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 § 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)(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.
Claim(s) 1-2, 6, 8, 10, 12 is/are rejected under 35 U.S.C. 102(a)(1) as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over Mao et al. (US2020/0218948).
To claim 1, Mao teach an image processing apparatus comprising:
a memory containing program instructions; and a processor coupled to the memory (Fig. 2), wherein the processor is configured to execute the program instructions to:
generate a trained model performing a plurality of mutually different inference tasks from an image (paragraphs 0051, 0067, 0080, may be trained in advance before being installed, which obviously means generated model may be trained),
wherein the trained model includes:
a first component that extracts a first feature value common to the plurality of inference tasks from the image (223 of Fig. 3B; paragraph 0078, encoder feature map);
a second component that is provided for each of the inference tasks and extracts a second feature value specific to the corresponding inference task from the first feature value (2241-2244 of Fig. 3B; paragraph 0078);
a third component that generates a third feature value by concatenating the second feature values extracted for the respective inference tasks (2245 of Fig. 3B; paragraph 0078); and
a fourth component that is provided for each of the inference tasks and outputs an inference result of the corresponding inference task from the third feature value (2246 of Fig. 3B; paragraph 0078).
To claim 10, Mao teach an image processing method by a computer (as explained in response to claim 1 above).
To claim 12, Mao teach a non-transitory computer-readable recording medium on which a program is recorded, the program comprising instructions for causing a computer to perform processes (as explained in response to claim 1 above).
To claim 2, Mao teach claim 1.
Mao teach wherein the third component is configured to set one of the second feature values as a reference feature value, change a size of the second feature value other than the reference feature value to match a size of the reference feature value, generate the third feature value by concatenating the second feature value other than the reference feature value after the change of the size and the reference feature value and, for each of the inference tasks, output the third feature value to the fourth component after changing a size of the third feature value to match an input size of the fourth component (paragraph 0078).
To claim 6, Mao teach claim 1.
Mao teach wherein the fourth component provided for each of the inference tasks is configured to perform 1×1 convolution on the input third feature value to reduce a number of dimensions of the third feature value (2246 of Fig. 3B; paragraph 0078).
To claim 8, Mao teach claim 1.
Mao teach wherein the processor is further configured to execute the instructions to output inference results of the plurality of inference tasks from an image by using the trained model (225 of Fig. 3B; paragraph 0078).
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.
Claim(s) 3-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mao et al. (US2020/0218948).
To claim 3, Mao teach claim 1.Mao teach wherein the third component includes a subcomponent corresponding to each of the inference tasks, and the subcomponent is configured to set the second feature value of the corresponding inference task as a reference feature value, change a size of the second feature value other than the second feature value of the corresponding inference task to match a size of the reference feature value, generate the third feature value by concatenating the second feature value other than the second feature value of the corresponding inference task after the change of the size and the reference feature value, and output the third feature value to the fourth component (paragraph 0078, wherein subcomponents inside third component would be obvious).
To claim 4, Mao teach claim 1.
Mao teach wherein: the processor is further configured to execute the instructions to, in the generation of the trained model, train the trained model in a plurality of training stages; and
the plurality of training stages include at least: a first training stage where any one of the plurality of inference tasks is set as a learning target task and, while parameters of the second component and the fourth component related to the inference task other than the learning target task and a parameter of the first component are fixed, parameters of the second component and the fourth component related to the learning target task are learned; and a second training stage where, while the parameter of the first component is fixed, parameters of the second components and the fourth components related to all the inference tasks are learned (paragraphs 0051, 0072, 0077, after training, the parameters are fixed, such that the model can be used to generate semantic segmentation in real application, wherein setting a learning target task would have been an obvious based on interpretation of learning target task as a real-world application or a training criteria on loss function).
Claim(s) 5, 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mao et al. (US2020/0218948) in view of Zhang et al. (US2021/0012567)
To claim 5, Mao teach claim 1.
But, Mao does not expressly disclose wherein the fourth component provided for each of the inference tasks is configured to set a weight defining a priority level of the second feature value of the corresponding inference task among the second feature values included by the third feature value to be larger than a weight defining a priority level of the other second feature value.
However, it would have been obvious due to lack of clarification on corresponding inference task, feature value, etc.
Zhang teach wherein the fourth component provided for each of the inference tasks is configured to set a weight defining a priority level of the second feature value of the corresponding inference task among the second feature values included by the third feature value to be larger than a weight defining a priority level of the other second feature value (paragraphs 0028-0031), which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the apparatus of Mao, in order to further convolution process.
To claim 7, Mao teach claim 1.
Mao teach wherein the plurality of inference tasks include an object detection task, a pose estimation task, and a semantic segmentation estimation task (without specific or corresponding limitation, object detection task, pose estimation task, and semantic segmentation estimation task are interpreted as being directly or indirectly performed in parallel or in series within the trained model; abstract, paragraphs 0007, 0120, 0126, semantic segmentation, object detection).
Zhang teach providing an understanding of the scene can comprises at least one of: semantic segmentation; localization; detection of objects in the scene; registration of objects in the scene or camera pose estimation, 3D structure inference (e.g. depth), scene completion, style transfer (e.g. a summer scene transferred to a winter scene) (paragraph 0026), which correspond to image of a scene in Mao, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the apparatus of Mao, in order to further understanding of a scene.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHIYU LU whose telephone number is (571)272-2837. The examiner can normally be reached Weekdays: 8:30AM - 5:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen R Koziol can be reached at (408) 918-7630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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ZHIYU . LU
Primary Examiner
Art Unit 2669
/ZHIYU LU/Primary Examiner, Art Unit 2665 January 24, 2026