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 .
Examiner’s Note
Examiner notes that the actual invention discussed in the paper used as associated provisional filing 63/465,613 (Which is just the inventors' research paper: Song, B., Yuan, C., Permenter, F., Arechiga, N., & Ahmed, F. (2023, August). Surrogate modeling of car drag coefficient with depth and normal renderings. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 87301, p. V03AT03A029). American Society of Mechanical Engineers.) is not recited in the claims, which do not include any links or limitations to integrating drag coefficient modeling into the process of using a diffusion model to generatively create car/automotive renderings beyond the tangential association of drag in claim 5.
It is highly unusual for the entire claim set to be so broad that it is effectively silent regarding an actual invention. This is unfortunate, because the specific technical features of the above-mentioned research paper do appear to be novel in view of the prior art, but this is currently wholly irrelevant, as the pending claims are not directed to that invention. Examiner respectfully requests that applicant be aware of the rules regarding election via original presentation when submitting claim amendments.
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 (mental processes and mathematical relationships) without significantly more. Claim 1 recites:
A system comprising: (this falls within the statutory categories of invention)
a processor; and a memory storing machine-readable instructions that, when executed by the processor, cause the processor to: (generic computer components recited in a manner equivalent to mere instructions to apply an exception, as per MPEP 2106.05(f).)
optimize a parameter of a shape in an image based on a predetermined constraint (a person can do this mentally or with aid of pencil and paper by altering the sketches based on mental observations, evaluations, and judgements)
using a diffusion model, (this is mere instructions to apply an exception using a generic machine learning model, as per MPEP 2106.05(f). Note that here, the claim recites only the idea of a solution or outcome. The claim fails to recite details of how a solution to a problem is accomplished. The diffusion model is merely being indicated as being “used” to accomplish the optimization, with no information or detail on how the diffusion model functions or operates when doing so.)
the parameter being a pixel value for each pixel forming the shape. (A person can mentally accomplish pixel-based image manipulation by imagining low complexity images or making use of graph paper and using the cells of the graph paper as pixels, well within the capability of a person to accomplish with mental evaluations and judgements. Note that no lower bound on complexity is present in the claims.)
This judicial exception is not integrated into a practical application. In particular, the claim only recites the following additional elements: 1) mere instructions to apply the exception using generic computer components (the processor/memory) and generic machine learning models (The diffusion model), 2) generally linking the use of the exception to the technical field of image generation, and 3) insignificant extra-solution activity in the form of selecting a particular data source or type of data to be manipulated (generating an image of the result, as implied by claim 1 and actually recited in claim 4). The processor/memory is recited at a high-level of generality (i.e., as a generic processor/memory performing a generic computer function of executing instructions and storing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application. The specification that a report is generated is only tangentially linked to the calculation and analysis steps, and does not meaningfully limit the claim. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor/memory to perform the claimed steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself. The addition of insignificant extra-solution activity does not amount to an inventive concept. The claim is not patent eligible.
Claims 2-6 recite only further details within the scope of the above analysis, with claim 5 as noted above also including the insignificant extra-solution activity of outputting an image (see MPEP 2106.05(g)).
Claim 7 additionally recites training the diffusion model on real images, but this is training the model as a regularizer, which is a mathematical function – this training therefore falls within the scope of mathematical relationships, as it is being done according to a mathematical algorithm, in a manner similar to 2024 AI SME example 47’s claim 2, where gradient-descent and back propagation training were analyzed as within the scope of mathematical relationships.
In view of the above, claims 2-7 remain ineligible.
Claims 8-14 are substantially similar to claims 1-7 respectively, and are rejected under the same grounds.
Claims 15-20 are substantially similar to claims 1-6 respectively, and are rejected under the same grounds.
Claim Rejections - 35 USC § 102
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.
Claims 1-4, 6, 8-11, 13, 15-18, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kasten (US 20240331280 A1).
Regarding Claim 1, Kasten teaches:
a processor; and (¶60 In an embodiment, the PPU 400 is a multi-threaded processor that is implemented on one or more integrated circuit devices.)
a memory storing machine-readable instructions that, when executed by the processor, cause the processor to: (¶30 various functions may be carried out by a processor executing instructions stored in memory; ¶50 For instance, various functions may be carried out by a processor executing instructions stored in memory.)
optimize a parameter of a shape in an image based on a predetermined constraint using a diffusion model, (¶26 Because the text-to-image diffusion model was trained on a vast number of diverse objects, it contains a strong prior about the shape and texture of objects, and that prior can be used for completing missing parts.; ¶27 The 3D model reconstruction system generates accurate and realistic 3D shapes from partial observations.; ¶40 The 3D model reconstruction system 100 optimizes for the complete object surface represented by a neural signed distance function; ¶41 The input distances may be used to constrain the optimized 3D surface 210 to match the depth sensor observations:)
the parameter being a pixel value for each pixel forming the shape. (¶35 the pixel coordinate u=(u, v)T∈R^2 is backprojected into a 3D ray ru, starting at t and going through the pixel u with a direction … where n is the number of pixels in the batch.; ¶41 Each rendered pixel i is associated with its expected rendered opacity and distance from the surface)
Regarding Claim 2, Kasten teaches:
optimize the parameter of the shape by constraining the pixel values such that the image appears to be a real image. (¶35 the pixel coordinate u=(u, v)T∈R^2 is backprojected into a 3D ray ru, starting at t and going through the pixel u with a direction … where n is the number of pixels in the batch.; ¶41 Each rendered pixel i is associated with its expected rendered opacity and distance from the surface; ¶27 he 3D model reconstruction system generates accurate and realistic 3D shapes from partial observations.; ¶39 The 3D model representation 110 may be further constrained using the sensor-compatibility loss in combination with the text-compatibility loss. The sensor-compatibility loss comprises multiple components including a points loss, distance loss, and absence loss.)
Regarding Claim 3, Kasten teaches:
optimize the parameter of the shape in image space. (¶26 Because the text-to-image diffusion model was trained on a vast number of diverse objects, it contains a strong prior about the shape and texture of objects, and that prior can be used for completing missing parts.; ¶27 The 3D model reconstruction system generates accurate and realistic 3D shapes from partial observations.; ¶40 The 3D model reconstruction system 100 optimizes for the complete object surface represented by a neural signed distance function; ¶41 The input distances may be used to constrain the optimized 3D surface 210 to match the depth sensor observations:)
Regarding Claim 4, Kasten teaches:
generate the image using the diffusion model. (¶57 The 3D model reconstruction system 100 leverages a pre-trained text-to-image diffusion model to reconstruct a complete 3D model of an object from a sensor-captured incomplete point cloud for the object and a textual description of the object.)
Regarding Claim 6, Kasten teaches:
optimize the parameter of the shape based on a plurality of images. (¶47 By initially applying the SDS loss on images rendered from the depth sensor's perspective, the colors of the observed part of the object are optimized first to be consistent with y, and then, when the sampling range increases, the rest of the object's colors and geometry are completed accordingly.; ¶54 additional images of the representation of the 3D object are rendered according to additional camera viewpoints. Sampled noise is combined with the additional images to produce additional noisy images and the text-compatibility loss is computed to reduce differences between the predicted noise and the sampled noise.)
Regarding claims 8-11 and 13:
Claims 8-11 and 13 are substantially similar to claims 1-4 and 6 respectively, and are rejected under the same grounds.
Regarding claims 15-18 and 20:
Claims 15-18 and 20 are substantially similar to claims 1-4 and 6 respectively, and are rejected under the same grounds.
Claim Rejections - 35 USC § 103
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.
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 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kasten (US 20240331280 A1) in view of Saha (Saha, S., Rios, T., Minku, L. L., Stein, B. V., Wollstadt, P., Yao, X., ... & Menzel, S. (2021, December). Exploiting generative models for performance predictions of 3D car designs. In 2021 IEEE symposium series on computational intelligence (SSCI) (pp. 1-9). IEEE.).
Regarding Claims 5, 12, and 19:
Kasten does not teach in particular, but Saha teaches:
wherein the predetermined constraint is based on one of: a drag coefficient; a manufacturability criterion; a vehicle dimension; a vehicle structural strength; or a vehicle weight distribution. (Abstract, We use machine learning-based surrogate models to predict the performances of car shapes based on the low-dimensional representation learned by 3D point cloud (variational) autoencoders. Furthermore, we exploit the stochastic nature of the representation learned by variational autoencoders to augment the training data for our surrogate models, since the limited amount of data is usually a challenge for surrogate modeling in engineering. We demonstrate that augmenting training with generated shapes improves prediction accuracy.; Section II.B., utilized a variational autoencoder for learning latent representations of 2D shapes and Gaussian process regression to predict the corresponding drag coefficients.; Section II.B., utilized a machine learning-based regression model to predict aerodynamic forces of 3D shapes, along with a time-averaged velocity field around the object.; Section III.B., we define the performances of a 3D design by its volume and aerodynamic drag coefficient)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the variational autoencoder based drag analysis of Saha to the diffusion model based reconstruction system of Kasten, in order to result in a system that offer powerful tools to support the engineering design process (Saha, Abstract).
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kasten (US 20240331280 A1) in view of Skrypnyk (US 20240355064 A1).
Regarding Claims 7 and 14:
Kasten does not teach in particular, but Skrypnyk teaches:
train the diffusion model on real images as a regularizer. (¶301 a validation phase may be performed evaluated on a separate dataset known as the validation dataset. The validation dataset is used to tune the hyperparameters of a model, such as the learning rate and the regularization parameter.)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the regularization and validation training/updating of Skrypnyk to the diffusion model based reconstruction system of Kasten, in order to improve the performance of the model (Skrypnyk, ¶301).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BIJAN MAPAR whose telephone number is (571)270-3674. The examiner can normally be reached Monday - Thursday, 11:00-8:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rehana Perveen can be reached at 571-272-3676. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BIJAN MAPAR/ Primary Examiner, Art Unit 2189
Prosecution Insights