Prosecution Insights
Last updated: April 19, 2026
Application No. 18/943,732

Large Language Model(s) System for Capturing, Maintaining, and Separating Copyrighted Information Within a Blockchain Network with Automatic Output of Information

Non-Final OA §103
Filed
Nov 11, 2024
Examiner
LE, CHAU D
Art Unit
2408
Tech Center
2400 — Computer Networks
Assignee
Pangee Inc.
OA Round
1 (Non-Final)
85%
Grant Probability
Favorable
1-2
OA Rounds
2y 10m
To Grant
99%
With Interview

Examiner Intelligence

Grants 85% — above average
85%
Career Allow Rate
453 granted / 532 resolved
+27.2% vs TC avg
Strong +17% interview lift
Without
With
+16.9%
Interview Lift
resolved cases with interview
Typical timeline
2y 10m
Avg Prosecution
13 currently pending
Career history
545
Total Applications
across all art units

Statute-Specific Performance

§101
14.9%
-25.1% vs TC avg
§103
40.9%
+0.9% vs TC avg
§102
15.2%
-24.8% vs TC avg
§112
8.8%
-31.2% vs TC avg
Black line = Tech Center average estimate • Based on career data from 532 resolved cases

Office Action

§103
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 . DETAILED ACTION The claims 1-20 are pending. Information Disclosure Statement The information disclosure statement (IDS) submitted on 11/22/24, 5/6/25 and 8/7/25 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Objections Claims 1-4, 9, 10, 13-17, 19 and 20 are objected to because of the following informalities: numerous use of “its” is objected to for being ambiguous, such as at Claim 1, line 9 “its corresponding object”. Appropriate correction is required. Examiner’s Notes The Specification has been reviewed and no known errors were found. However, the lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification. 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. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Seo et al. (US Pub No 2023/0231713) in view of Dods et al. (US Pub No 2024/0163097). Prior art Seo teaches: A computer-implemented method for tracking data input to a generative artificial intelligence model (generative AI) or a large language model (LLM), collectively referred to hereinbelow as “the model”, (e.g., tracking data using AI services ¶ 0028) comprising: receiving at an interface of a computing device a plurality of objects comprising the data input to the model (e.g., data processing ¶ 0031-0032 and AI pipeline NFT ¶ 0079); executing logic on the computing device for: generating a corresponding non-fungible token (NFT) for each object (e.g., mint a blockchain NFT ¶ 0015 & 0060); assigning a corresponding smart contract to each NFT [to control interactions with the NFT and its corresponding object] (e.g., assigning smart contract ¶ 0014 & a third protocol is NFT signature, and the NFT owner signs transaction details including NFT data, and transmits the transaction details to an NFT smart contract 0072-0075); recording the NFT and corresponding smart contract to a block for writing to a blockchain (e.g., “In a fourth protocol, the NFT smart contract receives the transaction details containing NFT data to mint the NFT. In a fifth protocol, when the transaction details for the NFT are confirmed on the blockchain network” ¶ 0072); and writing the block to the blockchain (e.g., committing the block to the blockchain network “with its own unique blockchain address as persistent proof” ¶0072). Seo discloses the claimed subject matter as discussed above with respect to assigning smart contract to each NFT (¶ 0072-0077), but does not explicitly discloses using smart contract to control interactions with the NFT and its corresponding object. However, analogous art from the same field of endeavor, Dobs teaches this by disclosing using smart contract to manage ownership of NFT and control interactions with the NFT (¶ 0161-0166). Therefore, based on Seo in view of Dobs, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Dobs to the system of Seo in order to provide “improvements in gathering, synthesizing, and analyzing data, anomaly identification, exception handling and root cause analysis without compromising security that would significantly improve safety and security for compliance tracking and reporting”. (@ Dods ¶ 0039). Hence, it would have been obvious to combine the references to obtain the invention as specified in the instant claims. The references above further teach claim: 2. The computer-implemented method of claim 1, wherein assigning the corresponding smart contract to each NFT to control interactions with the NFT and its corresponding object, comprises assigning a corresponding smart contract to each NFT that specifies permissions or restrictions to a type of object to which access to the corresponding NFT is being provided, access to the NFT and its corresponding object, duration of access to the NFT and its corresponding object, manner of use of the NFT and its corresponding object, distribution of the NFT and its corresponding object, or management of the NFT and its corresponding object (e.g., “smart contract receiving transaction containing the NFT metadata information and mint the AI pipeline NFT corresponding to the received transaction” @ Seo ¶ 0076 and smart contract is used to store and validate transaction @ Dobs ¶ 0164-0165). The references above further teach claim: 3. The computer-implemented method of claim 1, wherein assigning the corresponding smart contract to each NFT that specifies permissions or restrictions to access, use, or distribute the NFT and its corresponding object, comprises assigning a corresponding smart contract to each NFT that specifies remuneration for accessing, using, or distributing the NFT and its corresponding object (e.g., ownership information @ Seo ¶ 0011-00018 and ownership validation @ Dobs ¶ 0161-0166). The references above further teach claim: 4. The computer-implemented method of claim 1, further comprising: executing logic on the computing device for generating new data via the model based on the plurality of objects comprising the data input to the model and the corresponding smart contract for each NFT that specifies permissions or restrictions to access, use, or distribute, the NFT and its corresponding object (e.g., creating new AI pipeline NFT @ Seo ¶ 0056-0057 & creating another AI-NFT @Seo ¶ 0079 and @ Dobs ¶ 0185-0188). The references above further teach claim: 5. The computer-implemented method of claim 4, wherein receiving the plurality of objects comprising the data input to the model, comprises a chatbot and associated interface receiving the plurality of objects from a webpage in real time; and wherein generating the new data via the model, comprises generating the new data via the model based on the plurality of objects received from the webpage; and the computer-implemented method further comprising executing logic on the computing device for displaying in real-time the new data via the associated chatbot interface, separated from the plurality of objects received from the webpage (e.g., company S’s AI Assistant comprising dialogs repository teaching an AI chatbot with an interface chatting in real time @ Seo Fig. 3 and using web interface @ Dobs ¶ 0151 with chatbot @ Dobs ¶ 0185-0188). The references above further teach claim: 6. The computer-implemented method of claim 5, wherein displaying in real-time the new data via the associated chatbot interface, comprises displaying in real-time the new data including one or more of: one or more highlights of the plurality of objects received from the webpage; a heatmap representing a concentration, density, or distribution of a specific data set over a spatial area based on the plurality of objects received from the webpage; an automated web page summary; and one or more external links to curated or non-curated information or related multimedia content based on the plurality of objects received from the webpage (e.g., using dialogs repository and data for AI training from the web to respond in real-time @ Seo Fig. 3; using NFT media data @ Seo ¶ 0075; and web interface with multimedia input @ Dobs ¶ 0183-0188). The references above further teach claim: 7. The computer-implemented method of claim 4, further comprising executing logic on the computing device for analyzing the new data for accuracy and/or reliability (e.g., verify the transaction result value @ Seo ¶ 0057 and verify and validate NFT @ Dobs ¶ 0161-0165). The references above further teach claim: 8. The computer-implemented method of claim 7, wherein generating new data via the model based on the data input to the model, comprises generating new qualitative and/or quantitative data; and wherein analyzing the new data for accuracy or reliability comprises analyzing the new qualitative and/or quantitative data for accuracy and/or reliability (e.g., “event node F 113 may be configured to create a new AI pipeline NFT by selecting excellent tasks among execution values performed in parallel.” teaching both qualitative and quantitative data @ Seo ¶ 0056 & 0066 and mapping data @ Dobs ¶ 0094-0095). The references above further teach claim: 9. The computer-implemented method of claim 4, further comprising executing logic on the computing device for tracking how and when each of the plurality of objects comprising data input to the model is accessed according to the corresponding smart contract for each NFT that specifies permissions or restrictions to access, use, or distribute, the NFT and its corresponding object (e.g., tracking transaction with NFT metadata information @ Seo ¶ 0014-0015 & 0030 and “anomaly recognition and remediation generally include embodiments in which interface server(s) 102 implement via learning model(s) 134 a statistical learning based process (e.g., an “AI agent”) that performs tracking a global state of events in an accessed block chain, identifying anomalies, and deciding to trigger gathering additional information and/or filing a report to another entity” @ Dods ¶ 0094 with policies controlled by smart contracts @ Dobs ¶ 0151). The references above further teach claim: 10. The computer-implemented method of claim 9, wherein tracking how and when each of the plurality of objects comprising data input to the model is accessed according to the corresponding smart contract for each NFT that specifies permissions or restrictions to access, use, or distribute, the NFT and its corresponding object, comprises recording to a blockchain how and when each of the plurality of objects comprising data input to the model is accessed (e.g., “The AI pipeline NFT may include NFT metadata and an NFT smart contract.” @ Seo ¶ 0073 including ownership information and transaction information @ Seo ¶ 0074-0077; together with Dobs’ teaching of tracking NFT @ ¶ 0074-0075). The references above further teach claim: 11. The computer-implemented method of claim 9, wherein tracking how and when each of the plurality of objects comprising data input to the model is accessed comprises tracking whether a document comprising data input to the model is accessed through one of two types of access: a query, and opening the document and querying within the document itself (e.g., identify and isolate anomalies @ Dobs with detecting access types ¶ 0094-0095). The references above further teach claim: 12. The computer-implemented method of claim 11, further comprising executing logic on the computing device for assigning a respective weight to each of the two types of access; and measuring accesses to the document based on the respective weight assigned to each of the two types of access (e.g., analyze the input into vector representations with deep neural network learning model based on statistically learned results @ Dobs ¶ 0186). The references above further teach claim: 13. The computer-implemented method of claim 2, wherein assigning the corresponding smart contract to each NFT that specifies permissions or restrictions to access, use, or distribute, the NFT and its corresponding object, comprises establishing via the corresponding smart contract to each NFT an expiration or a renewal date to access, use, or distribute, the NFT and its corresponding object (e.g., smart contract are used for storing and validation @ Dobs ¶ 0161 and access restriction @ Dobs ¶ 0170-0180). Claim 14 is substantially similar to claim 1 above, and therefore the claim is likewise rejected. Claim 15 is substantially similar to claim 2 above, and therefore the claim is likewise rejected. Claim 16 is substantially similar to claim 3 above, and therefore the claim is likewise rejected. Claim 17 is substantially similar to claim 4 above, and therefore the claim is likewise rejected. Claim 18 is substantially similar to claim 5 above, and therefore the claim is likewise rejected. Claim 19 is substantially similar to claim 7 above, and therefore the claim is likewise rejected. Claim 20 is substantially similar to claim 13 above, and therefore the claim is likewise rejected. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Particularly, Milam et al. (US Pub No 2023/0043095) discloses relevant methods of using smart contracts to manage ownership. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAU LE whose telephone number is (571)270-7217. The examiner can normally be reached M-F 8:00-5:00. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, LINGLAN EDWARDS can be reached at (571) 270-5440. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /CHAU LE/Primary Examiner, Art Unit 2408
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Prosecution Timeline

Nov 11, 2024
Application Filed
Mar 01, 2026
Non-Final Rejection — §103 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
85%
Grant Probability
99%
With Interview (+16.9%)
2y 10m
Median Time to Grant
Low
PTA Risk
Based on 532 resolved cases by this examiner. Grant probability derived from career allow rate.

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