DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Office action is in response to correspondence received September 18, 2025.
Claims 1-20 are pending and have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim(s) 1 recite(s):
a repository of prompts; and a prompt analysis analyzing prompts based upon intellectual property issues.
The independent claim recites a mental process because a repository of prompts is simply data and the scope of prompts include plain English questions/commands to an AI app that a lay person would write to request something, so it’s simply a record of these plain English, lay statements. The prompt analysis analyzing prompts could be any form of analysis including counting prompts, interpreting the language of prompts, etc, which are all mental process steps of observation and judgment. Therefore claim 1 recites an abstract idea that is patent ineligible without a combination of additional elements that would integrate it into a practical application or significantly more.
This judicial exception is not integrated into a practical application. The additional elements include the scope of generic computing components including a system which could be any computing system anywhere capable of keeping text and running an algorithm (running on practically any computer whatsoever since the history of computing). Applications are synonymous to computer programs. In combination and the claims as a whole this amounts to no more than running the above abstract idea on a computer with no technical detail as to how this is accomplished. See MPEP 2106.05(f)(2). Therefore there is no practical application of the abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because for the same reasoning as the practical application section there is not significantly more. The reasoning is carried over here. This is explained in MPEP 2106.05(II). Therefore, there is not significantly more recited than the abstract idea.
Claims 2-20 are rejected as they either further describe the abstract idea or the additional elements are recited as apply it elements. Claims 2, 3 – defines the application but only in terms of outcome, this is apply it as there is not technical detail claimed as to how the application performs this. Therefore this is apply it. Claims 4-6 – describing the subject matter (in other words, not technical) of the data. Claim 7 – using a large language model – apply it. Claim 8 – apply it uses of LLM. Claims 10-11 descriptions of the kind of data (subject matter – lay person – not technical) – further describing the abstract idea. Claims 13-14 scoring – abstract idea, making a rule and following it. Claims 17-20 using vectors which are simply sets of related data. A row on a spreadsheet is a vector x cells long.
Section 33(a) of the America Invents Act reads as follows:
Notwithstanding any other provision of law, no patent may issue on a claim directed to or encompassing a human organism.
Claims 9 and 11 are rejected under 35 U.S.C. 101 and section 33(a) of the America Invents Act as being directed to or encompassing a human organism. See also Animals - Patentability, 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) (indicating that human organisms are excluded from the scope of patentable subject matter under 35 U.S.C. 101).
Claim 9 recites that the prompts are curated by legal partners and prompt creators. Claim 11 recites that a human … creator submits prompts. These are recitations of humans as positively recited elements in a system claim. This therefore is “directed to or … encompassing a human organism.” The claim should be amended to eliminate the humans claimed that are performing steps.
Therefore claims 1-20 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 2, and 7-14 is/are rejected under 35 U.S.C. 102(a)(1) is anticipated by Chandrasekaran, US PGPUB 20240320476 A1 ("Chandresekaran").
Per claim 1, Chandrasekaran teaches A prompt monitoring system for generative artificial intelligence systems, comprising: a repository of prompts in par 45: “The prompt capture and enrichment system 10 of the present invention identifies, captures, and manages prompts that produce effective results in a variety of domains that allow for wider use and fine tuning of the generative language models.”
Chandrasekaran then teaches and a prompt analysis application analyzing prompts based upon intellectual property issues in [0046] The prompt enrichment unit 20 enriches the prompts 18A by manipulating, such as ingesting, integrating, adding or modifying, one or more attributes associated with the prompts 18A so as to modify, enrich and curate the prompts 18A. Specifically, the prompt enrichment unit 20 can be configured to enrich the prompt by adding additional context, information, or details to the prompts to make the prompt 18A more specific, relevant, and/or useful for generating a higher-quality response from a generative language model. For example, the prompt 18A can be enriched by adding contextual data to help clarify the scope and purpose of the prompt 18A or the prompt 18A can be enriched by modifying or adjusting the scope of the prompt 18A. The prompt enrichment unit 20 can be configured to add any additional context, information, or details in metadata of the prompts 18A. See also 47. See also 65 to detect plagiarism.
Per claim 2, Chandrasekaran teaches the limitations of claim 1, above. Chandrasekaran then teaches wherein the prompt analysis application identifies prompts that infringe existing patents, copyrights, or other intellectual property rights in par 65: "r more text analysis techniques and based on one or more characteristics of the enriched prompt 50 and the author. For example, the existing prompt detection unit 64 can compare the writing style of an unknown author to a set of known authorship characteristics or attributes forming part of a profile in order to detect plagiarism by comparing the enriched prompt 50 to selected ones of the authorship profiles. The text analysis techniques can assume that each author has a unique writing style, which can be characterized by various linguistic and stylistic features, such as sentence length, vocabulary, use of punctuation, and grammatical patterns. By comparing the enriched prompt 50 to a set of known authorship characteristics in the profile, the technique can identify similarities and differences in these characteristics and determine based thereon whether a risk of plagiarism is present. The existing prompt detection unit 64 can generate as an output a similarity score 68 that is indicative of the similarity of the enriched prompt 50 to a preexisting prompt. "
Per claim 7, Chandrasekaran teaches the limitations of claim 1, above. Chandrasekaran further teaches wherein the prompt analysis application includes a Large Language Model in par 45: "The prompt capture and enrichment system 10 of the present invention identifies, captures, and manages prompts that produce effective results in a variety of domains that allow for wider use and fine tuning of the generative language models. The prompts can also serve to fine tune and/or train the generative language models. "
Per claim 8, Chandrasekaran teaches the limitations of claim 7, above. Chandrasekaran further teaches wherein the Large Language Model is created by fine-tuning an existing Large Language Model with data, training a Large Language Model from scratch, building conventional Machine Learning models, or prompt engineering with a multi-shot approach, or combined with embedding searches in par 45: "The prompt capture and enrichment system 10 of the present invention identifies, captures, and manages prompts that produce effective results in a variety of domains that allow for wider use and fine tuning of the generative language models. The prompts can also serve to fine tune and/or train the generative language models."
Per claim 9, Chandrasekaran teaches the limitations of claim 1, above. Chandrasekaran further teaches wherein the repository of prompts is curated by legal partners and prompt creators studying patents issued by the USPTO and copyright registrations issued by the Copyright Office of the Library of Congress, and identify those relating to prompts in par 046: "The prompt enrichment unit 20 enriches the prompts 18A by manipulating, such as ingesting, integrating, adding or modifying, one or more attributes associated with the prompts 18A so as to modify, enrich and curate the prompts 18A. Specifically, the prompt enrichment unit 20 can be configured to enrich the prompt by adding additional context, information, or details to the prompts to make the prompt 18A more specific, relevant, and/or useful for generating a higher-quality response from a generative language model. For example, the prompt 18A can be enriched by adding contextual data to help clarify the scope and purpose of the prompt 18A or the prompt 18A can be enriched by modifying or adjusting the scope of the prompt 18A. The prompt enrichment unit 20 can be configured to add any additional context, information, or details in metadata of the prompts 18A. This additional material in the metadata can be used to facilitate searches for the prompts 18A. The prompt enrichment unit 20 can enrich the prompt 18A so that the prompt 18A can be more easily identified for subsequent users. Additional information added to the metadata for prompts can include an identifier to enable a determination of the prompt author, information about the prompt context, location information for the prompt author or the prompt provider, or a prompt creation time, a prompt sharing time."
Per claim 10, Chandrasekaran teaches the limitations of claim 1, above. Chandrasekaran further teaches wherein the prompt analysis application studies new prompts to determine whether they are patented, copyrighted, or otherwise protected by intellectual property in Par 50: "The prompt enrichment subsystem 30 can also optionally include an auto-prompt classifier unit 34 for automatically classifying the ontology prompts 28 into one or more predefined classifications or categories associated with desired outputs of the generative language model. The auto-prompt classifier unit 34 enables automated processing and response generation for various applications, such as chatbots, search engines, recommendation systems, and the like. The auto-prompt classifier unit 34 can employ one or more machine learning models, where the model is trained on labeled examples of input prompts and corresponding categories that include the prompt. "
Per claim 11, Chandrasekaran teaches the limitations of claim 1, above. Chandrasekaran further teaches wherein a human or Al prompt creator submits prompts to the prompt analysis application for consideration as to whether they are patented, copyrighted, or otherwise protected by intellectual property in par 50: "The prompt enrichment subsystem 30 can also optionally include an auto-prompt classifier unit 34 for automatically classifying the ontology prompts 28 into one or more predefined classifications or categories associated with desired outputs of the generative language model. The auto-prompt classifier unit 34 enables automated processing and response generation for various applications, such as chatbots, search engines, recommendation systems, and the like. The auto-prompt classifier unit 34 can employ one or more machine learning models, where the model is trained on labeled examples of input prompts and corresponding categories that include the prompt. "
Per claim 12, Chandrasekaran teaches the limitations of claim 1, above. Chandrasekaran further teaches wherein a generative artificial intelligence system is connected to the repository of prompts and the prompt analysis application in par 50: "The prompt enrichment subsystem 30 can also optionally include an auto-prompt classifier unit 34 for automatically classifying the ontology prompts 28 into one or more predefined classifications or categories associated with desired outputs of the generative language model. The auto-prompt classifier unit 34 enables automated processing and response generation for various applications, such as chatbots, search engines, recommendation systems, and the like. The auto-prompt classifier unit 34 can employ one or more machine learning models, where the model is trained on labeled examples of input prompts and corresponding categories that include the prompt. The machine learning model then learns to recognize patterns in the ontology prompts 28 and predict an appropriate category for new, unseen prompts. The auto-prompt classifier unit 34 can then automatically classify the incoming ontology prompts 28 based on the various classifications associated with predefined output actions to be generated by the generative language model and trigger an appropriate response or action. This helps improve and enhance the efficiency and accuracy of the prompt capture and enrichment system 10, as it eliminates the need for human intervention in categorizing the ontology prompts 28 and improves the processing speed of the system by automatically performing the classification methodology. The categorization of the ontology prompts 28 allows the prompt capture and enrichment system 10 to place or assign a category to the enriched prompts and optionally to create a new classification if needed if the ontology prompt cannot be assigned to a preexisting category. For example, the prompts can be classified into topic categories based on subject matter associated with the prompt, an intent category based on an intent or purpose associated with the prompt, a complexity level category associated with a complexity of the prompt, a format category based on the format of the prompt (e.g., question, statement, etc.), a tone or sentiment category based on the tone or sentiment associated with the prompt, domain-specific categories, and the like."
Per claim 13, Chandrasekaran teaches the limitations of claim 1, above. Chandrasekaran further teaches wherein the prompt analysis application determines a score relating to the likelihood a prompt presents a problem regarding intellectual property in par 061: "The prompt filtering unit 60 can then generate an output score that is indicative of the degree of truthfulness associated with the language in the enriched prompt 50. The degree of truthfulness is the level of factual correctness or accuracy for the enriched prompt 50 that considers the polarity and toxicity of content in the enriched prompt 50 and the likelihood that material constituting propaganda is in the content of the enriched prompt 50. "
Per claim 14, Chandrasekaran teaches the limitations of claim 13, above. Chandrasekaran further teaches wherein a score that exceeds a predetermined threshold is considered to present a problem with regard to intellectual property in Par 08: "The truthfulness score can be determined based on a high degree of similarity to an existing prompt that can be indicative of copyright issues or plagiarism," Par 56: "The digital conversion unit 44 can optionally refrain from providing compensation until a score for the prompt is greater than or equal to a minimum threshold, and the digital conversion unit 44 can optionally provide compensation only where the score is greater than or equal to a minimum threshold. The digital conversion unit 44 can therefore provide an additional incentive to prompt authors and prompt owners to share their prompts."
Therefore, claims 1, 2, and 7-14 are rejected under 35 USC 102.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 3, 4, 6, and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chandrasekaran, US PGPUB 20240320476 A1 ("Chandresekaran") in view of Chang, WO 2024173577 A1 ("Chang").
Per claim 3, Chandrasekaran teaches the limitations of claim 1, above. Chandrasekaran does not teach wherein the prompt analysis application identifies patentable prompts.
Chang teaches using large language models to determine patentable subject matter. See abstract.
Chang teaches wherein the prompt analysis application identifies patentable prompts in page 10: "Optionally, the system includes Scorer Engine 118, which scores results from Semantic Engine 106 and/or Al Engine 112 based on multiple signals that are weighted (e.g., relatedness, confidence, etc.). System 100 can return results ranked based on scores, with an explanation of how the results were ranked. Here’s an example use case. The system is monitoring a mobile chip patent and detects a new laptop announced with 5G capabilities. The system flags the laptop as a potential hit, but only a soft “maybe,” potentially worth investigation, but not 100% deterministic. The scoring -based results enable the system to surface soft hits as a result without claiming de facto infringement."
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the prompt monitoring system of Chandrasekaran with the using patentable prompts teaching of Chang because Chang teaches in page 4 that infringement can be detected in real time or near real time and verifies products before they launch. As this benefit would increase the usability of Chandrasekaran and the truthfulness scores taught by that llm by including whether or not products are original or infringing, one would be motivated to modify Chang with Chandrasekaran.
Per claim 4, Chandrasekaran teaches the limitations of claim 1, above. Chandrasekaran does not teach wherein the repository of prompts is a collection of prompts determined to infringe existing patents, copyrights, or other intellectual property rights.
Chang teaches wherein the repository of prompts is a collection of prompts determined to infringe existing patents, copyrights, or other intellectual property rights Page 11: "As shown in FIG. 2, System 100 leverages LLMs that have been trained with general data and optionally LLMs that have been trained on patent data (the system uses post grant challenges - IPRs, PGRs, reexaminations, etc. - as labeled training data). Al Engine 112 injects one or more patent claims of a patent of interest into its LLMs’ 114 (and/or optionally an external LLM) and prompts the LLMs to find potentially invalidating prior art, which can be any combination of system prior art and printed publications (step 202). Within the same session, the Al engine also tasks the LLMs with computing and providing a confidence level for its responses. Here, the Al engine is leveraging the generative capabilities of LLMs. Al Engine 112 retrieves the LLM’s output, which is a long list of products and reorders them in order of confidence and prunes the low confidence results below an empirically determined threshold particular to the category of products (step 204). Al Engine 112 passes the pruned list to Hydrator, which retrieves product information from Product Database and supplements the pruned lists with additional information about the products listed and returns the enhanced and pruned list (step 206). Al Engine 112 injects the claims of interest and the pruned and hydrated lists of candidate prior art (system art and/or printed publications) into its LLMs and prompts them to self-verify (step 208). Al Engine 112 compares the LLMs’ current output with their previous output (step 210). Matching results are kept while nonmatching results are pruned. Steps 202 through 220 are repeated until results are stable (no more non-matching results) and confidence levels converge (decision 212)."
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the prompt monitoring system of Chandrasekaran with the collection of prompts teaching of Chang because Chang teaches in page 4 that infringement can be detected in real time or near real time and verifies products before they launch. As this benefit would increase the usability of Chandrasekaran and the truthfulness scores taught by that llm by including whether or not products are original or infringing, one would be motivated to modify Chang with Chandrasekaran.
Per claim 6, Chandrasekaran teaches the limitations of claim 4, above. Chandrasekaran further teaches Copyright Office information, information from other government agencies responsible for intellectual property in par 75: "Through a truthfulness assessment, the system 10 can filter out prompts identified with potential trust and safety concerns or issues, including those with low truthfulness scores due to issues like copyright violations,"
Chandrasekaran does not teach wherein the prompts maintained in the repository of prompts result from study of US Patent & Trademark Office (USPTO) information, court decisions concerning intellectual property, legal partners , and prompt creators
Chang teaches wherein the prompts maintained in the repository of prompts result from study of US Patent & Trademark Office (USPTO) information Page 14: "For example, the columns may include a ‘Patent #’ column 906 presenting the number of the analyzed patent, a ‘Patent Name’ column 908 presenting the title of the analyzed patent, a ‘Prior Art Found’ column 910 presenting the number of prior art items found by the system 100, a ‘Sources’ column 912 presented the sources searched by the system 100, a ‘Report Date’ column 914 presenting the date on which the system 100 the report for the respective patent, and a ‘Average Confidence’ column 916 presenting the confidence level as determined by the system 100. While the system 100 is analyzing the respective patent, an in-progress widget 918 may be presented at row 904." Patent # is uspto information.
Chang then teaches court decisions concerning intellectual property, legal partners in Page 17: "Litigations and External Proceedings o Alice briefings and opinions o Summary judgment briefings and opinions o Markman briefings and opinions o Invalidity briefings and opinions o Infringement briefings and opinions o Markman transcripts o Expert reports o ITC Proceedings o FDA Orange Book and Paragraph IV Proceedings. Expert reports teaches legal partners.
Chang then teaches and prompt creators in Page 17: ", While System 100 has the capability to prompt its LLMs and other ML models to perform its searches and analyses on the fly and on demand, it optionally pre-searches and pre-analyzes, both for validity and infringement, and may store what it learns in its Knowledge Graph 124, and results there can be retrieved and provided to end users, even when the LLMs are offline."
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the prompt monitoring system of Chandrasekaran with the collection of prompts teaching of Chang because Chang teaches in page 4 that infringement can be detected in real time or near real time and verifies products before they launch. As this benefit would increase the usability of Chandrasekaran and the truthfulness scores taught by that llm by including whether or not products are original or infringing, one would be motivated to modify Chang with Chandrasekaran.
Per claim 15, Chandrasekaran teaches the limitations of claim 1, above. Chandrasekaran does not teach wherein the prompt monitoring system further includes an interface providing screen notifications that allow for a user to request patent evaluation options, screen notifications that warn a user when a potential issue is identified, and/or options to explore new patent ideas associate with a given prompt.
Chang teaches wherein the prompt monitoring system further includes an interface providing screen notifications that allow for a user to request patent evaluation options, screen notifications that warn a user when a potential issue is identified, and/or options to explore new patent ideas associate with a given prompt See Page 28: "FIG. 22 is a flowchart of method 2200 for performing patent indexing in accordance with aspects of the invention. The procedure 2200 begins in step 2205 and continues to step 2210 where the indexing module 2135 receives a dictionary from a message broker 2150. The dictionary includes information relating to a particular patent, e.g., patent title, patent number, application number, the text of the specification, abstract, and claims related to a particular patent. The indexing module 2135 then, in step 2215, saves information in the database module 2105. The indexing module 2135 then utilizes the text embedding module 2115 to extract appropriate embeddings of the patent text components. These patent text component embeddings are then saved in the vector database in step 2225. The procedure 2200 completes in step 2230. In accordance with an implementation of the present invention, the patent indexing module may execute every day to index all patents issued on that day. For example, in the United States, patents are issued every Tuesday, so a message broker may retrieve the necessary information from the USPTO each Tuesday and feed it into the patent indexing module to update the system with the latest issued patents. In other jurisdictions, patents may be issued on different days, and the patent indexing system may execute on those days." See also Fig 8.
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the prompt monitoring system of Chandrasekaran with the collection of prompts teaching of Chang because Chang teaches in page 4 that infringement can be detected in real time or near real time and verifies products before they launch. As this benefit would increase the usability of Chandrasekaran and the truthfulness scores taught by that llm by including whether or not products are original or infringing, one would be motivated to modify Chang with Chandrasekaran.
Per claim 16, Chandrasekaran teaches the limitations of claim 1, above. Chandrasekaran does not teach wherein the prompt analysis application implements a vector processing system in its decision making process when determining a likelihood for either infringement or patentability.
Chang teaches wherein the prompt analysis application implements a vector processing system in its decision making process when determining a likelihood for either infringement or patentability In page 24: " These patent text component embeddings are then saved in the vector database in step 2225. The procedure 2200 completes in step 2230. In accordance with an implementation of the present invention, the patent indexing module may execute every day to index all patents issued on that day. For example, in the United States, patents are issued every Tuesday, so a message broker may retrieve the necessary information from the USPTO each Tuesday and feed it into the patent indexing module to update the system with the latest issued patents. In other jurisdictions, patents may be issued on different days, and the patent indexing system may execute on those days."
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the prompt monitoring system of Chandrasekaran with the collection of prompts teaching of Chang because Chang teaches in page 4 that infringement can be detected in real time or near real time and verifies products before they launch. As this benefit would increase the usability of Chandrasekaran and the truthfulness scores taught by that llm by including whether or not products are original or infringing, one would be motivated to modify Chang with Chandrasekaran.
Per claim 17, Chandrasekaran teaches the limitations of claim 16, above. Chandrasekaran does not teach wherein a prompt is first input into the prompt monitoring system and a vector check is performed to determine how closely the input prompt matches an existing patented prompt.
Chang teaches wherein a prompt is first input into the prompt monitoring system and a vector check is performed to determine how closely the input prompt matches an existing patented prompt In page 28: "FIG. 23 is a flowchart detailing the steps of a procedure 2300 for performing a patent semantic search in accordance with an implementation of the present invention. The procedure 2300 begins in step 2305 and continues to step 2310 where a product description is received from a message broker. This product description may consist of any form of data that describes a product including, for example, a press release, product documentation, technical manuals, source code, etc. The module then obtains appropriate embeddings of the product description by utilizing the embedding module in step 2315. Once embeddings have been received, the module then searches the vector database to identify one or more embeddings of most similar patent(s). The identified patent(s) are then obtained from the database module, in step 2325. The procedure then completes in step 2330."
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the prompt monitoring system of Chandrasekaran with the collection of prompts teaching of Chang because Chang teaches in page 4 that infringement can be detected in real time or near real time and verifies products before they launch. As this benefit would increase the usability of Chandrasekaran and the truthfulness scores taught by that llm by including whether or not products are original or infringing, one would be motivated to modify Chang with Chandrasekaran.
Per claim 18, Chandrasekaran teaches the limitations of claim 17, above. Chandrasekaran does not teach wherein vectors are generated based upon different information.
Chang teaches wherein vectors are generated based upon different information In page 28: "FIG. 23 is a flowchart detailing the steps of a procedure 2300 for performing a patent semantic search in accordance with an implementation of the present invention. The procedure 2300 begins in step 2305 and continues to step 2310 where a product description is received from a message broker. This product description may consist of any form of data that describes a product including, for example, a press release, product documentation, technical manuals, source code, etc. The module then obtains appropriate embeddings of the product description by utilizing the embedding module in step 2315. Once embeddings have been received, the module then searches the vector database to identify one or more embeddings of most similar patent(s). The identified patent(s) are then obtained from the database module, in step 2325. The procedure then completes in step 2330."
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the prompt monitoring system of Chandrasekaran with the collection of prompts teaching of Chang because Chang teaches in page 4 that infringement can be detected in real time or near real time and verifies products before they launch. As this benefit would increase the usability of Chandrasekaran and the truthfulness scores taught by that llm by including whether or not products are original or infringing, one would be motivated to modify Chang with Chandrasekaran.
Per claim 19, Chandrasekaran teaches the limitations of claim 18, above. Chandrasekaran does not teach wherein an abstract of a patent is used to produce a short embedding vector
Chang teaches wherein an abstract of a patent is used to produce a short embedding vector in page 29: " from the database module. Illustratively, the patent infringement module then splits the patent into chunks in step 2420. Illustratively, these chunks may be of varying size, e.g., paragraph, sentence, sub-sentence sized to enable good vector embeddings to be obtained"
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the prompt monitoring system of Chandrasekaran with the collection of prompts teaching of Chang because Chang teaches in page 4 that infringement can be detected in real time or near real time and verifies products before they launch. As this benefit would increase the usability of Chandrasekaran and the truthfulness scores taught by that llm by including whether or not products are original or infringing, one would be motivated to modify Chang with Chandrasekaran.
Per claim 20, Chandrasekaran teaches the limitations of claim 18, above. Chandrasekaran does not teach wherein the full text, description, and images of a patent are used to produce a long embedding vector.
Chang teaches wherein the full text, description, and images of a patent are used to produce a long embedding vector Page 29: "Illustratively, the large language module compares the various vector embeddings of the patent and the product description to determine whether there is a likelihood that the product meets the limitations of the claims of the patent. If no infringement is determined, the procedure 2400 then completes in step 2455."
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the prompt monitoring system of Chandrasekaran with the collection of prompts teaching of Chang because Chang teaches in page 4 that infringement can be detected in real time or near real time and verifies products before they launch. As this benefit would increase the usability of Chandrasekaran and the truthfulness scores taught by that llm by including whether or not products are original or infringing, one would be motivated to modify Chang with Chandrasekaran.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chandrasekaran, US PGPUB 20240320476 A1 ("Chandresekaran") in view of Chang, WO 20240173577 A1 ("Chang"), further in view of Ruan et al., US PGPUB 20250124055 A1 ("Ruan").
Per claim 5, Chandrasekaran teaches the limitations of claim 4, above. Chandrasekaran does not teach wherein the repository of prompts also includes prompts that have been denied intellectual property protection and are considered to be in the public domain.
Ruan teaches identifying related content and significance thereof with respect to some specified goal. Initial content is obtained based on a specified goal. See abstract.
Ruan teaches wherein the repository of prompts also includes prompts that have been denied intellectual property protection and are considered to be in the public domain in pars 0143-0147: "(b) Patentability—low [0144] (c) Patent infringement risk—high [0145] A highly relevant expired patent with succinct independent claim identified for the input inventive concept, where its 6-year statute of limitations on expired patents has elapsed, may raise, e.g., the following different flags for different objectives: [0146] (a) Freedom to Use—likely public domain [0147] "
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the prompt monitoring system of Chandrasekaran with the public domain teaching of Ruan because Ruan teaches using analytics to determine patentable possibilities, see pars 04-06. As Chandrasekaran is teaching a way to determine if something is legitimate, one would be motivated to modify Chandrasekaran with Ruan so that one would know that even though something is similar it is not violating a law. For these reasons one would be motivated to modify Chandrasekaran with Ruan.
Therefore claims 3-6 and 15-20 are rejected under 35 USC 103.
Prior Art Considered Relevant
The following prior art is considered relevant to Applicant’s disclosure but is not relied upon in the above rejection:
Wang et al., “IPEval: A Bilingual Intellectual Property Agency Consultation Evaluation Benchmark for Large Language Models” arxiv.org, published June 18, 2024, . available at: < https://arxiv.org/abs/2406.12386 >
Teaches on page 3 taking data from USPTO and CNIPA (China) and processing it to annotate the data for an LLM.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICHARD W. CRANDALL whose telephone number is (313)446-6562. The examiner can normally be reached M - F, 8:00 AM - 5:00 PM.
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/RICHARD W. CRANDALL/ Primary Examiner, Art Unit 3619
Prosecution Insights