DETAILED ACTION
This action is in response to the request for continuing examination received 12/16/2025. After consideration of applicant's amendments and/or remarks:
Applicant cancels claims 4, 6, 12, 14, and 19.
Examiner withdraws rejections under 35 USC § 101.
Claims 1-3, 5, 7-11, 13, 15-18, and 20 rejected under 35 USC § 103.
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, 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 1-3, 5, 9-11, 13, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Patil et al., U.S. PG-Publication No. 2015/0186535 A1, in view of Mermoud et al., U.S. PG-Publication No. 2023/0164029 A1, further in view of Zhu et al., U.S. PG-Publication No. 2015/0128156 A1.
Claim 1
Patil discloses an Information Handling System (IHS) orchestration system, comprising: at least one processor; and at least one memory coupled to the at least one processor. Patil discloses a “system 10 for predicting an active persona 100 of a user device 300” (system 10 → IHS orchestration system). Patil, ¶ 23.
Patil discloses the at least one memory having program instructions stored thereon that, upon execution by the at least one processor, cause the IHS to: identify a current persona of a user of the IHS. Patil discloses that system 10 includes a “persona server 200” that enables “a user device 300 to determine its active persona 100,” wherein the active persona is “indicative of an objective of a user of the device 300 at a time when the user is using the device 300” (determine active persona 100 → identify a current persona of a user). Id. Patil illustrates method 500 “for predicting an active persona 100 of a user device 300.” At 510, a “behavior monitor 316 captures a current user device state.” At 512, a “persona predictor 318 inputs the current user device state into a model 400.” The model “outputs one or more vectors,” and persona predictor 318 “matches the vector to an active persona category to obtain the active persona 100 … of the user device 300.” Id. at ¶¶ 75-78, FIG. 5.
Patil discloses the current persona comprising one of a plurality of modes of operating the IHS by the user. Active persona 100 is “indicative of an objective of a user of the user device 300,” wherein user device 300 “can have one or more users and each user can have one or more objectives when using the device 300” (plurality of objectives → plurality of modes). Id. Example persona objectives include “news objective,” “sports fan objective,” “mature video game objective,” “planning objective,” and “finance objective.” See Id. at ¶¶ 23-24, 32, 60; See Also ¶ 67 (“persona categories”).
Patil discloses identify an application using the current persona. Patil discloses that an operating system “can use the active persona 100 to reconfigure the home screen displayed by the user interface 340.” If an active persona indicates a movie watching mode “the operating system can reconfigure the home screen to display the media player applications icons prominently.” Id. at ¶ 73. In another example, “if the active persona 100 indicates that the user is likely to play a violent video game, the operating system can rearrange the home screen to show icons of the violent video game on the home screen.” Id. at ¶ 79. Accordingly, the predicted active persona can indicate an application (i.e. identify an associated application) and display that application’s icons more prominently in the user interface (i.e. prioritize the identified application).
Patil does not expressly disclose optimize, using Machine Learning (ML), one or more resources of the IHS used to execute the identified application.
Mermoud discloses optimize, using Machine Learning (ML), one or more resources of the IHS used to execute the identified application. Mermoud discloses a device that “associates application performance of an online application with network configuration changes implemented across one or more software-defined networks.” The device “trains a machine learning model to predict an effect of a configuration change on the application performance for any given portion of the one or more software defined networks,” and “generates a recommended configuration change for a particular portion of the one or more software-defined networks, using the machine learning model. The device causes the recommended configuration
change to be implemented in the particular portion of the one or more software-defined networks (i.e., optimize using ML one or more network resources used to execute an online application). Mermoud, ¶ 14; See Also ¶ 48 (primary networking goal is to “optimize the network to satisfy the requirements of the applications that it supports”), ¶ 64 (“leverage machine learning to recommend configuration changes in a network … based on their likelihood to improve the performance of the network”). In one embodiment, the “configuration recommendation process 249 may be used to implement a predictive application aware routing engine.” Id. at ¶¶ 66-67. The predictive application aware routing engine 412 “makes use of a high volume of network and application telemetry … so as to compute statistical and/or machine learning models to control the network with the objective of optimizing the application experience and reducing potential down times.” Id. at ¶ 61.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of identifying personas of a user and applications corresponding to the persona of Patil to incorporate optimizing an application using machine learning as taught by Mermoud. One of ordinary skill in the art would be motivated to integrate optimizing an application using machine learning into Patil, with a reasonable expectation of success, in order to obtain the benefits of “optimizing the application experience and reducing potential down times” using a model that “is biased toward producing configuration changes 806 that improve performance.” See Mermoud, ¶¶ 61, 95.
Patil-Mermoud does not expressly disclose the current persona identified by gathering a plurality of attributes of the application as it is being used on the IHS using a plurality of Application Program Interface (API) calls made to one or more APIs by the application to gather the attributes.
Zhu discloses the current persona identified by gathering a plurality of attributes of the application as it is being used on the IHS using a plurality of Application Program Interface (API) calls made to one or more APIs by the application to gather the attributes. Zhu discloses “an application programming interface (API) analytics system” comprising a memory including “API call data that identifies a set of API calls invoked in response to an unknown activity, and predetermined API usage patterns,” wherein each “predetermined API usage pattern may identify a series of API calls performed or invoked as a result of a corresponding use case.” The analytics system comprises a “usage identification module” that “may determine a type of the unknown activity based on the truncated API call data and the predetermined API usage patterns.” Zhu, ¶¶ 17-19. The system “API monitoring and pattern detection in API usage may occur in real-time” (real-time → as it is being used) and has “an ability to classify activity types based on a similarity to predetermined API usage patterns as opposed to exact matches to predetermined API usage patterns” (classify activity type → identify current persona). Id. at ¶ 21.
The API usage patterns 142 “identify a structure of API calls that generalizes a behavior of a series of API calls that are performed as a result of users … completing a user case and/or a set of functionalities,” wherein the “pattern of API calls may specify a call structure, for example, in which a first programmatic procedure may be called any number of times followed by a second programmatic procedure” (i.e., attributes of the application as it is being used). Id. at ¶ 31. A classification structure 144 is “generated by providing the API usage patterns 142 to a machine learning algorithm.” The classification structure 144 is used to classify API usage patterns as “indicating a type of activity,” wherein the “type of activity may b e considered a classification of the activity,” for example “’normal’, ‘alert’, ‘scripting’, ‘IT admin’, ‘unknown’ or any other type of activity” (classification of the activity → persona). Id. at ¶ 61. The usage identification module 140 determines “the type of activity or activities by matching … the extracted usage pattern with one of the predetermined API usage patterns 142.” Id. at ¶ 69.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of identifying a persona and optimizing applications of Patil-Mermoud to incorporate identifying activity classifications (i.e., personas) from monitored API calls as taught by Zhu. One of ordinary skill in the art would be motivated to integrate identifying activity classifications from monitored API calls into Patil-Mermoud, with a reasonable expectation of success, in order to improve task identification accuracy by providing “an ability to classify activity types based on a similarity to predetermined API usage patterns as opposed to exact matches to predetermined API usage patterns.” See Zhu, ¶ 21.
Claim 2
Patil discloses wherein the program instructions, upon execution, further cause the IHS to: perform an unsupervised Machine Learning (ML) process to derive a plurality of persona models that are different from one another. Patil discloses that “each user device 300 monitors itself and creates/updated models 400 that predict the active persona 100 of the device 300 based on the actions being performed by… the user device 300 at a given time” (i.e., an unsupervised ML process). The model 400 “receives a user device state as input” and “outputs one or more predicted clusters 238.” Patil, ¶¶ 30-31. The method “can identify clusters 238” used “to identify potential active personas 100 of a device 300,” wherein a “cluster 238 can refer to a group of data records that have one or more similar attributes” (clusters used to identify personas → plurality of persona models). Id. at ¶ 25.
Patil discloses gather data about how the user is using the one or more applications. Behavior monitor 316 “monitors the user device 300 and the commands issued by the user,” including “any applications being executed,” and provides observations “to the model builder 314 and the persona predictor 318.” Id. at ¶¶ 64-65. A model builder 314 “generates the machine learned model 400.” Model builder 314 “aggregates user behavior or other activity data,” and “utilizes the aggregated user behavior to seed the initial probabilities of the model 400.” The observed behavior includes “an active application (i.e., the application currently being used by the user),” “active media content,” and “the type of network connection that the user device 300 is currently using.” Id. at ¶¶ 61-62.
Patil discloses compare the gathered data against the persona models to identify the current persona of the user. Persona predictor 318 “predicts the active persona 100 of the user device 300 based on the current user device state” using machine learned model 400 that “outputs a set of probabilities,” wherein “each probability is associated with a different cluster 238.” The active persona 100 is “represented by the combination of probabilities of transitioning to each possible cluster from the current cluster.” The rules for matching vectors output by the model 400 to a persona category “can be learned over time by a machine learner” (i.e. unsupervised machine learning). Id. at ¶¶ 66-67.
Claim 3
Patil discloses wherein the program instructions, upon execution, further cause the IHS to: perform a supervised ML process to compare the gathered data against the persona models. The clustering of data records “can be done in a supervised manner, whereby a user manually selects the attributes, or representative data records by which the data records are clustered.” Patil, ¶ 43. The “user device states can correspond to the various clusters 238 that the user’s actions may implicate,” and the “implicated clusters 238 may be indicative of the active persona” (i.e., compare the gathered data against the persona models). Id. at ¶ 58.
Claim 5
Patil discloses wherein the type of application comprises at least one of a database type, a multimedia type, an enterprise type, an educational type, and a simulation type.1 Patil discloses that applications include “word processing applications … messaging applications, media streaming applications, social networking applications, and games.” Patil, ¶ 38.
Claims 9-11 and 13
Claims 9-13 are rejected utilizing the aforementioned rationale for Claims 1-3, 5; the claims are directed to a method performed by the system.
Claims 17-18
Claims 17-18 are rejected utilizing the aforementioned rationale for Claims 1 and 3; the claims are directed to a medium storing instructions executed by the system.
Claims 7-8, 15-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Patil et al., U.S. PG-Publication No. 2015/0186535 A1, in view of Mermoud et al., U.S. PG-Publication No. 2023/0164029 A1, further in view of Zhu et al., U.S. PG-Publication No. 2015/0128156 A1, further in view of Gandhi, U.S. PG-Publication No. 2019/0132219 A1.
Claim 7
Gandhi discloses wherein the program instructions, upon execution, further cause the IHS to: optimize the application for bandwidth usage over at least one of a plurality of active network connections of the IHS. Gandhi discloses a “connection manager 230” used “to dynamically optimize utilization of the available network connections across all of the executing applications,” wherein the “optimization may be performed continuously or periodically.” Gandhi, ¶ 36. Connection manager 230 uses “contextual data 300” describing “applications installed on the device, the user’s preferences, the user’s behavior” and the like. Id. at ¶ 44-46. Connection manager 230 uses contextual data 330 “derived from past user behaviors and/or application usage history, when determining an optimal allocation for network connections among the applications,” in order to “make predictions as to optimal network connection allocation … to ensure that the user experience is the best possible.” Id. at ¶ 48.
Connection manager 230 “can infer network requirements for a given application 204 based on its assigned type categorization.” Id. at ¶ 39. The network connections are “dynamically allocated by the connection manager to any of the bandwidth-consuming applications in an optimized manner across all of the application that are being executed” (i.e., optimize application for bandwidth usage over one of a plurality of network connections). The allocation may be performed “based on … application type 430, relevant context 435, and user preferences 440.” Id. at ¶ 50; See Also ¶ 100 (causing the computer system “to categorize applications by type and switch among available network connections based on categorized types”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of identifying personas of a user and applications corresponding to the persona of Patil-Mermoud-Zhu to incorporate selecting a network connection based on user context and application type as taught by Gandhi. One of ordinary skill in the art would be motivated to integrate selecting a network connection based on user context and application type into Patil-Mermoud-Zhu, with a reasonable expectation of success, in order to “improve the technical operations of the computing device,” wherein operations “may be optimized for a resource-efficient user experience by identifying application QoS requirements and user preferences and then responsibly managing network interfaces and connections to ensure more optimal utilization of available network connections.” See Gandhi, ¶ 9; See Also ¶ 36 (“improving the quality of user experience with various bandwidth consuming applications 204, e.g., by minimizing glitches and disruptions in the communication channel”).
Claim 8
Gandhi discloses wherein the program instructions, upon execution, further cause the IHS to: select one of the active network connections for use by the application to optimize the application. Connection manager 230 “can infer network requirements for a given application 204 based on its assigned type categorization.” Gandhi, ¶ 39. The network connections are “dynamically allocated by the connection manager to any of the bandwidth-consuming applications in an optimized manner across all of the application that are being executed” (i.e., optimize application for bandwidth usage over one of a plurality of network connections). Id. at ¶ 50. Gandhi discloses that the system can “switch network connections based on the predicted execution of the application or application feature,” and executes instructions “to categorize applications by type and switch among available network connections based on categorized type.” Id. at ¶ 100.
Claims 15-16
Claims 15-16 are rejected utilizing the aforementioned rationale for Claims 7-8; the claims are directed to a method performed by the system.
Claims 20
Claims 20 are rejected utilizing the aforementioned rationale for Claims 7; the claims are directed to a medium storing instructions executed by the system.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 7, and 19 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. See Zhu et al., U.S. PG-Publication No. 2015/0128156 A1.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANK D MILLS whose telephone number is (571)270-3172. The examiner can normally be reached M-F 10-6 ET.
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/FRANK D MILLS/Primary Examiner, Art Unit 2194 January 10, 2026
1 This limitation has no patentable weight, because it is directed to non-functional descriptive material. This limitation merely conveys meaning of the “type of application” to a human reader independent of the computer system. There is no functional relationship between the claimed system and a specific “type of application” because the functional programming of the system does not change based on what the “type of application” represents; these limitations merely describe the data being processed to a human reader. See MPEP 2111.05.