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 .
Status of the Claims
Claims 1, 8, 11-12, and 18-19 have been amended. Claims 1-20 are currently pending and have been considered by the Examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In claim 1, lines 13, the limitation “the execution of a task or process” renders the claim indefinite. Claim 1 already recites “solving a task” and “executing a plurality of sets of instructions for solving the task” in lines 1-4. It is unclear if “a task or process” in line 13 is the same or different from the task recited in lines 1-4. Examiner treats “the execution of a task or process” in line 13 as “the execution of the task”.
Claims 2-10 are rejected for failing to cure the deficiencies of claim 1.
Claim 11 recites the limitation "the execution of a task or process" in line 7. There is insufficient antecedent basis for this limitation in the claim. Line 2 recites “solving a task” and it is unclear if this is the same or different from the execution of a task or process. Examiner treats “the execution of a task or process” as “the solving of the task”.
Claims 12-17 are rejected for failing to cure the deficiencies of claim 11.
Claim 12 recites “designating a selected set of instructions based on the provided rank”. Claim 12 is rendered indefinite because it is unclear if this selected set of instructions is different from “a selected one of the plurality of sets of instructions” recited by claim 11, line 3. Examiner treats claim 12 to mean designating the same selected set of instructions.
In claim 18, line 9, the limitation “the execution of a task or process” renders the claim indefinite. Claim 18 already recites executing code for solving a task in lines 1-3. It is unclear if “a task or process” in line 9 is the same or different from the task recited in line 3. Examiner treats “the execution of a task or process” in line 13 as “the execution of the task”.
Claims 19-20 are rejected for failing to cure the deficiencies of claim 18.
Claim 19 recites the same indefinite limitation as claim 12 and is therefore rejected for at least the same reasons.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-10 recite a system comprising a processor, claims 11-17 recite a method, and claims 18-20 recite a non-transitory computer readable medium (a product). A system, a method, and a product each falls under one of the four statutory categories of patent eligible subject matter.
Claim 1
Step 2A Prong 1: Evaluating a set of instructions for solving a task is an evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Utilize the stored intermediate result and the received predicted next intermediate result to provide a rank of the selected one of the plurality of sets of instructions is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper. The claim recites an abstract idea.
Step 2A Prong 2: At least one processor capable of executing a plurality of sets of instructions for solving the task, the plurality of sets of instructions including one or more solvers amounts to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
At least one memory coupled to the at least one processor, the at least one processor being configured to perform operations amounts to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Provide, via the at least one memory, a task input to a selected one of the plurality of sets of instructions to perform the task amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Store, in the at least one memory, an intermediate result by the selected one of the plurality of sets of instructions at an intermediate stage while solving the task amounts to insignificant extra-solution activity under MPEP 2106.05(g).
The intermediate result being generated at a specific stage during the execution of a task or process, before reaching a final result amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Receive a prediction of a next intermediate result from the selected one of the plurality of sets of instructions amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Perform the task using a highest ranked set of instructions in the provided rank amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere insignificant extra solution activities as disclosed in combination with generic computer functions that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: At least one processor capable of executing a plurality of sets of instructions for solving the task, the plurality of sets of instructions including one or more solvers amounts to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
At least one memory coupled to the at least one processor, the at least one processor being configured to perform operations amounts to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Provide, via the at least one memory, a task input to a selected one of the plurality of sets of instructions to perform the task amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Store, in the at least one memory, an intermediate result by the selected one of the plurality of sets of instructions at an intermediate stage while solving the task is a well-understood, routine, conventional activity of storing information in memory, which the courts have recognized, under MPEP 2106.05(d)(II).
The intermediate result being generated at a specific stage during the execution of a task or process, before reaching a final result amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Receive a prediction of a next intermediate result from the selected one of the plurality of sets of instructions amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Perform the task using a highest ranked set of instructions in the provided rank amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are well-understood, routine and conventional activities as disclosed in combination with generic computer functions that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claim 2 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated.
Step 2A Prong 2 and Step 2B: The plurality of sets of instructions includes at least one domain-specific solver amounts to a mere field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claim 3 incorporates the rejection of claim 2.
Step 2A Prong 1: The abstract ideas of claim 2 are incorporated.
Step 2A Prong 2 and Step 2B: The at least one domain-specific solver includes at least one of a Monte Carlo, Particle Methods, and Sparse Solver amounts to a mere field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claim 4 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated.
Step 2A Prong 2 and Step 2B: The plurality of sets of instructions includes at least one machine learning (ML) solver amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f) and a mere field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claim 5 incorporates the rejection of claim 4.
Step 2A Prong 1: The abstract ideas of claim 4 are incorporated.
Step 2A Prong 2 and Step 2B: The at least one ML solver includes at least one of a Markov model, a decision tree, and a support vector machine (SVM) solver amounts to a mere field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claim 6 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated.
Step 2A Prong 2 and Step 2B: The plurality of sets of instructions includes at least one artificial intelligence (Al) solver amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f) and a mere field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claim 7 incorporates the rejection of claim 6.
Step 2A Prong 1: The abstract ideas of claim 6 are incorporated.
Step 2A Prong 2 and Step 2B: The at least one Al solver includes at least one of a Convolutional neural network solver and long short-term memory (LSTM) Neural Network solver amounts to a mere field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claim 8 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. Designating a set of instructions based on the provided rank is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: The at least one processor amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 9 incorporates the rejection of claim 8.
Step 2A Prong 1: The abstract ideas of claim 8 are incorporated.
Step 2A Prong 2 and Step 2B: The at least one processor uses the designated selected set of instructions for future processing of the task amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 10 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated.
Step 2A Prong 2 and Step 2B: Operating in an execution environment including an encrypted memory buffer amounts to a mere field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claims 11-13 each recites a method which implements the same features as the system of claims 1 and 8-9, respectively, and are therefore rejected for at least the same reasons.
Claim 14 incorporates the rejection of claim 11.
Step 2A Prong 1: The abstract ideas of claim 11 are incorporated.
Step 2A Prong 2 and Step 2B: The plurality of sets of instructions includes at least one set of instructions selected from at least one domain-specific solver amounts to a mere field of use and technological environment under MPEP 2106.05(h).
At least one machine learning (ML) solver and at least one artificial intelligence (AI) solver amount to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f) and mere fields of use and technological environments under MPEP 2106.05(h). The claim is not patent eligible.
Claims 15-17 each recite a method which implements the same features as the system of claims 3, 5, 7, respectively, and are therefore rejected for at least the same reasons.
Claim 18 recites a product which implements the same features as the system of claim 1 and is therefore rejected for at least the same reasons.
In Step 2A Prong 2 and Step 2B, a non-transitory computer readable medium including code stored thereon which when executed by a processor cause a system to perform a method amounts to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 19 recites a product which implements the same features as the system of claim 8 and is therefore rejected for at least the same reasons.
Claim 20 incorporates the rejection of claim 18.
Step 2A Prong 1: The abstract ideas of claim 18 are incorporated.
Step 2A Prong 2 and Step 2B: The plurality of sets of instructions includes at least one set of instructions selected from at least one domain-specific solver amounts to a mere field of use and technological environment under MPEP 2106.05(h).
At least one machine learning (ML) solver, and at least one artificial intelligence (AI) solver amount to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f) and mere fields of use and technological environments under MPEP 2106.05(h). The claim is not patent eligible.
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.
Claims 1, 4-9, 11-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Volodarskiy et al. (US 20200175354 A1, cited in the PTO-892 issued on 07/09/2024) in view of Bardy et al. (US 20180206752 A1, cited in the PTO-892 issued on 11/07/2025).
Regarding claim 1, Volodarskiy teaches: A system for evaluating a set of instructions for solving a task, the system comprising: at least one processor capable of executing a plurality of sets of instructions for solving the task, the plurality of sets of instructions including one or more solvers; and ([0029], lines 1-2 teaches a processor. [0054], lines 1-4, all of [0055], and [0056], lines 1-3 teach evaluating a number of machine-learning algorithms. The ML algorithms correspond to “sets of instructions”, regression or classification types correspond to “solvers”, and their predictions correspond to “solving a task”.)
at least one memory coupled to the at least one processor, the at least one processor being configured to: ([0031], lines 1-5)
provide, via the at least one memory, a task input to a selected one of the plurality of sets of instructions to perform the task; ([0053], lines 1-3; [0054], lines 1-4 and 12 to “algorithm” in line 13; [0055], lines 1-5; and [0056], lines 1-3 teaches providing inputs to a machine-learning algorithm.)
…
receive a prediction of the next intermediate result from the selected one of the plurality of sets of instructions; ([0055] and [0056], lines 1-3 indicate the selected machine-learning algorithm generates a prediction. A next intermediate result means a result before ranking the model.)
utilize ranking each model in order of the evaluation scores, and providing a leaderboard. The leaderboard may include only the highest-scoring model)
perform the task using a highest ranked set of instructions in the provided rank. ([0058] and [0059], lines 1-2)
Volodarskiy teaches a memory. However, Volodarskiy does not explicitly teach (underlines indicate limitations which are not taught): store, in the at least one memory, an intermediate result by the selected one of the plurality of sets of instructions at an intermediate stage while solving the task, wherein the intermediate result is generated at a specific stage during the execution of a task or process, before reaching a final result;
utilize the stored intermediate result and the received predicted next intermediate result to provide a rank
But Bardy teaches: store,
utilize the stored intermediate result and the received predicted next intermediate result to provide a rank. ([0102], lines 1-12. A testing result for a second sample constitutes a predicted next intermediate result, and determining an average accuracy corresponds to utilizing these results. A rank is the average accuracy.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have evaluated one of Volodarskiy’s models by using Bardy’s plurality of samples, and to have ranked the model based on Bardy’s average accuracy of testing data. In the combination of references, “an intermediate result” would be a prediction generated by a first sample, “a prediction of a next intermediate result” would be a prediction generated by a second sample, and “a final result” would be a prediction generated by a final sample. A motivation for the combination is to determine whether the model requires further training. (Bardy, [0102], final 5 lines)
Regarding claim 4, the combination of Volodarskiy and Bardy teaches: The system of claim 1.
Volodarskiy teaches: wherein the plurality of sets of instructions includes at least one machine learning (ML) solver. ([0026])
Regarding claim 5, the combination of Volodarskiy and Bardy teaches: The system of claim 4.
Volodarskiy teaches: wherein the at least one ML solver includes at least one of a Markov model, a decision tree, and a support vector machine (SVM) solver. ([0026], line 6 teaches a random forest algorithm, which comprises multiple decision trees.)
Regarding claim 6, the combination of Volodarskiy and Bardy teaches: The system of claim 1.
Volodarskiy teaches: wherein the plurality of sets of instructions includes at least one artificial intelligence (Al) solver. ([0026], lines 6-8 teaches deep neural networks.)
Regarding claim 7, the combination of Volodarskiy and Bardy teaches: The system of claim 6.
Volodarskiy teaches: wherein the at least one Al solver includes at least one of a
However, Volodarskiy does not explicitly teach: at least one of a Convolutional neural network solver and long short-term memory (LSTM).
But Bardy teaches: at least one of a Convolutional neural network solver and long short-term memory (LSTM). ([0096], lines 8-12)
A motivation for the combination is the same as the motivation given for claim 1.
Regarding claim 8, the combination of Volodarskiy and Bardy teaches:
The system of claim 1.
Volodarskiy teaches: wherein the at least one processor designates a set of instructions based on the provided rank. ([0058])
Regarding claim 9, the combination of Volodarskiy and Bardy teaches:
The system of claim 8.
Volodarskiy teaches: wherein the at least one processor uses the designated selected set of instructions for future processing of the task. ([0056], lines 6-11 discloses a model processes different inputs. At [0059], lines 1-3, the deployed model likewise processes different inputs. Processing a second input would corresponds to future processing of the task.)
Claims 11-13 each recite a method which implements the same features as the system of claims 1 and 8-9, respectively, and are therefore rejected for at least the same reasons.
Regarding claim 14, the combination of Volodarskiy and Bardy teaches: The method of claim 11.
Volodarskiy teaches: wherein the plurality of sets of instructions includes at least one set of instructions selected from at least one domain-specific solver, at least one machine learning (ML) solver, and ([0026] teaches machine learning solvers. Line 6 teaches a random forest algorithm. The limitation “at least one ML solver” corresponds to a random forest algorithm.)
at least one artificial intelligence (AI) solver. ([0026], lines 6-8 teaches deep neural networks.)
Claims 16-17 each recites method which implements the same features as the system of claims 5 and 7, respectively, and are therefore rejected for at least the same reasons.
Claim 18 recites a product which implements the same features as the system of claim 1 and is therefore rejected for at least the same reasons.
Volodarskiy teaches: A non-transitory computer readable medium including code stored thereon which when executed by a processor cause the system to perform a method ([0033])
Claim 19 recites a product which implements the same features as the system of claim 8 and is therefore rejected for at least the same reasons.
Regarding claim 20, the combination of Volodarskiy and Bardy teaches:
The computer readable medium of claim 18.
Volodarskiy teaches: wherein the plurality of sets of instructions includes at least one set of instructions selected from at least one domain-specific solver, at least one machine learning (ML) solver, and ([0026] teaches machine learning solvers. Line 6 teaches a random forest algorithm. The limitation “at least one ML solver” corresponds to a random forest algorithm.)
at least one artificial intelligence (AI) solver. ([0026], lines 6-8 teaches deep neural networks.)
Claims 2-3 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Volodarskiy et al. (US 20200175354 A1, cited in the PTO-892 issued 07/09/2024) in view of Bardy et al. (US 20180206752 A1, cited in the PTO-892 issued on 11/07/2025) and Sun et al. (US 20180046915 A1, cited in the PTO-892 issued 07/09/2024).
Regarding claim 2, the combination of Volodarskiy and Bardy teaches: The system of claim 1.
Volodarskiy teaches: wherein the plurality of sets of instructions includes at least one
However, Volodarskiy and Bardy do not explicitly teach: domain-specific
But Sun teaches: domain-specific solver (Examiner treats a domain-specific solver to mean a sparse solver. [0012], [0015] teaches a sparse neural network)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have pruned the deep neural network in the combination of Volodarskiy and Bardy according to Sun’s technique. A motivation for the combination is that by compressing a dense neural network into a sparse neural network, the computation amount and storage amount can be effectively reduced, achieving acceleration of running an ANN while maintaining its accuracy. (Sun, [0015])
Regarding claim 3, the combination of Volodarskiy, Bardy, and Sun teaches: The system of claim 1.
Volodarskiy and Bardy do not explicitly teach: wherein the at least one domain-specific solver includes at least one of a Monte Carlo, Particle Methods, and Sparse Solver.
But Sun teaches: wherein the at least one domain-specific solver includes at least one of a Monte Carlo, Particle Methods, and Sparse Solver. ([0012], [0015])
A motivation for the combination is the same as the motivation provided for the rejection of claim 2.
Claim 15 recites a method which implements the same features as the system of claims 2 and 3 and is therefore rejected for at least the same reasons.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Volodarskiy et al. (US 20200175354 A1, cited in the PTO-892 issued 07/09/2024) in view of Bardy et al. (US 20180206752 A1, cited in the PTO-892 issued on 11/07/2025) and Ho et al. (US 20200007931 A1, cited in the PTO-892 issued 07/09/2024).
Regarding claim 10, the combination of Volodarskiy and Bardy teaches: The system of claim 1.
Volodarskiy teaches: operating in an execution environment including
However, Volodarskiy and Bardy do not explicitly teach: an encrypted memory buffer.
But Ho teaches: an encrypted memory buffer ([0087], lines 1-10 and [0088] discloses an encrypted video is stored to a memory buffer, the encrypted video is retrieved from the memory buffer, and a neural network processor decrypts the video by applying a neural network. The limitation “an encrypted memory buffer” corresponds to a memory buffer storing encrypted video.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated Ho’s memory buffer storing an encrypted video into the combination of Volodarskiy and Bardy and to have used the combination’s neural network to decrypt the encrypted video. A motivation for the combination is to accelerate machine learning and artificial intelligence inference for video processing while maintaining the protection of the protected media content during the inference processing. (Ho, [0003])
Response to Arguments
The following is the examiner’s response to the applicant’s arguments filed 02/06/2026.
Applicant’s First Arguments Under 35 U.S.C. 103: On page 10, Applicant argues an intermediate result is the output generated at a specific stage during the execution of a task or process, before reaching the final result. That is, intermediate results are the outputs produced by the sets of instructions at various stages of solving a task. These results are used to predict the next steps and to rank the sets of instructions based on their performance in solving a task. This helps in determining which set of instructions is most efficient for the given task. Applicant has amended this language into the present claims for completeness.
Examiner’s Response: Applicant's arguments have been fully considered but they are not persuasive. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., intermediate results are used to predict the next steps) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In claim 1, the limitations in lines 12-18 recite generating an intermediate result, receiving a prediction of a next intermediate result, and using the intermediate result and predicted next intermediate result to provide a rank for a set of instructions. The claims do not recite any limitation of using intermediate results to “predict the next steps”. In Applicant’s argument, it is unclear whether “predict the next steps” refers to a prediction of a next intermediate result. Claim 1 does not recite an intermediate result is used to generate a prediction of a next intermediate result.
Applicant’s Second Arguments Under 35 U.S.C. 103: On page 10, Applicant argues the previous Office Action attempted to set forth that an intermediate result is generating a prediction based on inputs in a first one of the training subsets. This is misplaced based on the definition of an intermediate result is the output generated at a specific stage during the execution of a task or process, before reaching the final result.
On pages 11-12, Applicant respectfully submits that the amendments in claim 1 clarifies that the task is being solved with the intermediate result being at an intermediate stage of one of the plurality of sets of instructions, and not a completion of the set of instructions.
The previous Office Action continued by defining solving a task means making any prediction. Applicant respectfully submits this is inconsistent with the present claim, description and even the rest of the rejection. Applicant posits, that if solving a task is making a prediction, how could there be an intermediate result for this prediction?
Examiner’s Response: Examiner respectfully disagrees that Volodarskiy in view of Bardy allegedly fail to teach the features of pending claim 1. In response to Applicant’s argument that pending claim 1 “clarifies that the task is being solved with the intermediate result being at an intermediate stage of one of the plurality of sets of instructions, and not a completion of the set of instructions,” it is unclear if Applicant actually meant to write “an intermediate stage of solving the task” and “completion of the task”. The sets of instructions are solvers such as machine learning models, as recited in claims 2-7. It is unclear what an intermediate stage of a solver or completion of a solver means.
In the rejection of claim 1 over Volodarskiy in view of Bardy, solving a task corresponds to inputting a set of data samples to a prediction model to generate a set of predictions, an intermediate result generated at a specific stage during the execution of the task corresponds to a first prediction generated by inputting a first data sample from the set into the prediction model, receiving a prediction of a next intermediate result corresponds to generating a second prediction by inputting a second data sample into the prediction model, and reaching a final result corresponds to generating a final prediction by inputting a final data sample to the prediction model.
Volodarskiy at [0055], [0056], lines 1-3 and 13-15, and [0057], lines 1-6 discloses evaluating a selected prediction model by inputting a data sample to generate a prediction, and evaluating the model based on an accuracy of the prediction. Thus, Volodarskiy teaches at least the limitations “receive a prediction of the next intermediate result from the selected one of the plurality of sets of instructions” and “utilize… the received predicted next intermediate result to provide a rank of the selected one of the plurality of sets of instructions”.
However, Volodarskiy does not explicitly teach: store, in the at least one memory, an intermediate result by the selected one of the plurality of sets of instructions at an intermediate stage while solving the task, wherein the intermediate result is generated at a specific stage during the execution of a task or process, before reaching a final result;
utilize the stored intermediate result and the received predicted next intermediate result to provide a rank
But Bardy teaches: store,
utilize the stored intermediate result and the received predicted next intermediate result to provide a rank. ([0102], lines 1-12. A testing result for a second sample constitutes a predicted next intermediate result, and determining an average accuracy corresponds to utilizing these results. A rank is the average accuracy.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have evaluated one of Volodarskiy’s models by using Bardy’s plurality of samples, and to have ranked the model based on Bardy’s average accuracy of testing data. In the combination of references, “an intermediate result” would be a prediction generated by a first sample, “a prediction of a next intermediate result” would be a prediction generated by a second sample, and “a final result” would be a prediction generated by a final sample. A motivation for the combination is to determine whether the model requires further training. (Bardy, [0102], final 5 lines)
Applicant’s Third Arguments Under 35 U.S.C. 103: The second reference used in rejecting claim 1 presently is Bardy, which is provided solely to teach an intermediate result. Office Action page 15. As set forth in the Office Action, Bardy teaches a CNN on a number of samples and calculating an average accuracy of testing the samples setting forth that an intermediate result is the final result of the first sample. Applicant respectfully submits that this misplaces the intermediate result language.
Applicant respectfully submits that an intermediate result as provided in the present application is not taught by performing division of two numbers to completion because there is more later division of other numbers to be performed. Applicant instead respectfully submits that in line with the specification, an intermediate result is calculating, for example, the tens (or hundreds) place of the division, in a process where the entirety of division result is calculated. This initial part of the calculation is then used by the system.
Examiner’s Response: Applicant's arguments have been fully considered but they are not persuasive. As explained above, “an intermediate result” is a prediction generated by a first sample, “a prediction of a next intermediate result” is a prediction generated by a second sample, and “a final result” is a prediction generated by a final sample. The combination of Volodarskiy and Bardy teaches generating predictions for a number of samples using a model, and evaluating the model by calculating an average accuracy over all predictions.
Applicant’s arguments in the last paragraph of page 12 are unclear because the pending claims and references do not disclose division. Applicant appears to argue that the system of claim 1 uses an intermediate result achieved before reaching a final result, and a final result is not used. Applicant’s argument is not persuasive because claim 1, lines 16-18 does not prevent the final result from contributing to provide a rank. In the combination of Volodarskiy and Bardy, the final result can contribute to provide a rank.
Applicant’s Arguments Under 35 U.S.C. 101: On pages 14-15, Applicant argues the human mind alone cannot perform a task using a plurality of sets of instructions which includes AI and incumbent solvers and compare the results of the plurality of sets of instructions to determine a ranking of the sets of instructions for performing a task to be used by the processor when the task needs to be performed, as recited in the present claims.
Examiner’s Response: Applicant’s arguments are not fully persuasive. Examiner agrees that the human mind alone cannot perform a task using a plurality of sets of instructions which includes AI and incumbent solvers. However, a human mind can compare results obtained during the task performances. In Step 2A Prong 1, the feature of comparing the results of the plurality of sets of instructions to determine a ranking of the sets of instructions is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper. Instant specification paragraph [0115] discloses the ranking may be made by measuring performance and resource demands of each solver. The different measurements may guide ranking independently or via a weighted sum using some or all of the variables. This indicates the measurement is a numerical value. Comparing the results to determine a ranking would amount to ranking the sets of instructions from the best measurement to the worst measurement. A human mind can reasonably rank the sets of instructions if given the corresponding measurements. The claim recites an abstract idea.
In Step 2A Prong 2, the feature of performing a task using a plurality of sets of instructions which includes AI and incumbent solvers amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). Performing the task at a future time using a highest ranked set of instructions in the provided ranking amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere insignificant extra solution activities as disclosed in combination with generic computer functions that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Asher H. Jablon whose telephone number is (571)270-7648. The examiner can normally be reached Monday - Friday, 9:00 am - 6:00 pm.
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/A.H.J./Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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