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 .
Information Disclosure Statement
The information disclosure statements (IDS) filed on 1/16/2025 and /12/2025 were considered and placed on the file of record by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 11, 13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Dependent claims 2-10, 12, 14-20 are rejected based on their dependency.
The claims are generally narrative and indefinite, failing to conform with current U.S. practice. They appear to be a literal translation into English from a foreign document and are replete with grammatical and idiomatic errors.
The following highlighted elements in claim 1 are vague and indefinite:
“1. A computer-implemented machine-learning method, comprising:
obtaining a dataset comprising 2D images and corresponding layouts and viewpoints of 3D scenes; and
training a function based on the obtained dataset, the function configured to generate a 2D image of a 3D scene and including a scene encoder and a generative image model, the scene encoder taking as input a layout of the 3D scene and a viewpoint, and
outputting a scene encoding tensor, the generative image model taking as input the scene encoding tensor outputting by the scene encoder and outputting the generated 2D image.”
The following highlighted elements in claim 11 are vague and indefinite:
“11. A method of applying a function, comprising:
obtaining a dataset comprising 2D images and corresponding layouts and viewpoints of 3D scenes; and
training the function based on the obtained dataset, the function being machine-learnt by machine-learning including generating a 2D image of a 3D scene, the function including a scene encoder and a generative image model, the scene encoder taking as input a layout of the 3D scene and a viewpoint, and
outputting a scene encoding tensor, the generative image model taking as input the scene encoding tensor outputted by the scene encoder and outputting the generated 2D image, the training further comprising:
obtaining a layout of a 3D scene; and
applying the function to the layout of a 3D scene, thereby generating a 2D image of the 3D scene.”
“13. A device comprising:
a processor; and
a non-transitory computer-readable data storage medium having recorded thereon a computer program comprising instructions that when executed by the processor causes the processor to implement machine-learning of a function configured to generate a 2D image of a 3D scene, the function having a scene encoder and a generative image model, the scene encoder taking as input a layout of the 3D scene and a viewpoint, and outputting a scene encoding tensor, the generative image model taking as input the scene encoding tensor outputted by the scene encoder and outputting the generated 2D image, by the processor being configured to:
obtain a dataset comprising 2D images and corresponding layouts and viewpoints of 3D scenes; and
train the function based on the obtained dataset, and/or the processor is further configured to apply the function that is machine-learnt according to the machine-learning, the processor further configured to apply the function by being configured to:
obtain a layout of a 3D scene; and
apply the function to the layout of a 3D scene, thereby generating a 2D image of the 3D scene.”
Claims 3, 15, 19, 20 are vague and indefinite. The term “the bounding box” is not defined.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ribeiro et al. (US 2023/0281842) discusses generative AI models for image domain transfer and the labeling of target bones of 2D and 3D medical images.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNY A CESE whose telephone number is (571) 270-1896. The examiner can normally be reached on Monday – Friday, 9am – 4pm.
If attempts to reach the primary examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached on (571) 272-3838. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/Kenny A Cese/
Primary Examiner, Art Unit 2663
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