IPVectors.com Mission : Patent Analytics Done Right

Our mission is to empower companies to innovate with greater efficiency and confidence while safeguarding their inventions. Our AI driven analytics delivers the most comprehensive and precise patent analytics and comparisons. With these actionable insights, inventors and engineers as well as IP practioners can efficiently identify and navigate white space and innovation opportunities – and target the most promising acquisition and licensing candidates.

What We Do

IPV Patent Analysis generates detailed patent comparisons and reports using AI, large language models (LLMs) and proprietary scoring algorithms. This enables contextual searches and similarity scoring across large volumes of patents and produces easy to digest summaries enabling the user to dive deeper into the patents and claims that really matter.

Better Search Results

Far better than Boolean text searching: IPV Patent Analysis reports use generative AI, large language models and proprietary scoring algorithms to compare a Subject patent to potentially thousands of other patents within a defined CPC family. CPC stands for Cooperative Patent Classification (CPC) which is a hierarchical classification system that’s managed by the EPO and the US Patent and Trademark Office. It’s divided into nine sections which in turn are sub-divided into classes, sub-classes, groups and sub-groups. There are approximately 250,000 classification entries.

Better Precision

IPV Patent analysis reports deliver precise comparisons that identify similar patents, score them and then clearly reveal in an easy-to-read report what sets the Subject patent apart from others in its CPC family. It also suggests the probability of anticipated direct or indirect infringement based on the analysis of claims. Once similar patents within a CPC have been identified, users have access to an interactive dashboard enabling them to drill down and gain the insights they need to power smart decisions when developing a product strategy or when negotiating and building patent portfolios. These highly customizable dashboards bring together data from a wide variety of sources to reveal key factors like recent office actions, assignees, jurisdictions and a lot more. This provides an easy way to connect to virtually all publicly available data including litigation reports, bringing all of this information to the user in a form that's fast and easy to digest and act upon.

Advanced Analytics and the Future of IP Strategy

For law firms, adopting these advanced tools are becoming essential. Managing clients' patent portfolios requires not only filing and protecting intellectual property but also strategically guiding them through potential IP licensing or acquisition opportunities. The ability to more accurately identify white space, potential partners, or even competitors' vulnerabilities is invaluable. Additionally, improving the identification of prior art helps firms reduce the risk of infringement lawsuits, saving their clients from costly legal battles..

Documented Problems of LLM Patent Analysis Done Wrong

Key challenges that our AI Process Overcomes

“When it comes to patents...an LLM might confidently (but wrongly) infer that an invention is unique or that a certain prior art reference is sufficient to invalidate a claim. The pervasive errors observed in LLM legal reasoning” dho.stanford.edu .

There is a well-deserved lack of trust in AI-only results, recognizing that they often overlook subtle prior art or legal nuances, leading to inaccurate assessments of a patent’s true quality and uniqueness. Read on we'll explain the problems that other patent analytics often leave unresolved and see how IPVectors has overcome them.

From invented prior art references to superficial claim readings and flawed novelty conclusions, the examples below underscore the importance of skepticism and verification when using AI for patent work.

One of the most troubling behaviors is when an LLM “hallucinates” prior art – inventing patent citations or technical references that sound plausible but are incorrect or entirely fictional. Credible reports have documented LLMs providing misleading prior art search results that could derail patent analyses:

Fabricated Patent Citations: In tests of ChatGPT for prior art searching, the model often returned patent numbers and titles that were irrelevant or non-existent. UnitedLex found that ChatGPT would confidently list U.S. patent numbers supposedly related to a query, but the references were not actually pertinent to the technology in question . In some cases, the bibliographic details (titles, dates, inventors) didn’t match the patent numbers, revealing that the AI had simply generated plausible-sounding patents out of thin air. This misled the researchers and wasted time, because the “prior art” needed to be double-checked only to discover it was incorrect. An internal summary noted “the tool provides patent numbers that are irrelevant… and gives incorrect bibliographic details… mislead[ing] the prior art research” unitedlex.com

Expert Warnings of Fake References: Patent professionals have publicly warned that ChatGPT will make up references that don’t exist. In one discussion, a practitioner reported that the AI “completely fabricates titles, patent numbers, DOIs, abstracts, and more for documents”, creating fake prior art citations that look legitimate at first glance. This is a classic hallucination problem: the language model generates a convincing reference (often mixing real-sounding author names, publication venues, etc.) which cannot be found in any database. Relying on such output for patent invalidity searches or patentability opinions could be disastrous – an attorney might cite a phantom piece of prior art if they don’t verify each reference. The risk of fabricated prior art is so well-recognized that experts flatly state ChatGPT “is not a search engine” and cannot be trusted to retrieve actual patent documents reddit.com .

Incorrect Technical Classifications: Even when not outright fabricating sources, LLMs can still provide incorrect data about prior art. For instance, ChatGPT has been observed to suggest the wrong patent classification codes for a given technology. In patent research, searching by classification is a key strategy (since patents in the same class cover similar subject matter). However, UnitedLex found the model often reported wrong classification numbers or definitions, which could send a searcher down the wrong path. This kind of error shows that LLMs lack access to authoritative patent databases and may guess at related technical fields, again underscoring that human verification is mandatory.

Misleading Inferences on Patentability and Novelty

Assessing an invention’s novelty or obviousness requires comparing patent claims to prior art – a subtle task that LLMs do with mixed success. There are documented cases where an LLM’s analysis misjudged patent novelty or made unjustified inferences about patentability:

Missing Claim Elements in Prior Art (False Novelty): A 2023 academic study by Carnegie Mellon researchers tested large language models on patent novelty evaluation , arxiv.org. They found that while GPT-4 and other LLMs could generate explanations, the models often missed crucial overlaps between the claim and the prior art. For example, in one test the patent claim required creating and storing a parity bit in a memory system, and the cited prior art described transferring data to a register (which implicitly involves generating parity). The human examiner recognized that the prior art taught the parity feature indirectly, so the claim was not novel. GPT-4, however, failed to grasp this implication– because the reference didn’t state “parity” explicitly, the model concluded the claim element was missing and thus wrongly deemed the claim novel arxiv.org . The researchers note the model “missed it” when a reference taught something only implicitly, a common cause of LLM error in novelty analysis •. In general, subtle technical relationships (like a prior art using a synonym or a broader category of a claim element) can fool the AI, leading to an incorrect judgment about patentability.

Overlooking Implicit Disclosures: The same study reported multiple instances where the LLM’s explanation failed to recognize content implied in the prior art texts. In several test cases the ground truth was that the invention lacked novelty (because the prior art covered it), but the LLM predicted it was novel. The model’s answers showed it “missed the content implied in the text” or “failed to grasp the full content of [the] cited texts,” indicating it did not fully understand the prior art. These oversights resulted in overly optimistic novelty assessments. Essentially, the AI would declare an invention novel due to its own comprehension gap, not because of any real inventive difference. Such misleading inferences about novelty or obviousness could give inventors false confidence (or conversely, might wrongly dismiss a truly novel idea as already known, if the AI incorrectly imagines a prior disclosure).

Additional Sources:

Ikoma & Mitamura (2023) – Can AI Examine Novelty of Patents? (CMU study on GPT-4 and others assessing patent novelty) arxiv.org .

UnitedLex (2023) – Putting ChatGPT to the Patent Analysis Test (industry report on ChatGPT’s performance in prior art search, claim summarization, etc.) unitedlex.com .

Reddit r/patentlaw (2023) – Discussion “ChatGPT and Prior Art Searches?” (practitioner’s warning about fabricated references) reddit.com .

Upadhye Tang LLP (2024) – On the Use of Generative AI in Patent Litigation (law firm insights on LLM challenges in claim interpretation and the need for human review) ipfdalaw.com .

Stanford Law & HAI (2024) – Hallucinating Law: Legal Mistakes with LLMs (study finding pervasive AI errors in legal tasks, highlighting risks of false information) dho.stanford.edu .

IPVectors: Objective, Quantifiable Metrics

Here's how we overcome this problem:

Objective, Quantifiable Metrics

BERT-based scoring and cosine similarity provide numerical, reproducible measures of semantic similarity. By transforming patent texts into high-dimensional vectors, these methods enable precise calculations of how closely two pieces of text align. This is particularly useful when examining claim language, where subtle differences in wording can be legally significant. In contrast, LLMs, while capable of generating convincing narrative analyses, may produce outputs that include hallucinations or misinterpretations, which are harder to quantify or verify.

Grounding in Domain-Specific Data

Techniques that use a subject patent or abstract as the ground truth, combined with cosine similarity, allow the analysis to be anchored in a known reference. This approach mitigates the risks associated with LLMs, which can sometimes generate speculative or contextually inappropriate responses. In the realm of patent law, where precision is paramount, relying on objective statistical measures ensures that only truly relevant texts are flagged as similar or overlapping.

Consistency and Reliability

Statistical text analysis techniques, such as BERT scoring, are designed to be consistent across repeated evaluations. They eliminate the variability often seen with generative LLM outputs, which can vary from one instance to the next due to their probabilistic nature. This consistency is essential for rigorous patent analysis—whether determining infringement or assessing the novelty of a claim—where every detail must be scrutinized and documented.

Enhanced Accuracy in Overlap Detection

Patent claims often contain nuanced language and legal terminology that require careful interpretation. By leveraging BERT-based embeddings, analysts can capture these nuances and measure similarities with high accuracy. Cosine similarity then quantitatively compares these embeddings, ensuring that only documents with a meaningful semantic overlap are considered. This method avoids the pitfalls of advanced LLMs, which might “write on to a claim” with overconfidence but unverified interpretations, potentially leading to misleading conclusions.

The most efficient Economics, Compute time and Embedding Costs

On-Demand Vector Transformation: Dynamically selects only the most relevant CPC/IPCR patent classes, optimizing computational efficiency.
Eliminating Costly Overhead: Unlike competitors who precompute vectors for entire patent collections—leading to bloated databases and high update costs— we generate vectors only when necessary.
Targeted Insights, Minimal Waste: Our streamlined approach drastically reduces storage and computational expenses while maintaining access to the most relevant information.

Embeddings Explained

IPVectors.com uses LLMs, Scoring, and Vector Embedding to Revolutionize Patent Search and Analysis

The application of large language models (LLMs) combined with vector embeddings and scoring mechanisms represents a significant leap forward from traditional keyword and Boolean-based patent search methods. Historically, patent searches were limited by the rigid structure of keyword matching or Boolean logic, often missing critical pieces of prior art due to linguistic variation or ambiguity in descriptions. This was problematic because it led to either irrelevant results or missed prior art, making the patent search process inefficient and incomplete.

Embeddings, a key advancement in this new approach, are mathematical representations of words and phrases that capture not only their meaning but also their context. In simpler terms, embeddings convert words or entire texts into numerical vectors that are positioned in a high-dimensional space. Words or phrases with similar meanings will be closer together in this space, even if the actual terms used are different. This allows LLMs to recognize that two patents might describe the same invention in different ways, enabling a much more accurate and comprehensive prior art search.

This contextual understanding is crucial in patent law, where the nuances of language can define the difference between novelty and infringement. By leveraging embeddings and advanced scoring, LLMs can produce a much more precise and relevant set of results, helping both law firms and inventors navigate the increasingly complex patent landscape. Finding the most pertinent prior art early can help inventors avoid wasting time and resources on patents that may not stand up to scrutiny or might infringe on existing patents.

What is BERTs ?

And... What is Cosine Similarity?

The Basics of BERTScore

BERTScore represents a pivotal shift in LLM evaluation, moving beyond traditional heuristic-based metrics like BLEU and ROUGE to a learned approach that captures complex linguistic nuances. Unlike older n-gram-based methods, BERTScore excels at evaluating paraphrasing, coherence, relevance, and polysemy—essential features for modern AI applications.

BERTScore leverages transformer-based contextual embeddings and compares them using cosine similarity to assess the quality of model outputs. Its popularity endures due to its relatively low computational cost and greater interpretability compared to black-box methods like LLM-as-a-judge metrics.