DETAILED ACTION
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.
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because products that do not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") when claimed as a product without any structural recitations. See MPEP 2106.03
Claim Interpretation - 35 USC § 101
The limitations “calculating, via the generative model, at least one synthetic image representative of the predetermined target situation, from an output of the control model and descriptive data relating to the predetermined target situation” are considered a practical application of creating a generative image via a control model connected with a generative model.
Allowable Subject Matter
Claims 1-6, 8 are allowed.
Claim 7 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter: The closest prior art of record is Souza et al. (“Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models”, 2020.)(Hereinafter referred to as Souza).
Souza teaches A computer-implemented method for generating data, the computer-implemented method (Deep video action recognition models have been highly successful in recent years but require large quantities of manually annotated data, which are expensive and laborious to obtain. In this work, we investigate the generation of synthetic training data for video action recognition, as synthetic data have been successfully used to supervise models for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation, physics models and other components of modern game engines. With this model we generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for “Procedural Human Action Videos”. PHAV contains a total of 39,982 videos, with more than 1000 examples for each of 35 action categories. Our video generation approach is not limited to existing motion capture sequences: 14 of these 35 categories are procedurally-defined synthetic actions. In addition, each video is represented with 6 different data modalities, including RGB, optical flow and pixel-level semantic labels. These modalities are generated almost simultaneously using the Multiple Render Targets feature of modern GPUs. In order to leverage PHAV, we introduce a deep multi-task (i.e. that considers action classes from multiple datasets) representation learning architecture that is able to simultaneously learn from synthetic and real video datasets, even when their action categories differ. Our experiments on the UCF-101 and HMDB-51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance. Our approach also significantly outperforms video representations produced by fine-tuning state-of-the-art unsupervised generative models of videos. See abstract) comprising:
implementing a 3D engine to produce metadata relating to a reference scene generated via said 3D engine, the reference scene being representative of a predetermined target situation (Although not an image modality, our generator also produces extended metadata for every frame. This metadata includes camera parameters, 3D and 2D bounding boxes, joint locations in screen coordinates (pose), and muscle information (including muscular strength, body limits and other physical-based annotations) for every person in a frame. See page 1519, right col.)( We propose an interpretable parametric generative model of human action videos that relies on procedural generation, physics models and other components of modern game engines. See abstract)(See figure 17, game engine);
and
storing, in a data set, said at least one synthetic image that is calculated (Synthetic Fully-labeled videos for Related Tasks, dataset, see figure 17), but is silent to providing, as input to a control model coupled to a generative model, at least part of the metadata produced by the 3D engine ;
calculating, via the generative model, at least one synthetic image representative of the predetermined target situation, from an output of the control model and descriptive data relating to the predetermined target situation;.
The prior art of record alone or in combination is silent to the limitations “providing, as input to a control model coupled to a generative model, at least part of the metadata produced by the 3D engine;
calculating, via the generative model, at least one synthetic image representative of the predetermined target situation, from an output of the control model and descriptive data relating to the predetermined target situation” of claim 1 when read in light of the rest of the limitations in claim 1 and thus claim 1 is allowed.
The prior art of record alone or in combination is silent to the limitations “providing, as input to a control model coupled to a generative model, at least part of the metadata produced by the 3D engine; calculating, via the generative model, at least one synthetic image representative of the predetermined target situation, from an output of the control model and descriptive data relating to the predetermined target situation” of claim 7 when read in light of the rest of the limitations in claim 7 and thus claim 7 contains allowable subject matter.
The prior art of record alone or in combination is silent to the limitations “provide, as input to a control model coupled to a generative model, at least part of the metadata that is produced by the 3D engine; calculating, via the generative model, at least one synthetic image representative of the predetermined target situation, from an output of the control model and descriptive data relating to the predetermined target situation;” of claim 8 when read in light of the rest of the limitations in claim 8 and thus claim 8 is allowed.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure Ryland et al. (US 2024/0256866), generally refers to creating an AI data set using a 3D engine.
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/NICHOLAS R WILSON/Primary Examiner, Art Unit 2611
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