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 .
Response to Arguments
Applicant argued that Yu failed to disclose including analytics associated with the altered visual content and one or more additional recommendations, wherein the analytics and one or more recommendations are generated using one or more machine learning models…and retraining the one or more machine learning models based on the one or more additional recommendations implemented and not implemented by the practitioner. Ayodhimani taught these limitations as previously shown in the office action of 8 September 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.
Claims 1-4, 6, 8-11, 13, 15-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. (US 2013/0060579) in view of Ayodhimani et al. (US 2025/0239366).
In regard to claim 1, Yu disclosed a method for visual content privatization, the method comprising:
receiving visual content associated with a subject; Yu [0065], visual content is electronic medical images and records data, subject is patient, see Yu [0069]
altering the visual content using at least one or more image perturbations or one or more adversarial patches in response to a practitioner requesting an external consultation from a third party; Yu [0219], where the third-party processing entity (destination medical facility) receives instructions from the client server system at the destination medical facility to retrieve medical files and/or medical images. In Yu [0219]-[0222], the files are anonymized (altering the visual content using at least one or more image perturbations or one or more adversarial patches)
presenting an altered visual content to the practitioner within a user interface; Yu [0267], where the film librarian of the source facility reviews the located files for verification and approves or denies the request. and
transmitting the altered visual content to the third party following an approval by the practitioner. Yu [0219] sends the anonymized files to a third party processing entity.
Yu failed to disclose:
presenting analytics associated with the altered visual content and one or more additional recommendations, wherein the analytics and the one or more additional recommendations are generated using one or more machine learning models; and
retraining the one or more machine learning models based on the one or more additional recommendations implemented and not implemented by the practitioner.
However, Ayodhimani disclosed:
presenting analytics associated with the altered visual content and one or more additional recommendations, wherein the analytics and the one or more additional recommendations are generated using one or more machine learning models; Ayodhimani [0084], [0086] and
retraining the one or more machine learning models based on the one or more additional recommendations implemented and not implemented by the practitioner. Ayodhimani [0084], [0086]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to train the models in Yu for more effective anonymization of medical images.
Claims 2 are rejected under 35 U.S.C. 103 as being unpatentable over Yu in view of Ayodhimani as applied to claim 1 above, and further in view of Chung et al. (US 2025/0021824).
In regard to claim 2, Yu disclosed altering the visual content in response to the practitioner requesting the external consultation further comprises:
displaying one or more prompts to the practitioner in the user interface, wherein the one or more prompts are designed to gather information about a downstream task. Yu [0267] and Figure 6
Yu and Ayodhimani failed to disclose wherein adversarial algorithms, including a Fast Gradient Sign Method (FGSM), and utility metrics are leveraged in altering the visual content based on the downstream task.
However, Chung disclosed wherein adversarial algorithms, including a Fast Gradient Sign Method (FGSM), and utility metrics are leveraged in altering the visual content based on the downstream task. Chung [0028]-[0035]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a Fast Gradient Sign Method when altering image content in Yu / Ayodhimani to further anonymize the image data.
Claims 3 are rejected under 35 U.S.C. 103 as being unpatentable over Yu in view of Ayodhimani as applied to claim 1 above, and further in view of Ostadzadeh et al. (US 2021/0019425).
In regard to claim 3, Yu disclosed the altered visual content presented to the practitioner maximizes a utility of the visual content for the downstream task and minimizes a reidentification risk of the subject associated with the visual content. Yu [0267] and Figure 6
Yu and Ayodhimani failed to disclose wherein a degree of the image perturbations, locations, and pervasiveness are throttled depending on the downstream task.
However, Ostadzadeh disclosed wherein a degree of the image perturbations, locations, and pervasiveness are throttled depending on the downstream task. Ostadzadeh disclosed a privacy rating including a privacy recommendation based on the reconstruction rate or the quantification of risk, where the risk can be a numerical value or a category of risk such as low, medium, or high. This is a degree of the image perturbations. Setting the privacy recommendation based on a reconstruction rate or quantification of risk is throttling (privacy recommendation) based on a downstream task (risk of reconstruction). Ostadzadeh [0148]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to change the degree of image perturbations, locations, and pervasiveness (privacy recommendation) to allow for varying degrees of anonymization in the Yu and Ayodhimani combination in order to secure privacy of medical images and prevent reconstruction of the medical images.
In regard to claim 4, Yu disclosed the visual content is altered within a privacy-utility system, wherein the privacy-utility system is comprised of at least a feature recognition component, an evaluation component, a threshold component, and a visual content altering component. Yu [0220]-[0222]
In regard to claim 6, Yu disclosed wherein the approval by the practitioner is received in the user interface following an evaluation by the practitioner of the altered visual content in an interactive environment within the user interface. Yu [0267] and Figure 6
In regard to claim 7, Yu failed to disclose wherein actions associated with the evaluation by the practitioner are utilized for additional training of one or more machine learning models, such that the one or more machine learning models improve future alterations to new visual content in a manner specific to a downstream task or the practitioner.
However, Ayodhimani disclosed wherein actions associated with the evaluation by the practitioner are utilized for additional training of one or more machine learning models, such that the one or more machine learning models improve future alterations to new visual content in a manner specific to a downstream task or the practitioner. Ayodhimani [0084], [0086]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to train the models in Yu for more effective anonymization of medical images.
Claim 8 is rejected for substantially the same reasons as claim 1.
Claim 9 is rejected for substantially the same reasons as claim 2.
Claim 10 is rejected for substantially the same reasons as claim 3.
Claim 11 is rejected for substantially the same reasons as claim 4.
Claim 13 is rejected for substantially the same reasons as claim 6.
Claim 14 is rejected for substantially the same reasons as claim 7.
Claim 15 is rejected for substantially the same reasons as claim 1.
Claim 16 is rejected for substantially the same reasons as claim 2.
Claim 17 is rejected for substantially the same reasons as claim 3.
Claim 18 is rejected for substantially the same reasons as claim 4.
Claim 20 is rejected for substantially the same reasons as claim 6.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Yu in view of Ayodhimani as applied to claim 1 above, and further in view of Schmidtlein et al. (US 2024/0394408).
In regard to claim 22, Yu and Ayodhimani failed to disclose wherein the practitioner may adjust levels of acceptable utility reduction and a privacy threshold within the user interface based on the analytics associated with the altered visual content.
However, Schmidtlein disclosed wherein the practitioner may adjust levels of acceptable utility reduction and a privacy threshold within the user interface based on the analytics associated with the altered visual content. Schmidtlein [0070], [0076]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to alter the anonymization and noise levels in the images of Yu and Ayodhimani to improve anonymization of the images for enhanced privacy.
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Yu in view of Ayodhimani as applied to claim 2 above, and further in view of Giataganas et al. (US 2019/0279765).
In regard to claim 23, Yu in view of Ayodhimani failed to disclose wherein the retraining of the one or more machine learning models further utilizes interactions and actions of the practitioner within the user interface during an evaluation process to fine tune the one or more machine learning models specifically to the practitioner or the downstream task.
However, Giataganas disclosed wherein the retraining of the one or more machine learning models further utilizes interactions and actions of the practitioner within the user interface during an evaluation process to fine tune the one or more machine learning models specifically to the practitioner or the downstream task. Giataganas [0079]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to fine tune the machine learning models in Yu and Ayodhimani in order to properly train the models in Ayodhimani for the purpose of anonymizing data. See further Giataganas [0076] for data anonymization.
Allowable Subject Matter
Claim 21 is 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.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jeffrey R. Swearingen whose telephone number is (571)272-3921. The examiner can normally be reached M-F 8:00 am - 5:00 pm.
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Jeffrey R. Swearingen
Primary Examiner
Art Unit 2445
/Jeffrey R Swearingen/Primary Examiner, Art Unit 2445