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 .
Status of Claim
This action is in reply to the application filed on 30 of June 2023.
Claims 1-20 are currently pending and are rejected as described below.
Allowable Subject Matter
Claims 4-8 and 13 are objected to as being currently rejected as below, but would be allowable if the independent claims were amended in such a way as to overcome the 35 USC 101 rejection set forth in the action. The prior art of record most closely resembling the applicant’s claimed invention includes Uribe et. al. (US 10360508), Almecija et. al. (US 20210294617), Andress et. al. (US 20240354242), and Shabah (US 20180096588).
Uribe teaches computer-based techniques for generating personalized Markov chains useful in various kinds of computer recommendation systems. The present disclosure relates more specifically to techniques for recommending titles of content items based on personalized Markov chains.
Almecija teaches methods and system for adapting user interfaces are proposed. According to certain embodiments, a user experience level is determined based on usage of a software application and other detected factors. Based on the user experience level, at least in part, a user interface is adapted to provide an improved experience for the user of the adaptive user interface.
Andress teaches a method and system for testing functionality of a software program based on at least one modification to a software code of the software program. In one embodiment, the method includes: receiving software code of software program from one or more sources; identifying functions within the software code of the software program affected by the modification of software code; simulating the identified functions of the software program using digital twin; determining workflows of the software program based on the simulation of digital twin; identifying at least one impacted workflow from determined workflows based on one or more requirements of software program; and executing at least one impacted workflow critical to the functionality of the software program for testing the functionality of software program
Shabah teaches a workflow management system that includes a plurality of communication devices interconnected to form an ad hoc network. Mobile devices of the communication devices are each associated to a user and stores a user profile of the user. The system also includes a work-flow management module configured to assign tasks to users via task assignments transmitted to the mobile devices and to modify the assigned task based on changes to status of tasks, change in available mobile devices and/or changes in situational status affecting the ad hoc network. A method for managing workflow is also provided.
None of the above prior art explicitly teaches “updating the Markov chain model with the newly applicable user device” and “defining a software class for a Markov chain model based on software classes for a user, a user skill set, past results, and planned results”, and these are the reasons which adequately reflect the Examiner's opinion as to why Claims 4-8 and 13 are allowable over the prior art of record.
Claim Rejections - 35 USC § 101
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefore, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machines, article of manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception.
The claims are then analyzed to determine whether the claims are directed to a judicial exception. MPEP §2106.04(a). In determining, whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), and whether the claims recite additional elements that integrate the judicial exception into a practical application (Prong Two of Step 2A). See 2019 Revised Patent Subject Matter Eligibility Guidance (“PEG” 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (Jan. 7, 2019)).
With respect to 2A Prong 1, claim 18 recites “a processor set, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to: detect user interactions in a digital workflow via one or more workflow user interfaces of one or more user devices; model the user interactions in the digital workflow based on the user interactions in a Markov chain model of the user interactions; analyze the Markov chain model of the user interactions with reference to one or more performance goals of the digital workflow, to determine one or more prospective modifications to the user interactions that would increase performance of the user interactions as indicated by the one or more performance goals; generate one or more workflow modification recommendations based on the one or more prospective modifications to the user interactions; output the one or more workflow modification recommendations to at least one of the one or more user devices; receive a confirmation to select one of the workflow modification recommendations; and implement a modification to the digital workflow based on the selected workflow modification recommendation”. Claims 1 and 14 disclose similar limitations as Claim 1, and therefore recite an abstract idea.
More specifically, claims 1, 14, and 18 are directed to “Mathematical Concepts” in particular “mathematical calculations”, and “Mental Processes” in particular “concepts performed in the human mind (including an observation, evaluation, judgment, opinion)” as discussed in MPEP §2106.04(a)(2), and in the 2019-01-08 Revised Patent Subject Matter Eligibility Guidance. Accordingly, the claims recite an abstract idea.
Dependent claims 2-13, 15-17, and 19-20 further recite abstract idea(s) contained within the independent claims, and do not contribute to significant more or enable practical application. Thus, the dependent claims are rejected under 101 based on the same rationale as the independent claims.
Under Prong Two of Step 2A of the Alice/Mayo test, the examiner acknowledges that Claims 1, 14, and 18 recite additional elements yet the additional elements do not integrate the abstract idea into a practical application. In order for the judicial exception to be “integrated into a practical application”, an additional element or a combination of additional elements in the claim “will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.” PEG, 84 Fed. Reg. 54 (Jan. 7, 2019). The courts have identified examples in which a judicial exception has not been integrated into a practical application when “an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.” PEG, 84 Fed. Reg. 55 (Jan. 7, 2019); MPEP § 2106.05(h). The claims are directed to an abstract idea.
In particular, claims 1, 14, and 18 recite additional elements boldened and underlined above. These are generic computer components recited as performing generic computer functions that are mere instructions to apply an exception, because it does no more than merely invoke computers or machinery as a tool to perform an existing process. Further, the remaining additional element(s) italicized above reflect insignificant extra solution activities to the judicial exception, see MPEP 2106.05(g). Accordingly, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Dependent claims 7-8 recite additional elements “smartphone” and “smartwatch”. These are generic computer components recited as performing generic computer functions that are mere instructions to apply an exception, because it does no more than merely invoke computers or machinery as a tool to perform an existing process. Accordingly, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
With respect to step 2B, claims 1, 7-8, 14, and 18 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The claim recites the additional elements described above. These are generic computer components recited as performing generic computer functions that are mere instructions to apply an exception, because it does no more than merely invoke computers or machinery as a tool to perform an existing process, as evidenced by at least in ¶40 “FIG. 3 shows a flowchart of an exemplary method 300 in accordance with aspects of the present invention. Steps of the method may be carried out in the environment of FIG. 2 and are described with reference to elements depicted in FIG. 2. In method 300, workflow optimizer code 200 (e.g., Markov chain workflow analysis module 202 thereof as shown in FIG. 2) detects user interactions in a digital workflow via one or more workflow user interfaces of one or more user devices (302). In various embodiments, and as described with respect to FIG. 2, workflow optimizer code 200 (e.g., Markov chain workflow analysis module 202 thereof) models the user interactions in the digital workflow in a Markov chain model of the user interactions (304). Workflow optimizer code 200 (e.g., Markov chain workflow analysis module 202 thereof) analyzes the Markov chain model of the user interactions with reference to one or more performance goals of the digital workflow, to determine one or more prospective modifications to the user interactions that would increase performance of the user interactions as indicated by the one or more performance goals (306). Workflow optimizer code 200 (e.g., Markov chain workflow optimization module 204 thereof) generates one or more workflow modification recommendations based on the one or more prospective modifications to the user interactions (308). Workflow optimizer code 200 (e.g., Markov chain workflow optimization module 204 thereof) outputs the one or more workflow modification recommendations to at least one of the one or more user devices (310). Workflow optimizer code 200 (e.g., Markov chain workflow optimization module 204 thereof) receives a confirmation to select one of the workflow modification recommendations (312). Workflow optimizer code 200 (e.g., Markov chain workflow optimization module 204 thereof) implements a modification to the digital workflow based on the selected workflow modification recommendation (314)”.
Claims 2-6, 9-13, 15-17, and 19-20 do not disclose additional elements, further narrowing the abstract ideas of the independent claims and thus not practically integrated under prong 2A as part of a practical application or under 2B not significantly more for the same reasons and rationale as above.
After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298.
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 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.
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
Claims 1-3, 9-12, 14-16, and 18-19 are rejected under 35 U.S.C. 103 as being obvious by the combination of US 20210294617 to Almecija et. al. (hereinafter referred to as “Almecija”) in view of US 10360508 to Uribe et. al. (hereinafter referred to as “Uribe”).
(A) As per Claims 1, 14, and 18:
Almecija expressly discloses:
detecting, by a processor set, user interactions in a digital workflow via one or more workflow user interfaces of one or more user devices; (Almecija ¶42 user experience system 104 retrieves user UI interaction history and profile. Each user has a profile that is dynamically created. This profile includes their user interface actions, history, and preferences, as well as other information about them that may affect how a UI is adapted. These can be stored, and retrieved from, user UI profile store 146).
generating, by the processor set, one or more workflow modification recommendations based on the one or more prospective modifications to the user interactions; (Almecija ¶34-36 The whole software application workflow of completing a task that may require many steps/screens/buttons can be improved by adapting the workflow and user interface throughout the workflow. The user's workflow and user interfaces can be adapted dynamically and automatically. As an example, during their daily work, a user may perform repetitive actions that induce a lot of mouse travel. Systems and methods herein optimize user workflow and mouse travel by showing the next most probable actions the user will want to perform).
implementing, by the processor set, a modification to the digital workflow based on the selected workflow modification recommendation; (Almecija ¶46 at step 612, user experience system 104, through UI adaptive component 148 in an embodiment, adapts a user interface per user experience level and/or assigned grouping. Adapting the user interface can mean re-sizing the screen, changing the layout, reducing or adding buttons, changing menus, altering what content is shown, changing fonts, changing paradigms (e.g. visual to audible), changing icons, re-arranging UI assets, and more).
Although Almecija teaches methods and system for adapting user interfaces, where the system may use a Bayesian algorithm based on Markov chains, it doesn’t expressly disclose the specifics on how the Markov Chains are used in order to implement changes to digital workflow , however Almecija teaches:
modeling, by the processor set, the user interactions in the digital workflow based on the user interactions in a Markov chain model of the user interactions; (Uribe Col. 6 Lines 51-60 the personalized model generator 206 generates, based on the interactions stored in the user interaction database 204, one or more Markov chains likely to explain the interactions recorded in the user interaction database 204. In addition, for each user, the personalized model generator 206 calculates the mixture of those Markov chains likely to explain that user's interaction history. Thus, given a particular user, the personalized model generator 206 computes, from the one or more Markov chains and the known mixture, a personalized Markov chain for the particular user).
analyzing, by the processor set, the Markov chain model of the user interactions with reference to one or more performance goals of the digital workflow, to determine one or more prospective modifications to the user interactions that would increase performance of the user interactions as indicated by the one or more performance goals; (Uribe Col. 8 Lines 50-67 the closer the Markov chains are initialized to transition probabilities representing the underlying stochastic processes of the users, the quicker the personalized model generator 206 may converge on a solution that explains the known user transitions stored in the user interaction database 204. However, regardless of the initial transition probabilities chosen for the set of Markov chains, the personalized model generator 206 will eventually lock onto transition probabilities for the Markov chains that are likely to explain the recorded user transitions better than their initial value. Since the function that is optimized, a likelihood function, when tuning the Markov chain parameters can have multiple maxima, different initial conditions for the Markov chain probabilities can be used to end up with different estimates for these probabilities. In the end the set of parameters that achieves the largest value of the likelihood function being optimized is chosen as the final set of Markov chain parameters for the c Markov chains).
outputting, by the processor set, the one or more workflow modification recommendations to at least one of the one or more user devices; (Uribe Col. 14 Lines 8-14 in an embodiment, the CBP 108 sorts the one or more titles received from the recommendation engine 210 at block 502 of FIG. 5. The CBP 108 may sort the one or more titles by genre or any other applicable theme. The CBP 108 then displays the one or more titles in windows, such as selection window 601 and selection window 602, corresponding to the genres/themes).
receiving, by the processor set, a confirmation to select one of the workflow modification recommendations; (Uribe Col. 14 Lines as another example, the CBP 108 may provide, in the vicinity of titles that are available for playback, an option for displaying similar titles. When the option is selected by a user, the CBP 108 provides display 600 to guide the user to similar titles).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Almecija’s user's workflow and user interfaces adapted dynamically and automatically and generate using the personalized model generator one or more Markov chains of Uribe as both are analogous art which teaches improving user experience through UI adaptive component that adapts a user interface per user experience level and/or assigned grouping as taught in Almecija have optimized function chosen as the final set of Markov chain parameters for the c Markov chains as taught in Uribe.
Uribe teaches methods computer-readable medium and apparatus (e.g. system) in Col. 5 Lines 14-16.
(B) As per Claims 2 and 15:
Almecija expressly discloses:
modeling a user skill set based on the user interactions; wherein the modeling further comprises modeling the user interactions in the digital workflow and the user skill set based on the user interactions in the Markov chain model; (Almecija ¶33, 81 herein proposed are systems and methods to learn about users to determine the type of user, experience level, and working habits. The system can then tailor and/or adapt the user interface (imaging layouts, menu, buttons, toolbars, tools options, learning hints, input/output devices, etcetera) based on the determined type of user and experience user, as well as additional considerations that will be discussed herein throughout. The systems and methods for adaptive user interfaces determine what to present to the user, when to present it and how to present it. Further, the systems and methods for adaptive user interfaces determine when to not provide any user interface, instead inferring the actions the user would like and automatically automating or performing those actions based on user intent, history, experience level, and other factors described herein. The system provides the last i user actions to the algorithm and get the probabilities associated to the next possible actions. In other terms, with X∈Buttons, i∈N, user experience learning component estimates P(X(n)| X (n−1), X(n−2), . . . , X(n−i)). The system may use a Bayesian algorithm based on Markov chains, in an embodiment).
(C) As per Claims 3, 16, and 19:
Almecija expressly discloses:
wherein the generating the one or more workflow modification recommendations based on the one or more prospective modifications to the user interactions comprises generating a recommendation for a modification of user interface elements of one of the one or more workflow user interfaces; (Almecija ¶43 the user experience learning component 142 can automatically determine changes and patterns in the user's UI behavior and needs. For example, a user may have a similar way of working most sessions, but one session clicking buttons fast and furiously (i.e. shorter intervals between button clicks and faster mouse travel). Thus, the system can learn to group the user into a “speed needed” or “urgent” grouping that may dynamically update the user interface to show only the one anticipated next action or even automate some actions that would normally be clicks by the user in order to save time).
(D) As per Claim 9:
Almecija expressly discloses:
iteratively modeling the user interactions in the digital workflow based on the user interactions in the Markov chain model of the user interactions over time; (Uribe Col. Lines he personalized model generator 206 may generate any number of Markov chains by repeatedly applying Equation 2. It will be assumed that the personalized model generator 206 creates a total of c Markov chains).
(E) As per Claim 10:
Almecija expressly discloses:
wherein the generating the one or more workflow modification recommendations is further based on user preferences indicated by a user input via at least one of the one or more user devices; (Almecija ¶54 the user can see the factors that led to a certain user experience determination and outputted UI. And in some embodiments the user is allowed to change these factors or settings so as to communicate to the system their exact preferences or to edit potentially erroneous data in the system).
(F) As per Claim 11:
Almecija expressly discloses:
wherein generating the one or more workflow modification recommendations based on the one or more prospective modifications to the user interactions further comprises generating a recommendation to reduce or remove one or more user interface elements based on one or more user interactions of the user interactions that are faster than expected based on prior Markov chain modeling; (Almecija ¶46 at step 612, user experience system 104, through UI adaptive component 148 in an embodiment, adapts a user interface per user experience level and/or assigned grouping. Adapting the user interface can mean re-sizing the screen, changing the layout, reducing or adding buttons, changing menus, altering what content is shown, changing fonts, changing paradigms (e.g. visual to audible), changing icons, re-arranging UI assets, and more).
(G) As per Claim 12:
Almecija expressly discloses:
identifying other users with a skill set analogous to a given user; wherein, for the given user, generating the one or more workflow modification recommendations is further based on one or more user interactions of the other users with a skill set analogous to the given user; (Almecija ¶51-52 one example is that if a user uses the help menus more than an average amount, the user may be grouped into the “beginner” group and/or the “higher desire to learn” group. Another example is that if a user has logged in to this software application less than ten times, they may be grouped into the “beginner” group. But if that same user has logged into a software application in a software application suite that has a similar UI as the currently used software over 100 times, they may not be grouped in the “beginner group” and may be put into another group such as “advanced UI multi-app” (as shown more in FIG. 7).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATHEUS R STIVALETTI whose telephone number is 571-272-5758. The examiner can normally be reached on M-F 8:30-5:30.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached on 571-272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-1822.
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/MATHEUS RIBEIRO STIVALETTI/Primary Examiner, Art Unit 3623 8/4/2026
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