[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120354-en":3,"doc-seo-120354-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},120354,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",6,"Technology","DYNAMIC USER PERSONALIZATION USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING - Abstract","This document describes a framework for a personalization engine that suggests custom configuration parameters for each user by creating a “digital twin” of the user. It uses artificial intelligence and/or machine learning to predict preferred user experience and probable user behavior, while observing actual behavior. The model self-improves by comparing predicted outputs to ground truth behavior, trained on preferences, historical actions, and digital activity, then delivers real-time tailored recommendations and scalable, coordinated personalization across applications.","Technical Disclosure Commons  \nDefensive Publications Series  \n29 May 2025  \nDYNAMIC USER PERSONALIZATION USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING  \nRoger William Graves  \nFollow this and additional works at: [https://www.tdcommons.org/dpubs_series](https://www.tdcommons.org/dpubs_series)  \nRecommended Citation  \nGraves, Roger William, \"DYNAMIC USER PERSONALIZATION USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING\", Technical Disclosure Commons,(May 29, 2025)  \n[https://www.tdcommons.org/dpubs_series/8167](https://www.tdcommons.org/dpubs_series/8167)  \nThis work is licensed under a Creative Commons Attribution 4.0 License.  \nThis Article is brought to you for free and open access by Technical Disclosure Commons. It has been accepted for inclusion in Defensive Publications Series by an authorized administrator of Technical Disclosure Commons.  \nDYNAMIC USER PERSONALIZATION USING ARTIFICIAL INTELLIGENCE AND  \nMACHINE LEARNING  \nABSTRACT  \nThis document describes a framework for a personalization engine to suggest custom configuration parameters to each user, by creating a ‘digital twin’ of the user. The proposed  \nframework implements artificial intelligence and/or machine learning principles to predict the  \npreferred user experience and probable behavior of the user and observe actual behavior. The  \nmodel is designed to be self-improving by comparing the model output (predicted behavior) to  \nground-truth (actual behavior). As the machine learning model is trained on the user’s  \npreferences, historical behavior and digital actions, the model then provides real-time, custom  \ntailored personalization suggestions to the user. The model is either cached and pre-executed or  \nruns continuously at super-human speeds. The unified system can be efficiently scaled and  \ncoordinated, increasing the integration of user personalization across many applications. In some  \ninstances, the model assists in cooperative multitasking, which involves continuing one task  \nacross multiple applications. The model may also create and provide options and decision  \nsupport based on user activity and preferences in all applications from which it receives data.  \nDESCRIPTION  \nA computing device, such as shown in the example Figure 1 below, may include devices such as a smartphone, smart watch, laptop, fitness tracker, personal computer, etc. The disclosed techniques are envisioned to support personalization that is coherent and continuous across these various types of devices for a single user, understanding context that spans multiple devices. A  \nPublished by Technical Disclosure Commons, 2025 2  \ncomputing device will usually have applications ([e.g. email](e.g. email), messaging apps, games, web  \nbrowsers, etc.) installed. These applications may collect data authorized by the user and have  \npersonalization settings (e.g., themes, appearance, graphics, etc.) available for the user to  \nconfigure. Typically, a computing device and the applications therein will be connected to a  \ncloud system such that information (e.g., messages, notifications, system data, application data,  \netc.) is sent between the computing device and the cloud system.  \nHowever, each application often has its own set of personalization settings available that  \nthe user must individually configure, and the user must often navigate between separate  \napplications in order to achieve their goals. Separate applications often necessitate separate  \nworkflows and repetitive tasks even if the user is attempting to achieve similar goals using the  \ndevice. Furthermore, the cloud system each application connects to during routine operations  \n(e.g. performing updates, sending and receiving messages, online gaming, etc.) may be different than the cloud system other applications connect to during routine operations. This may lead to a  \nscenario where the user’s personalization settings and activities across applications are  \nuncoordinated, and when a new application i","cbCaibyLfeNI49Jl","https://ap.wps.com/l/cbCaibyLfeNI49Jl","pdf",243286,2,1,14,"English","en",105,"# Abstract\n# Description\n## Digital twin based personalization framework\n## Multi-application and cloud-coordinated settings","[{\"question\":\"How does the framework personalize configurations for each user?\",\"answer\":\"It creates a digital twin of the user and uses AI/ML to suggest custom configuration parameters based on predicted preferred experience and probable behavior.\"},{\"question\":\"How does the personalization model improve over time?\",\"answer\":\"It self-improves by comparing the model output (predicted behavior) against ground truth (actual behavior) and retraining on user preferences and historical actions.\"},{\"question\":\"How does the system handle personalization across multiple applications and devices?\",\"answer\":\"It coordinates personalization through a cloud-connected approach that can scale across many applications, supporting coherent and continuous personalization across devices for a single user.\"}]","DYNAMIC USER PERSONALIZATION USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING - 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