DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
1. This action is responsive to communications: Application filed on November 15, 2024, and Drawings filed on November 15, 2024.
2. Claims 1–20 are pending in this case. Claim 1, 11, 20 are independent claims.
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 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.
Allowable Subject Matter
Claim 1-20 would be allowable if rewritten to overcome the rejection(s) under double patenting, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Double Patenting
1. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/ patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/ patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 3, 4, 9, 10, 11, 13, 14, 19, 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over and anticipated by claims 1, 2, 6, 7 of U.S. Patent 11934637B2 and Gao, CN107607060A. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1, 3, 4, 9, 10, 11, 13, 14, 19, 20 under examination are anticipated, respectively, by claims 1, 2, 6, 7 of the reference patent. Every limitation in the application under examination claims is recited in the conflicting reference patent claims, and the differences between the claims are highlighted below by bolding all limitations that differ, italicizing additional limitations, and underlining limitations that will be addressed below.
Instant Application
12198407B2
1. An artificial intelligence-based image processing method, comprising: obtaining a first sample image of a source domain and a second sample image of a target domain, the first sample image of the source domain carrying a corresponding target processing result, and the first sample image and the second sample image being sample human tissue images;
encoding the first sample image according to an image distribution difference between a target encoding space and the first sample image on an image distribution rule, to obtain a first sample image code,
converting, according to an image distribution difference between the second sample image and the target encoding space, the first sample image into a target sample image, the target sample image carrying a corresponding target processing result, wherein the target sample image conforms to the image distribution rule of the second sample image;
training a first image processing model based on the target sample image and the target processing result corresponding to the target sample image, to obtain a second image processing model;
and inputting, in response to obtaining a human tissue image of the target domain, the human tissue image into the second image processing model, positioning, by the second image processing model, a target human tissue in the human tissue image, and outputting position information of the target human tissue in the human tissue image.
1. An artificial intelligence-based image processing method, comprising: obtaining a first sample image of a source domain and a second sample image of a target domain, the first sample image of the source domain carrying a corresponding target processing result, and the first sample image and the second sample image being sample human tissue images;
encoding, by an encoding network of an image conversion network, the first sample image according to an image distribution difference between a target encoding space and the first sample image, to obtain a first sample image code;
generating, by a generation network of the image conversion network, based on the first sample image code and according to an image distribution difference between the second sample image and the target encoding space, a target sample image that conforms to an image distribution rule of the second sample image, the target sample image carrying a corresponding target processing result;
training a first image processing model based on the target sample image and the target processing result corresponding to the target sample image, to obtain a second image processing model;
and inputting, in response to obtaining a human tissue image of the target domain, the human tissue image into the second image processing model, positioning, by the second image processing model, a target human tissue in the human tissue image, and outputting position information of the target human tissue in the human tissue image.
3. The method according to claim 1, wherein converting the first sample image into the target sample image comprises: inputting the first sample image into an image conversion network, and performing, by the image conversion network, operations of encoding and converting the first sample image, to obtain the target sample image.
2. The method according to claim 1, wherein training the image conversion network comprises: inputting the first sample image and the second sample image into an initial image conversion network, and encoding, by the initial image conversion network, the first sample image according to the image distribution difference of the target encoding space and the first sample image on the image distribution rule, to obtain the first sample image code; encoding the second sample image according to the image distribution difference of the target encoding space and the second sample image on the image distribution rule, to obtain a second sample image code;
4. The method according to claim 3, wherein the image conversion network includes an encoding network and a generation network;
and inputting the first sample image into the image conversion network includes:
inputting the first sample image into the encoding network of the image conversion network, and encoding, by the encoding network, the first sample image according to the image distribution difference of the target encoding space and the first sample image on the image distribution rule, to obtain the first sample image code;
and generating, by the generation network according to the image distribution difference of the second sample image and the target encoding space on the image distribution rule, the target sample image that conforms to the image distribution rule of the second sample image based on the first sample image code.
2. The method according to claim 1, wherein training the image conversion network comprises:
inputting the first sample image and the second sample image into an initial image conversion network,
and encoding, by the initial image conversion network, the first sample image according to the image distribution difference of the target encoding space and the first sample image on the image distribution rule, to obtain the first sample image code;
encoding the second sample image according to the image distribution difference of the target encoding space and the second sample image on the image distribution rule, to obtain a second sample image code; generating, according to the image distribution difference of the first sample image and the target encoding space on the image distribution rule, a third sample image and a fourth sample image based on the first sample image code and the second sample image code; generating, according to the image distribution difference of the second sample image and the target encoding space on the image distribution rule, a fifth sample image and a sixth sample image based on the first sample image code and the second sample image code; obtaining an image classification result of the first sample image, the second sample image, the third sample image, the fourth sample image, the fifth sample image, and the sixth sample image, the image classification result being used for indicating that an image is an original image or a generated image; and adjusting a network parameter of the initial image conversion network according to an accuracy rate of the image classification result, to obtain the image conversion network.
9. The method according to claim 1, wherein training the first image processing model comprises: inputting the target sample image into the first image processing model, positioning, by using the first image processing model, a target human tissue in the target sample image, and outputting predicted position information of the target human tissue in the target sample image; obtaining accuracy of the predicted position information based on the predicted position information and target position information of the target human tissue in the target processing result; and adjusting a model parameter of the first image processing model based on the accuracy, to obtain the second image processing model.
6. The method according to claim 1, wherein training the first image processing model comprises: inputting the target sample image into the first image processing model, positioning, by using the first image processing model, a target human tissue in the target sample image, and outputting predicted position information of the target human tissue in the target sample image; obtaining accuracy of the predicted position information based on the predicted position information and target position information of the target human tissue in the target processing result; and adjusting a model parameter of the first image processing model based on the accuracy, to obtain the second image processing model.
10. The method according to claim 1, wherein positioning the target human tissue in the human tissue image comprises: performing, by the second image processing model, feature extraction on the human tissue image, to obtain an image feature of the human tissue image; obtaining a feature map of the human tissue image according to the image feature, each pixel in the feature map being used for indicating whether one region of the human tissue image comprises the target human tissue; and obtaining and outputting the position information of the target human tissue according to the feature map and a target offset.
7. The method according to claim 1, wherein positioning the target human tissue in the human tissue image comprises: performing, by the second image processing model, feature extraction on the human tissue image, to obtain an image feature of the human tissue image; obtaining a feature map of the human tissue image according to the image feature, each pixel in the feature map being used for indicating whether one region of the human tissue image comprises the target human tissue; and obtaining and outputting the position information of the target human tissue according to the feature map and a target offset.
With regard to claim 1, 12198407B2 does not disclose the aspect wherein the image distribution rule being a grayscale distribution rule or a brightness distribution rule. However Gao, CN107607060A discloses the aspect wherein the image distribution rule being a grayscale distribution rule or a brightness distribution rule (projecting a pair to the standard white gray value are all gray image of a certain fixed value, the camera collecting image, counting the grey value distribution by the histogram according to the frequency number of different grey value interval to obtain the greyscale value distribution rule of the grey image, the region coding for the image, dividing the image into different regions.). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Gao to 12198407B2 so the system could use grayscale distribution rule to determine the distribution of the images and identify feature of the images in order to improve training on human tissue images.
Claims 11, 13, 14, 19 are rejected for the same reason as claims 1, 3, 4, 9. Claim 20 is rejected for the same reason as claim 1.
Pertinent Arts
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zheng, Pub. No.: 20200211189 A1, discloses techniques for generating an overlay map on a digital medical image of a slide are provided, and include cell detection and tissue classification processes.
Beck, Patent No..: US 11195279 B1: discloses a method of accessing annotated pathology images. Each of the images includes an annotation describing a tissue characteristic category for a portion of the image. A set of training patches and a corresponding set of annotations are defined using an annotated pathology image.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DI XIAO whose telephone number is (571)270-1758. The examiner can normally be reached 9Am-5Pm est M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen Hong can be reached at (571) 272-4124. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DI XIAO/Primary Examiner, Art Unit 2178
Prosecution Insights