DETAILED ACTION
This Office Action is in response to the application 18/907,414 filed on 10/04/2024.
Claims 1-20 have been examined and are pending in this application.
This application is a continuation of U.S. Patent Application No. 18/661,532 entitled “DYNAMIC, RESOURCE-SENSITIVE MODEL SELECTION AND OUTPUT GENERATION AND METHODS AND SYSTEMS OF THE SAME” and filed May 10, 2024, which is a continuation-in-part of U.S. Patent Application No. 18/661,519 entitled “DYNAMIC, RESOURCE-SENSITIVE MODEL SELECTION AND OUTPUT GENERATION AND METHODS AND SYSTEMS OF THE SAME” and filed May 10, 2024, and a continuation-in-part of U.S. Patent Application No. 18/633,293 entitled “DYNAMIC EVALUATION OF LANGUAGE MODEL PROMPTS FOR MODEL SELECTION AND OUTPUT VALIDATION AND METHODS AND SYSTEMS OF THE SAME” and filed April 11, 2024.
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 .
Election/Restrictions
For the record, the Examiner acknowledges that NO restrictions warranted at applicants initial time of filing for patent.
Priority
For the record, the Examiner acknowledges that NO foreign priority claimed at applicant’s initial time of filing for patent.
Information Disclosure Statement
The information disclosure statement (IDS), submitted on 12/16/2024 and 01/23/2025, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Oath/Declaration
For the record, the Examiner acknowledges that the Oath/Declaration submitted on 10/04/2024 has been accepted.
Drawings
For the record, the Examiner acknowledges that the drawings filed on 10/04/2024 has been accepted.
Specification
For the record, the Examiner acknowledges that the Applicant's specification filed on 10/04/2024 has been accepted.
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)(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.
Claims 1-20 are rejected under 35 U.S.C. 102 (a)(2) as being anticipated by Wang et al. (hereinafter Wang), Pub. No.: US 2023/0237367.
Referring to claim 1, Wang teaches a non-transitory computer-readable storage medium comprising instructions thereon, wherein the instructions when executed by at least one data processor of a system, cause the system to:
determine a performance metric associated with processing an output generation request comprising a prompt for generation of an output using a first large-language model (LLM) of a plurality of LLMs (abstract, para. 0012, metrics of regret for the model configurations; see also para. 0020);
determine a system state associated with system resources for processing requests using the first LLM of the plurality of LLMs (abstract, paras. 0019-0020, receives an input task and/or request to identify a model configuration);
compare a first estimated performance metric value for the determined performance metric with a threshold metric value associated with the determined performance metric (paras. 0020, 0072 and fig. 1-2, configuration selection manager 118 may compare a set of meta features for an input task to meta features);
in response to determining that the first estimated performance metric value satisfies the threshold metric value (paras. 0018-0020, the model configuration produces accurate predictions with respect to the training task having the similar set of meta features as the input task.):
provide the prompt to the first LLM to generate a first output by processing the prompt included in the output generation request (abstract, paras. 0011-0012, 0023 generate the configuration portfolio); and
enable access to the first output (abstract, para, 0023, first model configuration)
based on determining that the first estimated performance metric value does not satisfy the threshold metric value (paras. 0020, 0086, implement a zero-shot model selection):
provide the prompt to a second LLM of the plurality of LLMs to generate a second output by processing the prompt included in the output generation request (para. 0023, a second model configuration); and
enable access to the second output (para. 0023).
Referring to claim 2, Wang further teaches wherein the instructions further cause the system to: determine that the performance metric corresponds to a cost metric; determine a maximum cost value associated with output generation associated with the system; determine, based on the system state, a sum of cost metric values for previous output generation requests associated with the system; determine, based on the maximum cost value and the sum, an allowance value corresponding to the threshold metric value; and determine the threshold metric value comprising the allowance value (para. 0072-0075 and fig. 2, configuration selection manager 118).
Referring to claim 3, Wang further teaches wherein the instructions for determining the allowance value cause the system to: determine, based on the output generation request, a user identifier associated with a user; determine, using the user identifier, a first group of users, wherein the first group comprises the user; and determine the allowance value associated with the first group of users (paras. 0072-0075).
Referring to claim 4, Wang further teaches wherein the first estimated performance metric value corresponds to a number of input or output tokens, and wherein the threshold metric value corresponds to a maximum number of tokens (para. 0036, threshold percentage/other metrics).
Referring to claim 5, Wang further teaches wherein the instructions further cause the system to provide the prompt and an indication of the first LLM to a performance metric evaluation model to generate the first estimated performance metric value (para. 0023, first output).
Referring to claim 6, Wang further teaches wherein the instructions further cause the system to: obtain, from a first database, a plurality of training prompts and respective performance metric values associated with providing respective training prompts to the first LLM; and providing the plurality of training prompts and respective performance metric values to the performance metric evaluation model to train the performance metric evaluation model to generate estimated performance metric values based on prompts (abstract, paras, 0019-0020, training model).
Referring to claim 7, Wang further teaches wherein the instructions further cause the system to: determine that the performance metric corresponds to a usage metric for a computational resource; determine an estimated usage value for the computational resource based on an indication of an estimated computational resource usage by the first LLM when processing the prompt with the first LLM; determine a maximum usage value for the computational resource; determine, based on the system state, a current resource usage value for the computational resource; determine, based on the maximum usage value and the current resource usage value, an allowance value corresponding to the threshold metric value; and determine the threshold metric value comprising the allowance value (abstract, paras. 0013, 0015-0016, determine a model configuration).
Referring to claim 8, Wang further teaches wherein the instructions for providing the prompt to the second LLM cause the system to: in response to determining that the first estimated performance metric value does not satisfy the threshold metric value, transmit an LLM selection request to a user device; in response to transmitting the LLM selection request, obtain, from the user device, a selection of the second LLM; and provide the prompt to the second LLM associated with the selection (para. 0023, second model configuration).
Referring to claim 9, Wang further teaches wherein the instructions further cause the system to: determine that the performance metric comprises a composite metric associated with a plurality of system metrics; determine, based on the system state, a threshold composite metric value; determine a plurality of estimated metric values corresponding to the plurality of system metrics, wherein each estimated metric value of the plurality of estimated metric values indicates a respective estimated resource usage associated with processing the output generation request with the first LLM; determine, using the plurality of estimated metric values, a composite metric value associated with processing the output generation request with the first LLM; and determine the first estimated performance metric value comprising the composite metric value (para. 0023, first model configuration).
Referring to claim 10, Wang further teaches wherein the instructions for providing the prompt to the second LLM cause the system to: provide the output generation request and an indication of the plurality of LLMs to a selection model configured to generate recommendations for model selection based on user prompts; in response to providing the output generation request to the selection model, generate a recommendation to process the output generation request using the second LLM of the plurality of LLMs; and in response to generating the recommendation, provide the prompt to the second LLM to generate a third output (paras. 0023, 0062, 0081 and fig. 3).
Referring to claim 11, Wang further teaches wherein the instructions for providing the prompt to the second LLM cause the system to: in response to determining that the first estimated performance metric value does not satisfy the threshold metric value, generate, for display on a user interface of a user device, a request for user instructions, wherein the request for user instructions comprises a recommendation for processing the output generation request with the second LLM of the plurality of LLMs; in response to generating the request for user instructions, receive a user instruction comprising an indication of the second LLM; and in response to receiving the user instruction, provide the prompt to the second LLM (paras. 0023, 0081 and fig. 3).
Referring to claim 12, This claim is similar in scope to claim 1, and is therefore rejected under similar rationale.
Referring to claim 13, This claim is similar in scope to claim 2, and is therefore rejected under similar rationale.
Referring to claim 14, This claim is similar in scope to claim 3, and is therefore rejected under similar rationale.
Referring to claim 15, This claim is similar in scope to claim 4, and is therefore rejected under similar rationale.
Referring to claim 16, This claim is similar in scope to claim 7, and is therefore rejected under similar rationale.
Referring to claim 17, This claim is similar in scope to claim 1, and is therefore rejected under similar rationale.
Referring to claim 18, This claim is similar in scope to claim 2, and is therefore rejected under similar rationale.
Referring to claim 19, This claim is similar in scope to claim 3, and is therefore rejected under similar rationale.
Referring to claim 20, This claim is similar in scope to claim 4, and is therefore rejected under similar rationale.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,106,205. Although the claims at issue are not identical, they are not patentably distinct from each other because both patent application are related to a large language model (LLM) is a language model notable for its ability to achieve general-purpose language generation and other natural language processing tasks such as classification. LLMs acquire these abilities by learning statistical relationships from text documents during a computationally intensive self-supervised and semi-supervised training process. LLMs can be used for text generation, a form of generative AI, by taking an input text and repeatedly predicting the next token or word.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see the attached PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YONAS A BAYOU whose telephone number is (571)272-7610. The examiner can normally be reached Monday-Friday 7AM-4PM.
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/YONAS A BAYOU/Primary Examiner, Art Unit 2499 03/02/2026