Chatbook (using Google NotebookLM) for Patent Litigation book by Andrew Schulman

• This shared NotebookLM provides AI chat based on my extensive notes for a forthcoming book on Patent Litigation.
• https://notebooklm.google.com/notebook/811dfa64-7bb8-430a-ac60-42a62315ea49/preview
• 
• 
• I want an AI chatbook to play the role of an interactive book, based almost entirely on my rough notes, but without exposing those rough notes. To a reader, it should seem like the book has already been completed (not true) and that this chatbook will provide extensive answers based on what the book would say, mostly the way I would say it, but without exposing pieces of the book itself; typical LLM oracular pronouncements. “Talking Book” might be a good name, except I think Stevie Wonder and Ray Kurzweil already used that name for far far better things. Basically, this so-called “chatbook” has the effect of, “if this book actually existed, what would [might] it say?”
• Try asking something like “What are underlying purposes for claim charts?”, “How does collateral estoppel apply to patent invalidity?”, “Whats the book say about discovery during litigation?”, or “What are key cases on claim chart acceptability?”, or “The book seems to have a lot from scholars like romer, scotchmer, mokyr, jones, simon. What’s all that doing in a book on claim charts, source code, reverse engineering, etc.?”
• Quality of answers: sometimes quite subtle, nuanced; how much reflects NBLM/Gemini, vs. quality of my notes?; my notes very rough in some places xxx SHOW — quite amazing what NBLM doing from my notes, though of course must carefully read.
• The reader can configure the chat for longer or shorter responses, and for conversational style: analyst vs. guide vs. default vs. custom (reader can provide prompt instructions — I can preload, but 500 character max):
  
• Initial answers slow — answers are coming from the author’s uploaded files, not from LLM training; NBLM appears to answer ONLY from the uploaded hidden docs — but one version (NBLM or ChatGPT?) went out to internet to answer question about Tanner graph; note NBLM is RAG
• Once you start a chat, NotebookLM will suggest related follow-up questions.
• Or ask “What is the table of contents for the book?”, scan the reply for topics of interest, and then ask follow-up like “Give me a more detailed outline of chapter 5” (and keep in mind this is based on rough notes which are sometimes very rough), or “Drill down into the subsection titled “Claim Evolution and the Point of Novelty (PON)” — I’ll soon link here to AI-generated TOC, note differences from my draft TOC
• Or ask it, “What are 10 questions, specifically about patent litigation, that I can ask here?”; one possible reply you’ll receive (at least if you first configure chat settings for “Analyst” and for shorter answers):
  • “I can assist you as a thought partner by navigating the provided knowledge base on patent litigation, which includes detailed information on patent claims, limitations, infringement, invalidity, claim charts, Local Patent Rules, discovery, expert witnesses, and the strategic and economic aspects of patent disputes. I can help you quickly understand complex topics, find specific facts, apply relevant frameworks, and formulate well-supported recommendations. Here are 10 questions specifically about patent litigation that you can ask:
    1. What are the primary purposes of claim charts in patent litigation?
    2. How do patent claims and their “limitations” define the boundaries of a patented invention within litigation?
    3. What is “literal infringement,” and how does it differ from infringement under the “Doctrine of Equivalents” (DoE)?
    4. What are the primary grounds for challenging the “validity” of a patent in litigation, even after it has been granted by the PTO?
    5. How does “claim construction” (Markman hearing) impact the course and potential outcome of patent litigation?
    6. What are the requirements for “plausibility” and “reasonable pre-filing investigation” when a patent owner initiates an infringement lawsuit?
    7. What role do expert witnesses play in patent litigation, particularly concerning technical facts and infringement/invalidity analysis?
    8. How do Local Patent Rules (LPRs) influence the procedural requirements for infringement and invalidity contentions in federal district courts?
    9. What is the “multiplicity problem” in patent litigation, and what challenges does it present for litigants?
    10. What are the various private goals that motivate patent owners to engage in patent litigation, beyond simply protecting their products?
    To continue, we could start by delving into any of these questions, or you might want to explore how the strategic landscape of patent disputes impacts these individual aspects.”
• I’ll be showing sample interactions here, but want to be careful to always edit/comment, to avoid helping to train AI chatbots with their own self-regurgitated slop (“breathing their own fumes”) — “model collapse“
• More on the “chatbook” idea: must be careful to not include others’ writing in the sources, except for short paraphrases and small quotations
  • Basically a way to publish early versions of a book from author’s notes, without exposing the author’s notes, and letting readers get what they want from the book by asking an AI chatbot, which will synthesize likely material
  • Author must be careful to not include others’ writing in the sources, except for short paraphrases and small quotations, because otherwise this other material might get shown as if it were the author’s own
  • NotebookLM doesn’t know it’s a shared NotebookLM, and so may respond to a reader’s “What’s the book say about x?” with “Your notes for the book say…”, rather than “The notes for the book say…”; this can be fixed with prompt instructions in the GPT version (see below).
  • NBLM gives you 500 characters for a welcome screen; insufficient for what I want readers to initially see; but can pre-load a new NBLM with an initial question, then then share, and that might be used to show initial disclaimers, (c) info, link to author’s site, etc. — TODO
  • The current version either needs a prominent disclaimer (“this is not legal advice; no attorney/client relationship created”) if a reader asks a question about their own situation; this can be fixed with prompt instructions in the GPT version (see below; in fact, the GPT editor automatically created a legal-disclaimer instruction, without my asking).
  • It’s an LLM that’s directly saying it, only indirectly me, and sometimes LLMs, like kids, say the darnedest things. Hence this in the Welcome screen: “I’m not responsible for NotebookLM’s specific answers, though they’re derived from my notes”.
  • I also want to avoid even the appearance of giving legal advice, in case a user asks the book for what sounds like legal advice (see screenshots below). The copyright page of a printed law book would typically say “The publisher is not engaged in rendering legal or other professional advice, and this publication is not a substitute for the advice of an attorney…,” etc., based on a Declaration of Principles by the American Bar Association (ABA) and a Committee of Publishers and Associations. So what about a book that one can ask a question of?:
         
    I mean, nice that it knows to run through the doctrine of equivalence, and perhaps it might ask what role the specific type of cheese might play in an F/W/R analysis, but really better for it to say “I cannot give you legal advice. If you think you might have a cause of action you should promptly seek the advice of an attorney before any potential statute of limitations runs out,” etc.
  • Can NotebookLM be asked in chat to create different NotebookLM “Studio” output like audio, video? Looks like no:
    
  • Also see Manning “AI-in-book” which when combined with their MEAP (Manning Early Access Program) has something the same effect, though frankly I think the NotebookLM approach works a lot better so far
  • Steven Johnson (a co-developer of NotebookLM) shows an example of NBLM “source-grounded AI” using several chapters of his book Where Good Ideas Coming From; see also his “How to use NotebookLM as a research tool“. Johnson has developed an interactive game based on his terrific book The Infernal Machine: A True Story of Dynamite, Terror, and the Rise of the Modern Detective; the game uses Gemini Pro 1.5 and “a 400-word prompt that I wrote giving the model instructions on how to host the game, based on the facts contained in the book itself”. (Could it be reimplemented in NotebookLM?)
  • Unfortunately, Google does not seem to keep a repository of public shared notebooks. It has a small handful of featured public notebooks, including one I’ve used with Q1 earnings reports for Top 50 worldwide corporations; you can ask it to list all AI efforts disclosed in 1Q2025 reports, or all references to patents (which are mostly pro forma); it comes with NBLM briefing docs on AI and on tariffs.
• xxx More on recent changes to NotebookLM: video, flashcards excellent, reports superficial

There are also two ChatGPT versions of the Patent Litigation chatbook:

• https://chatgpt.com/share/68be1320-b1f8-8013-b06f-3cf43a2f0130 — it is not as seamless as the NotebookLM version (it shows names of my private files used as basis for the chat, though no one but me can access them), but the chat itself works nicely:
•      
• My son Matthew asked “In layman’s terms, and about 5 sentences, what’s this book about?”. With chatbot “temperature” randomizing, one of course gets a different answer each time; for example, repeated re-asking Matt’s question of the ChatGPT version of the chatbook results in something like:
  • In plain terms, the book is about what happens after a patent is granted. Instead of focusing on how to get a patent, it looks at lawsuits where companies (or universities, or individuals) accuse others of using their patented technology without permission. The central idea is that patents are made up of “claims,” which are like detailed recipes or parts lists, and lawsuits turn on whether an accused product matches every detail of one of those claims. The book explains how courts and experts go through those nitty-gritty details, how defendants push back by trying to show the patent is invalid, and how damages—sometimes in the billions—get calculated. Along the way, it asks the bigger policy question: why do we let people turn a few words, formulas, or diagrams into legal weapons worth enormous sums of money? Ultimately, it argues that while messy and often frustrating, patent litigation is central to how the U.S. patent system tries to “promote the progress of the useful arts”.
  • In simple terms, this book is about what happens when patents turn into lawsuits. It explains how patents aren’t just ideas, but bundles of very precise “claims,” and whether a company infringes depends on whether its product matches every detail of those claims. It shows how courts, lawyers, and experts pick apart those words, how defendants fight back by trying to knock patents out as invalid, and how damage awards can soar into the millions or billions. Alongside the courtroom battles, the book also looks at the bigger picture—whether this system really helps or hurts innovation, investment, and progress. In the end, it’s about why these dry legal documents can spark high-stakes conflicts that shape entire industries.
  • This book is about what happens when patents—those legal rights to inventions—end up in court. It explains how the fine print of a patent, especially the “claims” that define exactly what’s protected, can become the battlefield in lawsuits worth millions or even billions. It shows how lawyers, judges, and technical experts argue over whether a product really uses what a patent covers, or whether the patent itself should never have been granted. Beyond the courtroom drama, it also asks whether these fights truly encourage new ideas and investment, or whether they sometimes slow progress down. In short, it’s about how a few lines of legal text can reshape entire industries.
  • xxx What’s good about these — but some times see very generic facile answers (especially from new reports feature; yuck)
• GPT created with OpenAI’s GPT editor — in some ways better than the NotebookLM version
  • https://chatgpt.com/g/g-68be79d09e8081918e3aece0da3b94f8-patent-litigation-chatbook
  •    
  • I uploaded some docs (not as many as with NotebookLM yet) into the ChatGPT prompt, and this become part of what is shared. I could choose to hide my docs from readers when I configured the GPT.
  • Interactively create prompt instructions, upload files, set other configuration in GPT editor at chatgpt.com/gpts/editor:
  •    
  • Some of the interactively-created prompt instructions included:
    • The GPT should answer questions based on this body of material, always phrasing responses in clean, reader-ready language, but with flexibility depending on the quality of the draft text. When the uploaded drafts contain passages already written in the author’s own polished prose, the GPT should preferentially use that language directly (with only light adaptation for flow and context). When drafts are rough or fragmentary, the GPT should reformulate them into smooth, coherent answers….
    • The GPT should always incorporate the author’s distinctive catchphrases and stylistic flourishes (e.g., terms in quotation marks such as “ostrich position” or “dog in the manger”), weaving them naturally into answers rather than substituting generic phrasing. Its default tone should be less stiff or formal…
    • Where the GPT supplements with practical litigation consequences, illustrative case examples, or general knowledge not directly from the uploaded book materials, it must insert a clear inline note, but subtle in tone (e.g., in parentheses: “this part is from ChatGPT, not the book”). The same approach should be used for material retrieved from the web — always marked inline at the point where it appears.
    • When citing scholarship, case law, or examples, the GPT must surface **specific references** that appear in the uploaded drafts whenever available. If the drafts cite a particular article (e.g., an Ouellette paper), the GPT should name that work directly instead of referring generically to the author’s “studies.” When a reference comes from outside the drafts, the GPT should include a subtle inline note marking it as ChatGPT’s addition. [I’m not sure it’s really following this one yet.]
    • When referencing the uploaded drafts, outlines, or other background materials, do not describe them as belonging to the user (e.g., “your notes,” “your draft,” “your outline”). Instead, use neutral phrasing such as: “the draft materials”, “the manuscript”, …
  • It automatically added no-legal-advice to instructions, without me remembering to ask for this:
    • It should never provide individualized legal advice, but instead explain doctrine, strategy, and concepts in general terms. When information is missing or not explicit in the source material, it should reasonably infer and provide a thoughtful, coherent response to maintain the flow of conversation. [That reasonable inference might be a problem, but so far seems to work.]
  • What should be shocking, but is no longer surprising given prompt engineering and current “human prose is the new programming language”, these readable instructions are generally implemented.

I can happily and productive chat with Anthropic’s Claude over my book notes, but cannot yet publicly share the Claude project, because when generating a public shared Claude chat, it doesn’t yet allow providing chat over the uploaded docs without at the same time also giving public users access to the underlying docs — which, given the rough state of my notes, I don’t want. Anthropic now has “Claude-powered artifacts”, which sound perfect, except a current limitation is “No persistent storage”, so I would need to have Claude put all my docs right inline inside the Python it would generate for the artifact, which seems brittle.

I’ll soon create an AI chatbot front-end to this web site, using the major documents.