DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 06/12/2025 has been received, entered into the record, and considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
3. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
4. The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
5. Claims 8 and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Specifically, it is unclear as to whether the claimed “second content” in the limitation “wherein the generative AI model executes the first prompt to generate second content using a third set of data stored at the second location in memory” refers to the earlier claimed “second content” in the earlier limitation “responsive to detecting the first set of changes: inputting a second prompt to the generative AI model to obtain a second content that is based on the second set of data” in parent independent claims 1 and 12 respectively. For the purposes of examining the instant application, the examiner interprets the latter claimed “second content” as referring to the earlier claimed “second content” in parent independent claim 1.
Claim 10 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Specifically, it is unclear as to whether the claimed “first prompt” in the limitation “wherein the operations further comprise selectively monitoring data used to generate the first content based on a data type, at least by: generating a first prompt to include a pointer to a second location in memory storing a third set of data” refers to the earlier claimed “first prompt” in the earlier limitation “instructions which, when executed by one or more hardware processors, cause performance of operations comprising: generating a first prompt for a generative Artificial Intelligence (AI) model” in parent independent claim 1. For the purposes of examining the instant application, the examiner interprets the latter claimed “first prompt” as referring to the earlier claimed “first prompt” in parent independent claim 1.
Claim 11 recites the limitation “wherein executing the first set of machine-readable code results in displaying first visual content” in Page 3. There is insufficient antecedent basis for this limitation in the claim as no executing of the first set of machine-readable code is claimed earlier in the claim or in parent independent claim 1.
Claim 11 recites the limitation “wherein updating the first content comprises generating a second set of machine-readable code” in Page 3. There is insufficient antecedent basis for this limitation in the claim as no updating of the first content is claimed earlier in the claim or in parent independent claim 1.
Claim 11 recites the limitation “wherein executing the second set of machine-readable code results in displaying second visual content” in Page 3. There is insufficient antecedent basis for this limitation in the claim as no executing of the second set of machine-readable code is claimed earlier in the claim or in parent independent claim 1.
Claim Rejections - 35 USC § 101
6. 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.
7. Claims (1-11), (12-19), and (20) are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under the 2019 PEG, when considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (step 1). If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea) (step 2A prong 1), and if so, it must additionally be determined whether the claim is integrated into a practical application (step 2A prong 2). If an abstract idea is present in the claim without integration into a practical application, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself (step 2B).
In the instant case, claims (1-11), (12-19), and (20) are directed to one or more non-transitory computer readable media, method, and system respectively. Thus, each of the claims falls within one of the four statutory categories. However, the claims also fall within the judicial exception of an abstract idea.
Under Step 2A Prong 1, the test is to identify whether the claims are “directed to” a judicial exception. The examiner notes that the claimed invention is directed to an abstract idea in that the instant application is directed to mental processes, specifically generating content.
The examiner further notes that claims (1-11), (12-19), and (20) recite one or more non-transitory computer readable media, method, and system for generating content which is similar to themes defined above of method of mental processes such as performing the generation of content, and is similar to the abstract idea identified in the 2019 PEG in grouping “c” in that the claims recite certain methods of mental processes such as performing the generating of content. The limitations, substantially comprising the body of the claim, recite a process of generating content. The examiner notes that the claimed invention generates content. Because the limitations above closely follow the steps in generating content, and the steps of the claims involve mental processes, the claim recites an abstract idea consistent with the “mental processes” grouping set forth in the 2019 PEG.
Claim 1:
One or more non-transitory computer readable media comprising instructions which, when executed by one or more hardware processors, cause performance of operations comprising: generating a first prompt for a generative Artificial Intelligence (AI) model, the first prompt including instructions to generate a first content based on a first set of data stored at a first location in memory;
inputting the first prompt to the generative AI model to obtain the first content that is based on the first set of data;
subsequent to obtaining the first content from the generative AI model: monitoring, in real-time, the first set of data to detect changes to the first set of data;
detecting a first set of changes to the first set of data that modify the first set of data to a second set of data; and
responsive to detecting the first set of changes: inputting a second prompt to the generative AI model to obtain a second content that is based on the second set of data.
These limitations, as drafted, is an apparatus that, under its broadest reasonable interpretation, covers the performance of mental processes specifically generating content. Generating content has long before the modern computer was invented, and continues to be predominantly a product of human endeavor. The instant application is directed to generating content. Moreover, the generation of a defined prompt can be performed by a human via their mind and/or pen & paper. Additionally, the generation of first content can be performed by a human via their mind and/or pen & paper. Furthermore, the real-time monitoring for data change detection can be performed by a human via their mind and/or pen & paper. Moreover, the detection of changed data can be performed by a human via their mind and/or pen & paper. Additionally, the generation of second content can be performed by a human via their mind and/or pen & paper. Because the limitations above closely follow the steps of generating content, and the steps involved human judgments, observations and evaluations that can be practically or reasonably performed in the human mind and/or pen & paper, the claim recites an abstract idea consistent with the “mental process” grouping set forth in the 2019 PEG.
The mere nominal recitation of generic computing components such as a “One or more non-transitory computer readable media”, “one or more hardware processors”, and “memory” do not take the claim out of certain methods of mental processes grouping. Therefore, the limitation is directed to an abstract idea.
If the claims are directed toward the judicial exception of an abstract idea, it must then be determined under Step 2A Prong 2 whether the judicial exception is integrated into a practical application. The Examiner notes that considerations under Step 2A Prong 2 comprise most the consideration previously evaluated in the context of Step 2B. The Examiner submits that the considerations discussed previously determined that the claim does not recite “significantly more” at Step 2B would be evaluated the same under Step 2A Prong 1 and result in the determination that the claim does not integrate the abstract idea into a practical application. Specifically, the inputting of a first prompt to a model is simply a data transmission step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application. Moreover, the inputting of a second prompt to a model is simply a data transmission step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application.
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites words “apply it” (or an equivalent) with the judicial exception or merely includes instructions to implement an abstract idea. The instant application is directed to an apparatus instructing the reader to implement the identified apparatus of mental processes of generating content. The elements of the claim do not themselves amount to an improvement to the computer, to a technology or another technical field.
Here, the claim elements entirely comprise the abstract idea, leaving little if any aspects of the claim for further consideration under Step 2A Prong 2. In short, the claims have failed to integrate a practical application (see at least 84 Fed. Reg. (4) at 55). Under the 2019 PEG, this supports the conclusion that the claim is directed to an abstract idea, and the analysis proceeds to Step 2B.
While many considerations in Step 2A need not be reevaluated in Step 2B because the outcome will be the same. Here, on the basis of the additional elements other than the abstract idea, considered individually and in combination as discussed above, the Examiner respectfully submits that claim 1 does not contain any additional elements that individually or as an ordered combination amount to an inventive concept and the claims are ineligible.
With respect to the dependent claims do not recite anything that is found to render the abstract idea as being transformed into a patent eligible invention. The dependent claims are merely reciting further embellishments of the abstract idea and do not claim anything that amounts to significantly more than the abstract idea itself.
With respect to the dependent claims, they have been considered and are not found to be reciting anything that amounts to being significantly more than the abstract idea. Claims 2-11 are directed to further embellishments of the central theme of the abstract idea in that the claims are directed to further embellishments of the generating content of the steps of claim 1 and do not amount to significantly more.
Specifically, claim 2 recites the defining of the first prompt which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Furthermore, claim 3 recites the monitoring of writes which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Additionally, claim 4 recites the determining that changes meet a criteria which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Moreover, claim 5 recites the defining of the first and second prompts which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Furthermore, claim 6 recites the defining of the second prompt which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Additionally, claim 7 recites the periodic querying to a database server which is simply a data transmission step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application. Additionally, the obtaining of values is simply a data transmission step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application.
Moreover, claim 8 recites the defining of the first prompt and subsequent generation of second content which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more. Moreover, claim 8 recites the detection of changes that do not meet an update criteria which results in not updating the second content which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Additionally, claim 9 recites the defining first and second data types of the first set of data and first content respectively and subsequently converting the first set of data into a second data type which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Furthermore, claim 10 recites the selective monitoring of data via a generated defined prompt and generated defined first content which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Moreover, claim 11 recites the generation of the first and second code which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more. Moreover, the display of first and second visual content is simply a data output step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application.
Claim 12:
A method comprising: generating a first prompt for a generative Artificial Intelligence (AI) model, the first prompt including instructions to generate a first content based on a first set of data stored at a first location in memory;
inputting the first prompt to the generative AI model to obtain the first content that is based on the first set of data;
subsequent to obtaining the first content from the generative AI model: monitoring, in real-time, the first set of data to detect changes to the first set of data;
detecting a first set of changes to the first set of data that modify the first set of data to a second set of data; and
responsive to detecting the first set of changes: inputting a second prompt to the generative AI model to obtain a second content that is based on the second set of data.
These limitations, as drafted, is an apparatus that, under its broadest reasonable interpretation, covers the performance of mental processes specifically generating content. Generating content has long before the modern computer was invented, and continues to be predominantly a product of human endeavor. The instant application is directed to generating content. Moreover, the generation of a defined prompt can be performed by a human via their mind and/or pen & paper. Additionally, the generation of first content can be performed by a human via their mind and/or pen & paper. Furthermore, the real-time monitoring for data change detection can be performed by a human via their mind and/or pen & paper. Moreover, the detection of changed data can be performed by a human via their mind and/or pen & paper. Additionally, the generation of second content can be performed by a human via their mind and/or pen & paper. Because the limitations above closely follow the steps of generating content, and the steps involved human judgments, observations and evaluations that can be practically or reasonably performed in the human mind and/or pen & paper, the claim recites an abstract idea consistent with the “mental process” grouping set forth in the 2019 PEG.
The mere nominal recitation of generic computing components such as a “memory” does not take the claim out of certain methods of mental processes grouping. Therefore, the limitation is directed to an abstract idea.
If the claims are directed toward the judicial exception of an abstract idea, it must then be determined under Step 2A Prong 2 whether the judicial exception is integrated into a practical application. The Examiner notes that considerations under Step 2A Prong 2 comprise most the consideration previously evaluated in the context of Step 2B. The Examiner submits that the considerations discussed previously determined that the claim does not recite “significantly more” at Step 2B would be evaluated the same under Step 2A Prong 1 and result in the determination that the claim does not integrate the abstract idea into a practical application. Specifically, the inputting of a first prompt to a model is simply a data transmission step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application. Moreover, the inputting of a second prompt to a model is simply a data transmission step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application.
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites words “apply it” (or an equivalent) with the judicial exception or merely includes instructions to implement an abstract idea. The instant application is directed to an apparatus instructing the reader to implement the identified apparatus of mental processes of generating content. The elements of the claim do not themselves amount to an improvement to the computer, to a technology or another technical field.
Here, the claim elements entirely comprise the abstract idea, leaving little if any aspects of the claim for further consideration under Step 2A Prong 2. In short, the claims have failed to integrate a practical application (see at least 84 Fed. Reg. (4) at 55). Under the 2019 PEG, this supports the conclusion that the claim is directed to an abstract idea, and the analysis proceeds to Step 2B.
While many considerations in Step 2A need not be reevaluated in Step 2B because the outcome will be the same. Here, on the basis of the additional elements other than the abstract idea, considered individually and in combination as discussed above, the Examiner respectfully submits that claim 12 does not contain any additional elements that individually or as an ordered combination amount to an inventive concept and the claims are ineligible.
With respect to the dependent claims do not recite anything that is found to render the abstract idea as being transformed into a patent eligible invention. The dependent claims are merely reciting further embellishments of the abstract idea and do not claim anything that amounts to significantly more than the abstract idea itself.
With respect to the dependent claims, they have been considered and are not found to be reciting anything that amounts to being significantly more than the abstract idea. Claims 13-19 are directed to further embellishments of the central theme of the abstract idea in that the claims are directed to further embellishments of the generating content of the steps of claim 12 and do not amount to significantly more.
Specifically, claim 13 recites the defining of the first prompt which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Furthermore, claim 14 recites the monitoring of writes which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Additionally, claim 15 recites the determining that changes meet a criteria which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Moreover, claim 16 recites the defining of the first and second prompts which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Furthermore, claim 17 recites the defining of the second prompt which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Additionally, claim 18 recites the periodic querying to a database server which is simply a data transmission step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application. Additionally, the obtaining of values is simply a data transmission step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application.
Moreover, claim 19 recites the defining of the first prompt and subsequent generation of second content which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more. Moreover, claim 8 recites the detection of changes that do not meet an update criteria which results in not updating the second content which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Claim 20:
A system comprising: at least one device including a hardware processor;
the system being configured to perform operations comprising: generating a first prompt for a generative Artificial Intelligence (AI) model, the first prompt including instructions to generate a first content based on a first set of data stored at a first location in memory;
inputting the first prompt to the generative AI model to obtain the first content that is based on the first set of data;
subsequent to obtaining the first content from the generative AI model: monitoring, in real-time, the first set of data to detect changes to the first set of data;
detecting a first set of changes to the first set of data that modify the first set of data to a second set of data; and
responsive to detecting the first set of changes: inputting a second prompt to the generative AI model to obtain a second content that is based on the second set of data.
These limitations, as drafted, is an apparatus that, under its broadest reasonable interpretation, covers the performance of mental processes specifically generating content. Generating content has long before the modern computer was invented, and continues to be predominantly a product of human endeavor. The instant application is directed to generating content. Moreover, the generation of a defined prompt can be performed by a human via their mind and/or pen & paper. Additionally, the generation of first content can be performed by a human via their mind and/or pen & paper. Furthermore, the real-time monitoring for data change detection can be performed by a human via their mind and/or pen & paper. Moreover, the detection of changed data can be performed by a human via their mind and/or pen & paper. Additionally, the generation of second content can be performed by a human via their mind and/or pen & paper. Because the limitations above closely follow the steps of generating content, and the steps involved human judgments, observations and evaluations that can be practically or reasonably performed in the human mind and/or pen & paper, the claim recites an abstract idea consistent with the “mental process” grouping set forth in the 2019 PEG.
The mere nominal recitation of generic computing components such as a “a hardware processor” and “memory” do not take the claim out of certain methods of mental processes grouping. Therefore, the limitation is directed to an abstract idea.
If the claims are directed toward the judicial exception of an abstract idea, it must then be determined under Step 2A Prong 2 whether the judicial exception is integrated into a practical application. The Examiner notes that considerations under Step 2A Prong 2 comprise most the consideration previously evaluated in the context of Step 2B. The Examiner submits that the considerations discussed previously determined that the claim does not recite “significantly more” at Step 2B would be evaluated the same under Step 2A Prong 1 and result in the determination that the claim does not integrate the abstract idea into a practical application. Specifically, the inputting of a first prompt to a model is simply a data transmission step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application. Moreover, the inputting of a second prompt to a model is simply a data transmission step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application.
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites words “apply it” (or an equivalent) with the judicial exception or merely includes instructions to implement an abstract idea. The instant application is directed to an apparatus instructing the reader to implement the identified apparatus of mental processes of generating content. The elements of the claim do not themselves amount to an improvement to the computer, to a technology or another technical field.
Here, the claim elements entirely comprise the abstract idea, leaving little if any aspects of the claim for further consideration under Step 2A Prong 2. In short, the claims have failed to integrate a practical application (see at least 84 Fed. Reg. (4) at 55). Under the 2019 PEG, this supports the conclusion that the claim is directed to an abstract idea, and the analysis proceeds to Step 2B.
While many considerations in Step 2A need not be reevaluated in Step 2B because the outcome will be the same. Here, on the basis of the additional elements other than the abstract idea, considered individually and in combination as discussed above, the Examiner respectfully submits that claim 1 does not contain any additional elements that individually or as an ordered combination amount to an inventive concept and the claims are ineligible.
Claim Rejections - 35 USC § 102
8. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
9. Claims 1, 3-4, 9, 12, 14-15, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Doggett et al. (U.S. PGPUB 2024/0394965).
10. Regarding claims 1, 12, and 20, Doggett teaches one or more non-transitory computer readable media, method, and system comprising:
A) instructions which, when executed by one or more hardware processors, cause performance of operations comprising: generating a first prompt for a generative Artificial Intelligence (AI) model (Paragraphs 58-59);
B) the first prompt including instructions to generate a first content based on a first set of data stored at a first location in memory (Paragraphs 58-59);
C) inputting the first prompt to the generative AI model to obtain the first content that is based on the first set of data (Paragraphs 58-59);
D) subsequent to obtaining the first content from the generative AI model: monitoring, in real time, the first set of data to detect changes to the first set of data (Paragraphs 58-60);
E) detecting a first set of changes to the first set of data that modify the first set of data to a second set of data (Paragraphs 58-60); and
F) responsive to detecting the first set of changes: inputting a second prompt to the generative AI model to obtain a second content that is based on the second set of data (Paragraphs 58-60).
The examiner notes that Doggett teaches “instructions which, when executed by one or more hardware processors, cause performance of operations comprising: generating a first prompt for a generative Artificial Intelligence (AI) model” as “FIG. 3B illustrates the conversion of chat history 224 associated with a virtual character into a representation that is included in a prompt 328, according to various embodiments. As shown in FIG. 3B, chat history 224 includes a sequence of messages 322(1)-322(A), 322(A+1)-322(A+B), and 322(A+B+1)-322(A+B+C) (each of which is referred to individually herein as message 322) ordered by time” (Paragraph 58) and “As chat history 224 is updated with messages 322, one or more summaries 324 and 326 are generated from older messages 322 in chat history 224. More specifically, a certain number of the earliest messages 322 (e.g., messages 322(1)-322 (A)) in chat history 224 can be converted into an initial summary 324. For example, summary 324 could be generated by prompting an LLM (or another type of machine learning model with summarization capabilities) with (i) these earliest messages and (ii) one or more instructions to generate a summary of these messages” (Paragraph 59). The examiner further notes that an initial prompt to an LLM (i.e. the claimed AI model) for generating a summary of a chat history teaches the claimed generating of a first prompt. The examiner further notes that Doggett teaches “the first prompt including instructions to generate a first content based on a first set of data stored at a first location in memory” as “FIG. 3B illustrates the conversion of chat history 224 associated with a virtual character into a representation that is included in a prompt 328, according to various embodiments. As shown in FIG. 3B, chat history 224 includes a sequence of messages 322(1)-322(A), 322(A+1)-322(A+B), and 322(A+B+1)-322(A+B+C) (each of which is referred to individually herein as message 322) ordered by time” (Paragraph 58) and “As chat history 224 is updated with messages 322, one or more summaries 324 and 326 are generated from older messages 322 in chat history 224. More specifically, a certain number of the earliest messages 322 (e.g., messages 322(1)-322 (A)) in chat history 224 can be converted into an initial summary 324. For example, summary 324 could be generated by prompting an LLM (or another type of machine learning model with summarization capabilities) with (i) these earliest messages and (ii) one or more instructions to generate a summary of these messages” (Paragraph 59). The examiner further notes that an initial prompt (i.e. the claimed first prompt) is sent to an LLM for generating a summary (i.e. first content) of stored chat history 224 of messages (i.e. the claimed first set of data). The examiner further notes that Doggett teaches “inputting the first prompt to the generative AI model to obtain the first content that is based on the first set of data” as “FIG. 3B illustrates the conversion of chat history 224 associated with a virtual character into a representation that is included in a prompt 328, according to various embodiments. As shown in FIG. 3B, chat history 224 includes a sequence of messages 322(1)-322(A), 322(A+1)-322(A+B), and 322(A+B+1)-322(A+B+C) (each of which is referred to individually herein as message 322) ordered by time” (Paragraph 58) and “As chat history 224 is updated with messages 322, one or more summaries 324 and 326 are generated from older messages 322 in chat history 224. More specifically, a certain number of the earliest messages 322 (e.g., messages 322(1)-322 (A)) in chat history 224 can be converted into an initial summary 324. For example, summary 324 could be generated by prompting an LLM (or another type of machine learning model with summarization capabilities) with (i) these earliest messages and (ii) one or more instructions to generate a summary of these messages” (Paragraph 59). The examiner further notes that an initial prompt (i.e. first prompt) sent to an LLM (i.e. the claimed AI model) results in a generated summary 324 that is based off of a chat history 224 of messages (i.e. the claimed first set of data). The examiner further notes that Doggett teaches “subsequent to obtaining the first content from the generative AI model: monitoring, in real time, the first set of data to detect changes to the first set of data” as “FIG. 3B illustrates the conversion of chat history 224 associated with a virtual character into a representation that is included in a prompt 328, according to various embodiments. As shown in FIG. 3B, chat history 224 includes a sequence of messages 322(1)-322(A), 322(A+1)-322(A+B), and 322(A+B+1)-322(A+B+C) (each of which is referred to individually herein as message 322) ordered by time” (Paragraph 58), “As chat history 224 is updated with messages 322, one or more summaries 324 and 326 are generated from older messages 322 in chat history 224. More specifically, a certain number of the earliest messages 322 (e.g., messages 322(1)-322 (A)) in chat history 224 can be converted into an initial summary 324. For example, summary 324 could be generated by prompting an LLM (or another type of machine learning model with summarization capabilities) with (i) these earliest messages and (ii) one or more instructions to generate a summary of these messages” (Paragraph 59), and “After a certain number of additional messages 322 (e.g., messages 322(A+1)-322 (A+B)) have been added to chat history 224, summary 324 is combined with these additional messages 322 into an updated summary 326. Continuing with the above example, summary 326 could be generated by prompting the LLM and/or machine learning model with (i) summary 324, (ii) the additional messages 322, and (iii) one or more instructions to generate a new summary 326 from the older summary 324 and additional messages 322. This process can be repeated periodically and/or continually (e.g., at a certain frequency and/or after a certain number of additional messages 322 has been added to chat history 224) to generate an updated summary 326 that acts as a form of short-term memory for the virtual character” (Paragraph 60). The examiner further notes that determining that a certain number of additional messages have been added (i.e. changed) to the chat history 224 (i.e. the claimed first set of data) after the generation of the initial summary 324 in a continuous manner (i.e. real-time in the broadest reasonable interpretation) entails monitoring the chat history 224 in the first place. The examiner further notes that Doggett teaches “detecting a first set of changes to the first set of data that modify the first set of data to a second set of data” as “FIG. 3B illustrates the conversion of chat history 224 associated with a virtual character into a representation that is included in a prompt 328, according to various embodiments. As shown in FIG. 3B, chat history 224 includes a sequence of messages 322(1)-322(A), 322(A+1)-322(A+B), and 322(A+B+1)-322(A+B+C) (each of which is referred to individually herein as message 322) ordered by time” (Paragraph 58), “As chat history 224 is updated with messages 322, one or more summaries 324 and 326 are generated from older messages 322 in chat history 224. More specifically, a certain number of the earliest messages 322 (e.g., messages 322(1)-322 (A)) in chat history 224 can be converted into an initial summary 324. For example, summary 324 could be generated by prompting an LLM (or another type of machine learning model with summarization capabilities) with (i) these earliest messages and (ii) one or more instructions to generate a summary of these messages” (Paragraph 59), and “After a certain number of additional messages 322 (e.g., messages 322(A+1)-322 (A+B)) have been added to chat history 224, summary 324 is combined with these additional messages 322 into an updated summary 326. Continuing with the above example, summary 326 could be generated by prompting the LLM and/or machine learning model with (i) summary 324, (ii) the additional messages 322, and (iii) one or more instructions to generate a new summary 326 from the older summary 324 and additional messages 322. This process can be repeated periodically and/or continually (e.g., at a certain frequency and/or after a certain number of additional messages 322 has been added to chat history 224) to generate an updated summary 326 that acts as a form of short-term memory for the virtual character” (Paragraph 60). The examiner further notes that determining that chat history 324 has added a certain number of additional messages (i.e. first set of changes) teaches the claimed detecting. Such a chat history with the additional messages (i.e. first set of changes) teaches the claimed second set of data. The examiner further notes that Doggett teaches “responsive to detecting the first set of changes: inputting a second prompt to the generative AI model to obtain a second content that is based on the second set of data” as “FIG. 3B illustrates the conversion of chat history 224 associated with a virtual character into a representation that is included in a prompt 328, according to various embodiments. As shown in FIG. 3B, chat history 224 includes a sequence of messages 322(1)-322(A), 322(A+1)-322(A+B), and 322(A+B+1)-322(A+B+C) (each of which is referred to individually herein as message 322) ordered by time” (Paragraph 58), “As chat history 224 is updated with messages 322, one or more summaries 324 and 326 are generated from older messages 322 in chat history 224. More specifically, a certain number of the earliest messages 322 (e.g., messages 322(1)-322 (A)) in chat history 224 can be converted into an initial summary 324. For example, summary 324 could be generated by prompting an LLM (or another type of machine learning model with summarization capabilities) with (i) these earliest messages and (ii) one or more instructions to generate a summary of these messages” (Paragraph 59), and “After a certain number of additional messages 322 (e.g., messages 322(A+1)-322 (A+B)) have been added to chat history 224, summary 324 is combined with these additional messages 322 into an updated summary 326. Continuing with the above example, summary 326 could be generated by prompting the LLM and/or machine learning model with (i) summary 324, (ii) the additional messages 322, and (iii) one or more instructions to generate a new summary 326 from the older summary 324 and additional messages 322. This process can be repeated periodically and/or continually (e.g., at a certain frequency and/or after a certain number of additional messages 322 has been added to chat history 224) to generate an updated summary 326 that acts as a form of short-term memory for the virtual character” (Paragraph 60). The examiner further notes that after determining that a certain number of additional messages have been added to the chat history 224 (i.e. the claimed first set of data), an additional prompt (i.e. the claimed second prompt) is sent to the LLM (i.e. the claimed AI model) for generating a new summary 326 (i.e. the claimed second content) that is based on the updated chat history with additional messages (i.e. the claimed second data).
Regarding claims 3 and 14, Doggett further teaches one or more non-transitory computer readable media and method comprising:
A) wherein monitoring the first set of data comprises monitoring write operations corresponding to the first location in memory (Paragraphs 58-60).
The examiner further notes that Doggett teaches “wherein monitoring the first set of data comprises monitoring write operations corresponding to the first location in memory” as “FIG. 3B illustrates the conversion of chat history 224 associated with a virtual character into a representation that is included in a prompt 328, according to various embodiments. As shown in FIG. 3B, chat history 224 includes a sequence of messages 322(1)-322(A), 322(A+1)-322(A+B), and 322(A+B+1)-322(A+B+C) (each of which is referred to individually herein as message 322) ordered by time” (Paragraph 58), “As chat history 224 is updated with messages 322, one or more summaries 324 and 326 are generated from older messages 322 in chat history 224. More specifically, a certain number of the earliest messages 322 (e.g., messages 322(1)-322 (A)) in chat history 224 can be converted into an initial summary 324. For example, summary 324 could be generated by prompting an LLM (or another type of machine learning model with summarization capabilities) with (i) these earliest messages and (ii) one or more instructions to generate a summary of these messages” (Paragraph 59), and “After a certain number of additional messages 322 (e.g., messages 322(A+1)-322 (A+B)) have been added to chat history 224, summary 324 is combined with these additional messages 322 into an updated summary 326. Continuing with the above example, summary 326 could be generated by prompting the LLM and/or machine learning model with (i) summary 324, (ii) the additional messages 322, and (iii) one or more instructions to generate a new summary 326 from the older summary 324 and additional messages 322. This process can be repeated periodically and/or continually (e.g., at a certain frequency and/or after a certain number of additional messages 322 has been added to chat history 224) to generate an updated summary 326 that acts as a form of short-term memory for the virtual character” (Paragraph 60). The examiner further notes that determining that a certain number of additional messages have been added (i.e. write operations) to the stored chat history 224 (i.e. the claimed first set of data) teaches the claimed monitoring.
Regarding claims 4 and 15, Doggett further teaches one or more non-transitory computer readable media and method comprising:
A) wherein inputting the second prompt to generative AI model is further responsive to determining that the first set of changes meet content update criteria (Paragraphs 58-60).
The examiner notes that Doggett teaches “wherein inputting the second prompt to generative AI model is further responsive to determining that the first set of changes meet content update criteria” as “FIG. 3B illustrates the conversion of chat history 224 associated with a virtual character into a representation that is included in a prompt 328, according to various embodiments. As shown in FIG. 3B, chat history 224 includes a sequence of messages 322(1)-322(A), 322(A+1)-322(A+B), and 322(A+B+1)-322(A+B+C) (each of which is referred to individually herein as message 322) ordered by time” (Paragraph 58), “As chat history 224 is updated with messages 322, one or more summaries 324 and 326 are generated from older messages 322 in chat history 224. More specifically, a certain number of the earliest messages 322 (e.g., messages 322(1)-322 (A)) in chat history 224 can be converted into an initial summary 324. For example, summary 324 could be generated by prompting an LLM (or another type of machine learning model with summarization capabilities) with (i) these earliest messages and (ii) one or more instructions to generate a summary of these messages” (Paragraph 59), and “After a certain number of additional messages 322 (e.g., messages 322(A+1)-322 (A+B)) have been added to chat history 224, summary 324 is combined with these additional messages 322 into an updated summary 326. Continuing with the above example, summary 326 could be generated by prompting the LLM and/or machine learning model with (i) summary 324, (ii) the additional messages 322, and (iii) one or more instructions to generate a new summary 326 from the older summary 324 and additional messages 322. This process can be repeated periodically and/or continually (e.g., at a certain frequency and/or after a certain number of additional messages 322 has been added to chat history 224) to generate an updated summary 326 that acts as a form of short-term memory for the virtual character” (Paragraph 60). The examiner further notes that determining that a certain number (i.e. update criteria) of additional messages (i.e. first set of changes) have been added to the chat history 224 results in an additional prompt (i.e. the claimed second prompt) being sent to the LLM (i.e. the claimed AI model). Reaching the certain number of additional messages teaches the claimed content update criteria.
Regarding claim 9, Doggett further teaches one or more non-transitory computer readable media comprising:
A) wherein the first set of data is of a first data type (Paragraphs 58-59);
B) wherein the first content is of a second data type (Paragraphs 58-59); and
C) wherein generating the first content by the generative AI model includes converting the first set of data from the first data type into the second data type (Paragraphs 58-59).
The examiner notes that Doggett teaches “wherein the first set of data is of a first data type” as “FIG. 3B illustrates the conversion of chat history 224 associated with a virtual character into a representation that is included in a prompt 328, according to various embodiments. As shown in FIG. 3B, chat history 224 includes a sequence of messages 322(1)-322(A), 322(A+1)-322(A+B), and 322(A+B+1)-322(A+B+C) (each of which is referred to individually herein as message 322) ordered by time” (Paragraph 58) and “As chat history 224 is updated with messages 322, one or more summaries 324 and 326 are generated from older messages 322 in chat history 224. More specifically, a certain number of the earliest messages 322 (e.g., messages 322(1)-322 (A)) in chat history 224 can be converted into an initial summary 324. For example, summary 324 could be generated by prompting an LLM (or another type of machine learning model with summarization capabilities) with (i) these earliest messages and (ii) one or more instructions to generate a summary of these messages” (Paragraph 59). The examiner further notes that chat history (i.e. the claimed first set of data) is of a first data type. The examiner further notes that Doggett teaches “wherein the first content is of a second data type” as “FIG. 3B illustrates the conversion of chat history 224 associated with a virtual character into a representation that is included in a prompt 328, according to various embodiments. As shown in FIG. 3B, chat history 224 includes a sequence of messages 322(1)-322(A), 322(A+1)-322(A+B), and 322(A+B+1)-322(A+B+C) (each of which is referred to individually herein as message 322) ordered by time” (Paragraph 58) and “As chat history 224 is updated with messages 322, one or more summaries 324 and 326 are generated from older messages 322 in chat history 224. More specifically, a certain number of the earliest messages 322 (e.g., messages 322(1)-322 (A)) in chat history 224 can be converted into an initial summary 324. For example, summary 324 could be generated by prompting an LLM (or another type of machine learning model with summarization capabilities) with (i) these earliest messages and (ii) one or more instructions to generate a summary of these messages” (Paragraph 59). The examiner further notes that the generated summary 324 (i.e. the claimed first content) is of a second data type. The examiner further notes that Doggett teaches “wherein generating the first content by the generative AI model includes converting the first set of data from the first data type into the second data type” as “FIG. 3B illustrates the conversion of chat history 224 associated with a virtual character into a representation that is included in a prompt 328, according to various embodiments. As shown in FIG. 3B, chat history 224 includes a sequence of messages 322(1)-322(A), 322(A+1)-322(A+B), and 322(A+B+1)-322(A+B+C) (each of which is referred to individually herein as message 322) ordered by time” (Paragraph 58) and “As chat history 224 is updated with messages 322, one or more summaries 324 and 326 are generated from older messages 322 in chat history 224. More specifically, a certain number of the earliest messages 322 (e.g., messages 322(1)-322 (A)) in chat history 224 can be converted into an initial summary 324. For example, summary 324 could be generated by prompting an LLM (or another type of machine learning model with summarization capabilities) with (i) these earliest messages and (ii) one or more instructions to generate a summary of these messages” (Paragraph 59). The examiner further notes that the chat history 224 (i.e. first set of data) with a first data type is “converted” into a generated summary with a second data type via the LLM (i.e. generative AI model). Specifically, a chat history is of a different data type versus a summary.
Claim Rejections - 35 USC § 103
11. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
12. 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.
13. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
14. Claims 2, 5, 13, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Doggett et al. (U.S. PGPUB 2024/0394965) as applied to claims 1, 3-4, 9, 12, 14-15, and 20 above, and further in view of Goyal et al. (Article entitled “News Summarization and Evaluation in the Era of GPT-3”, dated 23 May 2023).
15. Regarding claims 2 and 13, Doggett does not explicitly teach one or more non-transitory computer readable media and method comprising:
A) wherein the first prompt (a) identifies the first location in memory storing the first set of data for use by the generative AI model and (b) does not identify the first set of data.
Goyal, however, teaches “wherein the first prompt (a) identifies the first location in memory storing the first set of data for use by the generative AI model and (b) does not identify the first set of data” as “Figure 3 shows an example of different length GPT3-D2 summaries for the same news article, using the following prompt format: Article: {{article}} Summarize the above article in N sentences” (Section 2.2).
The examiner further notes that Goyal teaches the concept of an LLM summarization prompt including a URL (i.e. a location in memory in the broadest reasonable interpretation) without the actual article text (which is interpreted as the not identifying of the first set of data in the broadest reasonable interpretation) as depicted in Figures 1 and 3. The combination would result in the prompts of Goyal to have memory locations solely for its summarization prompts.
It would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to combine the teachings of the cited references because teaching Goyal’s would have allowed Doggett’s to provide a method for providing adaptable summarization based on various settings, as noted by Goyal (Section 1).
Regarding claims 5 and 16, Doggett does not explicitly teach one or more non-transitory computer readable media and method comprising:
A) wherein the second prompt and the first prompt are (a) identical and (b) identify the first location in memory.
Goyal, however, teaches “wherein the second prompt and the first prompt are (a) identical and (b) identify the first location in memory” as “Figure 3 shows an example of different length GPT3-D2 summaries for the same news article, using the following prompt format: Article: {{article}} Summarize the above article in N sentences” (Section 2.2).
The examiner further notes that Goyal teaches the concept of an LLM summarization prompt submitted by a user including a URL (i.e. a location in memory in the broadest reasonable interpretation) as depicted in Figures 1 and 3. Such a user-submitted prompt can be submitted once again at a future time resulting in a second prompt that is identical. The combination would result in the first prompt of Goyal to have a location and be submitted again with a location.
It would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to combine the teachings of the cited references because teaching Goyal’s would have allowed Doggett’s to provide a method for providing adaptable summarization based on various settings, as noted by Goyal (Section 1).
16. Claims 6, 11, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Doggett et al. (U.S. PGPUB 2024/0394965) as applied to claims 1, 3-4, 9, 12, 14-15, and 20 above, and further in view of Russell et al. (U.S. PGPUB 2024/0296295).
17. Regarding claims 7 and 18, Doggett does not explicitly teach one or more non-transitory computer readable media and method comprising:
A) wherein the operations further comprise generating the second prompt, the second prompt identifying (a) the second set of data and/or (b) a second location in memory that stores the second set of data.
Russell, however, teaches “wherein the operations further comprise generating the second prompt, the second prompt identifying (a) the second set of data and/or (b) a second location in memory that stores the second set of data” as “Prompt templates for summarization requests may be similar to the prompt templates for answering a question and may include one or more of the same or similar elements as the question template discussed above. An example summarization prompt template is provided below: #Document File Name: {{Filename} Filepath: {{Path}} Filecontent: {{Body Content}} #Instructions Considering only the document above, generate one representative sentence from the document as a summary of the document. Include at least one verbatim quote from the above document to support the representative sentence. Just provide the best one, no other options. The text must be unformatted” (Paragraphs 34-43).
The examiner further notes that Russell teaches the concept of a summarization prompt including the entirety of text to be summarized and its filepath (i.e. location in memory). The combination would result in the second prompt of Doggett to have such the location of the modified chat history as well as the entire chat history for summarizing.
It would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to combine the teachings of the cited references because teaching Russell’s would have allowed Doggett’s to provide a method for providing reduced hallucinations in LLM produced summaries, as noted by Russell (Paragraph 11).
Regarding claim 11, Doggett does not explicitly teach one or more non-transitory computer readable media comprising:
A) wherein generating the first content comprises generating a first set of machine-readable code, wherein executing the first set of machine-readable code results in displaying first visual content; and
B) wherein updating the first content comprises generating a second set of machine-readable code, wherein executing the second set of machine-readable code results in displaying second visual content.
Russell, however, teaches “wherein generating the first content comprises generating a first set of machine-readable code, wherein executing the first set of machine-readable code results in displaying first visual content” as “a prior-request UI element 310 indicates that a prior request was to “Generate a summary of the document.” The responsive results to the request are displayed in a responsive-results UI element 312. The example responsive results in FIG. 3 include a summary 314 that was generated from the LLM” (Paragraphs 61) and “wherein updating the first content comprises generating a second set of machine-readable code, wherein executing the second set of machine-readable code results in displaying second visual content” as “The pane 306 may include a text input box 308 that receives inputs from a user for a request and/or question for the source document 322. When the request or question is entered into the input box 308, the request or question is provided to the productivity application 110 for processing along with the content of the source document 322…a prior-request UI element 310 indicates that a prior request was to “Generate a summary of the document.” The responsive results to the request are displayed in a responsive-results UI element 312. The example responsive results in FIG. 3 include a summary 314 that was generated from the LLM” (Paragraphs 60-61).
The examiner further notes that although Doggett clearly teaches first and second prompts, there is no explicit teaching of displaying the corresponding summaries (i.e. first and second content) back to a user. Nevertheless, Russell teaches the concept of multiple prompts 308 and 310 that when executed will result in the display of generated content (See the generated summary 314). The combination would result in the display of the generated first and second contents of Doggett.
It would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to combine the teachings of the cited references because teaching Russell’s would have allowed Doggett’s to provide a method for providing reduced hallucinations in LLM produced summaries, as noted by Russell (Paragraph 11).
18. Claims 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Doggett et al. (U.S. PGPUB 2024/0394965) as applied to claims 1, 3-4, 9, 12, 14-15, and 20 above, and further in view of Serio et al. (U.S. PGPUB 2007/0198493).
19. Regarding claims 7 and 18, Doggett does not explicitly teach one or more non-transitory computer readable media and method comprising:
A) wherein monitoring the first set of data comprises periodically querying a database server to obtain values for the first set of data stored at the first location in memory.
Serio, however, teaches “wherein monitoring the first set of data comprises periodically querying a database server to obtain values for the first set of data stored at the first location in memory” as “A system and method for monitoring transactions associated with a person or a property may include the steps of storing (and continuously, periodically or randomly updating) information pertaining to a person or a property in a database, receiving subscriber information including a name to monitor or a property address to monitor, continuously or periodically searching the database for the name or the property address, identifying a transaction associated with the name or the property address, and transmitting the transaction (via email address or other mediums) to the affected person(s)” (Paragraph 06).
The examiner further notes that Serio teaches the concept of periodically querying a database for data monitoring purposes. The combination would result in Doggett periodically querying for its data that it monitors.
It would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to combine the teachings of the cited references because teaching Serio’s would have allowed Doggett’s to provide a method for providing continuously monitoring remote data, as noted by Serio (Paragraphs 4 and 13).
Conclusion
20. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. PGPUB 2025/0272475 issued to Cunningham et al., on 28 August 2025. The subject matter disclosed therein is pertinent to that of claims 1-20 (e.g., methods to prompt AI models to generate content).
U.S. PGPUB 2025/0217427 issued to Gelli et al., on 03 July 2025. The subject matter disclosed therein is pertinent to that of claims 1-20 (e.g., methods to prompt AI models to generate content).
Contact Information
21. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Mahesh Dwivedi whose telephone number is (571) 272-2731. The examiner can normally be reached on Monday to Friday 8:20 am – 4:40 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Rones can be reached (571) 272-4085. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
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Mahesh Dwivedi
Primary Examiner
Art Unit 2168
August 11, 2026
/MAHESH H DWIVEDI/Primary Examiner, Art Unit 2168
