DETAILED ACTION
Preliminary Remarks
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The Examiner notes the reply of 03/02/2026 cancels claims 10-22, 28 and 29 and adds new claims 30-44.
Election/Restriction
Applicant's election with traverse of Group I, claims 1-9 and 23-27 in the reply filed on 03/02/2026 is acknowledged. The traversal is on the ground(s) that since the systems and methods of the different groups of claims are related there is not a serious burden of search present on the examiner. This is not found persuasive because adjusting points of a 3D look-up table as detailed by the non-elected invention at not explicitly required for by the elected claim group thus searching for those specifics in Group II claims would require the extra undue burden.
The requirement is still deemed proper and is therefore made FINAL.
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 7 and 8 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.
In reference to claims 7 and 8, the term “upper range” in the context of “an ambient brightness level” is a relative term which renders the claims indefinite. The term “upper range” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. These claims, nor does claim 1 from which these claims ultimately depend upon, explicitly or even implicitly infer what is meant by the term “upper range,” by giving any sort of range or set of values thereto to allow one of ordinary skill in the art to properly interpret a value or set of values to equate thereto. Thus, the claims are seen as indefinite for failing to particularly point out that which Applicant regards as the invention.
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, 2, 23, 24, 30, 31, 42 and 43 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Vyas et al. (U.S. Publication 2023/0289930).
In reference to claim 1, Vyas et al. discloses an electronic device comprising image processing circuitry (see paragraphs 2, 34, 38, 95, 97 and Figures 30-31 wherein Vyas et al. discloses an electronic device in the form of a computer system such as a mobile phone, tablet, or laptop computer that performs image processing in the form of illumination control via lightweight machine learning. Vyas et al. discloses the computer system to comprise of typical computer circuitry for example, a processor, memory storage and I/O and communication interfaces.), the image processing circuitry configured to:
receive image data in a first color space (see paragraphs 95-96 and #3010 of Figure 30 wherein Vyas et al. discloses a method of the invention wherein an input image is accessed, and therefore at least inherently “received,” which is associated with an initial illumination and comprises RGB image data.);
generate, based at least in part on converting the image data from the first color space to an opponent (OPP) color space, converted image data (see paragraph 96 and #3020 of Figure 30 wherein Vyas et al. discloses converting the input image from the RGB format to a luminance and chrominance color space such as CIElab or YCbCr. Note, the Examiner interprets at least the CIElab color space of Vyas et al. functionally equivalent to Applicant’s “opponent color space” in particular due to the explicitly usage of explanation of the term in Applicant’s specification paragraph 63. Further it is clear that the converted image data in Vyas et al. is functionally equivalent to Applicant’s “converted image data.”);
generate, based at least in part on adjusting the converted image data in the OPP color space, adjusted image data (see paragraph 96 and #3030-3060 of Figure 30 wherein Vyas et al. discloses down-sampling the converted image data to produce a lower resolution version of the image data. Vyas et al. then discloses performing specific illumination correction processing upon the converted image data again, the converted image data within either CIElab or YCbCr color space formats as seen above.); and
generate, based at least in part on converting the adjusted image data from the OPP color space to the first color space, updated image data (see paragraph 96 and #3070 of Figure 30 wherein Vyas et al. discloses generating corrected image data based upon the input image data having gone through the color space conversion and illumination correction processes, back into RGB format (e.g. the “first color space.”).
In reference to claim 2, Vyas et al. discloses all of the claim limitations as applied to claim 1 above in addition, Vyas et al. explicitly discloses of what is referred to as a “light transfer network” which assumes the input image has been captured in ambient lighting such that YCbCr color space data is processed by individually processing a luminance correction via a luminance correction network and a chroma correction via a correction network (see paragraph 91 and #2710, 2715, 2720, 2725, 2730, 2735, 2740, 2745 of Figure 27) of which the Examiner interprets functionally equivalent to Applicant’s ‘luminance compensation function” and “chrominance compensation function” respectively.
In reference to claim 23, Vyas et al. discloses processing circuitry (see paragraphs 2, 34, 38, 95, 97 and Figures 30-31 wherein Vyas et al. discloses an electronic device in the form of a computer system such as a mobile phone, tablet, or laptop computer that performs image processing in the form of illumination control via lightweight machine learning. Vyas et al. discloses the computer system to comprise of typical computer circuitry for example, a processor, memory storage and I/O and communication interfaces.), comprising:
first conversion circuitry configured to generate, based on converting image data corresponding to a display pixel of an electronic display from a first color space to an opponent (OPP) color space, converted image data (see paragraphs 77, 95-96 and #3010, 3070 of Figure 30 wherein Vyas et al. discloses the invention comprising a multitude of different hardware configurations some of which include an ASIC (application specific integrated circuit) or GPU for performing the techniques of an input image being accessed and associated with an initial illumination and comprises RGB image data and generating corrected image data based upon the input image data having gone through the color space conversion and illumination correction processes, back into RGB format (e.g. the “first color space.”). Note, it is clear that at least the ASIC or GPU of Vyas et al. are functionally equivalent to Applicant’s “conversion circuity.”);
chromatic correction circuitry configured to generate, based on adjusting luminance data of the converted image data by a first amount and adjusting chrominance data of the converted image data by a second amount, adjusted image data in the OPP color space (see paragraphs 77-80, 95-96, #2710, 2715, 2720, 2725, 2730, 2735, 2740, 2745 of Figure 27 and #3030-3060 of Figure 30 wherein Vyas et al. discloses down-sampling the converted image data to produce a lower resolution version of the image data. Vyas et al. then discloses performing specific illumination correction processing upon the converted image data again, the converted image data within either CIElab or YCbCr color space formats as seen above. Vyas et al. discloses the correction being performed on each of luma and chroma channels of the color space at different scaling and offsets. Further, Vyas et al. also discloses such corrections being performed via a “luma correction network” and “chroma correction network” while again disclosing that the invention comprises a multitude of different hardware configurations some of which include an ASIC (application specific integrated circuit) or GPU. Note, it is clear that the combination of the ”networks” and at least ASIC or GPU processing entities are functionally equivalent to Applicant’s “chromatic correction circuity.”); and
second conversion circuitry configured to generate, based on converting the adjusted image data from the OPP color space to the first color space, updated image data (see paragraph 96, #2750-2755 of Figure 27 and #3070 of Figure 30 wherein Vyas et al. discloses generating corrected image data based upon the input image data having gone through the color space conversion and illumination correction processes, back into RGB format (e.g. the “first color space.”). Further, Vyas et al. also discloses such conversion being performed via a “network generation” step of the “electronic device” while again disclosing that the invention comprises a multitude of different hardware configurations some of which include an ASIC (application specific integrated circuit) or GPU. Note, it is clear that at least ASIC or GPU processing entities comprise functionally equivalent elements as Applicant’s “second conversion circuity.”).
In reference to claim 24, Vyas et al. discloses all of the claim limitations as applied to claim 23 above in addition, since Vyas et al. discloses generating corrected image data based upon the input image data having gone through the color space conversion and illumination correction processes, from CIElab or YCbCr back into RGB format (see at least paragraph 96), the Examiner interprets the conversion process in Vyas et al. to at least inherently be “based at least in part on color conversion data.”
In reference to claim 30, Vyas et al. discloses a method (see paragraphs 2, 34, 38, 95, 97 and Figures 30-31 wherein Vyas et al. discloses an electronic device in the form of a computer system such as a mobile phone, tablet, or laptop computer that performs image processing in the form of illumination control via lightweight machine learning.), comprising:
receiving image data in a first color space (see paragraphs 95-96 and #3010 of Figure 30 wherein Vyas et al. discloses a method of the invention wherein an input image is accessed, and therefore at least inherently “received,” which is associated with an initial illumination and comprises RGB image data.);
generating, based at least in part on converting the image data from the first color space to an opponent (OPP) color space, converted image data (see paragraph 96 and #3020 of Figure 30 wherein Vyas et al. discloses converting the input image from the RGB format to a luminance and chrominance color space such as CIElab or YCbCr. Note, the Examiner interprets at least the CIElab color space of Vyas et al. functionally equivalent to Applicant’s “opponent color space” in particular due to the explicitly usage of explanation of the term in Applicant’s specification paragraph 63. Further it is clear that the converted image data in Vyas et al. is functionally equivalent to Applicant’s “converted image data.”);
adjusting, based at least in part on an ambient brightness value and a target brightness value, the converted image data in the OPP color space to generate adjusted image data (see paragraphs 91, 96, #2705, 2710, 2715, 2720, 2725, 2730, 2735, 2740, 2745 of Figure 27 and #3030-3060 of Figure 30 wherein Vyas et al. discloses down-sampling the converted image data to produce a lower resolution version of the image data. Vyas et al. then discloses performing specific illumination correction processing upon the converted image data again, the converted image data within either CIElab or YCbCr color space formats as seen above. Vyas et al. explicitly discloses of what is referred to as a “light transfer network” which assumes the input image has been captured in ambient lighting such that YCbCr color space data is processed by individually processing a luminance correction via a luminance correction network and a chroma correction via a correction network. Vyas et al. discloses using a user specified lighting and shading specification to perform the color corrections.); and
generating, based at least in part on converting the adjusted image data from the OPP color space to the first color space, updated image data (see paragraph 96 and #3070 of Figure 30 wherein Vyas et al. discloses generating corrected image data based upon the input image data having gone through the color space conversion and illumination correction processes, back into RGB format (e.g. the “first color space.”).
In reference to claim 31, Vyas et al. discloses all of the claim limitations as applied to claim 30 above in addition, Vyas et al. explicitly discloses of what is referred to as a “light transfer network” which assumes the input image has been captured in ambient lighting such that YCbCr color space data is processed by individually processing a luminance correction via a luminance correction network and a chroma correction via a correction network (see paragraph 91 and #2710, 2715, 2720, 2725, 2730, 2735, 2740, 2745 of Figure 27) of which the Examiner interprets functionally equivalent to Applicant’s ‘luminance compensation function” and “chrominance compensation function” respectively.
In reference to claim 42, Vyas et al. discloses all of the claim limitations as applied to claim 1 above in addition, Vyas et al. explicitly discloses the electronic device comprising a display of any architecture such as AMLCD, AMOLED, micro-LED and so forth (see paragraph 36).
In reference to claim 43, Vyas et al. discloses all of the claim limitations as applied to claim 23 above. Vyas et al. discloses an electronic device in the form of a computer system such as a mobile phone, tablet, or laptop computer that performs image processing in the form of illumination control via lightweight machine learning (see paragraphs 2, 34, 38, 95, 97 and Figures 30-31). Vyas et al. discloses the computer system to comprise of typical computer circuitry for example, a processor, memory storage and I/O and communication interfaces (see paragraphs 2, 34, 38, 95, 97 and Figures 30-31). Vyas et al. explicitly discloses the electronic device comprising a display of any architecture such as AMLCD, AMOLED, micro-LED and so forth (see paragraph 36).
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) 38, 40, 41 and 44 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vyas et al. (U.S. Publication 2023/0289930) and Metcalfe et al. (U.S. Patent 10,986,250).
In reference to claim 38, Vyas et al. discloses an electronic device comprising image processing circuitry (see paragraphs 2, 34, 38, 95, 97 and Figures 30-31 wherein Vyas et al. discloses an electronic device in the form of a computer system such as a mobile phone, tablet, or laptop computer that performs image processing in the form of illumination control via lightweight machine learning. Vyas et al. discloses the computer system to comprise of typical computer circuitry for example, a processor, memory storage and I/O and communication interfaces.), the image processing circuitry configured to:
receive image data in a first color space (see paragraphs 95-96 and #3010 of Figure 30 wherein Vyas et al. discloses a method of the invention wherein an input image is accessed, and therefore at least inherently “received,” which is associated with an initial illumination and comprises RGB image data.);
generate, based at least in part on converting the image data from the first color space to an opponent (OPP) color space, converted image data (see paragraph 96 and #3020 of Figure 30 wherein Vyas et al. discloses converting the input image from the RGB format to a luminance and chrominance color space such as CIElab or YCbCr. Note, the Examiner interprets at least the CIElab color space of Vyas et al. functionally equivalent to Applicant’s “opponent color space” in particular due to the explicitly usage of explanation of the term in Applicant’s specification paragraph 63. Further it is clear that the converted image data in Vyas et al. is functionally equivalent to Applicant’s “converted image data.”);
generate, based at least in part on a white point adjustment of the converted image data in the OPP color space, adjusted image data (see paragraph 96 and #3030-3060 of Figure 30 wherein Vyas et al. discloses down-sampling the converted image data to produce a lower resolution version of the image data. Vyas et al. then discloses performing specific illumination correction processing upon the converted image data again, the converted image data within either CIElab or YCbCr color space formats as seen above.); and
generate, based at least in part on converting the adjusted image data from the OPP color space to the first color space, updated image data (see paragraph 96 and #3070 of Figure 30 wherein Vyas et al. discloses generating corrected image data based upon the input image data having gone through the color space conversion and illumination correction processes, back into RGB format (e.g. the “first color space.”).
Although Vyas et al. does disclose performing luminance correction upon the image data, Vyas et al. does not explicitly disclose generating the image data in the OPP color space based upon a white point adjustment. Metcalfe et al. discloses a system and method for background adjustment in an image (see column 1, lines 15-18). Metcalfe et al. further explicitly discloses the image made from pixels being converted to a chrominance-luminance space such as L*a*b (see column 4, lines 41-51). Metcalfe et al. discloses processing image background pixels to drive the pixels towards a “pure white” being based upon a background strength of the pixel in question and also a luminance strength of the pixel (see column 4, lines 4-18 and Figures 6-7). It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to implement the white point based color processing techniques of Metcalfe et al. with the color image correction techniques of Vyas et al. in order to compensate for variations in ambient brightness so that images that are output aren’t perceived to be “too blue” or “too yellow” due to such variations.
In reference to claim 40, claim 40 is similar in scope to claim 38 and is therefore rejected under like rationale. Claim 40 recites a “method” of the invention of which the Examiner deems has at least inherently been disclosed by the teachings of Vyas et al. and Metcalfe et al. in the rejection of claim 38 above.
In reference to claim 41, Vyas et al. and Metcalfe et al. disclose all of the claim limitations as applied to claim 40 above. Metcalfe et al. discloses processing image background pixels to drive the pixels towards a “pure white” being based upon a background strength of the pixel in question and also a luminance strength of the pixel (see column 4, lines 4-18 and Figures 6-7).
In reference to claim 44, Vyas et al. and Metcalfe et al. disclose all of the claim limitations as applied to claim 38 above. Vyas et al. explicitly discloses outputting the image via the electronic device which comprises a display of any architecture such as AMLCD, AMOLED, micro-LED and so forth (see paragraphs, 36, 91 and Figure 27). Metcalfe et al. explicitly discloses rendering an image on an image output device (see at least Figure 1).
Allowable Subject Matter
Claims 3-6, 9, 25-27, 32-37 and 39 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.
References Cited
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Nasiriavanaki et al. (U.S. Publication 2016/0086572)
Nasiriavanaki et al. discloses methods, devices, controllers and systems for color calibration of displays involving perceptually uniform spreading of color points.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Antonio Caschera whose telephone number is (571) 272-7781. The examiner can normally be reached Monday-Friday between 6:30 AM and 2:30 PM EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Said Broome, can be reached at (571) 272-2931.
Any response to this action should be mailed to:
Mail Stop ____________
Commissioner for Patents
P.O. Box 1450
Alexandria, VA 22313-1450
or faxed to:
571-273-8300 (Central Fax)
See the listing of “Mail Stops” at http://www.uspto.gov/patents/mail.jsp and include the appropriate designation in the address above.
Any inquiry of a general nature or relating to the status of this application or proceeding should be directed to the Technology Center 2600 Customer Service Office whose telephone number is (571) 272-2600.
/Antonio A Caschera/
Primary Examiner, Art Unit 2612
4/30/26
Prosecution Insights