DETAILED ACTION
Status of the Claims
Claims 1-20 are pending.
Notice of AIA Status
The present application, filed on or after 16 March 2013, is being examined under the first inventor to file provisions of the AIA .
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.
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 of this title, 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, 13-16, and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Swift et al., US 2023/0199014 A1, in view of Cirlig et al., US 2024/0311473 A1.
Regarding claim 1, Swift discloses a method for identifying one or more patterns within a screen layout of an electronic device having a negative impact on a user of the electronic device, the method comprising:
Detecting, by an identification module of the electronic device, at least one of one or more user interface (UI) elements and one or more user experience (UX) elements within the screen layout, and one or more characteristics associated with the at least one of one or more UI elements and one or more UX elements. Swift teaches identifying a plurality of UI elements. Swift ¶ 47, fig. 3(step 304). Swift further teaches detecting functionality and/or information associated with a UI element. Id. ¶ 32.
Identifying, by a machine learning (ML) module, based on one or more UI-related parameters, the one or more patterns associated with at least one of one or more detected UI elements and one or more detected UX elements within the screen layout having the negative impact on the user based on one or more predefined rules. Swift teaches using a CNN (a type of ML model) to detect that a UI element has a risk score indicating it being a dark pattern. Swift ¶ 48, fig. 3 (step 306). A CNN comprises parameters. The identification of a dark pattern is based on preference data (i.e., rules). Id. ¶ 41.
Swift alone does not disclose, but the combination of Swift with Cirlig renders obvious determining, by a display controller module of the electronic device (100), one or more UI elements that have to be placed on top of at least one of one or more identified negative UI elements and one or more identified negative UX elements within the screen layout. Swift teaches executing a mitigating action to counteract the identified dark pattern. Swift ¶ 51. The mitigating action may include graphically blocking negative UI elements. Id. Swift does not explicitly disclose graphically blocking by placing a UI element on top of (i.e., overlaying) a negative UI element. However, Cirlig teaches placing a UI element on top of malicious content in order to graphically block the malicious content. Cirlig ¶¶ 23-24, 28, fig. 2.
It would have been obvious before the effective filing date of the claimed invention to a person with ordinary skill in the art to modify Swift’s process of identifying and mitigating dark patters with Cirlig’s process of overlaying a UI element on top of malicious content. Such a modification would “improve[] the operation of a computing device by increasing the speed at which malicious content is detected and actions performed to impede the user from selecting malicious content.” See Cirlig ¶ 13.
Regarding claim 13, which depends on claim 1, Swift discloses wherein the one or more UI-related parameters comprise at least one of a historical behavioral pattern of the user and a current behavioral pattern of the user associated with a single or multi-program scenario associated with the electronic device, and wherein the multi-program scenario comprises one or more programs running on cross platforms. Swift teaches training the CNN (i.e., optimizing the parameters) based on a patter of historical behavior. Swift ¶¶ 21-24.
Regarding claim 14, which depends on claim 1, Cirlig discloses wherein the one or more detected UI elements and the one or more detected UX elements, along with the one or more characteristics, are dynamically or statically displayed over the UI of an active program or a background program associated with the electronic device. Cirlig teaches statically displaying a UI element on top of malicious content of an active program. Cirlig ¶¶ 23-24, 28, fig. 2.
Regarding claim 15, which depends on claim 1, Cirlig discloses wherein the one or more characteristics associated with the at least one of one or more UI elements and one or more UX elements comprises at least one of feature information, position information, and functionality information. Cirlig teaches determining position information since the UI element is “positioned” over the malicious content. Cirlig ¶ 28. Cirlig further teaches determining feature/functionality information since the UI element impedes the user from selecting the malicious content. Id. ¶ 24.
Claim 16 is drawn to a system that implements the method recited in claim 1. Accordingly, this claim is rejected for substantially the same reasons as indicated in the above rejection of the corresponding claim.
Claim 20 is drawn to instructions stored in a medium that implement the method recited in claim 1. Accordingly, this claim is rejected for substantially the same reasons as indicated in the above rejection of the corresponding claim.
Claims 2 and 17 are rejected under 35 U.S.C. § 103 as being unpatentable over Swift et al., US 2023/0199014 A1, in view of Cirlig et al., US 2024/0311473 A1, in view of Mitra et al., US 2023/0022396 A1.
Regarding claim 2, which depends on claim 1, the combination of Swift with Mitra renders obvious: updating, using a reinforcement learning technique, one or more parameters associated with the identification module based on the feedback; and personalizing UI/UX elements based on at least one of one or more identified negative UI elements, one or more identified negative UX elements, and the one or more updated parameters. Swift teaches using “reinforcement learning” to improve the process of detecting and mitigating dark patterns. Swift ¶¶ 44-45. Swift does not explicitly disclose receiving feedback from a user to affect the reinforcement learning. However, the technique of reinforcement learning from human feedback (RLHF) to train an ML model is known in the art. See, e.g., Mitra ¶ 3.
It would have been obvious before the effective filing date of the claimed invention to a person with ordinary skill in the art to modify Swift’s process of identifying and mitigating dark patters with the use of RLHF to train an ML model. Such a modification would avoid the difficult and time-consuming task of manually generating examples to use in training a model.
Claim 17 is drawn to a system that implements the method recited in claim 2. Accordingly, this claim is rejected for substantially the same reasons as indicated in the above rejection of the corresponding claim.
Allowable Subject Matter
Claims 3-12 and 18-19 contain allowable subject matter.
Claims 3-12 and 18-19 are 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
Although particular portions of the prior art may have been cited in support of the rejections, the specified citations are merely representative of the teachings. Other passages and figures in the cited prior art may apply. Accordingly, Applicant should consider the entirety of the cited prior art for potentially teaching all or part of the claims.
The following prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Najaraja et al., US 2021/0105298 A1, discloses a process for detecting malicious content on a web page.
Jakobson et al., US 2010/0259560 A1, discloses a process for placing a security window over content that user wishes to protect.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Asher D Kells whose telephone number is (571)270-7729. The examiner can normally be reached Mon. - Fri., 8 a.m. - 4 p.m..
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Asher D. Kells
Primary Examiner
Art Unit 2171
/Asher D Kells/Primary Examiner, Art Unit 2171