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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application on March 30, 2026 after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed on March 13, 2026 has been entered and made of record.
Claim Objections
Claims 4, 11, and 18 are objected to because of the following informalities:
Claim 4 lines 2-3: “receiving feedback on results of the medical imaging analysis task from a plurality of users comprises” should read -- receiving feedback on results of the medical imaging analysis task from the plurality of users comprises --
Claim 11 lines 1-2: “receiving feedback on results of the medical imaging analysis task from a plurality of users comprises” should read -- receiving feedback on results of the medical imaging analysis task from the plurality of users --
Claim 18 lines 2-3: “receiving feedback on results of the medical imaging analysis task from a plurality of users comprises” should read -- receiving feedback on results of the medical imaging analysis task from the plurality of users --
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3, 7, 8, 10, 14, 15, and 17 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ciritsis et al. (U.S. Pub. No. 2024/0242349).
Re claims 1, 8 and 15: Ciritsis et al. disclose a computer-implemented method (i.e., “classifying, using an artificial intelligence, medical images showing a body portion”, Abstract)/an apparatus (i.e., “system 100”, Paragraph [0286]) comprising/a non-transitory computer readable medium (i.e., “computer-readable medium”, Paragraph [0176]) storing computer program instructions, the computer program instructions when executed by a processor cause the processor to perform operations comprising:
receiving/means for (i.e., “images are provided, for example via the communication unit 130, to the analysis unit 110”, Paragraph [0247]) receiving one or more input medical images (i.e., “providing an image taken during normal operation of the user’s system 100”, Paragraph [0209]; and “the image is a radiologic image”, Paragraph [0211]);
performing/means for (i.e., “the analysis unit 110 where the AI configured according to the current model 1 classifies the images”, Paragraph [0247]) performing a medical imaging analysis task (i.e., “the provided image is the input to the AI configured according the current model 1 and a classification, this means a label indicating the category to which the body portion shown in the image belongs according to the AI, is the output of the AI”, Paragraph [0214]) on the one or more input medical images using a trained AI (artificial intelligence) system (i.e., “classifying the image provided using the current model 1, this means the AI configured according to the current model”, Paragraph [0213]);
receiving/means for (i.e., “communication unit 130 displays the images and their classification according to the AI (step S23 of displaying the image and the classification) to a user and receives a user input comprising an approval or correction of the classifications of the images displayed”, Paragraph [0248]) receiving feedback on results of the medical imaging analysis task from a plurality of users (See for example, “adapting a model of an AI to a specific user, this means to a particular user of a plurality of actual or potential users”, Paragraph [0036]; and “approving or correcting, by the user, the classification according to the AI configured according to the current model 1”, Paragraph [0220]);
for each respective user of the plurality of users, retraining/means for (i.e., “the AI is trained in the training unit 120 on the training data set 27, wherein the output of the training is the adapted model 2”, Paragraph [0255]), for each respective user of the plurality of users, retraining the trained AI system based on the feedback from the respective user, without using feedback from any other user, to generate a user-specific AI system for the respective user (See for example, “generating an adapted model 2 by training the AI for classifying images of the body portion on a training data set 27 comprising a user-specific data element 24 of the data set 20 comprises a step S31 of automatic generation of a training data set 27 using the data set 20”, Paragraph [0228]; and “generating an adapted model 2 comprises further a step S33 of automatic generation of the adapted model 2 by retraining the current model 1 using the training data set 27 generated”, Paragraph [0231]);
validating/means for (i.e., “classification performance is determined in the training unit 120”, Paragraph [0292]) validating the user-specific AI system for each of the plurality of users (i.e., “testing the AI configured according to the current model and the AI configured according to the adapted model on the test data set 28 generated”, Paragraph [0234]; and “replacing the current model 1 with the adapted model 2 if a replacement criterion is fulfilled comprises a step S51 of automatic determination whether the classification performance of the AI configured according to the adapted model 2 is sufficient or not”, Paragraph [0240]); and
outputting/means for (i.e., “the current model will be replaced in the analysis unit 110”, Paragraph [0285]) outputting the validated user-specific Al system, for each of the plurality of users (See for example, “defining the adapted model 2 as current model”, Paragraph [0243]; and “storing the adapted model 2, and hence replacing the current model 1”, Paragraph [0244]).
Re claims 3, 10 and 17: Ciritsis et al. disclose wherein the feedback comprises one or more of acceptance of the results, rejection of the results, editing of the results, or creation of new results (i.e., “approving or correcting, by the user, the classification according to the AI configured according to the current model 1”, Paragraph [0220]).
Re claims 7 and 14: Ciritsis et al. disclose wherein the medical imaging analysis task comprises detection of abnormalities (See for example, “the characteristic according to which the image is classified is the mammographic density (MD), also called breast density, this means the relative amount of fibroglandular breast tissue and fat tissue in the breast. Then, the categories into which the radiologic image showing a breast is classifies may be the categories according to ACR BI-RADS”, Paragraph [0212]).
Claim Rejections - 35 USC § 103
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.
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Ciritsis et al. in view of Fang et al. (U.S. Pub. No. 2022/0301156). The teachings of Ciritsis et al. have been discussed above.
As to claims 2, 9 and 16, Ciritsis et al. teaches wherein validating/the means for validating the user-specific AI system for each of the plurality of users comprises, for the user-specific AI system for each respective user of the plurality of users/comprises: performing/means for performing a medical imaging analysis validation task on a validation dataset using the user-specific Al system, for the respective user (i.e., “testing the AI configured according to the current model and the AI configured according to the adapted model on the test data set 28 generated”, Paragraph [0234]).
However, Ciritsis et al. does not explicitly disclose wherein validating/the means for validating the user-specific AI system for each of the plurality of users comprises, for the user-specific AI system for each respective user of the plurality of users/comprises: determining/means for determining a performance of the user-specific AI system for the respective user by comparing results of the medical imaging analysis validation task with ground truth annotations of the validation dataset; and comparing/means for comparing the performance of the user-specific AI system for the respective user with a performance threshold.
Fang et al. teaches validating/the means for validating (i.e., “model training device 202”, Paragraph [0050]) a user-specific AI system (i.e., “updated learning model”, Paragraph [0034]) for each of the plurality of users comprises, for the user-specific AI system for each respective user of the plurality of users/comprises: determining/means for determining a performance of the user-specific AI system for the respective user by comparing results of the medical imaging analysis validation task with ground truth annotations of the validation dataset (i.e., “the error estimator's performance can be evaluated through workflow 400, to directly compare the ground-truth error of main model 404 (e.g., the difference between ground-truth results 410 and the main model result 408) obtained on this validation set with the error estimation output by error estimator 406. In another embodiment, the error estimator's performance can be evaluated by evaluating the updated main model's performance on this validation set through workflow 450”, Paragraph [0061]); and comparing/means for comparing the performance of the user-specific AI system for the respective user with a performance threshold (i.e., “compare it against the initial main model’s performance”, Paragraph [0061]).
Ciritsis et al. and Fang et al. are analogous art because they are from the field of digital image processing for medical imaging.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Ciritsis et al. by incorporating the means for determining a performance of the user-specific AI system for the respective user by comparing results of the medical imaging analysis validation task with ground truth annotations of the validation dataset, and the means for comparing the performance of the user-specific AI system for the respective user with a performance threshold, as taught by Fang et al.
The suggestion/motivation for doing so would have been to provide extra assurance that the model is performing well.
Therefore, it would have been obvious to combine Fang et al. with Ciritsis et al. to obtain the invention as specified in claims 2, 9, and 16.
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Ciritsis et al. in view of Austin et al. (WO 2024/036374). The teachings of Ciritsis et al. have been discussed above.
As to claims 4, 11, and 18, Ciritsis et al. does not explicitly disclose wherein receiving/the means for receiving feedback on results of the medical imaging analysis task from a plurality of users comprises: storing/means for storing the feedback received from each respective user of the plurality of users on a database associated with the respective user.
Austin et al. teaches receiving/the means for receiving feedback on results of the medical imaging analysis task from a plurality of users (See for example, “the RIAS 110 forwards this data to an interactive viewer component 701, which communicates the predicted Al radiology findings to the user and receives user feedback associated with the Al radiology findings, to communicate user feedback to the server 70”, page 11 lines 28-32) comprises: storing/means for storing the feedback received from each respective user of the plurality of users on a database associated with the respective user (See for example, “A distributed message queueing service (DMQS) 710 stores user feedback metadata. User feedback data associated with the results ID is stored in a separate database”, page 12 lines 22-33; page 17 lines 18-27; and “Once the feedback data has been collected from the user input, it is stored in a database”, page 23 lines 1-5).
Ciritsis et al. and Austin et al. are analogous art because they are from the field of digital image processing for medical imaging.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Ciritsis et al. by incorporating the means for storing the feedback received from each respective user of the plurality of users on a database associated with the respective user, as taught by Austin et al.
The suggestion/motivation for doing so would have been to enable multiple integration and injection pathways to facilitate interoperability and deployment in various existing computer environments such as PACS.
Therefore, it would have been obvious to combine Austin et al. with Ciritsis et al. to obtain the invention as specified in claims 4, 11, and 18.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ciritsis et al. in view of Shoura et al. (U.S. Pub. No. 2023/0118546). The teachings of Ciritsis et al. have been discussed above.
As to claims 6, 13, and 20, Ciritsis et al. does not explicitly disclose wherein outputting/the means for outputting the validated user-specific AI system for each of the plurality of users comprises: deploying/means for deploying the validated user-specific AI system for each of the plurality of users for use in a clinical setting.
Shoura et al. teaches outputting/means for outputting (i.e., “apparatus for performing the operations herein. This apparatus may be a particular machine that is specially constructed for the required purposes, or it may comprise a computer otherwise selectively activated or reconfigured by a computer program stored in the computer”, Paragraph [0053]) the validated user-specific AI system for each of the plurality of users (i.e., “In the production block 304, the tailored Al model 317 is evaluated and deployed 319”, Paragraph [0021]) comprises: deploying/means for deploying the validated user-specific AI system for each of the plurality of users for use in a clinical setting (i.e., Paragraph [0018]; and “In the production block 304, the tailored Al model 317 is evaluated and deployed 319”, Paragraph [0021]).
Ciritsis et al. and Shoura et al. are analogous art because they are from the field of digital image processing for medical imaging.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Ciritsis et al. by incorporating the means for deploying the validated user-specific AI system for each of the plurality of users for use in a clinical setting, as taught by Shoura et al.
The suggestion/motivation for doing so would have been to quickly deploy self-developed algorithms without extensive coding.
Therefore, it would have been obvious to combine Shoura et al. with Ciritsis et al. to obtain the invention as specified in claims 6, 13, and 20.
Response to Arguments
Claim Rejections - 35 USC § 103
With respect to claims 1-4, 6-11, 13-18, and 20, Applicant’s arguments (Remarks dated March 13, 2026, pages 7-9) have been fully considered. However, they are moot in view of the new ground(s) of rejection (Refer to Claim Rejections - 35 USC § 102, and Claim Rejections - 35 USC § 103 Sections above).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSE M TORRES whose telephone number is (571)270-1356. The examiner can normally be reached Monday thru Friday; 10:00 AM to 6:00 PM EST.
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/JOSE M TORRES/Examiner, Art Unit 2664 06/12/2026
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