DETAILED ACTION
This Office Action is sent in response to Applicant’s Communication received 11/15/2023 for application number 18/509,359.
Claims 1-20 are pending.
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 .
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 1-7, 10-14, 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claim 19 (representative of claims 1-5 and 16-17) recites:
A computer program product comprising a computer readable program stored on a computer readable storage medium, wherein the computer readable program, when executed on a processor system, causes the processor to perform processor system operations comprising: inputting a first input into a plansformer comprising a transformer-based neural network (NN), the first input comprising symbols and a problem; and in response to the inputting, receiving as output from the plansformer a plan for solving the problem. wherein the symbols comprise computer code; wherein the plan comprises more symbols; and training the plansformer via submitting training data to a large language model (LLM); wherein the training data comprises planning problems and associated plans generated from a test domain.
(2A, prong 1) The underlined portions of the claim recite an abstract idea, specifically a mental process. A human can use a problem and symbols to generate a solution including a plan for solving the problem and code.
(2A, prong 2) This judicial exception is not integrated into a practical application. The claims recite the additional elements of [a] receiving input comprising symbols and a problem, [b] a plansformer comprising a transformer-based neural network, [c] training the plansformer via training data input to a large language model, and [d] generic computer components including processor and memory. Element [a] is insignificant extra-solution activity because it acts as mere necessary data gathering for the abstract idea. Elements [b] and [c] are mere instructions to apply the exception because these elements merely recite the idea of a solution (i.e. that the plansformer solves a problem and the plansformer is trained with training data input to a LLM) and does not recite the technical details of how the solution is accomplished (i.e. how the plansformer operates to make the prediction or any details on how it is trained). Element [d] is also a mere instruction to apply the exception because it only adds generic computer components to the abstract idea after-the-fact. Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not integrate the abstract idea into a practical application because they only add insignificant extra-solution activity and mere instructions to apply the exception to the mental process.
(2B) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Element [a] is well-understood, routine, and conventional, analogous to storing and retrieving information in memory, see MPEP 2106.05(d) citing Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Elements [b], [c], and [d] are mere instructions to apply the exception, as explained above. Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not amount to significantly more than the abstract idea itself because they only add insignificant extra-solution activity that is well-understood, routine, and conventional and mere instructions to apply the exception to the abstract idea.
With respect to dependent claims 6 and 10-14, these claims (2A, prong 1) recite further abstract idea limitations. A human can mentally: (claim 6) create problems and progression plans, (claim 10) mentally judge a plan and produce a confidence score, (claim 11-12) evaluate a plan for validity and optimality, including apply relaxation conditions, (claim 13) evaluate using metrics, (claim 14) and make a plan with a series of time steps and actions for the time steps.
With respect to dependent claims 7, 18, and 20, these claims recite (2A, prong 2) the additional elements of [e] the LLM is a code-aware encoder-decoder. These additional elements do not integrate the abstract idea into a practical application because they are mere instructions to apply the exception because these elements merely recite the idea of a solution do not recite the technical details of how the solution is accomplished. That is to say, that the LLM comprises a code-aware encoder-decoder does not give any technical details of how the LLM trains the planformer, or how the planformer is able to create solutions to input problems.
The examiner notes that claims 8-9 and 15 are not rejected under 35 USC § 101 because they recite additional elements that integrate the abstract idea into a practical application because the elements do not fall under the considerations outlined in MPEP § 2106.05(d)-(h).
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.
Claim(s) 1-7, 14-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chalvatzaki et al., Learning to reason over scene graphs: a case study of finetuning GPT-2 into a robot language model for grounded task planning, (see NPL[U], Notice of Reference Cited).
In reference to claim 1, Chalvatzaki discloses a computer-implemented method comprising: inputting a first input into a plansformer comprising a transformer-based neural network (NN) (model is transformer, specifically a finetuned LLM, pages 7-8 and 2), the first input comprising symbols and a problem (task description includes code symbols and problem, page 4-7); and in response to the inputting, receiving as output from the plansformer a plan for solving the problem (based on input task, a plan is created, pages 7, 10-11).
In reference to claim 2, Chalvatzaki discloses the computer-implemented method of claim 1, wherein the symbols comprise computer code (the syntactic rules shown on page 4 are computer markup code).
In reference to claim 3, Chalvatzaki discloses the computer-implemented method of claim 1, wherein the plan comprises more symbols (output plan also has symbols, pages 5, 7).
In reference to claim 4, Chalvatzaki discloses the computer-implemented method of claim 1, further comprising training the plansformer via submitting training data to a large language model (LLM) (model is trained by finetuning a LLM, GPT-2, using training data, pages 7-8).
In reference to claim 5, Chalvatzaki discloses the computer-implemented method of claim 4, wherein the training data comprises planning problems and associated plans generated from a test domain (training data from ALFRED, and includes problem and validation data, pages 3, 7, 9-10).
In reference to claim 6, Chalvatzaki discloses the computer-implemented method of claim 5, wherein: the planning problems are received from a problem generator in response to a domain model being input into the problem generator; and the associated plans are received from a progression planner in response to the planning problems being input into the progression planner (the problems and solutions in the AFLRED training data exist, so they must have been created, pages 3, 7, 9-10).
In reference to claim 7, Chalvatzaki discloses the computer-implemented method of claim 4, wherein the LLM comprises a code-aware encoder-decoder architecture (GPT-2 is an encoder-decoder architecture, 3.7 Training, page 7, and has been trained on natural language including program code, page 5).
In reference to claim 14, Chalvatzaki discloses the computer-implemented method of claim 1, wherein the plan comprises a series of time steps and one or more actions to instantiate for each of the time steps (plan has series of steps and actions, pages 5, 7).
In reference to claim 15, Chalvatzaki discloses the computer-implemented method of claim 1, wherein the plansformer comprises a tokenizer that produces planning-language specific tokens (tokens are for planning, pages 7-8).
In reference to claim 16, this claim is directed to a system associated with the method claimed in claim 1 and is therefore rejected under a similar rationale.
In reference to claim 17, this claim is directed to a system associated with the method claimed in claim 5 and is therefore rejected under a similar rationale.
In reference to claim 18, this claim is directed to a system associated with the method claimed in claims 6 and 7 and is therefore rejected under a similar rationale.
In reference to claim 19, this claim is directed to a computer-readable storage medium (the Examiner notes the specification defines storage media as excluding transitory signals, see para. 0035 as published) associated with the method claimed in claims 1-5 and is therefore rejected under a similar rationale.
In reference to claim 20, this claim is directed to a computer-readable storage medium associated with the method claimed in claims 6 and 7 and is therefore rejected under a similar rationale.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chalvatzaki et al., Learning to reason over scene graphs: a case study of finetuning GPT-2 into a robot language model for grounded task planning, (see NPL[U], Notice of Reference Cited) in view of Chan et al. (US 2024/0403438 A1).
In reference to claim 8, Chalvatzaki does not explicitly teach the computer-implemented method of claim 7, wherein the code-aware encoder-decoder architecture is pre-trained using one or more code-related tasks selected from a group consisting of code summarization, code generation, code translation, code refinement, code defect detection, code clone detection, and text-code matching.
Chan teaches the computer-implemented method of claim 7, wherein the code-aware encoder-decoder architecture is pre-trained using one or more code-related tasks selected from a group consisting of code summarization, code generation, code translation, code refinement, code defect detection, code clone detection, and text-code matching (LLM can be trained for code defect detection, para. 0016-28).
It would have been obvious to one of ordinary skill in art, having the teachings of Chalvatzaki and Chan before the earliest effective filing date, to modify the LLM of Chalvatzaki to include the code pretraining of Chan.
One of ordinary skill in the art would have been motivated to modify the LLM of Chalvatzaki to include the pretraining of Chan because Chalvatzaki does not explicitly discuss the pretraining of the LLM, but given that that the LLM is intended to output code, it would be logical to perform the coding pretraining of Chan.
In reference to claim 9, Chan teaches the computer-implemented method of claim 4, wherein the code-aware encoder-decoder architecture implements masked language modeling (para. 0026).
Claim(s) 10 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chalvatzaki et al., Learning to reason over scene graphs: a case study of finetuning GPT-2 into a robot language model for grounded task planning, (see NPL [U], Notice of Reference Cited) in view of Ahn et al., Do As I Can, Not As I Say: Grounding Language in Robotic Affordances (see NPL [V], Notice of Reference Cited).
In reference to claim 10, Chalvatzaki does not explicitly teach the computer-implemented method of claim 1, further comprising evaluating the plan to produce a confidence score.
Ahn teaches the computer-implemented method of claim 1, further comprising evaluating the plan to produce a confidence score (steps of plan are scored for likelihood of progressing towards goal, pages 2-3, fig. 6 on page 8).
It would have been obvious to one of ordinary skill in art, having the teachings of Chalvatzaki and Ahn before the earliest effective filing date, to modify the plan of Chalvatzaki to include the confidence score of Ahn.
One of ordinary skill in the art would have been motivated to modify the plan of Chalvatzaki to include the confidence score of Ahn because it helps enhance automated planning for robotic tasks (Ahn, page 8).
In reference to claim 13, Chalvatzaki does not explicitly teach the computer-implemented method of claim 1, further comprising evaluating the plan via a natural language metric tool.
Ahn teaches the computer-implemented method of claim 1, further comprising evaluating the plan via a natural language metric tool (steps of plan are scored using LLM, which is a natural language metric tool, pages 2-3, fig. 6 on page 8).
It would have been obvious to one of ordinary skill in art, having the teachings of Chalvatzaki and Ahn before the earliest effective filing date, to modify the plan of Chalvatzaki to include the evaluation of Ahn.
One of ordinary skill in the art would have been motivated to modify the plan of Chalvatzaki to include the evaluation of Ahn because it helps enhance automated planning for robotic tasks (Ahn, page 8).
Claim(s) 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chalvatzaki et al., Learning to reason over scene graphs: a case study of finetuning GPT-2 into a robot language model for grounded task planning, (see NPL [U], Notice of Reference Cited) in view of Pereira et al., Manipulation Task Planning and Motion Control Using Task Relaxations (see NPL [W], Notice of Reference Cited).
In reference to claim 11, Chalvatzaki does not explicitly teach the computer-implemented method of claim 1, further comprising evaluating the plan for validity and optimality.
Pereira teaches the computer-implemented method of claim 1, further comprising evaluating the plan for validity and optimality (constraints of robotic control plan are checked and constraints relaxed, pages 1105-07).
It would have been obvious to one of ordinary skill in art, having the teachings of Chalvatzaki and Pereira before the earliest effective filing date, to modify the plan of Chalvatzaki to include the evaluation of Pereira.
One of ordinary skill in the art would have been motivated to modify the plan of Chalvatzaki to include the evaluation of Pereira because it helps speed robotic control (Pereira, pages 1103-04).
In reference to claim 12, Pereira teaches the computer-implemented method of claim 11, wherein the evaluating for validity and optimality comprises applying relaxation conditions to the plan and the first input (constraints of robotic control plan are checked and constraints relaxed, pages 1105-07).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew T. Chiusano whose telephone number is (571)272-5231. The examiner can normally be reached M-F, 10am-6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 571-272-4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW T CHIUSANO/Primary Examiner, Art Unit 2144
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