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 .
Response to Arguments
Objections to the Drawings
The amendment to par. [0123] is sufficient to overcome the corresponding objection.
Applicant's arguments regarding figure 8 have been fully considered but they are not persuasive.
… the specification indicates that "FIG. 8 is a diagram illustrating an example system architecture for implementing certain aspects described herein, in accordance with some aspects of this disclosure." Applicants As-Filed Application, para. [0023]. The application further states, "[f]or instance, a computing device with the computing device architecture of the computing system 800 shown in FIG. 8 can implement the operations of FIG. 6 and/or the components and/or operations described herein with respect to any of FIGs. 2, 3, 4, SA and/or SB." Application, para. [0085]. Thus, Applicant submits that FIG. 8 is not considered "Prior Art" and a label to that effect is, therefore, not required.
The examiner respectfully disagrees. Figure 8 shows a prior art computer architecture and does not show that architecture performing or being configured to perform the disclosed embodiments.
First it is noted that the cited parts of the specification only describe what is shown in figure 8 as an “system architecture” or a “computing device architecture” and go on to describe uses to which it could be put. However, figure 8 does not show the architecture performing or being configured to perform the disclosed embodiments. With the exception of services 1-3 (832-836) all that is shown is basic high-level representations of standard computer hardware. Services 1-3 are disclosed as “any general purpose … software service 832, 834, and 836” (par. [0124]). Accordingly, software service(s) shown in figure 8. are disclosed as being within the prior art.
Amending figure 8 to show, e.g., the “components and/or operations” of figs. 2, 3, 4, 5 would overcome the objection without requiring a prior art label.
Claim Rejections under 35 U.S.C. §102
Applicant's arguments have been fully considered but they are not persuasive.
For example, Chen fails to describe a policy that "receives input-output data of one or more tasks as input" and generating a set of programs based on such a policy, as claimed. Rather, Chen describes receiving a task input such as a natural-language task description and then generating a training input from task input, task output, and feedback. Chen, paras. [0025], [0028]-[0030], [0079]-[0081].
Chen fig. 3’s shows “task input 102“including the line “assert ([1,2,3],[2,3,4])=[1,4]”. This discloses input (“[1,2,3],[2,3,4]”) and output (“[1,4]”) data. Accordingly, Chen discloses receiving input-output data of a task. Furter Chen discloses code generation based on this “input-output data” (see e.g. par. [0022] “generate … a code segment … generate student predicted code”). Accordingly, contrary to applicant’s assertion Chen discloses receiving input-output data and generating a set of programs.
Chen also fails to describe generated programs being used in the claimed sense as items that are added to a training dataset together with task input-output data, much less than being reused through an iterative updated-policy process. Rather, Chen describes that the task output 202 can be "a programming language segment." Chen, para. [0025]. Chen further describes that "a training input is generated by incorporating the task input, the task output, and the feedback with a pre-defined refinement template," that "a training pair is stored in a training dataset," and that "the student model is trained using the training dataset." Id at paras. [0079]-[008 l]. Thus, even assuming, for argument purposes, that Chen's task output 202 is code, Chen uses that code as part of a teacher-student refinement example for training the student model, not as a claimed "first set of programs" that is added to a training dataset together with input-output data of one or more tasks corresponding to the first set of programs.
The claim recites adding the “first set of programs” and the “input-output data”. As claimed the “first set of programs” are generated based on the “input-output data” and subsequently added to a training dataset.
Chen par. [0034] discloses code (i.e. “student model 120’s task output 202”) generated based on input-output data (par. [0034] “conditioned on task input 102”) is added, together with, the input-output data (“task input 102”) to a training dataset (par. [0035] “The training sample 204”).
To the extent the applicant is arguing that Chen differs from what is claimed in that the generated “programming language segment” does not constitute a “program”. It is noted that Applicant does not provide an explicit definition of a “program”. Instead, applicant discloses, e.g., a “program p that outputs the test example outputs when executed on test example inputs” (par. [0071], also see e.g. par. [0033]). Chen discloses executing a “code segment” (e.g. par. [0025] “the code segment may be executed by an executor”) a “to solve the task described in task input 102” (par. [0022]). Accordingly, except in name, Chen’s code segments appear to fall within a reasonably broad understanding of the claimed “set of programs” and applicant’s arguments are not persuasive of a patentable distinction..
Drawings
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
THIS ACTION IS MADE FINAL. 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 2012/0011152 to Gulwani discloses an LLM for generating programs.
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
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