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 .
Response to Arguments
Applicant's arguments filed on 03/11/2026, with respect to the rejection of claim 1, 14, and 18, under 35 U.S.C 102(a)(1) have been fully considered but they are not persuasive. Regarding independent claims Applicant argues that “Claims 1, 14, and 18, were rejected over Liba (2022/0230323). The rejections are respectfully traversed.” (please see Remarks, page 1, Rejections under 35 U.S.C 102). Examiner respectfully disagrees, First of all, Examiner noticed that nowhere in the Remarks Applicant presented specific arguments with respect to Liba reference neither Applicant highlighted any claim limitations which are not taught by the reference. Hence, Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Therefore the rejection of claims 1, 14, and 18, with respect to 35 U.S.C 102(a)(1), is being maintained.
Applicant arguments with respect to rejection of claims 1, 14, and 18, under 35 U.S.C 103, have been fully considered but they are not persuasive. Regarding independent claims Applicant argues that “The prior art references, Brandt (2024/0169624), Kalra (2022/0044441), and Sato (2024/0362907), fail to teach or disclose techniques of processing peripheral and focal image regions differently for reduced latency, energy consumption, and communication bandwidth usage, as claimed by Applicant.” (please see Remarks, page 6, first paragraph).
Examiner respectfully disagrees, First of all, In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “processing peripheral and focal image regions differently for reduced latency, energy consumption, and communication bandwidth usage”, please see Remarks, page 6, first paragraph) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Secondly, Examiner noticed throughout the Remarks Applicant pointing out to the specification of the pending Application without explaining how the claimed limitations are different from the cited reference. Furthermore, as previously cited by Examiner Brandt in view of Karala discloses the argued limitations for instance Brandt discloses a graphical user interface configured to present an image captured by an image sensor and receive user interactions with the image (Brandt, Fig. 2:202:206, Fig. 3:312, and paragraphs 84 and 91); and generate, based on the user interactions with the graphical user interface, a region mask configured to identify the plurality of regions of pixels in the image sensor (Brandt, Fig. 3:312, and paragraphs 60, 188, 199 and 200); and further Kalra discloses generate, according to the region mask and based on a deep neural network model a computing model for analyzing image data, captured by the image sensor (Kalra, Fig. 6:607:632, and paragraphs 179, 184-186, 219, and 221). Similarly, Brandt in view of Sato also discloses the claim limitations as presented by the Applicant as being explained below. Therefore, Brandt, Karala and Sato references reads on the argued limitations as presented by the Applicant. Examiner suggests Applicant to further elaborate on deep neural network model and/or how the computing model is generated rather than merely claiming generate, according to the region mask and based on a deep neural network model a computing model for analyzing image data to overcome the cited references.
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.
Claim(s) 1, 14, and 18, is/are rejected under 35 U.S.C. 102(a)(1)as being anticipated by Liba (US PGPUB 2022/0230323 A1).
As per claim 1, Liba discloses an apparatus (Liba, Fig. 1:100, and Fig. 3:300), comprising:
a graphical user interface configured to present an image captured by an image sensor and receive user interactions with the image (Liba, paragraphs 25, 37, 80, and 85-88); and
a processor (Liba, Fig. 1:100) configured to:
generate, based on the user interactions with the graphical user interface, a region mask configured to identify the plurality of regions of pixels in the image sensor (Liba, Fig. 4:402-412, and paragraphs 25, 37, 80, 85-89); and
generate, according to the region mask and based on a deep neural network model a computing model for analyzing image data, captured by the image sensor (Liba, paragraphs 34, 42, and 47, discloses the camera module 108 re-trains the machine-learned model based on the refined mask that is output from the guided filter to improve future segmentations performed by the machine-learned model on other images).
As per claim 14, Liba discloses a method (Liba, Fig. 1:100, and Fig. 3:300), comprising:
storing a deep neural network model configured according to a resolution of an image sensor (Liba, Fig. 1:100, and Fig. 2:3108:200, and paragraphs 26, 42 and 50);
presenting, in a graphical user interface, an image captured by the image sensor (Liba, Fig. 1:106, and paragraphs 86-88);
receiving, in the graphical user interface, user interactions with the image (Liba, paragraphs 25, 37, 80, and 85-88);
generating, based on the user interactions with the graphical user interface, a region mask configured to identify the plurality of regions of pixels in the image sensor (Liba, paragraphs 86-89, discloses at 412, the computing device 100 automatically segments the image into discrete regions to generate a mask for each region);
deriving, according to the region mask and from the deep neural network model, a computing model (Liba, paragraphs 34, 42, and 47, discloses the camera module 108 re-trains the machine-learned model based on the refined mask that is output from the guided filter to improve future segmentations performed by the machine-learned model on other images); and
analyzing, using the computing model, image data, captured by the image sensor (Liba, paragraphs 34, 42, and 47, discloses the camera module 108 re-trains the machine-learned model based on the refined mask that is output from the guided filter to improve future segmentations performed by the machine-learned model on other images).
As per claim 18, Liba discloses a non-transitory computer storage medium storing instructions which, when executed in a computing device, cause the computing device to perform a method (Liba, paragraph 73), the method comprising:
presenting, in a graphical user interface, an image captured by an image sensor (Liba, Fig. 1:106, and paragraphs 86-88);
receiving, in the graphical user interface, user interactions with the image (Liba, paragraphs 25, 37, 80, and 85-88);
removing, from a deep neural network model configured according to a resolution of the image sensor, a portion of computations in regions (Liba, Fig. 3:200:210, and paragraphs 30, and 60, discloses camera module 108 re-trains the machine-learned model); and
generating, based on the removing, a computing model customized to analyze image data, captured by the image sensor (Liba, paragraphs 34, 42, and 47, discloses the camera module 108 re-trains the machine-learned model based on the refined mask that is output from the guided filter to improve future segmentations performed by the machine-learned model on other images).
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.
Claim(s) 1 and 14, is/are also rejected under 35 U.S.C. 103 as being unpatentable over Brandt (US PGPUB 2024/0169624 A1) and further in view of Kalra (US PGPUB 2022/0044441 A1).
As per claim 1, Brandt discloses an apparatus (Brandt, Figs. 1-51), comprising:
a graphical user interface configured to present an image captured by an image sensor and receive user interactions with the image (Brandt, Fig. 2:202:206, Fig. 3:312, and paragraphs 84 and 91); and
a processor (Brandt, Fig. 3:300) configured to:
generate, based on the user interactions with the graphical user interface, a region mask configured to identify the plurality of regions of pixels in the image sensor (Brandt, Fig. 3:312, and paragraphs 60, 188, 199 and 200); and
Brandt does not explicitly disclose generate, according to the region mask and based on a deep neural network model a computing model for analyzing image data, captured by the image sensor.
Kalra discloses generate, according to the region mask and based on a deep neural network model a computing model for analyzing image data, captured by the image sensor (Kalra, Fig. 6:607:632, and paragraphs 179, 184-186, 219, and 221).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Brandt teachings by implementing a model training device to the system, as taught by Kalra.
The motivation would be to improve performance in detecting and classifying objects encountered in manufacturing environment (paragraph 179), as taught by Kalra.
As per claim 14, Brandt discloses a method (Brandt, Figs. 1-51), comprising:
storing a deep neural network model configured according to a resolution of an image sensor (Brandt, Fig. 1: 102:110a, and paragraphs 56, 80, 84-85, 86, and 413);
presenting, in a graphical user interface, an image captured by the image sensor (Brandt, Fig. 2:202:206, and paragraphs 84 and 91);
receiving, in the graphical user interface, user interactions with the image (Brandt, Fig. 2:202:206, Fig. 3:312, and paragraphs 84 and 91);
generating, based on the user interactions with the graphical user interface, a region mask configured to identify the plurality of regions of pixels in the image sensor (Brandt, paragraphs 60, 188, 199 and 200);
Brandt does not explicitly disclose deriving, according to the region mask and from the deep neural network model, a computing model; and
analyzing, using the computing model, image data, captured by the image sensor.
Kalra discloses deriving, according to the region mask and from the deep neural network model, a computing model (Kalra, Fig. 6:607:632, and paragraphs 179, 184-186, 219, and 221); and
analyzing, using the computing model, image data, captured by the image sensor (Kalra, Fig. 6:632).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Brandt teachings by implementing a model training device to the system, as taught by Kalra.
The motivation would be to improve performance in detecting and classifying objects encountered in manufacturing environment (paragraph 179), as taught by Kalra.
Claim(s) 18, is/are also rejected under 35 U.S.C. 103 as being unpatentable over Brandt (US PGPUB 2024/0169624 A1) and further in view of Sato (US PGPUB 2024/0362907 A1).
As per claim 18, Brandt discloses a non-transitory computer storage medium storing instructions which, when executed in a computing device, cause the computing device to perform a method (Brandt, paragraphs 2 and 505), the method comprising:
presenting, in a graphical user interface, an image captured by an image sensor (Brandt, Fig. 2:202:206, and paragraphs 84 and 91);
receiving, in the graphical user interface, user interactions with the image (Brandt, Fig. 2:202:206, Fig. 3:312, and paragraphs 84 and 91);
Brandt does not explicitly disclose removing, from a deep neural network model configured according to a resolution of the image sensor, a portion of computations in regions; and
generating, based on the removing, a computing model customized to analyze image data, captured by the image sensor.
Sato discloses removing, from a deep neural network model configured according to a resolution of the image sensor, a portion of computations in regions (Sato, Fig. 1:1, and Fig. 56:1A:2:101:106A, and Fig. 57:2, and paragraphs 309-310 and 313); and
generating, based on the removing, a computing model customized to analyze image data, captured by the image sensor (Sato, Fig. 1:1, Fig. 56:1A:2:106A, and Fig. 57:2, and paragraphs 74, 310 and 313, discloses That is, even if the mask pattern of the mask of the first camera 101 is changed, the new identification model more suitable for the changed mask pattern of the mask is created by the learning processing of the learning device 2. Therefore, even if the mask pattern of the mask of the first camera 101 is changed, the image identification unit 103 performs the identification processing using the created new identification model, thus implementing the highly accurate identification processing).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Brandt teachings by implementing a learning device to the system, as taught by Sato.
The motivation would be to achieve more accurate object identification performance (paragraph 68), as taught by Sato.
Allowable Subject Matter
Claims 2-13, 15-17, and 19-20, are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SYED Z HAIDER whose telephone number is (571)270-5169. The examiner can normally be reached MONDAY-FRIDAY 9-5:30 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SAM K Ahn can be reached at 571-272-3044. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SYED HAIDER/Primary Examiner, Art Unit 2633
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