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 .
1. Claims 11-30 are currently pending in this application.
Claims 11, 18, and 25 are amended as filed on 11/07/2025.
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) 11-16, 18-23, and 25-30 are rejected under 35 U.S.C. 103 as being unpatentable over Dirik et al. (Pre-Grant Publication No. US 2018/0240216 A1), hereinafter Dirik, in view of Kuta et al. (Pre-Grant Publication No. US 2022/0012362 A1), hereinafter Kuta.
2. With respect to claims 11, 18, and 25, Dirik taught a method for image data desensitization (0021-0029, where the hiding of the image data is the data desensitization), comprising: determining a target image channel, in a native image, on which target recognition needs to be performed (0064, where any of the R, G, or B channels is a single channel from the image), wherein the target image channel is determined from a plurality of single-channel images corresponding to the native image based on system settings or based on image quality of the plurality of single-channel images (0064, where this, at least, inherently teaches the system settings limitation as the system is performing its functions according to its programming, which includes the system’s settings); performing image restoration on native data corresponding to the target image channel, to obtain a single-channel image corresponding to a color component of the target image channel (0064the selected channel to by anonymized); and performing data desensitization processing on the native data in the sensitive area (0064, performing the anonymization).
However, while Dirik did recognize the noise to be removed and could be argued that that represents a plurality of objects under broadest reasonable interpretation, in order to maintain a more compact prosecution, it will be taken that Dirik did not explicitly state that it performed recognizing a target object from the single-channel image; determining a sensitive area in the native image based on the target object. On the other hand, Kuta did teach recognizing a target object from the single-channel image (0046, where the objects can be seen in 0026, where the single-channel was previously shown in Dirik: 0064); determining a sensitive area in the native image based on the target object (0046, where the removed object from the image is the sensitive area). Both of the systems of Dirik and Kuta are directed towards desensitizing image data and therefore, it would have been ordinary to a person having ordinary skill in the art, at the time of the effective filing of the invention, to modify the teachings of Dirik, to utilize removing specific targeted objects from the image, as taught Kuta, in order provide another layer of protection of a user that desires as much.
3. As for claims 12, 19, and 26, they are rejected on the same basis as claims 1, 18, and 25 (respectively). In addition, Dirik taught determining a plurality of single-channel images corresponding to the native image based on array distribution information of an image sensor, wherein image sizes of the plurality of single-channel images are all smaller than that of the native image; and determining the target image channel based on the plurality of single-channel images (0064, where the different layers would make up the entire native image and are thus, individual smaller than the whole under broadest reasonable interpretation from a data-centric point of view).
4. As for claims 13, 20, and 27, they are rejected on the same basis as claims 1, 18, and 25 (respectively). In addition, Dirik taught performing image interpolation processing on the native image to determine a plurality of single-channel images corresponding to the native image, wherein image sizes of the plurality of single-channel images are all same as that of the native image; and determining the target image channel based on the plurality of single-channel images (0064, where the different layers would cover the entire image and thus, teach the same size under broadest reasonable interpretation from a geometric point of view).
5. As for claims 14, 21, and 28, they are rejected on the same basis as claims 1, 18, and 25 (respectively). In addition, Kuta taught recognizing the target object from the single-channel image by means of a pre-trained recognition model (0016, where the training can be seen).
6. As for claims 15, 22, and 29, they are rejected on the same basis as claims 1, 18, and 25 (respectively). In addition, Dirik taught setting a pixel within the sensitive area to a target pixel value, wherein a difference value between the target pixel value and a pixel boundary value is within a preset difference range (0050. See also: Kuta: 0024).
7. As for claims 16, 23, and 30, they are rejected on the same basis as claims 1, 18, and 25 (respectively). In addition, Kuta taught performing image blurring processing on the sensitive area (0003).
Claim(s) 17 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Dirik, in view of Kuta, and in further view of Pranskevichus et al. (Pre-Grant Publication No. US 2023/0267583 A1), hereinafter Prans.
8. As for claims 17 and 24, they are rejected on the same basis as claims 16 and 23 (respectively). However, Dirik did not explicitly state performing image blurring processing on the sensitive area of the native image by means of a preset convolution kernel. On the other hand, Prans did teach performing image blurring processing on the sensitive area of the native image by means of a preset convolution kernel (0030). Both of the systems of Kuta and Del are directed towards obfuscating an image and therefore, it would have been ordinary to a person having ordinary skill in the art, at the time of the effective filing of the invention, to modify the teachings of Kuta, to utilize specific techniques for image blurring, as taught Prans, in order to utilize efficient methods for data obfuscation.
Response to Arguments
Applicant's arguments filed 11/07/2025 have been fully considered but they are not persuasive.
9. The applicant argues on page 9 that “what is actually hidden in Dirik is a source of the photo/video content, that is, a source device, and the privacy of the source device is protected, but it is not the hiding of the image data. Since data desensitization in the art refers to hiding sensitive information included in data to prevent sensitive information from being leaked, Dirik does not involve hiding image data nor desensitization of image data”.
However, image desensitization includes any data associated with the image under broadest reasonable interpretation. The claim only requires that a sensitive area be determined and desensitized. Accordingly, as can be seen in Dirik, the sender information is part of the image data, is a sensitive area, and thus can be targeted for sanitization.
10. The applicant further argues on page 9 that “Dirik discloses that the image (I) can be an image which consists of different color channels, in which case, the anonymization is processed on each of these color channels, and as a result, PRNU noise on the image (I) is eliminated. For example, if the image (I) is of RGB format, anonymization is applied for R, G and B color channels of the image (I), separately. Elaborately, R, G, and B color channels of the image (I) are separated and anonymization method is applied on each of these separate channels. It can be seen that Dirik anonymizes each color channel, but does not involve the process of selecting the target color channel from multiple channels.” However, R, G, B, represents 3 single separate images channels, which is a plurality of single-channel images.
11. The applicant argues on page 10 that “the PRNU noise unique to the digital imaging sensor in Dirik is not private image data, nor is it sensitive information. PRNU noise only needs to be cancelled and does not need to be protected. There are essential differences between noise and private image data and cannot be equivalent to each other. Then, the removal of noise in the digital image in Dirik does not desensitize the private image data in the digital image.”
However, the cited portion of Dirik (0064) explicitly states that it is performing image anonymization by removing PRNU noise. Image anonymization means that it is altering the image so that a person, sender, receiver, etc. (for example), cannot be identified. Thus, this is teaching removing/desensitizing private and/or sensitive data under broadest reasonable interpretation.
12. The applicant argues on page 11 that “it is impossible to anonymize only one color channel, and there is no process of determining one color channel for subsequent processing from a plurality of color channels. If only the R color channel is processed anonymously, and the G and B channels are abandoned, the whole image can't be anonymized, and the image noise can't be eliminated.” However, the argument is unclear as the cited paragraph (0064) explicitly states that it anonymizes each channel (R,G,B) separately.
13. The applicant argues on page 13 that “the target object in the single-channel image is different in size from the sensitive area in the native image, which can also indicate that the target object is not a sensitive area. Therefore, on the basis that the object removed in Kuta is equivalent to the sensitive area but the target object in the present disclosure is not equivalent to the sensitive area, and in combination that the object removed in Kuta is in the source image but the target object in the present disclosure can be obtained in the single-channel image, the object removed in the source image in Kuta is not equivalent to the target object in the single-channel image in the present disclosure, and Kuta does not involve the technical feature of "recognizing a target object from the single-channel image"”.
However, the single channel anonymization was taught by Dirik in 0064 (for example). Kuta was only utilized, under 35 U.S.C. 103, to more explicitly show that the anonymization could be for a specific identified target object within the image (0046, where the objects can be seen in 0026).
14. The applicant argues on page 14 that “the source identity determination and swap in Kuta are implemented in the encoded space which is a non-visual space.” 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. visual space) 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).
15. The applicant argues on page 15 that “It can be seen that the focus of Prans is to perform image enhancement processing on the focused object in the image, which has nothing to do with the desensitization of image data in the present disclosure, and Prans does not disclose the technical features related to the desensitization of image data.” However, desensitizing data was previously shown by Dirik (0064). Prans was utilized under 35 U.S.C. 103 to specifically teach techniques relating to blurring images (0030).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/JOSEPH L GREENE/Primary Examiner, Art Unit 2443
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