Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement(s) (IDS) was/were submitted on
10/05/2023, 2/21/2025, 7/15/2025, 9/9/2025, 12/4/2025, 5/27/2026
The submission(s) is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Claim Objections
Claim(s) 4, 13, and 20 are objected to because of the following informalities:
Claims 4, 13, and 20 line 3 (4 for claim 20) reads: “is a received from”. It should read: “is received from”. Remove “a”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
According to the first part of the analysis, in the instant case, claims 1-4 are directed to a system, claims 5-13 are directed to a method, and claims 14-20 are directed to non-transitory computer-readable media. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding Claim 1:
Step 2A Prong 1:
identify one or more portions of a shared template to complete according to a classification determined for the natural language request; (This step for identifying one or more portions of a shared template to complete according to a classification determined for the natural language request is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).))
generate a prompt to perform the natural language task with the identified one or more portions of the shared template completed; (This step for generating a prompt to perform the natural language task with the identified one or more portions of the shared template completed is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).))
Step 2A Prong 2: This judicial exception is not integrated into a practical application
Additional elements:
A system, comprising:
A plurality of computing devices, respective comprising at least one processor and a memory, configured to implement a generative machine learning system configured to: (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic classifier as a tool to perform the abstract idea (i.e., generating/identifying) – see MPEP 2106.05(f).)
receive, via an interface, a natural language request to perform a natural language task; (This step is directed to receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
instruct a generative machine learning model to perform the prompt generated according to the shared template, wherein the generative machine learning model was tuned to perform a plurality of natural language tasks, including the natural language task, according to completing identified portions of the shared template for the plurality of natural language tasks using a tuning data set; ; (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
return, via the interface, a response to the natural language request based, at least in part, on a result received from the generative machine learning model. (This step is directed to presenting information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra solution activity in combination of mere instructions to perform generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A system, comprising:
A plurality of computing devices, respective comprising at least one processor and a memory, configured to implement a generative machine learning system configured to: (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic classifier as a tool to perform the abstract idea (i.e., generating/identifying) – see MPEP 2106.05(f).)
receive, via an interface, a natural language request to perform a natural language task (This step is directed to transmitting or receiving information and is a well understood, routine and conventional activity as identified by the court (MPEP2106.05(d)(ll)(i)))).
instruct a generative machine learning model to perform the prompt generated according to the shared template, wherein the generative machine learning model was tuned to perform a plurality of natural language tasks, including the natural language task, according to completing identified portions of the shared template for the plurality of natural language tasks using a tuning data set; ; (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
return, via the interface, a response to the natural language request based, at least in part, on a result received from the generative machine learning model. (This step is directed to presenting offers and gathering statistics/information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional as identified by the court (MPEP2106.05(d)(ll)(i)))).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 2:
Incorporates the rejection of claim 1.
Step 2A Prong 1:
template when generating the prompt. (This step for searching one or more data repositories to obtain data is practically performable in the human mind and is understood to be a recitation of a mental process (i.e. evaluation).)
Step 2A Prong 2 & 2B: This judicial exception is not integrated into a practical application.
Additional elements:
The system of claim 1, wherein the generative machine learning system is further configured to (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic classifier as a tool to perform the abstract idea (i.e., searching) – see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are merely adding the words “apply it” with the judicial exception that are implemented to perform the disclosed abstract idea above.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are merely adding the words “apply it” with the judicial exception that are implemented to perform the disclosed abstract idea above.
Regarding Claim 3:
Incorporates the rejection of claim 1.
Step 2A Prong 1: The claim does not recite additional abstract ideas.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
The system of claim 1, wherein the generative machine learning model was tuned according to a specified mixture of different natural tasks included in a tuning data set. (tuning a model (e.g. generative machine learning model) is understood as mere instructions to implement an abstract idea (e.g., evaluate labels) on a computer – see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere instructions/generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The system of claim 1, wherein the generative machine learning model was tuned according to a specified mixture of different natural tasks included in a tuning data set. (tuning a model (e.g. generative machine learning model) is understood as mere instructions to implement an abstract idea (e.g., evaluate labels) on a computer – see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 4:
Incorporates the rejection of claim 1.
Step 2A Prong 1:
The claim does not recite additional abstract ideas.
Step 2A Prong 2 & 2B: This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The system of claim 1, wherein the generative machine learning system is a natural language generative application service offered by a provider network and wherein the natural language request is a received from a natural language generative application created at the natural language generative application service. (Amounts to generally linking the abstract ideas to a particular technological environment or field of use, as discussed in MPEP 2106.05(h).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are linking the abstract ideas to a particular technological environment or field of use that does not impose any meaningful limits on practicing the abstract ideas.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are merely linking the abstract ideas to a particular technological environment or field of use.
Regarding Claim 5:
Step 2A Prong 1:
identifying one or more portions of a shared template to complete as part of generating a prompt to perform the natural language task; (This step for identifying one or more portions of a shared template to generate a prompt to perform the natural language task is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).))
Step 2A Prong 2: The judicial exception is not integrated into a practical application
Additional elements:
A method, comprising: receiving, via an interface of a generative machine learning system, a natural language request to perform a natural language task; (This step is directed to receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
instructing a generative machine learning model to perform the prompt generated according to the shared template, wherein the generative machine learning model was tuned to perform a plurality of natural language tasks, including the natural language task, according to completing identified portions of the shared template for the plurality of natural language tasks using a tuning data set; (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
returning, via the interface of the generative machine learning system, a response to the natural language request based, at least in part, on a result received from the generative machine learning model. (This step is directed to presenting information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra solution activity in combination of mere instructions to perform generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A method, comprising: receiving, via an interface of a generative machine learning system, a natural language request to perform a natural language task; (This step is directed to transmitting or receiving information and is a well understood, routine and conventional activity as identified by the court (MPEP2106.05(d)(ll)(i)))).
instructing a generative machine learning model to perform the prompt generated according to the shared template, wherein the generative machine learning model was tuned to perform a plurality of natural language tasks, including the natural language task, according to completing identified portions of the shared template for the plurality of natural language tasks using a tuning data set; (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
returning, via the interface of the generative machine learning system, a response to the natural language request based, at least in part, on a result received from the generative machine learning model. (This step is directed to presenting offers and gathering statistics/information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional as identified by the court (MPEP2106.05(d)(ll)(i)))).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 7:
Incorporates the rejection of claim 5
2A Prong 1:
The method of claim 5, wherein the prompt is generated without obtaining data from one or more data repositories to complete a portion of the prompt. (This step for generating the prompt without obtaining data from one or more data repositories is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).))
Step 2A Prong 2 & 2B: The claim does not recite any additional elements
Regarding Claim 8:
Incorporates the rejection of claim 5
Step 2A Prong 1:
The method of claim 5, wherein identifying the one or more portions of the shared template to complete as part of generating a prompt to perform the natural language task comprises determining an intent classification for the natural language request to perform the natural language task, wherein the intent classification is mapped to the one or more portions of the shared template to complete. (This step for identifying one or more portions of a shared template to generate a prompt to perform the natural language task, determining an intent classification, and mapping to portions of the shared template is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).))
Step 2A Prong 2 & 2B: The claim does not recite any additional elements
Regarding Claim 9:
Incorporates the rejection of claim 5
Step 2A Prong 1: The claim does not recite additional abstract ideas.
Step 2A Prong 2 & 2B: This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The method of claim 5, wherein at least one portion of the shared template is not completed as part of generating the prompt. (Amounts to generally linking the abstract ideas to a particular technological environment or field of use, as discussed in MPEP 2106.05(h).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are linking the abstract ideas to a particular technological environment or field of use that does not impose any meaningful limits on practicing the abstract ideas.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are merely linking the abstract ideas to a particular technological environment or field of use.
Regarding Claim 10:
Incorporates the rejection of claim 5
Step 2A Prong 1: The claim does not recite additional abstract ideas.
Step 2A Prong 2 & 2B: This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The method of claim 5, wherein the shared template comprises an instruction portion, a context portion, a history portion, and a query portion. (Amounts to generally linking the abstract ideas to a particular technological environment or field of use, as discussed in MPEP 2106.05(h).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are linking the abstract ideas to a particular technological environment or field of use that does not impose any meaningful limits on practicing the abstract ideas.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are merely linking the abstract ideas to a particular technological environment or field of use.
Regarding Claim 11:
Incorporates the rejection of claim 5
Step 2A Prong 1:
. (This step for selecting a machine learning model is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).)
Step 2A Prong 2 & 2B: This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The method of claim 5. wherein the generative machine learning model is one of a plurality of generative machine learning models available for performing natural language tasks (Amounts to generally linking the abstract ideas to a particular technological environment or field of use, as discussed in MPEP 2106.05(h).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are linking the abstract ideas to a particular technological environment or field of use that does not impose any meaningful limits on practicing the abstract ideas.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are merely linking the abstract ideas to a particular technological environment or field of use.
Regarding Claim 14:
Step 2A Prong 1:
identifying one or more portions of a shared template to complete as part of generating a prompt to perform the natural language task; (This step for identifying one or more portions of a shared template to generate a prompt to perform the natural language task is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).))
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
One or more non-transitory, computer-readable storage media, storing program instructions that when executed on or across one or more computing devices cause the one or more computing devices to implement: (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic classifier as a tool to perform the abstract idea (i.e., searching) – see MPEP 2106.05(f).)
receiving, via an interface, a natural language request to perform a natural language task; (This step is directed to receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
causing a generative machine learning model to perform the prompt generated according to the shared template, wherein the generative machine learning model was tuned to perform a plurality of natural language tasks, including the natural language task, according to completing identified portions of the shared template for the plurality of natural language tasks using a tuning data set; (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
returning, via the interface a response to the natural language request based, at least in part, on a result received from the generative machine learning model. (This step is directed to presenting information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra solution activity in combination of mere instructions to perform generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
One or more non-transitory, computer-readable storage media, storing program instructions that when executed on or across one or more computing devices cause the one or more computing devices to implement: (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic classifier as a tool to perform the abstract idea (i.e., searching) – see MPEP 2106.05(f).)
receiving, via an interface, a natural language request to perform a natural language task; (This step is directed to transmitting or receiving information and is a well understood, routine and conventional activity as identified by the court (MPEP2106.05(d)(ll)(i)))).
causing a generative machine learning model to perform the prompt generated according to the shared template, wherein the generative machine learning model was tuned to perform a plurality of natural language tasks, including the natural language task, according to completing identified portions of the shared template for the plurality of natural language tasks using a tuning data set; (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
returning, via the interface a response to the natural language request based, at least in part, on a result received from the generative machine learning model. (This step is directed to presenting offers and gathering statistics/information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional as identified by the court (MPEP2106.05(d)(ll)(i)))).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 6 and 15:
Incorporates the rejections of claim 5 and claim 14 respectively.
Claims 6 and 15 are method and product claims having similar limitations as the system claim of Claim 2. Therefore, claims 6 and 15 are rejected for the same reasons as disclosed for claim 2 above.
Regarding Claims 12 and 19:
Incorporates the rejections of claim 5 and claim 14 respectively.
Claims 12 and 19 are method and product claims having similar limitations as the system claim of Claim 3. Therefore, claims 12 and 19 are rejected for the same reasons as disclosed for claim 3 above.
Regarding Claims 13 and 20:
Incorporates the rejections of claim 5 and claim 14 respectively.
Claims 13 and 20 are method and product claims having similar limitations as the system claim of Claim 4. Therefore, claims 13 and 20 are rejected for the same reasons as disclosed for claim 4 above.
Regarding Claim 16:
Incorporates the rejection of claim 14
Claims 16 is a product claim having similar limitations as the system claim of Claim 7. Therefore, claim 16 is rejected for the same reasons as disclosed for claim 7 above.
Regarding Claim 17:
Incorporates the rejection of claim 14
Claims 17 is a product claim having similar limitations as the system claim of Claim 8. Therefore, claim 17 is rejected for the same reasons as disclosed for claim 8 above.
Regarding Claim 18:
Incorporates the rejection of claim 14
Claims 18 is a product claim having similar limitations as the system claim of Claim 9. Therefore, claim 18 is rejected for the same reasons as disclosed for claim 9 above
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claim(s) 1-8, 10-17 and 19-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Schillace et al. (US 20240202452 A1, herinafter "Schillace").
Regarding Claim 1:
Schillace discloses the system of claim 1:
A system, comprising: a plurality of computing devices, respective comprising at least one processor and a memory, configured to implement a generative machine learning system, configured to: ([FIG 6, FIG 1, Para 50, Para 61-62] discloses a computing device with at least one processor and memory to implement the system. Paragraph 50 discloses that the Generative model package may be implemented in computing devices such as FIG 1. Paragraphs 61-62 link Figure 6 to the system with at least one processor and memory to implement the generative machine learning system.)
receive, via an interface, a natural language request to perform a natural language task; ([Para 19-20, Para 67] discloses receiving input that may be in the form of natural language and sending it to the request wrapper to complete the task. In addition, it describes how input can be received, AKA an interface of some form. Paragraph 67 further discloses what interfaces can be used for input of requests.)
identify one or more portions of a shared template to complete according to a classification determined for the natural language request; ([Para 23-24, Para 27] Paragraphs 23-24 disclose how the task objective module identifies portions of the request to focus on for completing the task and sends it to the prompt generator, which is further disclosed to comprise of prompt templates. Paragraph 27 further discloses how a prompt generator associates portions of the task and populates prompt templates. It also discloses selecting a prompt associated with the task.)
generate a prompt to perform the natural language task with the identified one or more portions of the shared template completed; ([Para 27] discloses generating a prompt according to populated prompt templates through a prompt generator.)
instruct a generative machine learning model to perform the prompt generated according to the shared template, wherein the generative machine learning model was tuned to perform a plurality of natural language tasks, including the natural language task, according to completing identified portions of the shared template for the plurality of natural language tasks using a tuning data set; ([Para 28, Para 47-48] discloses the prompt being provided to a machine learning model as input to generate an output e.g. performing the task as described by the input. Paragraphs 47-48 discloses how the generative machine model is pre-trained/tuned on a variety of inputs, i.e. a training/tuning dataset.)
return, via the interface, a response to the natural language request based, at least in part, on a result received from the generative machine learning model. ([Page 3-4, Para 29, Schillace] discloses how a response evaluator checks the response and returns the output response based on the task to the user. [Page 8, Para 67, Schillace] discloses how the output can be received through an interface.)
Regarding Claim 2:
Schillace discloses: The system of claim 1, wherein the generative machine learning system is further configured to search one or more data repositories to obtain data to include in a context portion of the shared template when generating the prompt. ([Para 23] discloses how the system may store context in a data store which can later be retrieved to populate portions of the prompt template with context.)
Regarding Claim 3:
Schillace discloses: The system of claim 1, wherein the generative machine learning model was tuned according to a specified mixture of different natural tasks included in a tuning data set. ([Para 47-48] discloses how the generative model is pre-trained on a variety of natural language inputs.)
Regarding Claim 4:
Schillace discloses: The system of claim 1, wherein the generative machine learning system is a natural language generative application service offered by a provider network and wherein the natural language request is a received from a natural language generative application created at the natural language generative application service. ([Para 30, Para 50] discloses the generative model system being implemented on an application which may be on a network for communication from the user to provide input and receive output. Paragraph 50 discloses the application being distributable across systems through a generative model package.)
Regarding Claim 5:
Claim 5 recites a process (method) that is performed on the system as described in claim 1, with similar limitations to claim 1. Therefore, claim 5 is rejected under the same reasons mentioned for claim 1 above. There are no additional elements for claim 5 with respect to claim 1.
Regarding Claim 7:
The method of claim 5, wherein the prompt is generated without obtaining data from one or more data repositories to complete a portion of the prompt. ([Para 26, Para 39] discloses multiple ways a prompt may be generated. One of these methods comprise obtaining data, whereas in contrast, another method discloses directly generating a prompt. Paragraph 39 further discloses generating a new prompt when there is no prior data to be obtained.)
Regarding Claim 8:
The method of claim 5, wherein identifying the one or more portions of the shared template to complete as part of generating a prompt to perform the natural language task comprises determining an intent classification for the natural language request to perform the natural language task, wherein the intent classification is mapped to the one or more portions of the shared template to complete. ([Para 22-24] discloses the request processor that identifies the intent of the task and generating embeddings that are used to identify portions of the task to complete. This is sent to the prompt generator in which the respective portions of the prompt templates are completed.)
Regarding Claim 10:
wherein the shared template comprises an instruction portion, a context portion, a history portion, and a query portion. ([Para 24-25, Para 27, Para 29] discloses portions of the templates that include context and history. Paragraph 27 discloses the instruction portion of the template. Paragraph 27 further discloses triggering retrieval of semantic information i.e. querying.)
Regarding Claim 11:
The method of claim 5. wherein the generative machine learning model is one of a plurality of generative machine learning models available for performing natural language tasks, and wherein the generative machine learning model is selected to perform the natural language request for the natural language task. ([Para 28, Para 20] discloses a model repository with a variety of machine learning models. Paragraph 20 further discloses the request processor that analyzes the models in the repository to select the model that is tuned for a particular task.)
Regarding Claim 14:
Claim 14 recited the article of manufacture that constitutes the system as described in claim 1, with similar limitations to claim 1. Therefore, claim 14 is rejected under the same reasons mentioned for claim 1. The additional elements of claim 14 are addressed below by Schillace:
Claim 14:
One or more non-transitory, computer-readable storage media, storing program instructions that when executed on or across one or more computing devices cause the one or more computing devices to implement: ([Para 36] discloses one or more non-transitory, computer readable storage media, storing instructions executed by a computer system.)
Regarding Claim 6 and 15:
Claims 6 and 15 recite a process (method) and the article of manufacture from claim 5 and 14 respectively, which is performed on the system as described in claim 2. Therefore, claims 6 and 14 are rejected under the same reasons mentioned for claim 2.
Regarding Claim 12 and 19:
Claims 12 and 19 recite a process (method) and the article of manufacture from claims 5 and 14 respectively, which is performed on the system as described in claim 3. Therefore, claims 12 and 19 are rejected under the same reasons mentioned for claim 3. There are no additional elements for claim 12 with respect to claim 3.
Regarding Claim 13 and 20:
Claims 13 and 20 recite a process (method) and article of manufacture from claims 5 and 14 respectively, which is performed on the system as described in claim 4. Therefore, claims 13 and 20 are rejected under the same reasons mentioned for claim 4
Regarding Claim 16:
Claim 16 recites an article of manufacture which performs the method as described in claim 7, with limitations similar to claim 7. Therefore, claim 16 is rejected under the same reasons mentioned for claim 7.
Regarding Claim 17:
Claim 17 recites an article of manufacture which performs the method as described in claim 8, with limitations similar to claim 8. Therefore, claim 17 is rejected under the same reasons mentioned for claim 8.
Claim Rejections - 35 USC § 103
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 9 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schillace et al. (US 20240202452 A1, hereinafter “Schillace”) in view of Sodhi et al. (US 20240386216 A1, hereinafter “Sodhi”).
Regarding Claim 9:
Schillace discloses: The method of claim 5, as discussed above.
Schillace does not explicitly disclose: wherein at least one portion of the shared template is not completed as part of generating the prompt.
However, Sodhi discloses in the same field of endeavor: wherein at least one portion of the shared template is not completed as part of generating the prompt. ([Para 70] discloses that portions of the prompt template may be omitted.)
Schillace and Sodhi are both analogous art to the present invention because both are from the same field of endeavor directed to utilizing template-based prompts to perform natural language tasks with a generative model system.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the system of performing natural language requests with a model through prompt generation with prompt templates by Schillace, and the similar system for performing natural language requests through prompts for the model to perform the task by Sodhi. One would be motivated to add the feature of omitting portions of the template in prompt generation to give the invention more flexibility in completing tasks.
Regarding Claim 18:
Claim 18 recites an article of manufacture from claim 14 which is performs the method as described in claim 9. Therefore, claim 18 is rejected under the same reasons mentioned for claim 9.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Reza et al. (US 20230237277 A1) describes generating prompts using templates that categorize aspects of the natural language request to certain portions.
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/SUMAIR RASHED CHOWDHURY/
Examiner, Art Unit 2127 6/24/2026
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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