DETAILED ACTION
This Non-Final Office action is in response to Applicant’s filing on 10/13/2022. Claims 1-21 are pending. The effective filing date of the claimed invention is 10/13/2022.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed are directed to abstract idea.
Step 1 – Claims 1-19 are process claims; claim 20 is a machine claim; and, claim 21 is a manufacture claim. Accordingly, step 1 is satisfied.
Step 2A, Prong 1 – Exemplary claim 1 recites the following abstract idea:
A method comprising: processing an input dataset to generate a synthetic dataset that is targeted to a specified downstream task (see e.g. MPEP 2106.04(a)(2)(II)(C) referring to Voter Verified for accept input of the votes and generating a dataset of the votes; see also data manipulation according to mathematical concept at MPEP 2106.04(a)(2)(I) citing Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of ‘‘managing a stable value protected life insurance policy by performing calculations and manipulating the results’’ as an abstract idea).); and outputting the synthetic dataset (see e.g. MPEP 2106.04(a)(2)(III)(A) a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)).
When these abstract concepts are viewed alone and in ordered combination, the examiner finds claim 1 to recite abstract idea.
Step 2A, Prong 2 – Exemplary claim 1 does not integrate the abstract idea with practical application. Claim 1 recites the following additional element: at a device [perform the abstract idea]. The examiner does not find this to improve the device at all. The examiner refers to MPEP 2106.05(f), “As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do “‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on “the draftsman’s art”).” The examiner finds that claim 1 uses the “at a device” as a tool in “apply it” manner and therefore does not improve the device technology. See also MPEP 2106.05(g)(3) where the claimed outputting is found to be mere data gathering/outputting. When the additional element is viewed alone and in combination with the rest of the claim, Claim 1 is found to be directed to abstract idea.
Step 2B – Claim 1 is not found to recite significantly more. The additional element analysis from Step 2A, Prong 2 is equally applied to Step 2B. Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry. This consideration is only evaluated in Step 2B of the eligibility analysis. Claim 1 has limitations that touch on the iii. Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining “shadow accounts”); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. See MPEP 2106.05(d)(II), where these concepts are found to be WURC. Accordingly, claim 1 is found to be directed to abstract idea.
Dependent Claims – Claim 2-4 relates to more abstract idea and data gathering of the specific dataset(s). MPEP 2106.05(g). Claim 5-19 recites more abstract idea such as mathematical concepts from MPEP 2106.04(a)(2)(I).
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.
Claims 1-6, 9-14, and 16-21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kar et al. (April 25 2019). Meta-Sim: Learning to Generate Synthetic Datasets (hereinafter “Kar”).
With regard to claims 1, 20, and 21, Kar discloses the claimed method comprising:
at a device (see e.g. page 1, computing environment with graphics engine):
processing an input dataset to generate a synthetic dataset that is targeted to a specified downstream task (see e.g. Abstract, dataset generator with generative model of synthetic scenes); and
outputting the synthetic dataset (see e.g. Abstract, rendered outputs).
With regard to claim 2, Kar further discloses where the input dataset includes: an input synthetic dataset, and an input real world dataset (see e.g. page 2, We, on the other hand, aim to align the synthetic and real distributions through a direct optimization on the attributes and through a meta objective of optimizing for performance on a down-stream task).
With regard to claim 3, Kar further discloses where the input dataset includes labeled samples (see e.g. page 1-2, In particular, we assume that the structure of the scenes sampled from the grammar are correct (e.g. a driving scene has a road and cars)).
With regard to claim 4, Kar further disclose where the input synthetic dataset includes a greater number of samples than the input real world dataset (e.g. page 3 at figure).
With regard to claim 5, Kar further discloses where the processing is performed using a meta-learning algorithm (see e.g. page 2-3, under 3. Meta-Sim).
With regard to claim 6, Kar further discloses where the meta-learning algorithm reweights a plurality of synthetic samples included in the input dataset (e,g, page 9-10).
With regard to claim 10, Kar further discloses where the synthetic dataset is curated from the input dataset (see e.g. abstract).
With regard to claim 11, Kar further discloses where the synthetic dataset is curated from the input dataset (see e.g. abstract) by: determining a defined number of top-weighted synthetic samples included in the input dataset, and selecting the top-weighted synthetic samples as the synthetic dataset (see e.g. page 4-5, optimizing task performance).
With regard to claim 12, Kar further discloses the synthetic dataset is actively synthesized from the input dataset (see e.g. page 8).
With regard to claim 13, Kar further discloses where the synthetic dataset includes newly generated synthetic samples that augment the input dataset (see e.g. page 2, optimizing simulators section).
With regard to claim 14, Kar further discloses where the newly generated synthetic samples include additional synthetic samples generated over a plurality of iterations (see e.g. page 4).
With regard to claim 16, Kar further discloses where the target downstream task is a computer vision task (see e.g. page 9, computer vision).
With regard to claim 17, Kar further discloses where the target downstream task is a natural language processing task (see e.g. page 5, under MNIST).
With regard to claim 18, Kar further discloses where the synthetic dataset is output as a training dataset for training a machine learning model for the target downstream task (see e.g. page 2, Meta-Sim also optimizes a meta objective of adapting the simulator to improve downstream real-world performance of a Task Network trained on the datasets synthesized by our model).
With regard to claim 19, Kar further discloses training the machine learning model for the target downstream task, using the synthetic dataset (see e.g. page 2).
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.
Claim(s) 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Kar in view of Ren et al. (May 5, 2019). Learning to reweight examples for robust deep learning. Retrieved at https://arxiv.org/pdf/1803.09050 (hereinafter referred to as “Ren”).
With regard to claim 7-8, Kar discloses generating synthetic data optimized for a downstream task, and an overall meta-objective that improve downstream task performance. See above. Kar does not disclose “for each synthetic sample . . . an importance” and wherein the importance is indicated as a weight.
Ren teaches at e.g. abstract, page 3-4, that it would have been obvious to one of ordinary skill in the DL art to include the ability to assign an importance to each sample, where the importance is indicated as a weight. The advantage of such combination is “to minimize the loss on a clean unbiased validation set.” See Ren at abstract.
Therefore, it would have been obvious to one of ordinary skil in the DNN art before the effective filing date of the claimed invention to modify Kar to include such per sample importance learning with respect to a task, and importance indicated as a weight, as taught by Ren, where this is beneficial in that such combination is “to minimize the loss on a clean unbiased validation set.” See Ren at abstract.
Claim(s) 15 is rejected under 35 U.S.C. 103 as being unpatentable over Kar in view of Mehta et al. (July 10, 2019). Active domain randomization. Retrieved at https://arxiv.org/pdf/1904.04762. (hereinafter referred to as “Mehta”).
With regard to claim 15, Kar discloses synthetic data generation, downstream task optimization, and parameterized generator, as shown above. Kar does not disclose the below limitations of claim 15.
However, Mehta teaches at e.g. abstract, page 1-3, section 5.2, section 3, etc. that it would have been obvious to one of ordinary skill in the DL art to include the ability to where the newly generated synthetic samples include additional synthetic samples generated by: determining a defined number of top-weighted synthetic samples included in the input dataset (see Mehta, section 5.2, Similarly, ADR searches for what environments may be most useful to the agent at any given time. Active learners, like BO methods discussed in Section 3, often require an acquisition function (derived from a notion of model uncertainty) to chose the next sample. Since ADR handles this decision through the explore-exploit framework of RL and the α in SVPG, ADR sidesteps the well-known scalability issues of both active learning and BO [26].), computing a generative parameter distribution of the top-weighted synthetic samples included in the input dataset (see e.g. page 2, We cast this active search as a Reinforcement Learning (RL) problem where the ADR sampling policy is parameterized with Stein Variational Policy Gradient (SVPG) [3].), selecting a plurality of synthesis parameters, based on the generative parameter distribution (e.g. page 2), and generating the additional synthetic samples based on the plurality of synthesis parameters (see e.g. page 3, the system iteratively generates new environments to improve policy learning).
Therefore, it would have been obvious to one of ordinary skill in the DL art before the effective filing date of the claimed invention to modify Kar’s synthetic datasets generation with such active synthesis framework of Mehta, in order to explicitly identify top-weighted synthetic samples and more accurately estimate a generative parameter distribution from those samples, thereby further improving the efficiency and effectiveness of synthetic data generation, as shown in Mehta pages 1-3.
Conclusion
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/PETER LUDWIG/Primary Examiner, Art Unit 3627