DETAILED ACTION
Status of Application
This action is a Non-Final Rejection. This action is in response to the application filed on November 16, 2023.
Claims 1-20 are pending.
Claims 1-5 and 18-20 have been withdrawn from examination in light of the telephone election of claims 6-17 for examination on the merits..
Claims 6-17 are rejected.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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 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.
Election/Restrictions
During a telephone conversation with Shirley Fung on September 18, 2026, a provisional election was made to prosecute the invention of Group II, claims 6-17. Affirmation of this election must be made by applicant in replying to this Office action. Claims 1-5 and 18-20 are withdrawn from further consideration by the examiner, 37 CFR 1.142(b), as being drawn to a non-elected invention.
The following is the restriction requirement:
Restriction to one of the following inventions is required under 35 U.S.C. 121:
I. Claims 1-5, drawn to analysis for content understanding, classified in G06F 40/20. See Figure 15.
II. Claims 6-17, drawn to building synthetic user memories, classified in G06F 16/2453. See Figure 16.
II. Claims 18 and 20, drawn to training a model and analyzing responses from the model, classified in G06F 30/27. See Figure 13.
Inventions I, II, and III are related as subcombinations usable together in a single combination. The subcombinations are distinct if they do not overlap in scope and are not obvious variants, and if it is shown that at least one subcombination is separately usable.
In the instant case, each subcombination has separate utility as described above and shown in the table below. See MPEP 806.05(d).
Group I
Claims 1-5
Group II
Claims 6-17
Group II
Claims 18-20
1. A method, comprising:
inputting a first prompt to a model, the first prompt comprising a first description of a first persona, a first description of a first shot of a content item, and a question, and the model being trained using one or more of: data collected from a content streaming platform, user communication data, market research data, and user research data;
receiving a first response from the model in response to the first prompt, the first response indicating a first understanding of the first description of the first shot of the content item by the first persona;
inputting a second prompt to the model, the first prompt comprising the first description of a first persona, a second description of a second shot of the content item following the first shot, and the question;
receiving a second response from the model in response to the second prompt, the second response indicating a second understanding of the second description of the second shot of the content item by the first persona;
determining whether the first understanding and the second understanding are different; and
in response to determining that the first understanding and the second understanding are different, marking a cue point in the content item between the first shot and the second shot as a location to insert content.
6. A computer-implemented system, comprising:
one or more processors; and
one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to:
determine first user data corresponding to a first user of a content streaming platform;
convert the first user data into first natural language log entries of a first memory log;
prompt a model using a first subset of the first natural language log entries extracted from the first memory log using an extraction function;
receive a first generated response in response to prompting using the first subset;
incorporate the first generated response into the first memory log;
after incorporating the first generated response, extract a second subset of the first natural language log entries from the first memory log using the extraction function;
prompt the model using the second subset of the first natural language log entries; and
receive a second generated response in response to the prompting using the second subset.
18. One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to:
transform user data into training data, the user data comprising data collected from a content streaming platform, and user communication data, and the training data comprising prompts and responses to the prompts;
update parameters of a model using the training data;
input a first prompt to the model, the first prompt comprising a first description of a first persona, a context description, and a question;
receive a first response from the model in response to the first prompt;
input a second prompt to the model, the second prompt comprising a second description of a second persona different from the first persona, the context description, and the question;
receive a second response from the model in response to the second prompt; and
analyze the first response and the second response.
As shown in the above table, group I is directed to analysis for content understanding, group II is directed to building synthetic user memories, and group III is directed to training a model and analyzing responses from the model. Each of these groups includes unique limitations.
The examiner has required restriction between subcombinations usable together. Where applicant elects a subcombination and claims thereto are subsequently found allowable, any claim(s) depending from or otherwise requiring all the limitations of the allowable subcombination will be examined for patentability in accordance with 37 CFR 1.104. See MPEP § 821.04(a). Applicant is advised that if any claim presented in a divisional application is anticipated by, or includes all the limitations of, a claim that is allowable in the present application, such claim may be subject to provisional statutory and/or nonstatutory double patenting rejections over the claims of the instant application.
Restriction for examination purposes as indicated is proper because all the inventions listed in this action are independent or distinct for the reasons given above and there would be a serious search and/or examination burden if restriction were not required because (1) the inventions have acquired a separate status in the art in view of their different classifications, (2) the inventions have acquired a separate status in the art due to their recognized divergent subject matter, and (3) the inventions require a different field of search using different search strategies and search queries. Because the inventions have distinct limitations and features, they will require separate consideration of statutory issues and separate searches of the prior art. Therefore, there is a search and examination burden without a restriction.
Applicant is advised that the reply to this requirement to be complete must include (i) an election of an invention to be examined even though the requirement may be traversed (37 CFR 1.143) and (ii) identification of the claims encompassing the elected invention.
The election of an invention may be made with or without traverse. To reserve a right to petition, the election must be made with traverse. If the reply does not distinctly and specifically point out supposed errors in the restriction requirement, the election shall be treated as an election without traverse. Traversal must be presented at the time of election in order to be considered timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are added after the election, applicant must indicate which of these claims are readable upon the elected invention.
Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other invention.
Applicant is reminded that upon the cancellation of claims to a non-elected invention, the inventorship must be corrected in compliance with 37 CFR 1.48(a) if one or more of the currently named inventors is no longer an inventor of at least one claim remaining in the application. A request to correct inventorship under 37 CFR 1.48(a) must be accompanied by an application data sheet in accordance with 37 CFR 1.76 that identifies each inventor by his or her legal name and by the processing fee required under 37 CFR 1.17(i).
Information Disclosure Statement
The information disclosure statement (IDS) submitted on November 17, 2023 has been considered by the examiner.
Claim Interpretation
Applicant should be aware that there is claim language that does not serve to differentiate the claims from the prior art and/or provide an additional element that can be a consideration for eligibility1. See MPEP 2103(c).
Intended Use
Intended use language is generally not given patentable weight. See MPEP 2114(II) ("A claim containing a 'recitation with respect to the manner in which a claimed apparatus is intended to be employed does not differentiate the claimed apparatus from a prior art apparatus’ if the prior art apparatus teaches all the structural limitations of the claim. Ex parte Masham, 2 USPQ2d 1647 (Bd. Pat. App. & Inter. 1987).”); see also MPEP 2103(C). Examples of claim limitations that are often found to precede intended use include “adapted to,” “capable of,” “sufficient to,” “whereby,” and “for.”
The following limitations include intended use limitations:
Claim 11: wherein the one or more processors to prompt the model using the first subset of the first natural language log entries comprises: the one or more processors to input a question and the first subset of the first natural language log entries to the model to generate an opinion about the first subset of the first natural language log entries
Claim 12: wherein the one or more processors to prompt the model using the first subset of the first natural language log entries comprises: the one or more processors to input a question and the first subset of the first natural language log entries to the model to generate a statement about the first subset of the first natural language log entries and a reasoning behind the statement
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 6-17 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Does the Claim Fall within a Statutory Category? (see MPEP 2106.03)
Yes, with respect to claims 6-17, which recite a system and, therefore, are directed to the statutory class of machine or manufacture.
Step 2A, Prong One: Is a Judicial Exception Recited? (see MPEP 2106.04(a))
The following claims identify the limitations that recite the abstract idea in regular text and that recite additional elements in bold:
6. A computer-implemented system, comprising:
one or more processors; and
one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to:
determine first user data corresponding to a first user of a content streaming platform;
convert the first user data into first natural language log entries of a first memory log;
prompt a model using a first subset of the first natural language log entries extracted from the first memory log using an extraction function;
receive a first generated response in response to prompting using the first subset;
incorporate the first generated response into the first memory log;
after incorporating the first generated response, extract a second subset of the first natural language log entries from the first memory log using the extraction function;
prompt the model using the second subset of the first natural language log entries; and
receive a second generated response in response to the prompting using the second subset.
7. The computer-implemented system of claim 6, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
incorporate the second generated response into the first memory log.
8. The computer-implemented system of claim 6, wherein the extraction function comprises:
a scoring function that scores the first natural language log entries based on freshness and accuracy.
9. The computer-implemented system of claim 8, wherein the accuracy is measured based on an evaluation of a response generated by the model against the first user data.
10. The computer-implemented system of claim 6, wherein the extraction function comprises:
a selection function that selects one or more responses generated by the model.
11. The computer-implemented system of claim 6, wherein the one or more processors to prompt the model using the first subset of the first natural language log entries comprises:
the one or more processors to input a question and the first subset of the first natural language log entries to the model to generate an opinion about the first subset of the first natural language log entries.
12. The computer-implemented system of claim 6, wherein the one or more processors to prompt the model using the first subset of the first natural language log entries comprises:
the one or more processors to input a question and the first subset of the first natural language log entries to the model to generate a statement about the first subset of the first natural language log entries and a reasoning behind the statement.
13. The computer-implemented system of claim 6, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
determine second user data corresponding to a second user of the content streaming platform; and
convert the second user data into second natural language log entries of a second memory log; and
incorporate one or more third responses generated by the model using one or more second natural language log entries of the second memory log into the second memory log.
14. The computer-implemented system of claim 13, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
analyzing (1) a response generated by the model in response to a test question and one or more first natural language log entries of the first memory log, and (2) a response generated by the model in response to the test question and one or more second natural language log entries of the second memory log.
15. The computer-implemented system of claim 6, wherein the first user data comprises demographic information about the first user, one or more survey questions, and one or more survey answers.
16. The computer-implemented system of claim 6, wherein the first user data comprises user interactivity data of the first user on the content streaming platform logged during an experiment.
17. The computer-implemented system of claim 16, wherein the one or more processors to convert the first user data into the first natural language log entries of the first memory log comprises: the one or more processors to translate the user interactivity data into natural language descriptions of the user interactivity data.
Yes. But for the recited additional elements as shown above in bold, the remaining limitations of the claims recite certain methods of organizing human activity. The claims are directed to generating synthetic user memories for estimating user reactions and preferences. This type of method of organizing human activity is a commercial interaction such as marketing or sales activities or behaviors.
The claims also recite mental processes. For example, determining first user data is evaluation or judgment, converting the first user data is evaluation or judgment, prompting a model is evaluation, receiving a first generated response is judgment or opinion, incorporating the first generated response is evaluation or judgment, extracting a second subset of the first natural language log entries is evaluation, prompting the model using the second subset of data is evaluation, and receiving a second generated response is judgment or opinion. Thus, the claims recite an abstract idea.
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application? (see MPEP 2106.04(d))
No. The claims as a whole merely use a computer as a tool to perform the abstract idea. The computing components (i.e., additional elements that are in bold above) are recited at a high level of generality and are merely invoked as a tool to implement the steps. For example, only a programmed general purpose computing device (i.e., claimed “one or more processors”) is needed to implement the claimed process. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, there is no improvement to the functioning of a computer or technology. Therefore, the abstract idea is not integrated into a practical application.
Step 2B: Does the Claim Provide an Inventive Concept? (see MPEP 2106.05)
No. As discussed with respect to Step 2A, Prong 2, the additional elements in the claims, both individually and in combination, amount to no more than tools to perform the abstract idea. Merely performing the abstract idea using a computer cannot provide an inventive concept. Therefore, the claims do not provide an inventive concept.
As such, the claims are not patent eligible.
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 6, 7, and 10-14 are rejected under 35 U.S.C. 103 as being unpatentable over Park et al., "Generative Agents: Interactive Simulacra of Human Behavior," arXiv:2304.03442v2 [cs.HC] 6 Aug 2023, UIST '23, October-November 2023, 22 pages.
Claim 6:
Park teaches:
one or more processors; and one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to: (see at least Park, a computer is inherently needed to implement the processes described in Park).
determine first user data corresponding to a first user… (see at least Park, page 8, left column (“At the center of our architecture is the memory stream, a database that maintains a comprehensive record of an agent’s experience.”)).
convert the first user data into first natural language log entries of a first memory log (see at least Park, page 2, right column (“The first is the memory stream, a long-term memory module that records, in natural language, a comprehensive list of the agent’s experiences.”); page 8, left column (“Everything in the architecture is recorded and reasoned over as a natural language description, allowing the architecture to leverage a large language model.”); page 8, right column (“The memory stream maintains a comprehensive record of the agent’s experience. It is a list of memory objects, where each object contains a natural language description, a creating timestamp, an a most recent access timestamp.”)).
prompt a model using a first subset of the first natural language log entries extracted from the first memory log using an extraction function (see at least Park, page 8, left column (“From the memory stream, records are retrieved as relevant to plan the agent’s actions and react appropriately to the environment.”); page 9, Figure 6 caption (“The memory stream comprises a large number of observations that are relevant and irrelevant to the agent’s current situation. Retrieval identifies a subset of these observations that should be passed to the language model to condition its response to the situation.”)).
receive a first generated response in response to prompting using the first subset; incorporate the first generated response into the first memory log; after incorporating the first generated response, extract a second subset of the first natural language log entries from the first memory log using the extraction function; prompt the model using the second subset of the first natural language log entries; and receive a second generated response in response to the prompting using the second subset (see at least Park, page 4, right column (“prompt chain”); page 8, Figure 5 (Data is fed back into the memory stream.); page 11, right column (“Generative agents operate in an action loop where, at each time step, they perceive the world around them and those perceived observations are stored in their memory stream. We prompt the language model with these observations to decide whether the agent should continue with their existing plan, or react.”)).
Park does not explicitly teach:
…of a content streaming platform.
Park, however, teaches:
Generative agents may be used for other purposes. See Park, page 17, left column (“Generative agents have vast potential applications that extend beyond the sandbox demonstration presented in this work, especially in domains that would benefit from a model of human behavior based on long-term experience.”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate this feature with Park’s generative agents. One of ordinary skill in the art would have been motivated to incorporate this feature for the purpose of generating proxies of human behavior to receive feedback relating to content streaming platforms in a manner that is cost efficient and useful. This may be very useful for marketing, research, and business decisions.
Claim 7:
Park further teaches:
incorporate the second generated response into the first memory log (see at least Park, page 8, Figure 5 (Data is fed back into the memory stream.); page 11, right column (“Generative agents operate in an action loop where, at each time step, they perceive the world around them and those perceived observations are stored in their memory stream. We prompt the language model with these observations to decide whether the agent should continue with their existing plan, or react.”)).
Claim 10:
Park further teaches:
wherein the extraction function comprises: a selection function that selects one or more responses generated by the model (see at least Park, page 8, Figure 5 (Data is fed back into the memory stream.); page 11, right column (“Generative agents operate in an action loop where, at each time step, they perceive the world around them and those perceived observations are stored in their memory stream. We prompt the language model with these observations to decide whether the agent should continue with their existing plan, or react.”)).
Claim 11:
Park further teaches:
wherein the one or more processors to prompt the model using the first subset of the first natural language log entries comprises: the one or more processors to input a question and the first subset of the first natural language log entries to the model to generate an opinion about the first subset of the first natural language log entries (see at least Park, page 10, left column (“The first step in reflection is for the agent to determine what to reflect on, by identifying questions that an be asked given the agent’s most recent experiences. We query the large language model with the 100 most recent records in the agent’s memory stream (e.g., Klaus Mueller is reading a book on gentrification,” “Klaus Mueller is conversing with a librarian about his research project”, “desk at the library is currently unoccupied”) and prompt the language model, “Given only the information above, what are 3 most salient high-level questions we can answer about the subjects in the statements?” The model’s response generates candidate questions: for example, What topic is Klaus Mueller passionate about? And What is the relationship between Klaus Mueller and Maria Lopez? We use these generated questions as queries for retrieval, and gather relevant memories (including other reflections) for each question.”)).
Claim 12:
Park further teaches:
wherein the one or more processors to prompt the model using the first subset of the first natural language log entries comprises: the one or more processors to input a question and the first subset of the first natural language log entries to the model to generate a statement about the first subset of the first natural language log entries and a reasoning behind the statement (see at least Park, page 10, left column (“The first step in reflection is for the agent to determine what to reflect on, by identifying questions that an be asked given the agent’s most recent experiences. We query the large language model with the 100 most recent records in the agent’s memory stream (e.g., Klaus Mueller is reading a book on gentrification,” “Klaus Mueller is conversing with a librarian about his research project”, “desk at the library is currently unoccupied”) and prompt the language model, “Given only the information above, what are 3 most salient high-level questions we can answer about the subjects in the statements?” The model’s response generates candidate questions: for example, What topic is Klaus Mueller passionate about? And What is the relationship between Klaus Mueller and Maria Lopez? We use these generated questions as queries for retrieval, and gather relevant memories (including other reflections) for each question.”)).
Claim 13:
Park further teaches:
determine second user data corresponding to a second user of the content streaming platform; and convert the second user data into second natural language log entries of a second memory log; and incorporate one or more third responses generated by the model using one or more second natural language log entries of the second memory log into the second memory log (see at least Park, this document shows that the process can be performed for multiple generative agents.)).
Claim 14:
Park further teaches:
analyzing (1) a response generated by the model in response to a test question and one or more first natural language log entries of the first memory log, and (2) a response generated by the model in response to the test question and one or more second natural language log entries of the second memory log (see at least Park, pages 5 and 6 (Unique agents interact and communicate, which requires model output with respect to each agent to be analyzed.)).
Claims 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Park et al., "Generative Agents: Interactive Simulacra of Human Behavior," arXiv:2304.03442v2 [cs.HC] 6 Aug 2023, UIST '23, October-November 2023, 22 pages and Atcheson, U.S. Patent Application Publication Number 2020/0160229 A1.
Claim 8:
Park further teaches:
wherein the extraction function comprises: a scoring function that scores the first natural language log entries based on freshness (see at least Park, page 8, left column (“Recency assigns a higher score to memory objects that were recently accessed, so that events from a moment ago or this morning are likely to remain in the agent’s attention sphere.”)).
Park does not explicitly teach, but Atcheson, however, does teach:
and accuracy (see at least Atcheson, paragraph 0090 (Accurate data is fed back into the model.)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Atcheson’s method of using accurate data with Park’s generative agents. One of ordinary skill in the art would have been motivated to incorporate this feature for the purpose of having the score of Park reflect the data that will provide the best results, and accurate data will provide a more accurate and useful result than inaccurate data.
Claim 9:
Park does not explicitly teach, but Atcheson, however, does teach:
wherein the accuracy is measured based on an evaluation of a response generated by the model against the first user data (see at least Atcheson, paragraph 0090 (“The user interface 1302 additionally includes a selectable radio button 1308, which indicates whether the model should be updated with subsequent behavior information from a user profile for which the user experience 106 is generated. As described herein, this feedback information is useable to improve an accuracy of subsequent outputs generated by the selected classifier model 1304. For instance, in the context of a car prediction classifier model, the output of the model may provide a prediction of a type, make, and model of car that the given user profile(s) are likely to purchase, with at least a 55% confidence. Given this example output, a user experience generated by the user experience system 104 may include an image of the make and model of car identified by the selected classifier model 1304, such as a web page configured to target certain user profiles. Feedback information may then be gleaned from a respective user profile, such as information describing an amount of time that the user profile dwells on the web page including the picture of the make and model of car before navigating to a different web page. In this manner, long dwell times can be associated with positive feedback to the selected classifier model 1304, indicating that the output was accurate, while short dwell times can be associated with negative feedback, indicating that the model's output was inaccurate. In response to receiving selection of the radio button 1308, the user experience system 104 may monitor a corresponding one or more user profiles 108 for behavior information 110 associated with a generated user experience 106 and subsequently apply the behavior information 110 as feedback to the selected classifier model 1304 in a manner similar to the similarity criterion 306, as described with respect to FIG. 3.”)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Atcheson’s method of using accurate data with Park’s generative agents. One of ordinary skill in the art would have been motivated to incorporate this feature for the purpose of having the score of Park reflect the data that will provide the best results, and accurate data will provide a more accurate and useful result than inaccurate data.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Park et al., "Generative Agents: Interactive Simulacra of Human Behavior," arXiv:2304.03442v2 [cs.HC] 6 Aug 2023, UIST '23, October-November 2023, 22 pages and Lee et al., WO 2024/077103 A2.
Claim 15:
Park does not explicitly teach, but Lee, however, does teach:
wherein the first user data comprises demographic information about the first user, one or more survey questions, and one or more survey answers (see at least Lee, paragraph 0022 (“In some embodiments of the present disclosure, machine learning models can be trained and/or used to predict user progress on a therapy plan. For example, a machine learning model may be trained to evaluate input such as the user’s responses to survey questions, the demographics of the user (e.g., their age, race, ethnicity, gender, marital status, education, employment, and the like), and the like, in order to generate predicted progress measures (e.g., indicating the probability that they will reach a given stage of the plan, such as completing enrollment, indicating the stage they will likely reach in the plan, and the like).”); paragraph 0031 (“In the illustrated example, the user device 110 provides a set of user data 1 15 to the machine learning system 120. The user data 1 10 can generally include a wide variety of data relating to the user 105, such as their demographics (e.g., age, weight, height, and the like), survey data (e.g., their responses to survey questions), sleep and/or medical history, and the like. In at least one embodiment, the user data 1 15 is provided to the machine learning system 120 at the start or beginning of the enrollment process for a therapy (e.g., for PAP therapy). For example, the user data 1 15 may be collected as part of a survey or questionnaire, where the user 105 is a prospective or potential PAP patient and provides the survey responses for purposes of determining whether to proceed with PAP (e.g., to determine whether they have sleep apnea, whether PAP therapy may help, and the like).”)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Lee’s demographic and survey data with Park’s generative agents. One of ordinary skill in the art would have been motivated to incorporate this feature for the purpose of generating output that simulates how different users would respond to questions regarding a content streaming platform. This would allow for a platform to implement improvements without having to survey actual users.
Claims 16 and 17 are not rejected under 35 U.S.C. 103. The limitation of claim 16, as combined with independent claim 6, was not found to be disclosed in or obvious in light of the prior art.
Relevant Prior Art
The following references are relevant to Applicant’s invention:
Ho, Nhu. “Harnessing Generative AI: Prompt Chaining & Multi-Model Orchestration,” https://www.cognigy.com/product-updates/generative-ai-prompt-chaining-llm-orchestration (June 19, 2023). This reference discusses prompt chaining.
Email Communications
Per MPEP 502.03, Applicant may authorize email communications by filing Form PTO/SB/439, available at https://www.uspto.gov/sites/default/files/documents/sb0439.pdf, via the USPTO patent electronic filing system.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIZABETH H ROSEN whose telephone number is (571) 270-1850 and email address is elizabeth.rosen@uspto.gov. The examiner can normally be reached Monday - Friday, 10 AM ET - 7 PM ET.
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, Michael Anderson, can be reached at 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ELIZABETH H ROSEN/Primary Examiner, 3693
1 See MPEP 2106.04(d)(2) (“Examiners should keep in mind that in order to qualify as a "treatment" or "prophylaxis" limitation for purposes of this consideration, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition. An example of such a limitation is a step of "administering amazonic acid to a patient" or a step of "administering a course of plasmapheresis to a patient." If the limitation does not actually provide a treatment or prophylaxis, e.g., it is merely an intended use of the claimed invention or a field of use limitation, then it cannot integrate a judicial exception under the "treatment or prophylaxis" consideration. For example, a step of "prescribing a topical steroid to a patient with eczema" is not a positive limitation because it does not require that the steroid actually be used by or on the patient, and a recitation that a claimed product is a "pharmaceutical composition" or that a "feed dispenser is operable to dispense a mineral supplement" are not affirmative limitations because they are merely indicating how the claimed invention might be used.”)
Prosecution Insights