DETAILED ACTION
This action is responsive to the filing of 3/27/24. Claims 1-20 are pending and have been considered below.
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 § 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by
Almecija (20180365025.)
Claim 1, 11, 20: Almecija discloses a system, comprising: a processor coupled to a memory that includes instructions that, when executed by the processor, cause the processor to:
receive an electronic document (Fig. 12: 1200 User Interface with images / toolbars);
determine an interaction event (par. 57, at step 208, a user action occurs. User IO 102 receives an input to perform some action directed towards the software application; par. 59, logic to track the user input and actions; Fig. 5: Input tracking) for at least one section (top toolbar section; par. 68, clicking on the magnifying glass button / click the right most button in top toolbar 1206) of the electronic document;
determine an expertise level of a user of the electronic document based on the interaction event (user input causes output of hints / dynamic shortcuts that are tailored based on their level (i.e. the program needs to fetch / determine their level); par. 68, shows the likely next buttons the user will want after clicking on the magnifying glass button; par. 64, hints can be tailored to the user based on user experience level; par. 5, user experience level may be determined from current session user interaction history);
assign a rule (par. 68, rule to show hints / dynamic shortcuts) to the at least one section (Fig. 12: toolbar section) based on the interaction event (par. 68, clicking on the magnifying glass button / click the right most button in top toolbar 1206 ) and the expertise level of the user (par. 32, The system can then tailor and/or adapt the user interface (imaging layouts, menu, buttons, toolbars, tools options, learning hints, input/output devices, etc) based on the determined type of user and experience user; par. 64, hints can be tailored to the user based on user experience level); and
implement an action to the at least one section of the electronic document based on a determination that the interaction event has occurred and the rule (Fig. 12-13, display dynamic shortcuts.)
Claim 2, 12: Almecija discloses the system of claim 1 wherein the instructions further cause the processor to: organize the electronic document into a plurality of layers including a base content layer (Fig. 12: 1200 UI) that contains the electronic document and a first layer that contains information (Fig. 13: predicted dynamic shortcuts) corresponding to the base content layer, wherein the information contained in the first layer is generated by a machine learning model (par. 6, user experience learning component can apply at least one of machine learning, deep learning, or a neural network to analyze the registered user actions and perform the prediction of the next intended action.)
Claim 3, 13: Almecija discloses the system of claim 2 wherein the plurality of layers includes a second layer (Fig. 15: 1510, Hints, layered over) that contains information corresponding to the first layer, wherein the information contained in the second layer is generated by the machine learning model (par. 43, Each time user experience learning component 142 is applied, the UI system can improve in its understanding of user needs and preferences, and thus output improved adapted user interfaces.)
Claim 4, 14: Almecija discloses the system of claim 2 wherein the instructions further cause the processor to: identify at least one layer of the plurality of layers, wherein the at least one layer corresponds to the expertise level of the user (par. 64, hints can be tailored to the user based on user experience level as well as various aspects of their user profile); and display the at least one layer to the user (Fig. 15: 1510, Hints, layered over.)
Claim 5, 15: Almecija discloses the system of claim 2, wherein the machine learning model is trained on a library of previous interaction events and is configured to determine the expertise level of the user based on the interaction event (par. 60, user experience system 104, through user experience learning component 142 in an embodiment, updates the learning training base. The learning training base includes data, models, and analysis to empower the predictive features of step 210.)
Claim 6, 16: Almecija discloses the system of claim 1, wherein a machine learning model determines the action to be taken on the at least one section of the electronic document based on the expertise level of the user (par. 6, user experience learning component can apply at least one of machine learning, deep learning, or a neural network to analyze the registered user actions and perform the prediction of the next intended action.)
Claim 7, 17: Almecija discloses the system of claim 1, wherein the action comprises providing a summary of the at least one section of the electronic document, (Fig. 15: 1510 Hints summarizing the function) wherein the summary corresponds to the expertise level of a user of the system (par. 32, The system can then tailor and/or adapt the user interface (imaging layouts, menu, buttons, toolbars, tools options, learning hints, input/output devices, etc) based on the determined type of user and experience user; par. 64, hints can be tailored to the user based on user experience level.)
Claim 8, 18: Almecija discloses the system of claim 1, wherein the interaction event comprises at least one of a text input or a voice input (par. 101, a user can interact with a software application in many different user interface paradigms, from touch, mouse, voice, eye movement, and so forth.)
Claim 9, 19: Almecija discloses the system of claim 1, wherein the instructions further cause the processor to generate a second electronic document based on the interaction event (Fig. 15: 1510, hint document.)
Claim 10: Almecija discloses the system of claim 9, wherein the second electronic document is generated by a machine learning model configured to predict a need for the second electronic document based on the interaction event (par. 6, user experience learning component can apply at least one of machine learning, deep learning, or a neural network to analyze the registered user actions and perform the prediction of the next intended action.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Malviya (2025/0272480) dynamic UI for automated document creation.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREY BELOUSOV whose telephone number is (571) 270-1695 and Andrew.belousov@uspto.gov email. The examiner can normally be reached Mon-Friday EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Adam Queler, can be reached at telephone number 571-272-4140. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Andrey Belousov/
Primary Examiner
Art Unit 2145
12/12/2025