Prioritization of processes based on user persona and reinforcement learning

Settings on an information handling system may be adjusted to set priority levels for processes executing on the information handling system in view of desired operational characteristics of the information handling system for a user persona and in view of expected future events for the information handling system. A score may be generated based on a user persona (e.g., whether a user is a light gamer, heavy gamer, corridor warrior, or desk worker) and expected future computer contexts (e.g., an expectation that a user will play a game in one hour). That score may be used to determine policies (e.g., high performance, balanced, or battery saver) to implement through settings on the computer system. Consideration of user persona classifications, associated group behaviors, and dynamic system contexts (including resource extremas, location, temporal context, and predicted future events) improve use of system resources through prioritization and governing of diverse optimization methods.

FIELD OF THE DISCLOSURE

The instant disclosure relates to performance enhancement of information handling systems. More specifically, portions of this disclosure relate to automated prioritization adjustment for information handling system processes.

BACKGROUND

Information handling systems may execute multiple system processes concurrently. For example, users may run multiple applications, such as gaming applications, productivity applications, system applications, video streaming applications, communications applications, and other applications, simultaneously, and each application may include multiple associated system processes. Different processes may require allocation of different amounts of resources. Allowing processes that are unimportant to a user to consume resources and/or starving processes that are important to a user of needed resources can negatively impact a user experience. For example, starving important processes of resources can cause decreases in frame rate, network lagging, application crashes, and other negative events.

In some cases, users may manually prioritize certain processes and/or applications, configuring an information handling system to prioritize allocating resources to specific applications or processes that a user deems important. However, manual prioritization can be time consuming and may require a level of technical knowledge that users may not possess. Furthermore, such prioritization may require a user to frequently interact with a prioritization user interface to adjust priorities as usage needs change. In other cases, processes and/or applications may be automatically prioritized based on resource consumption, such that processes that consume substantial system resources are prioritized over those that do not, or based on a designation of processes as foreground and background processes, such that foreground processes are prioritized over background processes. Such prioritization, however, may negatively impact a user experience, as processes that require minimal system resources may be more important to a user than processes that require substantial system resources, and background processes may, in some cases, be more important to a user experience than foreground processes.

Shortcomings mentioned here are only representative and are included simply to highlight that a need exists for improved information handling systems. Embodiments described herein address certain shortcomings but not necessarily each and every one described here or known in the art. Furthermore, embodiments described herein may present other benefits than, and be used in other applications than, those of the shortcomings described above.

SUMMARY

Settings on an information handling system may be adjusted to set priority levels for processes executing on the information handling system in view of desired operational characteristics of the information handling system for a user persona and in view of expected future events for the information handling system. In some embodiments, a score may be generated based on a user persona (e.g., whether a user is a light gamer, heavy gamer, corridor warrior, or desk worker) and expected future computer contexts (e.g., an expectation that a user will play a game in one hour). That score may be used to determine policies (e.g., high performance, balanced, or battery saver) to implement through settings on the computer system. Consideration of user persona classifications, associated group behaviors, and dynamic system contexts (including resource extremas, location, temporal context, and predicted future events) improve use of information handling system resources through prioritization and governing of diverse optimization methods. The synthesis of a user experience score from telemetric sources to use for feedback generation can be used to continuously train personalized reinforcement learning algorithms to serve client intelligence use cases.

An optimization importance score may be calculated by weighting the values of sets of system effects relative to their impact to key performance indicators (KPIs) particular to a user based on a user's persona, that persona's KPIs, and the information handling system's system state, system history, and system context. This score provides a relative indicator to the KPIs of system components to be used in evaluating which optimization to be applied to a given system. For example, a gamer belonging to a persona of visual enthusiasts (an “Immersive Gamer”) has a baseline KPI importance matrix that is weighted towards graphics quality, stable performance, low skin temperatures, and communication-optimized networking.

In some embodiments, a user experience score is determined from telemetry data to generate a closed loop on reinforcement learning systems without requiring user engagement. User experience scoring is, at a high level, the reward function for machine learning algorithms to determine whether a policy (and the resulting outcome) was desired or not. Therefore, a positive score reinforces the prediction when encountered with a similar input state, while a negative score allows the algorithm to explore other outcomes. The score may be calculated by synthesizing various telemetric features relevant to a particular outcome reached by a generated policy. The importance/relevance of an outcome is evaluated by incorporating the user's persona and system context features and incorporated into the outcome score.

For example, a reinforcement learning algorithm targeted at learning a user's system startup behaviors could use a reward function that assigned a positive score to correctly predicting which applications and files the user would open and placing them in the correct locations on screen, with a negative score being assigned for applications/files that are closed by the user without interaction or moved to a different location on the screen. Additionally, the algorithm making the prediction may not act on it to assess correctness of a generated policy. If the user manually reaches a predicted state by their own action, the model can assign a positive score to its predicted policy and predict with a higher confidence in subsequent scenarios. Further embodiment illustrations and telemetry features may be found in the subsequent material.

In one embodiment, a method may include determining a user persona based on a user's usage of the information handling system. A priority level for processes executing on the information handling system may be determined based on the user persona. Processes executed on the information handling system may be adjusted based on the determined priority level by adjusting one or more settings of the information handling system, such as through adjusting the operating system. In some embodiments, the priority levels may also be adjusted based on future expected events determined from the user's persona or telemetry data. Reinforcement learning may be applied by monitoring performance parameters of the information handling system after the adjustment and adjusting policies based on the monitored performance parameters, in which the policies are used to determine priority levels of future executing processes. An information handling system may include a memory and a processor for performing the steps described herein. Instructions for performing the steps described herein may be stored on a non-transitory computer readable medium.

DETAILED DESCRIPTION

An information handling system may adjust prioritization of process execution in order to enhance a user experience. For example, certain processes that are more important to a user and/or have more impact on a user experience may be prioritized over processes that are less important to the user or have less of an impact on the user experience. For example, the information handling system may devote more resources (such as processor time and/or memory space) to certain processes while devoting fewer resources to other processes. An example information handling system100may execute an operating system102. The information handling system100may also execute one or more applications108. The operating system102and the applications108may each execute one or more system processes. A user may interact with the applications108and/or the operating system102via a user interface132. Hardware sensors and controls110of the information handling system100may monitor and control hardware operation of the information handling system100. An operating system telemetry module104may collect telemetry data from the operating system102. A telemetry collector106may collect telemetry data from the operating system telemetry module104, the one or more applications108, and the hardware sensors110. The telemetry data collected by the telemetry collector106may be provided to a dynamic user experience score module112to determine a dynamic user experience score. The dynamic user experience score may provide a rating of the information handling system based on a user experience. A higher dynamic user experience score may indicate a better user experience. Telemetry data from the telemetry collector106may also be provided to a persona experience importance model116and used in determining a persona for the user of the information handling system and how different performance characteristics of the information handling system may affect users with different personas. For example, different personas may be assigned to different users based on behavior observed in the telemetry data, such as frequent gaming, offline-to-online or online-to-offline status changes, frequent use of video editing applications, and other user behaviors. The persona experience importance model116may rank processes executed by the information handling system, such as processes of the applications108and operating system102based on importance of the processes to a user experience of users with similar behavior patterns. The persona experience importance model116may be located on the information handling system100, located in an external cloud-based system, or a combination of local and remote systems.

An optimization importance model114may receive persona experience importance information from the persona experience importance model116and telemetry data from the telemetry collector106. The optimization importance model114may also receive expected future events data from an expected future events module124. The optimization importance model114may determine an importance of optimization of one or more processes of the information handling system100and may pass the importance data to the dynamic user experience score module112. The dynamic user experience score module112may pass a dynamic user experience score to a policy discovery module120. The telemetry collector106may also feed telemetry data to a state aggregation module118which may aggregate state information for the information handling system100. The state aggregation module118may feed state information to the policy discovery module120. Based on the state information and the dynamic user experience score, the policy discovery module120may determine policy information and may feed the policy information to a policy observations database122. The policy observations database122may feed the policy information to the expected future events module124which may determine one or more expected future events of the information handling system100based on the policy information.

State aggregation information from the state aggregation module118and expected future events information from the expected future events module124may be fed to a decision module126. The decision module126may determine one or more adjustments to be performed based on the state aggregation information and the expected future events. For example, the decision module126may determine one or more adjustments to be performed based on a process that is determined to be a highest-ranking process by the importance model114. The decision module126may make decisions with low confidence or high confidence. If an adjustment decision is made with low confidence, the decision module126may feed instructions to a mock application module128. The mock application module128may perform a simulation of the adjustments determined by the optimization decision module126and feed the results of the simulation to the dynamic user experience score module112. The dynamic user experience score module112may then determine if the simulation indicates that the optimization would increase or decrease the dynamic user experience score. That information may then be fed to the policy discovery module120and may eventually be used by the decision module126to determine one or more adjustments that should be made. For example, a positive result from the mock application module128may increase the confidence of the decision module126decision. If the decision module126decides with high confidence, the decision module may feed the adjustment to an application module130. The application module130may instruct the operating system102and/or hardware sensors and controls110to make one or more adjustments based on the decision. The adjustments may include, for example, allocating additional system resources to a system process with high priority. The results of the adjustments may then be reflected in collected telemetry data and an updated dynamic user experience score. The optimization importance model114, persona experience importance model116, dynamic user experience score module112, telemetry collector106, state aggregation module118, policy discovery module120, policy observations database122, expected future events module124, decision module126, mock application module128, and application module130may all be components of a reinforcement learning system of the information handling system for improving information handling system performance in an automated manner using intelligence from previous decisions. Thus, an information handling system may dynamically adjust optimization of system processes, and the system overall, based on a dynamic user experience score, to enhance a user experience.

An information handling system may implement a reinforced learning algorithm to determine whether adjustments performed on an information handling system enhance a user experience. An example reinforcement learning loop200is shown inFIG. 2. The reinforcement learning loop200may include an environment204and an agent202. The agent202may, for example, include one or more applications configured to monitor, configure, and communicate with applications, an operating system, firmware, and hardware of an information handling system. The environment204may, for example, be an operating environment of the agent202, such as a software stack of the information handling system, which may include an embedded controller, a basic input/output system (BIOS), an operating system (OS), and applications executed by the information handling system. In some embodiments, the environment204may be restricted to only an OS, applications, or user-created data.

The agent202may monitor the environment204and may adjust the environment based on the monitoring to enhance performance. The agent may monitor state information, such as system power state information, such as sleep, standby, and off, performance state information, such as power profiles, overclocking data, hyper-threading data, Vsync data, caching performance data, and system settings, and repair state information of the information handling system, such as system backup information, recovery information, diagnostic test information, and SOS mode information. The agent202may also perform state changes, such as disabling a fast charging capability of the information handling system or reducing a top charge voltage. For example, the agent202may monitor a state stat time t. The agent202may monitor performance parameters of the information handling system. Based on monitored state information and performance information, the agent202may select one or more policies to govern adjustments of the information handling system. Policies may be used to determine actions to be performed based on a current state of the information handling system to cause decisions to maximize a particular reward. Policies may, for example, include a policy for extending battery runtime, an interactive fault tolerance policy, a battery service life policy to extend a battery service lifetime, an interactive fan adjustment policy, and other policies.

The agent202may take one or more actions (at) that impact the environment at time t. For example, the agent202may adjust one or more settings of the information handling system to adjust operation of the information handling system. For example, the agent202may take a series of actions based on a performance improvement policy to remove applications and/or processes operating in the background of an information handling system. The agent202may turn off non-essential hardware components based on a battery maximization policy. Agents may also be configured to record backup data and accept or reject driver updates. Actions may include adjustments to system hardware settings such as fan speed, screen brightness, speaker volume, and Bluetooth settings, adjustment to application settings, such as removing an application from automatic execution at startup and enabling or disabling notifications, and OS settings, such as adjustments to sizes and locations of page files and application or rejection of updates to the OS.

After adjusting settings, the agent202may monitor the environment204to determine if the adjustments improved performance (e.g., responsiveness, frame rate, lower processor utilization, more processor time available for a top-ranked process, lower processor temperature, longer battery life, etc.) of the information handling system. If the adjustments did improve performance, the agent202may calculate a reward, increasing prioritization of such adjustments in the future. If the adjustments reduced performance, the agent202may calculate a negative reward, deprioritizing such adjustments in the future. The reward may be a metric used to determine the success or failure of the agent's action. As one example, if the agent202reduces screen brightness to improve battery life and a user overrides the change to increase brightness, a calculated reward may be negative. For example, the reward rt+1may be calculated at time t+1 when a state st+1is observed in the environment204. Various factors can be considered in determining whether the adjustments enhanced performance of the information handling system, such as a user experience, performance statistics of the information handling system, security of the information handling system, and management of a health of the information handling system. The reinforcement learning algorithm may learn from user interactions with the information handling system and performance of the information handling system and may adapt continuously to a changing environment. Such operation may provide advantages over a supervised labelled data environment, such as reduced expense, applicability to specific problem areas, and enhanced responsiveness to user feedback. A reinforcement learning algorithm may be applied to an information handling system to determine processes that should be prioritized and settings adjustments that should be made based on the process prioritization.

An information handling system user environment300, shown inFIG. 3, may include multiple components. For example, the user environment300may include user-initiated applications304, which may each include multiple processes. The information handling system may, by default, prioritized user-initiated applications304and associated processes. The user environment300may also include other applications and processes306that were not user initiated. The user environment300may also include instrumentation302for measuring one or more parameters, such as internal performance statistics, associated with the information handling system and for monitoring user interaction308with the user environment300.

An information handling system may prioritize one or more processes being executed by the information handling system and may adjust one or more settings of the information handling system associated with the processes to optimize operation of the information handling system. The reinforcement learning system described with reference toFIG. 1,FIG. 2, andFIG. 3may be used in the training of a model for prioritization based on user persona as described in the embodiments below.

A method of prioritizing processes for execution on an information handling system may be performed based on a user persona that characterizes the manner in which the user uses the information handling system.FIG. 4is a flow chart illustrating an example method for adjusting operation of an information handling system based on a user persona with reinforcement learning according to some embodiments of the disclosure. A method400includes, at block402, determining a user persona for a user based on the user's usage of the information handling system. Examples of user personas include corridor warrior, light gamer, and immersive gamer. Next, one or more settings of the information handling system associated with executing one or more processes based, at least in part, on the expected future event and the user persona. For example, priority levels for processes executing on the information handling system may be determined based on the user persona. In one example application, a user persona of “immersive gamer” may result in prioritization of foreground gaming processes over other processes. As another example, a user persona of corridor warrior may result in reduction in prioritization of processes to reduce battery consumption. The prioritization may also be based on location context. For example, a corridor warrior in the office may have less reduction in prioritization based on the availability of a power supply than a corridor warrior in a conference room where no power supply is connected. At block406, the information handling system implements the prioritizations on the processes, such as by modifying parameters in an operating system executing on the information handling system.

Changes in performance of the information handling system in response to the user persona adjustments to prioritization may be used in a reinforced learning system to improve operation of the information handling system. For example, the method400may continue to block408with monitoring performance parameters for the information handling system after adjusting execution of the processes based on the user persona. Then, at block410, policies that are applied to adjust execution based on the user persona may be adjusted to reflect the success or failure at improving performance of the information handling system.

The prioritization of processes may be determined based on an optimization importance model that is based on the user persona class. That model may be based on the user persona and other factors, such as game context, location context, what processes are currently executing on the information handling system, and what future events are expected to occur on the information handling system.FIG. 5is a block diagram illustrating the application of a user persona to operation of an information handling system according to some embodiments of the disclosure. Telemetry collector502obtains telemetry data that includes information about how the user uses the information handling system and/or how other users use other information handling systems. The telemetry data is used to determine a user persona512for the user and a context514for the current use of the information handling system. The user persona512and context514are used to determine a base optimization importance516, informed by a persona experience importance model504. The model504may be generated based on the telemetry data from telemetry collector. In some embodiments, the model504is generated on a remote server and retrieved for storage on a local information handling system. The base optimization importance516may specify policies for operating the information handling system. For example, for an “immersive gamer” that executes full-screen resource-intensive applications the base optimization importance516may set a policy with a graphics quality importance of 60%, a framerate importance of 20%, and a network latency importance of 20%.

The optimization importance model510may also take into consideration other processes executing on the information handling system. For example, a resource KPI522may specify that a game is waiting to be updated in the background, and a KPI margin524may indicate the update is 800 MB is size. An expected future event526for the information handling system may indicate that a social gaming session will begin in approximately one hour. Blocks522,524, and526may feed into a persona effect importance model530that modifies a policy from the base optimization importance516. In the illustrated example, the modified policy may set an importance of graphics quality to 45%, a framerate importance to 15%, a network latency importance to 15%, and a background traffic importance to 25%. By increasing an importance of the background traffic importance relative to other processes, the update to the game is allowed to occur despite the importance to the immersive gamer of graphics quality because there is an expected future event requiring the game to be updated within one hour. The persona effect importance model530may be used to generate a dynamic user experience score532.

As processes and importance levels change on an information handling system, the person effect importance model may be updated by further modifying the policy of the base optimization importance516.FIG. 6is a block diagram illustrating a modification of a persona effect importance model according to some embodiments of the disclosure. The illustrated example ofFIG. 6is similar to that ofFIG. 5but fifteen minutes of time have elapsed, and 400 MB of the game update have been downloaded. The optimization importance model516may determine that the KPI margin624has decreased and there is sufficient time to complete the update before the expected future event. Thus, the persona effect importance model630may be set by modifying the base optimization importance516to have a lower background traffic importance than that shown inFIG. 5. For example, the persona effect importance model630may have a graphics quality importance of 55%, a framerate importance of 18%, a network latency importance of 18%, and a background traffic importance of 8%. In bothFIG. 5andFIG. 6, the game context indicated the game was non-competitive. However, if the game context was competitive, the optimization importance model516may prioritize the graphics quality over the background traffic because a competitive game is more important than an update for a social gaming session. The relative importance based on context may be set through the persona experience importance model504based on telemetry data collected from information handling systems indicating how much an importance level can be changed for a particular hardware configuration of an information handling system without affecting the graphics quality.

Another example of adjusting importance levels for processes on an information handling system based on user persona is shown inFIG. 7.FIG. 7is a block diagram illustrating another user persona class according to some embodiments of the disclosure. Another user persona712determined from the telemetry data may be a “corridor warrior,” referring to a user that works in an office but attends frequent meetings requiring short periods out of the office without an available external power source. An expected future event for such a user may indicate an amount of time until the information handling system is back at a desk for charging. For example, an expected future event726is charging of the information handling system at a desk in approximately one hour. Based on that event, a KPI margin724is set at 2 hours of runtime, indicating that the battery should last at least two hours to allow the information handling system to operate until returning to the desk with some safety margin. A base optimization importance716for a corridor warrior, determined from the model504, may be to set an importance level of runtime duration at 90% and an importance level for performance at 10%, thus indicating a high preference for operating the information handling system to ensure a desired runtime duration (in this example 2 hours). Based on determined KPI margin724, the information handling system may determine that no modification to the base optimization importance716is needed because the hardware's available battery reserve is sufficient to ensure the desired runtime duration. Thus, the persona effect importance model730is equal to the base optimization importance716.

A user belonging to a persona of mobile productivity workers (a “Corridor Warrior”), such as demonstrated inFIG. 7, has a baseline KPI importance matrix that is weighted towards fast battery charging, responsive compute tuning, low skin temperatures, communication-optimized networking, and standard office display brightness settings. Additionally, their system state shows medium relative state of charge (RSoC) and absolute state of charge (ASoC) battery levels, communication activity, and office ambient light. The system context further details that the user is in their nominal use environment (a corporate office) and has a 200% battery runtime margin until the next expected charging time (based on history and calendar events). The system is prioritizing a battery runtime optimization policy. Then, the user starts up a moderate utilization program and shares the window via their communication session. The system now has an option to start dynamically optimizing the application by allocating more thermal headroom and reducing runtime optimization levels due to the surplus in expected runtime before the next expected charge event. Based on the importance of communication traffic to the user and the context of the other system resources, the system balances the performance optimizations for the communication activity, stepping the runtime optimization policy down, resulting in a half hour loss in expected runtime but still maintaining margin to the next expected charge event.

FIG. 8is a block diagram illustrating a generation of a policy score according to embodiments of the disclosure. Several factors may be used to determine an optimal policy for implementation on an information handling system when multiple policies are available as candidates. Policy scores802may be generated corresponding to a set of available policies810. The scores may be based on a current system state814, forecasted events812, optimization importance levels816, and a user experience scorecard832. Each of the policy scores802may be weighted and used to arrive at an optimal policy804, in which “optimal” refers to a policy with the best usage of system resources in view of the information available at the time regardless of whether other policies may perform better based on unforeseen circumstances. The determined optimal policy may be provided as feedback to the available policies810. The available policies810may be adjusted over time as the feedback system trains using additional circumstances.

FIG. 9is a block diagram illustrating a process for reinforcement learning of policies according to some embodiments of the disclosure. A dataset of historical states for information handling systems904is generated over time from a current system state908. The historical state dataset904is used in applying feedback to policies at block902, in combination with determined optimal policies906. The feedback902is applied, such as through periodic updates, to optimization states914. The optimal policy906may be determined for a current system state908by examining available optimizations920and selecting particular optimization combinations912. The state impact914of those optimization combinations912in view of forecasted events922is determined, and a raw user experience score impacts916calculated. The raw user experience score impacts916may be importance-weighted at block918, such as based on an optimization importance model910that includes a user persona, such as that shown inFIG. 5.

The experience score reflects experiences that the user prefers more when there are multiple experiences that could be optimized. For example, when there is a trade-off between higher performance with longer battery life, a user may prefer high performance given current and predicted tasks. The user experience score may rank performance over long battery runtime. The Reinforcement Learning (RL) framework described above, such as with respect toFIG. 1,FIG. 2, and/orFIG. 3, may generate a candidate score based on how the user is using the information handling system and the choices made by the user over time. After an initial period of learning, the reinforcement learning system may begin making choices for the user. If the choices are incorrect then the reinforcement learning penalizes the choices. In some embodiments, the reinforcement learning may be trained externally on a simulator to initialize a default experience scoring policy. The user experience score918may be a numerical output of the combination of candidate policies912, resulting system state impact projections914(e.g., incorporating impacts916forecasted event likelihoods912), weighted by optimization importance models910(such as calculated at block730). The user experience score may be used as a metric used for determining the optimal policy at each decision point.

An example of the operation inFIG. 9for one example case is shown inFIG. 10.FIG. 10is a block diagram illustrating an example determination of a policy according to some embodiments of the disclosure. A current system state1008indicates a 60% RSOC battery level. Available optimizations1020include runtime duration and application performance, and combinations1012of those optimizations1020include maximizing runtime or maximizing application performance. A forecasted event1022indicates a 90% confidence in access to a charging power supply in one hour. The impacts to runtime and performance are determined for the combinations1012to determine that maximizing runtime would provide a 2-hour margin in runtime and that maximizing performance would increase application performance by 25% at the cost of only having a 1-hour margin in runtime. The impacts1014are used to determine raw scores1016. For example, the maximum performance optimization combination may receive a score of −10 indicating a 10% chance of empty battery (which is a −100 score) with a score of +15 for the increase in application performance. As another example, the maximum runtime optimization combination may receive a score of zero, which includes a zero for runtime score for having no likelihood of emptying the battery and a zero-performance score for not increasing performance of the applications. The raw scores1016may be weighted1018based on an optimization importance model1010. For example, the model1010may specify a runtime importance of 10% and a performance importance of 90%, such that the −10-runtime penalty is weighted to a −1-runtime penalty and such that the +15-performance increase is weighted to a +13.5 performance increase. An optimal policy1006may be determined that selects a maximum application performance.

Another example of the reinforcement learning process is shown inFIG. 11.FIG. 11is a block diagram illustrating a process for reinforcement learning of policies according to some embodiments of the disclosure. A user calendar1104may be used to forecast events1106, along with telemetry data from telemetry collector1102. The telemetry data may also be used to determine an optimization importance model1104, such as described above with reference toFIG. 5. The telemetry data may further be used for optimization and reinforcement in a dynamic user experience scoring process1110. The telemetry data may be used to project hardware change impacts, and how those changes positively/negatively change the user experience. The telemetry data may also be used to evaluate policy predictions against user actions to determine policy accuracy. An importance of various changes may be determined from the policy accuracy and the projected hardware change impacts to generate a user sentiment score indicating whether the user was satisfied with the performance of the information handling system. The sentiment score may be used for positive or negative reinforcement of an available set of policies, and those policies adjusted based on the reinforcement. In some embodiments, a mock application1140of the policies may be used to evaluate policies and used alone or in combination with real-world application of the policies for reinforcement learning.