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 .
Priority
This application is a continuation of PCT/JP2022/022229 filed 05/31/22 which is claims priority of JP 2021-141805 filed 08/31/21.
Continued Examination Under 37 CFR 1.114
Receipt is acknowledged of a request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e) and a submission, filed on 05/27/2026.
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) 1-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vaughn (U.S. Publication 2004/0021784) and Adams et al. (GB 2498238 A).
In reference to claim 1, Vaughn discloses a data generation method of generating first image data which is image data obtained by imaging a subject via an imaging apparatus and which includes accessory information (see paragraphs 9, 19-22, 26-27, 39 and Figures 1 and 5 wherein Vaughn discloses a method of improving a digital image captured by a digital camera. Vaughn discloses the method utilizing a digital camera which comprises of at least one processor and that produces digital images using an image sensor. Vaughn discloses the digital camera comprising an image processor that performs color processing upon the image data utilizing regression processing of color chart data forming either profile or color matrix type data of which the Examiner interprets functionally equivalent to Applicant’s “accessory information.”), the data generation method comprising:
a first generation step and a second generation step (see paragraphs 19, 27, 39 and #500, 502, 505 of Figure 5 wherein Vaughn discloses capturing and analyzing an image of a color chart and then through a regression procedure deriving a color correction matrix or profile.),
wherein the image data is used in machine learning (see paragraphs 19, 27, 39 and #502-504 of Figure 5 wherein Vaughn discloses utilizing the color chart in a regression procedure which utilizes captured color patch data with reference color patch data to produce either an ICC profile or a color matrix that colorimetrically balances the image for that set of conditions that the images are captured under. Note, the Examiner interprets such regression procedure in Vaughn functionally equivalent to a “machine learning” since it learns a mapping from data. Thus taking the broadest definition of “machine learning” as any data-driven model for improvement of a task, the techniques of Vaughn can surely be considered equivalent thereto.),
the first generation step generating the first image data by associating the accessory information with processed image data generated by performing first image processing via the imaging apparatus (see paragraphs 19, 27 and 39 wherein Vaughn explicitly discloses the digital camera capturing and analyzing an image of a color chart under the illuminant or in the venue used to capture subsequent images.); and
the second generation step generating first information based on image processing information related to the first image processing, as information included in the accessory information (see paragraphs 19, 27, 39 and #502-504 of Figure 5 wherein Vaughn discloses utilizing the color chart in a regression procedure which utilizes captured color patch data with reference color patch data to produce either an ICC profile or a color matrix that colorimetrically balances the image for that set of conditions that the images are captured under.).
Although Vaughn does disclose the digital camera capturing and analyzing an image of a color chart under the illuminance used to capture subsequent images, Vaughn does not explicitly disclose the image data “including” the accessory information and the accessory information associated with processed image data generating by performing a first image processing. Adams et al. discloses techniques for image storage management in the context of digital images captured via digital cameras (see paragraphs 2-3). Adams et al. discloses a digital camera connected to a storage device, the digital camera capturing images which comprise of structured metadata associated and stored with the image body (see paragraphs 30-32 and Figure 2). Adams et al. explicitly discloses examples of metadata to comprise image resolution, compression type, exposure time, aperture value, color space, exposure compensation, to name a few (see paragraph 32). Note, the Examiner interprets the actual capturing of the image and compressing (via the inherent “compression type” metadata) of the image in Adams et al. functionally equivalent to Applicant’s “first image processing.” It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to implement the captured digital image metadata association techniques of Adams et al. with the color regression digital image improvement techniques of Vaughn in order to reliably reproduce, archive and/or feed into downstream color-managed workflows, digital images by storing associated metadata documenting capturing conditions and processing parameters therewith.
In reference to claims 2 and 16, Vaughn and Adams et al. disclose all of the claim limitations as applied to claims 1 and 15 respectively. Vaughn explicitly discloses the digital camera capturing an image of a color chart under the illuminant or in the venue used to capture subsequent images (see paragraphs 19, 27 and 39 and Figure 2).
In reference to claim 3, Vaughn and Adams et al. disclose all of the claim limitations as applied to claim 2 above. Vaughn explicitly discloses the digital camera capturing an image of a color chart under the illuminant or in the venue used to capture subsequent images (see paragraphs 19, 27 and 39 and Figure 2). Vaughn discloses capturing color patches from the color chart (see paragraph 27 and Figure 2).
In reference to claim 4, Vaughn and Adams et al. disclose all of the claim limitations as applied to claim 2 above. Vaughn discloses utilizing the captured color patch data with reference color patch data (see paragraph 27) which either of, can be interpreted as “stored in advance” as there lacks any sort of basis as to compare “timing” to such data therefore allowing for a broadest interpretation of the term to one of ordinary skill in the art.
In reference to claims 5-7, Vaughn and Adams et al. disclose all of the claim limitations as applied to claim 2 above. Vaughn discloses capturing color patches from the color chart (see paragraph 27 and Figure 2). Since Vaughn explicitly discloses the patch data comprising red, green, blue channel information, the Examiner interprets that least “one or two of chroma saturation, lightness or hue” are represented thereby (see paragraphs 29-34, 39 and Figure 2). Further, one of ordinary skill in the art would surely find equivalent the color patch red, green and blue channel information equivalent to Applicant’s “one or two of the first signal value, the second signal value or the third signal value are different between the plurality of color patches, and the rest are the same.”
In reference to claim 8, Vaughn and Adams et al. disclose all of the claim limitations as applied to claim 2 above. Vaughn explicitly discloses the digital camera capturing an image of a color chart under the illuminant or in the venue used to capture subsequent images (see paragraphs 19, 27 and 39 and Figure 2). Vaughn discloses utilizing the captured color patch data with reference color patch data (see paragraph 27) which the Examiner interprets as at least inherently being “color chart data” from “a reference imaging apparatus.”
In reference to claims 9 and 10, Vaughn and Adams et al. disclose all of the claim limitations as applied to claim 2 above. Vaughn explicitly discloses the digital camera capturing an image of a color chart under the illuminant or in the venue used to capture subsequent images (see paragraphs 19, 27 and 39 and Figure 2). Vaughn explicitly discloses the patch data comprising red, green, blue channel information (see paragraphs 29-34, 39 and Figure 2) therefore, the Examiner interprets that the color chart information generated is at least inherently based on spectral characteristics of the imaging apparatus and chart colors themselves.
In reference to claim 11, Vaughn and Adams et al. disclose all of the claim limitations as applied to claim 2 above. Vaughn discloses further performing a color correction matrix or profile creation process utilizing the regression processed data (see paragraph 39 and #504 of Figure 5). Vaughn discloses utilizing such data to apply corrections for subsequently captured images (see paragraphs 20, 26 and 27).
In reference to claim 12, Vaughn and Adams et al. disclose all of the claim limitations as applied to claim 1 above. Vaughn discloses further performing a color correction matrix or profile creation process utilizing the regression processed data (see paragraph 39 and #504 of Figure 5). Adams et al. further discloses the process of storing the captured image by computing a hash which involves obtaining the image metadata and creating the hash from the obtained metadata (see at least #503-504 of Figure 6).
In reference to claim 13, Vaughn and Adams et al. disclose all of the claim limitations as applied to claim 1 above. Vaughn further explicitly discloses performing white balance correction based upon the color chart information and a specific middle gray patch from the chart whereby a gain for each red, green, blue color channel is computed (see paragraphs 29-35). Adams et al. explicitly discloses examples of metadata to comprise image resolution, compression type, exposure time, aperture value, color space, exposure compensation, to name a few (see paragraph 32).
In reference to claim 14, Vaughn and Adams et al. disclose all of the claim limitations as applied to claim 11 above. Although Vaughn discloses capturing color chart data via a digital camera, utilizing reference color chart data to perform a regression and output corrected color matrix/profile information neither Vaughn or Adams et al. explicitly disclose performing a learning method executing machine learning using images subsequently captured via the corrected processed color data. It is well known in the art of image processing to perform color correction and processing techniques utilizing machine learned techniques. Adopting machine learning as a superior alternative to conventional image processing techniques offers improved accuracy, adaptability, and automation while building directly on known data acquisition methods (Official Notice). It would have been obvious to one of ordinary skill in the art for the combination of Vaughn and Adams et al. who already teach performing image capturing techniques utilizing captured color data and associating metadata therewith, to use machine-learned techniques in the process because adopting machine learning as a superior alternative to conventional image processing techniques offers improved accuracy, adaptability, and automation.
In reference to claim 15, claim 15 is similar in scope to claim 1 and is therefore rejected under like rationale. In addition to the rationale as applied in the rejection of claim 1 above, claim 15 further recites, “An imaging apparatus comprising: an image sensor; and a processor….” Vaughn discloses the camera comprising an image such as for example a CCD image sensor (see at least paragraph 19). Vaughn discloses the method utilizing a digital camera which comprises of at least one processor and that produces digital images using an image sensor (see paragraphs 19, 22 and #62, 66 of Figure 1). Additionally, Adams et al. discloses a digital camera connected to a storage device, the digital camera capturing images which comprise of structured metadata associated and stored with the image body (see paragraphs 30-32 and Figure 2). Adams et al. discloses the storage device to be implemented via a PC which explicitly comprises a CPU and memory (see at least paragraphs 19-20 and Figure 1).
In reference to claim 17, claim 17 is similar in scope to claim 1 and is therefore rejected under like rationale. In addition to the rationale as applied in the rejection of claim 1 above, claim 17 further recites, “A non-transitory computer-readable storage medium storing a program executable by a computer to perform…” Vaughn discloses the method utilizing a digital camera which comprises of at least one processor and that produces digital images using an image sensor (see paragraphs 19, 22 and #62, 66 of Figure 1). Vaughn discloses the at least one processor performing processing to execute the invention as determined by firmware stored in a firmware memory such as an EPROM memory (see paragraph 20 and #66, 70 of Figure 1). Additionally, Adams et al. discloses the invention implemented via a PC which comprises a CPU which executes a program stored in memory (see paragraphs 19-20 and Figure 1).
Response to Arguments
Applicant’s arguments, see pages 6-10 of Applicant’s Remarks, filed 05/27/26, with respect to the rejection(s) of claim(s) 1-14 under 35 USC 102 & 103 in view of Vaughn have been fully considered and are persuasive. The Remarks in combination with Applicant’s claim amendments overcome the prior art rejection therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Vaughn and Adams et al..
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:
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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
6/5/26
Prosecution Insights