DETAILED ACTION
This office Action is in response to the Amendment filed on 01/21/2026.
In the instant application, claims 1, 8 and 15 are amended independent claims; Claims 1-20 have been examined and are pending. This action is made final.
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 .
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were effectively filed absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned at the time a later invention was effectively filed in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 5-10, 12-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Meijer et al. (“Meijer,” US 2013/0159228), published on 20 June 2013 in view of Ekron (US 2022/0365989), published on 17 November 2022 and further in view of Basu et al. (US 2010/0082516), published on April 1, 2010.
Regarding claim 1, Meijer teaches A method comprising:
[selecting a neurological condition associated with a user];
determining, based on historical user data, a user experience level (Meijer: ¶0033; the update component 110 includes an analysis component which is configured to interpret, translate, or otherwise analyze the feedback 114 to determine one or more attributes included in the user model 116. For example, the analysis component 302 can determine a skill level of the user 106 regarding a first feature of the UX 104 based on feedback 114. The analysis component 302 can include a user emotion component 306, and a classifier component 308. ¶0036; the classification component 402 is configured to classify the user 106 based in part on the user model 116 associated with the user 106. For example, the user 106 can be classified as a novice, intermediate, or expert user based on the user model 116, and the adaptation component 112 can modify the UX 104 based on the classification);
generating a user interface based on [the selected neurological condition] and the user experience level (Meijer: ¶0036; the adaptation component 112 is configured to modify the UX 104 based in part on a user model 116 associated with a user 106. The classification component 402 is configured to classify the user 106 based in part on the user model 116 associated with the user 106. For example, the user 106 can be classified as a novice, intermediate, or expert user based on the user model 116, and the adaptation component 112 can modify a user interface associated with the UX 104 based on the classification);
receiving, from a tracer configured to log a user's activity, a tracking log comprising information regarding the user's activity (Meijer: ¶0057; a user’s interaction with a user experience (UX) is monitored to obtain usage feedback. ¶0059; an emotional state of the user can be determined based on the sensed characteristics, query feedback and/or usage feedback. ¶0024; the update component 110 is configured to analyze or interpret the feedback 114, and adjust, modify, or otherwise update a user model 116 associated with the user 106 based in part on the analysis or interpretation);
determining a behavioral metric by analyzing the tracking log using a machine learning model trained by processing prior user activity, wherein the behavioral metric represents an emotional state of the user (Meijer: ¶0059; the user’s emotional state can be determined based on comparisons with a set of emotional state data relating to other users. Additionally or alternatively, the user’s emotional state can be determined based on a set of training data associated with previous emotional states of the user. ¶0047; the update component 110 can also employ intelligent determinations or inferences in connection with analyzing feedback, and/or updating a user model 116. Any of the foregoing inferences can potentially be based upon machine learning techniques related to historical analysis, feedback, and/or other determinations or inferences);
[identify, by the machine learning model, a specific user interface element correlated with a change in the emotional state by analyzing patterns in the tracking log associated with the user interface element]; and
modifying an operational aspect of the user interface element based on the change associated with the behavioral metric (Meijer: ¶0035-0036; the adaptation component 112 is configured to modify the UX 104 based in part on a user model 116 associated with a user 106. The classification component 402 is configured to classify the user 106 based in part on the user model 116 associated with the user 106. For example, the user 106 can be classified as a novice, intermediate, or expert user based on the user model 116, and the adaptation component 112 can modify a user interface associated with the UX 104 based on the classification. ¶0024; the update component 110 is configured to analyze or interpret the feedback 14, and adjust, modify, or otherwise update a user model 116 associated with the user 106 based in part on the analysis or interpretation. The user model 116 can contain a set of attributes that indicate the user’s 106 ability, comfort, familiarity, etc. with regard to various features or functions of the UX 104) and [based on the neurological condition].
Meijer does not explicitly teach: selecting a neurological condition associated with a user; modifying an operational aspect of the user interface element based on the change associated with the behavioral metric and based on the neurological condition; and identify, by the machine learning model, a specific user interface element correlated with a change in the emotional state by analyzing patterns in the tracking log associated with the user interface element.
Ekron teaches: selecting a neurological condition associated with a user (Ekron: ¶0113-0120 and Figs. 4A-6; display of a browser which is adjusted pursuant to user selection of an accessibility/disability profile. The accessibility/disability profile may be Epilepsy/ADHD/Cognitive related, etc).
Accordingly, it would have been obvious to one of ordinary skill in the art , before the effective filing date of the claimed invention, having the teachings of Ekron and Meijer in front of them to include the method of adjusting user interface based on selected accessibility profile as disclosed by Ekron with the dynamic user experience adaptation as taught by Meijer to conform with needs of a user having the disability associated with the selected accessibility profile (Ekron: ¶0006).
The combination of Meijer and Ekron teach: modifying an operational aspect of the user interface element based on the change associated with the behavioral metric (Meijer: ¶0035-0036; the adaptation component 112 is configured to modify the UX 104 based in part on a user model 116 associated with a user 106. The classification component 402 is configured to classify the user 106 based in part on the user model 116 associated with the user 106. For example, the user 106 can be classified as a novice, intermediate, or expert user based on the user model 116, and the adaptation component 112 can modify a user interface associated with the UX 104 based on the classification. ¶0024; the update component 110 is configured to analyze or interpret the feedback 14, and adjust, modify, or otherwise update a user model 116 associated with the user 106 based in part on the analysis or interpretation. The user model 116 can contain a set of attributes that indicate the user’s 106 ability, comfort, familiarity, etc. with regard to various features or functions of the UX 104) and based on the neurological condition (Ekron: ¶0081; upon receiving the user’s selection of a visual impaired profile, disclosed embodiments may include implementing a predefined template associated with a visual impaired profile to alter the website display parameter “font size parameter” associated with all the titles to have a value of 20 pt, and to adjust the alter website display parameter “saturation parameter” to increase display intensity).
Meijer and Ekron teach all the limitation above but do not appear to teach: identify, by the machine learning model, a specific user interface element correlated with a change in the emotional state by analyzing patterns in the tracking log associated with the user interface element.
However Basu teaches a method for modifying a system in response to indications of user frustration. Basu further teaches: identify, by the machine learning model, a specific user interface element correlated with a change in the emotional state by analyzing patterns in the tracking log associated with the user interface element (Basu: ¶0048; the prediction module 220 can determine that certain sequences of occurrences in the target system 104 are correlated with frustration events. For example, the prediction module 220 can determine that certain trends in features appear to terminate in frustration events. As such, the prediction module 220 may take system history in account in making its decision as to whether or not the user is likely to be frustrated. ¶0051; the policy selection module 222 can make user of the prediction module 220 in selecting appropriates policies. ¶0052; upon finding a policy that is likely to reduce user frustration, the policy selection module 222 can apply this policy to the target system 104. ¶0068; the frustration mapping module 702 can identify the features that accompany incidents of user frustration. The frustration mapping module 702 can then identify statistically significant patterns in such data. These patterns correlate the presence of certain features with incidents of user frustration. The frustration mapping module 702 can user various techniques to identify such patterns, such as any king of statistical/machine learning technique. The frustration mapping module 702 can also analyze the features per se to identify instance in which the features deviate from their normal respective behavior. The frustration mapping module 702 can use the results of this per se analysis to help identify occasions in which a user is likely to express frustration. ¶0033; the concepts disclosed herein are not limited to the operating system environment. In another case, for instance, the target system 104 may correspond to an individual application running on a computer system of any nature).
Accordingly, it would have been obvious to one of ordinary skill in the art , before the effective filing date of the claimed invention, having the teachings of Basu, Meijer and Ekron in front of them to include the method of modifying a system in response to indications of user frustration as disclosed by Basu with the dynamic user experience adaptation as taught by Meijer to modify the operation of the target system to reduce the likelihood that the user will be frustrated in the future and thus increasing user experience (Basu: ¶0005).
Regarding claim 2, Meijer, Ekron and Basu teach the method of claim 1,
Meijer, Ekron and Basu further teach: wherein the neurological condition is selected from a list of neurological conditions comprising one or more of autism, Parkinson's disease, epilepsy, and attention-deficit hyperactivity disorder. (Ekron: ¶0113-0120 and Figs. 4A-6; display of a browser which is adjusted pursuant to user selection of an accessibility/disability profile. The accessibility/disability profile may be Epilepsy/ADHD/Cognitive related, etc).
Regarding claim 3, Meijer, Ekron and Basu teach the method of claim 1,
Meijer, Ekron and Basu further teach: wherein the historical user data includes a measurement of time the user has operated an application (Ekron: ¶0378; in an example customized profile, values of the multiple default website display parameters may be determined based on previous user interactions with an accessibility GUI, such as values of website display parameter modifications that were previously requested by the user. These previous user interactions may include any aspect of the interactions, such as duration, frequency, or type of interaction).
Regarding claim 5, Meijer, Ekron and Basu teach the method of claim 1,
Meijer, Ekron and Basu further teach: recording a user facial expression; and evaluating the user facial expression, using a machine learning model trained by processing prior user facial expressions, to update the behavioral metric (Meijer: ¶0058; the set of sensed characteristics can include virtually any characteristic of the user, including, but not limited to, heart rate, perspiration, body temperature, voice or audio data, facial images, and so forth. ¶0059; an emotional state of the user can be determined based on the sensed characteristics, query feedback, and/or usage feedback. The user’s emotional state can be determined based on comparisons with a set of emotional state data relating to other users. For example, the user's emotional state can be determined by compare an image of the user to images of other users. Additionally or alternatively, the user's emotional state can be determined based on a set of training data associated with previous emotional states of the user. For example, the training data can include previous sensed characteristics for the user that are correlated with prior usage feedback and/or query feedback to determine emotional states of the user).
Regarding claim 6, Meijer, Ekron and Basu teach the method of claim 1,
Meijer, Ekron and Basu further teach: recording a user's voice; and evaluating the user's voice, using a machine learning model trained by processing prior user voice interactions, to update the behavioral metric (Meijer: ¶0058; the set of sensed characteristics can include virtually any characteristic of the user, including, but not limited to, heart rate, perspiration, body temperature, voice or audio data, facial images, and so forth. ¶0059; an emotional state of the user can be determined based on the sensed characteristics, query feedback, and/or usage feedback. The user’s emotional state can be determined based on comparisons with a set of emotional state data relating to other users. For example, the user's emotional state can be determined by compare an image of the user to images of other users. Additionally or alternatively, the user's emotional state can be determined based on a set of training data associated with previous emotional states of the user. For example, the training data can include previous sensed characteristics for the user that are correlated with prior usage feedback and/or query feedback to determine emotional states of the user).
Regarding claim 7, Meijer, Ekron and Basu teach the method of claim 2,
Meijer, Ekron and Basu further teach: wherein each neurological condition of the list of neurological conditions is associated with a template user interface (Ekron: ¶0081; Upon receiving the user's selection, the method may include implementing a predefined template to alter one or more default website display parameters to conform with the needs of the specific disability of the website user); and wherein the user interface is generated using the template (Ekron: ¶0081; For example, upon selection of a visual impaired profile, disclosed embodiments may include implementing a predefined template associated with a visual impaired profile to alter the website display parameter “font size parameter” associated with all the titles to have a value of 20 pt, and to adjust the alter website display parameter “saturation parameter” to increase display intensity).
Regarding claims 8-10, these claims are directed to a system comprising a non-transitory computer-readable medium and a processor (Meijer: ¶0080-0081 and Fig. 11) executing the method as claimed in claims 1-3, respectively; Claims 8-10 are similar scope to claims 1-3, respectively and are therefore rejected under similar rationale.
Regarding claims 12-14, these claims are directed to a system comprising a non-transitory computer-readable medium and a processor (Meijer: ¶0080-0081 and Fig. 11) executing the method as claimed in claims 5-7, respectively; Claims 12-14 are similar scope to claims 5-7, respectively and are therefore rejected under similar rationale.
Regarding claims 15-17, these claims are directed to a non-transitory computer-readable medium executing the method as claimed in claims 1-3, respectively; Claims 15-17 are similar scope to claims 1-3, respectively and are therefore rejected under similar rationale.
Regarding claims 19-20, these claims are directed to a non-transitory computer-readable medium executing the method as claimed in claims 5-6, respectively; Claims 19-20 are similar scope to claims 5-6, respectively and are therefore rejected under similar rationale.
Claims 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Meijer, Ekron and Basu as applied to claim 1 above and further in view of PRICE et al. (“Price,” US 2022/0058731), published on 24 February, 2022.
Regarding claim 4, Meijer, Ekron and Basu teach the method of claim 1,
Meijer, Ekron and Basu do not appear to teach: wherein the tracking log includes one or more of: mouse movements, mouse hover, periods of mouse inactivity, typing speed, spelling errors, abandoned sessions, and pageviews.
Price teaches: wherein the tracking log includes one or more of: mouse movements, mouse hover, periods of mouse inactivity, typing speed, spelling errors, abandoned sessions, and pageviews (Price: ¶0066; tracking interactions of a visitor with the electronic store. Based on the tracked interactions of the visitor, determining whether the visitor is a serious customer or a non-serious customer. And Altering the electronic store based on the determination. In some embodiments, the determining is performed in response to one or more of tracking of the visitor’s interactions over a predetermined period of time. An interaction may include, for example, a mouse movement, a mouse hover, a click, a selection, a view of a website page or portion of the electronic store, a search, combinations thereof and/or patterns formed thereby).
Accordingly, it would have been obvious to one of ordinary skill in the art , before the effective filing date of the claimed invention, having the teachings of Price, Meijer, Ekron and Basu in front of them to include the method of altering the electronic store based on tracked visitor’s interactions as disclosed by Price with the dynamic user experience adaptation as taught by Meijer for personalizing a visitor experience of an electronic store (Price: ¶0066 ).
Regarding claim 11, the claim is directed to a system comprising a non-transitory computer-readable medium and a processor (Meijer: ¶0080-0081 and Fig. 11) executing the method as claimed in claim 4; Claim 11 is similar scope to claim 4 and is therefore rejected under similar rationale.
Regarding claim 18, the claim is directed to a non-transitory computer-readable medium executing the method as claimed in claim 4; Claim 18 is similar scope to claim 4 and is therefore rejected under similar rationale.
Response to Arguments
Applicants’ arguments filed on 01/21/2026, have been fully considered but are moot in view of the new grounds of rejection presented above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. 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 extension fee 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.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tam T. Tran whose telephone number is (571) 270-5029. The examiner can normally be reached M-F: 7:30 AM - 5:00 PM.
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/TAM T TRAN/Primary Examiner, Art Unit 2174
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