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 Amendment
The Amendment filed 12/18/2025 has been entered. Claims 21-40 are presented for examination.
Allowable Subject Matter
The allowability of claims 26 and 37 as indicated in the Office Action mailed 10/01/2025 has been withdraw (see rejection below for detail).
Claim Objections
Claims 26 and 37 are objected to because of the following informalities:
Claim 26, line 6, missing “,” before the phrase “to the user-specific machine learning”. Line 8, the phrase “a threshold confidence level” should be “the threshold confidence level”.
Claim 37, line 6, missing “,” before the phrase “to the user-specific machine learning”. Line 8, the phrase “a threshold confidence level” should be “the threshold confidence level”.
Appropriate corrections are required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 26 and 37 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 26 and 37 recited “determining, for the user, that a user-specific machine learning model has at least a threshold confidence level after obtaining a sufficient amount of user interaction data for the user and training the user-specific machine learning model using the user interaction data for the user, and switching, from the trained machine learning model to the user-specific machine learning model for one or more subsequent updates to the UI for the user in response to determining that the user-specific machine learning model has at least a threshold confidence level”. However, there is nowhere in the specification that contains this subject matter. Therefore, claims 26 and 37 contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
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.
Claim 21-23, 29, 32-34 and 39-40 are rejected under 35 U.S.C. 103 as being unpatentable over Srivastava et al. (US 20190361579), (hereafter Srivastava), and in view of PK et al. (US 20200019418) (hereafter PK).
Srivastava and PK were cited in the previous office action.
Regarding claim 21, Srivastava teaches a method performed by a computing device (fig. 1 processing device), the method comprising:
generating, using a trained machine learning model (paragraphs [0004] and [0005], classifying user using machine learning; paragraph [0017] model training to generate the trained machine learning model to select additional user-interface elements based on, e.g., classification of the user and/or the interaction with the application; paragraph [0041] further, model training based on labeled training data or learning patterns compared to other users), a set of user interest scores for a user (paragraph [0016] user interaction based on, e.g., interest level based on a value of a scale; paragraph [0027] user scores such as a proficiency level, an interest level, and a behavioral level, each level comprising a value on a scale), wherein each user interest score is indicative of the user’s interest in accessing information corresponding to a user interface (UI) element of an application (paragraph [0027] monitored interaction for user classification such as an interest level, each level comprising a value on a scale);
determining that at least one user interest score of the set of user interest scores positively indicates the user’s interest in accessing information corresponding to a particular UI element that was not included in an initial UI presented to the user (paragraph [0028] determining additional user interface elements are desired based on the classification of the user selected from a library, invoked upon a threshold);
in response to determining that the at least one user interest score of the set of user interest scores positively indicates the user’s interest in accessing the information corresponding to the particular UI element, dynamically modifying the initial UI, including incorporating the particular UI element into the initial UI to generate an updated UI that includes the particular UI element that was not included in the initial UI (paragraphs [0028]-[0029] determining and retrieving one or more additional user interface elements to display on the user interface);
presenting the updated UI that includes the particular UI element to the user (figures 2-8, paragraph [0029] iterative updating and presentation to user interface);
monitoring, after presenting the updated UI to the user, further user interactions with the updated UI (paragraph [0029] iterative updating based on further monitoring for updating based on additional interaction or usage with the application based on prior changes);
updating the trained machine learning model based on the further user interactions (paragraph [0029] iterative training); and
selecting an updated set of UI elements that are presented in the updated UI based on the updated machine learning model (paragraph [0029] iterative training such that the interaction or usage of the application changes over time and the interface is dynamically modified such as involving reclassifying the user based on a different usage or patterns of interaction with the additional user interface elements).
Srivastava did not specifically teach wherein the trained machine learning model is trained using a set of training data comprising a set of features related to user interactions with UI elements of one or more UIs of the application, the features being selected for training the trained machine learning model based on respective durations for which UI elements corresponding to the features were presented to one or more users.
However, PK teaches wherein the trained machine learning model is trained using a set of training data comprising a set of features related to user interactions with UI elements of one or more UIs of the application, the features being selected for training the trained machine learning model based on respective durations for which UI elements corresponding to the features were presented to one or more users (paragraphs [0003], [0045], 0049], [0052]-[0059]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to have incorporated the concept of wherein the trained machine learning model is trained using a set of training data comprising a set of features related to user interactions with UI elements of one or more UIs of the application, the features being selected for training the trained machine learning model based on respective durations for which UI elements corresponding to the features were presented to one or more users as suggested in PK into the system of Srivastava because both of these system are addressing the need to personalizing the user interface. By incorporating the concept of PK into Srivastava would improve the personalize interface of Srivastava by reducing computer operations for example, reducing the number of improper user input events and the time, processor use, and memory use in carrying out an original action and then potentially undoing such operations before user carries out “correct” interaction (PK, paragraph [0040]).
Regarding claim 22, the combination of Srivastava teaches the invention as claimed in claim 21 including wherein the trained machine learning model is one of (1) a logistical regression model, (ii) a machine learning model that combines a wide linear model for memorization with a deep neural network for generalization across a plurality of features, or (iii) a long short-term memory model (PK paragraph [0059], [0063] regression based machine learning model trained using training data [0006]; logistic regression).
Regarding claim 23, the combination of Srivastava and PK teaches the invention as claimed in claim 21 including determining that a particular user interest score of the set of user interest scores associated with a further UI element is less than a threshold score, wherein the further UI element is not displayed to the user in the updated UI based on determining that the particular user interest score associated with the further UI element is less than the threshold (PK paragraph [0028], [0029] selection based on threshold such that only elements meeting a threshold are enabled to be additionally be displayed on the UI).
Regarding claim 29, the combination of Srivastava and PK teaches the invention as claimed in claim 21 including wherein the set of features related to the user interactions with the UI elements of the one or more UIs of the application comprises one or more of (1) features related to zooming to view content of particular UI elements, (11) features related to marking particular content as being helpful, or (ii1) features related to submitting a query for additional content (PK paragraph [0054]).
Regarding claims 32-34 and 39-40, they are system and medium claims that corresponding to method claims 21-23 and 29. Therefore, they are rejected for the same reason as method claims 21-2 and 29 above.
Claims 24, 25, 28, 31, 35, 36, and 38 are rejected under 35 U.S.C. 103 as being unpatentable over Srivastava et al. (US 20190361579), (hereafter Srivastava), and in view of PK et al. (US 20200019418) (hereafter PK), as applied in claims 21 and 32 above, and further in view of Chen et al. (US 20190188272) (hereafter Chen).
Chen was cited in the previous office action.
Regarding claim 24, the combination of Srivastava and PK teaches the invention as claimed in claim 21. The combination of Srivastava and PK did not teach receiving a set of user interaction data indicating a group of multiple different users’ interactions with one or more UI elements; generating, from the set of user interaction data, a set of user group training data; and training the trained machine learning model using the user group training data.
However, Chen teaches receiving a set of user interaction data indicating a group of multiple different users’ interactions with one or more UI elements; generating, from the set of user interaction data, a set of user group training data; and training the trained machine learning model using the user group training data (paragraph [0042], ratings for various and similar users [0035] that training corresponding with machine learning modeling and for making recommendations based on interaction data and/or contextual data for the users).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to have incorporated the concept of receiving a set of user interaction data indicating a group of multiple different users’ interactions with one or more UI elements; generating, from the set of user interaction data, a set of user group training data; and training the trained machine learning model using the user group training data as suggest by Chen into the combination of Srivastava and PK because all of these systems are addressing the need of personalize the user interface. By incorporating Chen into the combination of Srivastava and PK would personalize content authoring driven by recommendations the content made more relevant among multiple users who may use the same content management system to produce content such as writers and designers, the system learning from a larger group of users and making relevant content more useful for those users (Chen paragraphs [0001], [0014] and [0015]).
Regarding claim 25, the combination of Srivastava, PK and Chen teaches the invention as claimed in claim 24 including wherein the set of user interaction data relates to two or more different topics, and each topic of the two or more different topics is assigned a different weight based on an importance of the topic to at least one of the multiple different users (Chen, paragraphs [0020], [0021], [0028]-[0030] e.g., a type of interaction and a type of content associated with the interaction; assigned numeric score for each individual feature/element calculated from interaction data with relative weights that reflect relative importance of each feature).
Regarding claim 28, the combination of Srivastava and PK teaches the invention as claimed in claim 21 above including wherein the trained machine learning model is trained for the user (Srivastava, abstract, figures 2-8). The combination of Srivastava and PK did not teach identifying a second user that is determined to be similar to the user; and selecting, using the trained machine learning model and/or the updated machine learning model, UI elements to present to the second user based on the second user being determined to be similar to the user.
However, Chen teaches identifying a second user that is determined to be similar to the user; and selecting, using the trained machine learning model and/or the updated machine learning model, UI elements to present to the second user based on the second user being determined to be similar to the user (paragraph [0035]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to have incorporated the concept of identifying a second user that is determined to be similar to the user; and selecting, using the trained machine learning model and/or the updated machine learning model, UI elements to present to the second user based on the second user being determined to be similar to the user as suggest by Chen into the combination of Srivastava and PK because all of these systems are addressing the need of personalize the user interface. By incorporating Chen into the combination of Srivastava and PK would personalize content authoring driven by recommendations the content made more relevant among multiple users who may use the same content management system to produce content such as writers and designers, the system learning from a larger group of users and making relevant content more useful for those users (Chen paragraphs [0001], [0014] and [0015]).
Regarding claim 31, the combination of Srivastava and PK teaches the invention as claimed in claim 21 including initially training the trained machine learning model based on user interaction data for the user (Srivastava, paragraph [0029]). The combination of Srivastava and PK did not teach identifying one or more second users that share a common user characteristic with the user; and using the trained machine learning model to select UI elements for presentation to the one or more second users.
However, Chen teaches identifying one or more second users that share a common user characteristic with the user; and using the trained machine learning model to select UI elements for presentation to the one or more second users (paragraphs [0033], [0034], [0035], [0039]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to have incorporated the concept of identifying one or more second users that share a common user characteristic with the user; and using the trained machine learning model to select UI elements for presentation to the one or more second users as suggest by Chen into the combination of Srivastava and PK because all of these systems are addressing the need of personalize the user interface. By incorporating Chen into the combination of Srivastava and PK would personalize content authoring driven by recommendations the content made more relevant among multiple users who may use the same content management system to produce content such as writers and designers, the system learning from a larger group of users and making relevant content more useful for those users (Chen paragraphs [0001], [0014] and [0015]).
Regarding claims 35-36 and 38, they are system claims that corresponding to method claims 24-25 and 28. Therefore, they are rejected for the same reason as method claims 24-25 and 28 above.
Claim 27 is rejected under 35 U.S.C. 103 as being unpatentable over Srivastava et al. (US 20190361579), (hereafter Srivastava), and in view of PK et al. (US 20200019418) (hereafter PK), as applied in claim 21 above, and further in view of Blair (US 20080172610).
Regarding claim 27, the combination of Srivastava and PK teaches the invention as claimed in claim 21 above. The combination of Srivastava and PK did not teach wherein at least one of the initial UI or the updated UI is presented to the user by a game console.
However, Blair teaches wherein at least one of the initial UI or the updated UI is presented to the user by a game console (abstract, paragraphs [0005], [0007]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to have incorporated the idea of having at least one of the initial UI or the updated UI is presented to the user by a game console as suggested in Blair into the combination of Srivastava and PK because all of these system are addressing the need of personalize the user interface. By incorporating the teaching of Blair into the system of Srivastava would allow a customized user control interface to control an electronic device with an original user control interface that is different from the customized user control interface (Blair, paragraph [0005]).
Claim 30 is rejected under 35 U.S.C. 103 as being unpatentable over Srivastava et al. (US 20190361579), (hereafter Srivastava), and in view of PK et al. (US 20200019418) (hereafter PK), as applied in claim 21 above, and further in view of Tang et al. (US 10,977,297) (hereafter Tang).
Tang was cited in the previous office action.
Regarding claim 30, the combination of Srivastava and PK teaches the invention as claimed in claim 21 above. The combination of Srivastava did not teach identifying a particular UI element in which the user is determined to no longer have interest; and preventing presentation of the particular UI element to the user for at least a specified period of time.
However, Tang teaches identifying a particular UI element in which the user is determined to no longer have interest; and preventing presentation of the particular UI element to the user for at least a specified period of time (abstract, figure. 6).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to have incorporated the concept of identifying a particular UI element in which the user is determined to no longer have interest; and preventing presentation of the particular UI element to the user for at least a specified period of time as suggested by tang into the system of Srivastava and PK because all of these system are addressing the need of personalize the user interface. By incorporating the teaching of Tang into Srivastava and PK’s system would improving the user viewing experience by decluttering the user interface particularly related to small screens to allow users to access content presented quickly and efficiently and only for a limited time if the user is not interested, thus promoting user’s interaction with other content items (Tang, col. 1 , lines 35-col. 2 lines 60).
Response to Arguments
In the response filed 12/18/2025, Applicant submitted a Terminal Disclaimers which has been approved. Therefore, the Obvious Type Double Patenting rejection is withdrawn.
Applicant’s arguments, see pages 9-11, filed 12/18/2025, with respect to 35 USC 103 rejection of claims 21-40 have been considered and are persuasive. However, a new ground of rejection for claims 21-40 under 35 USC 103 have been given (see rejection above).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JENNIFER N WELCH whose telephone number is (571)272-7212. The examiner can normally be reached M-T 5:30AM - 4:00PM.
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JENNIFER N. WELCH
Supervisory Patent Examiner
Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143