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 .
Specification
The disclosure is objected to because of the following informalities:
Par. [0077] “… The process can those programs and evolve them …”. This appears to be missing a word (between “can” and “those”).
Pars. [0093] and [0096] appear to be duplicates.
Appropriate correction is required.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description:
Par. [0123] “processing unit (CPU or processor) 810”.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Figure 8 should be designated by a legend such as --Prior Art-- because only that which is old is illustrated. See MPEP § 608.02(g). Corrected drawings in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. The replacement sheet(s) should be labeled “Replacement Sheet” in the page header (as per 37 CFR 1.84(c)) so as not to obstruct any portion of the drawing figures. If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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.
Claim(s) 1-19 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 2024/0428079 to Chen et al. (Chen).
Claims 1 and 11: Chen discloses an apparatus to generate a program in an iterative process, comprising:
at least one memory (par. [0037] “memory 420”); and
at least one processor coupled to the at least one memory (par. [0037] “processor 410”) and configured to:
generate, based on a policy that receives input-output data of one or more tasks as input, a first set of programs; (par. [0076] “At step 754, the student model generates a task output in response to a task input”, see e.g. Fig. 3A “assert ([1,2,3],[2,3,4])=[1,4]”)
add the first set of programs and the input-output data to a training dataset to generate an updated training dataset (par. [0077] “the student model obtains … a feedback relating to an accuracy of the task output”);
train the policy based on the first set of programs and the input-output data to generate an updated policy (par. [0020] “using training samples built on the student model’s own output”);
identify, based on the updated policy, a second set of programs for second input-output data for a second set of tasks (par. [0079] “At step 760, a training input is generated by incorporating the task input, the task output, and the feedback with a pre-defined refinement template”);
add the second set of programs and second input-output data to the updated training dataset to generate a second updated training dataset (par. [0081] “At step 764, a training pair is stored in a training dataset”); and
train the updated policy based on the second set of programs and the second input-output data to generate a second updated policy (par. [0081] “At step 766, the student model is trained using the training dataset”).
Claims 2 and 12: Chen discloses claims 1 and 11, wherein the at least one processor is configured to pre-train the policy on the input-output data and corresponding programs from a dataset (par. [0098] “pre-train for 20 epochs”).
Claims 3 and 13: Chen discloses claims 1 and 11, wherein the policy comprises one of transformer-based policy network, a language model, a large language model, a CodeT5 model, a decoder-only model, an encoder-decoder model, or a vision-language model parsing grids using convolutional encoders (par. [0021] “a student model 120 (such as a language model of a smaller size)”).
Claims 4 and 14: Chen discloses claims 1 and 11, wherein the first set of programs and the input-output data and the second set of programs and the second input-output data are each corrected or annotated without human intervention (par. [0078] “the teacher model generates a refinement output”, par. [0026] “task output 202 may be compared with a reference output to provide feedback 204”).
Claim 5 and 15: Chen discloses claims 1 and 11, wherein the at least one processor is configured to add the first set of programs, the input-output data, and information associated with an intermediate state to the training dataset stored in the at least one memory to generate an updated training dataset (e.g. par. [0079] “training input is generated by incorporating … the feedback with a pre-defined refinement template”).
Claims 6 and 16: Chen discloses claims 1 and 11, wherein the at least one processor is configured to train the policy based on the first set of programs and the input-output data to generate the updated policy based on an intermediate state generated from evaluating a program or partial program (par. [0079] “At step 760, a training input is generated by incorporating the task input, the task output, and the feedback with a pre-defined refinement template”).
Claims 7 and 18: Chen discloses The apparatus of claims 1 and 11, wherein the at least one processor is configured to train the policy based on the first set of programs and the input-output data to generate the updated policy based on a policy-sampled action (par. [0077] “obtains … a feedback relating to an accuracy of the task output”, par. [0035] a training sample using the task input 103 as a training input”).
Claims 8 and 19: Chen discloses claims 7 and 18, wherein the at least one processor is configured to train the policy based on the first set of programs and the input-output data to generate the updated policy based on the policy-sampled action and an intermediate state (par. [0079] “a training input is generated … the feedback with a pre-defined refinement template”).
Claims 9 and 17: Chen discloses claims 8 and 16, wherein the at least one processor is configured to add the first set of programs, the input-output data, and the intermediate state to the training dataset stored in the at least one memory to generate an updated training dataset (par. [0079] “At step 760, a training input is generated by incorporating the task input, the task output, and the feedback with a pre-defined refinement template”).
Claim 10: Chen discloses the apparatus of claim 8, wherein the at least one processor is configured to add the first set of programs, the input-output data, the intermediate state, and the policy-sampled action to the training dataset stored in the at least one memory to generate the updated training dataset (par. [0079] “At step 760, a training input is generated by incorporating the task input, the task output, and the feedback with a pre-defined refinement template”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON D MITCHELL whose telephone number is (571)272-3728. The examiner can normally be reached Monday through Thursday 7:00am - 4:30pm and alternate Fridays 7:00am 3:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lewis Bullock can be reached at (571)272-3759. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JASON D MITCHELL/Primary Examiner, Art Unit 2199