentence embeddings to map claims into a vector space and identify “holes” (unclaimed technological areas)?
  • How can Masked Language Modeling (MLM) be used to assess patent claim novelty or non-obviousness by testing the predictability of masked tokens? …
  • How well can LLMs answer questions about whether a particular code construct is unusual or common? …
  • How can we compare interpretability results against a baseline from random data to distinguish actual learning patterns from architectural artifacts? …
  • If AI can help us reverse engineer the software we create, how long will it be until we can effectively use AI to reverse engineer the thinking processes within other AI models, and what would that transparency reveal about the true nature of intelligence? …
  • Why is “attention” considered “all you need” in foundational papers, and how central is it to modern AI? …
  • What is the relationship between next-token prediction training and the emergence of logical abilities in LLMs? …
  • To what extent do AI chatbot answers about their own implementation reflect actual “introspection” versus generalized descriptions based on their training data? …
  • How do AI systems like Google Gemini examine, summarize, and compare source-code files, including heavily-obfuscated ones? …
  • How do AI systems handle disassembled and decompiled code listings, and how do they infer higher-level functionality (e.g., regression models) from low-level details without explicit mentions?
  • How can AI identify common software design patterns in binary form, and how might this be used for “Big Code” applications like finding prior art or security holes?
  • To what extent should one trust an AI’s explanation of how it performed a reverse engineering process, given its acknowledged lack of true introspection?
  • … etc., etc. (sheesh)
• NotebookLM (NBLM) generated an interesting “Briefing Doc” covering “AI Capabilities in Software Analysis and Reverse Engineering”, “Patent Litigation and Claim Charts”, and “General Principles of Reverse Engineering”. It’s useful, but before posting it, I’m going to see if I can get a better version that gives sufficient coverage to source code generally, and to non-patent software litigation.
• There are also NBLM-generated FAQ, Study Guide, and MindMap which similarly are useful but disproportionately covers AI on the one hand over source code on the other. NotebookLM can probably tell that I’m all about AI right now, but that doesn’t mean that’s primarily what the entire web site is about. I’ll either get more balanced versions of these documents by adding instructions before re-generating them, or I’ll post them as-is with a critique.
• NBLM-generated audio for SoftwareLitigationConsulting.com — somewhat strange discussion between two AI characters e.g. contrasting how LLMs think “in contrast to how we do”; I’ll soon try joining in on the Interactive Mode, and will soon post an annotated transcript as I did for the NBLM-generated audio on Patent Litigation`
• NBLM-generated video; I’ll soon post an annotated transcript, similar to the one I did for the NBLM-generated video on Patent Litigation
• Some sample chatbot answers to questions about software litigation and AI