US-20260127445-A1 - AI-DRIVEN ENERGY OPTIMIZATION ARCHITECTURE

Abstract

Methods and systems for intelligently managing user interactions and experiences with content delivery networks (CDNs)to promote energy efficiency, user privacy, and security are provided. A CDN collects data relating to one or more user’s interactions with an application. Machine learning (M/L) models analyze and train on the usage data to predict user behavior patterns, application performance trends and device metrics to reduce energy consumption. The predicted outputs may be used to generate a real-time adaptive user interaction policy configured to enable proactive system energy consumption savings and optimizations.

Inventors

• Mahuya Ghosh

Assignees

• DELL PRODUCTS L.P.

Dates

Publication Date

20260507

Application Date

20241107

Claims (20)

1. 1 . A method comprising: training a plurality of machine learning (M/L) models to predict one or more energy-saving policies using a modeling dataset comprising a plurality of training samples, each training sample generated from a corpus of historical interaction data, each training sample of the plurality of training samples to adjust weights in the plurality of M/L models, wherein training the plurality of M/L models includes inputting different portions of the modeling dataset and comparing predictions of customer actions with target values of the training samples to adjust weights in the plurality of M/L models; receiving usage data from an application; generating a plurality of feature vectors from the usage data; inputting one or more of the plurality of feature vectors into one of the plurality of M/L models to predict one or more energy-saving policies; and generating a user interaction policy based on the one or more energy-saving policies.
2. 2 . The method of claim 1 wherein the plurality of M/L models includes at least a federated learning model and an unsupervised learning model.
3. 3 . The method of claim 1 wherein the plurality of M/L models includes at least, a hybrid federated learning model, a gradient boosting machine (GBM) model, a meta-learning model, a convolutional neural network (CNN), a recurrent neural network (RNN), a Differential Privacy model, and Secure Multi-party Computation model.
4. 4 . The method of claim 1 wherein the usage data includes at least one of user interaction data, contextual data, and system data.
5. 5 . The method of claim 4 wherein the user interaction data includes at least one of device usage metrics, content preferences, and user feedback.
6. 6 . The method of claim 4 wherein the contextual data includes at least one of an environmental condition and a temporal dynamic.
7. 7 . The method of claim 4 wherein the system data includes multi-device synchronization data.
8. 8 . The method of claim 1 wherein the one or more energy-saving policies include one or more of a display and interface adjustment, a content delivery optimization, a data management and processing optimization, a device and system level optimization, a services and integration optimization.
9. 9 . The method of claim 1 further comprising generating an energy savings report and predictive alerts based on the user interaction policy.
10. 10 . A system comprising: a memory; and at least one processor that is operatively coupled to the memory, the at least one processor being configured to perform the operations of: training a plurality of machine learning (M/L) models to predict one or more energy-saving policies using a modeling dataset comprising a plurality of training samples, each training sample generated from a corpus of historical interaction data, each training sample of the plurality of training samples to adjust weights in the plurality of M/L models, wherein training the plurality of M/L models includes inputting different portions of the modeling dataset and comparing predictions of customer actions with target values of the training samples to adjust weights in the plurality of M/L models; receiving usage data from an application; generating a plurality of feature vectors from the usage data; inputting one or more of the plurality of feature vectors into one of the plurality of M/L models to predict one or more energy-saving policies; and generating a user interaction policy based on the one or more energy-saving policies.
11. 11 . The system of claim 10 wherein the plurality of M/L models includes at least a federated learning model and an unsupervised learning model.
12. 12 . The system of claim 10 wherein the plurality of M/L models includes at least, a hybrid federated learning model, a gradient boosting machine (GBM) model, a meta-learning model, a convolutional neural network (CNN), a recurrent neural network (RNN), a Differential Privacy model, and Secure Multi-party Computation model.
13. 13 . The system of claim 10 wherein the usage data includes at least one of user interaction data, contextual data, and system data.
14. 14 . The system of claim 13 wherein the user interaction data includes at least one of device usage metrics, content preferences, and user feedback.
15. 15 . The system of claim 13 wherein the contextual data includes at least one of an environmental condition and a temporal dynamic.
16. 16 . The system of claim 13 wherein the system data includes multi-device synchronization data.
17. 17 . The system of claim 10 wherein the one or more energy-saving policies include one or more of a display and interface adjustment, a content delivery optimization, a data management and processing optimization, a device and system level optimization, a services and integration optimization.
18. 18 . The system of claim 10 further comprising generating an energy savings report and predictive alerts based on the user interaction policy.
19. 19 . A non-transitory computer-readable medium storing one or more processor-executable instructions, which when executed by at least one processor cause the at least one processor to perform the operations of: training a plurality of machine learning (M/L) models to predict one or more energy-saving policies using a modeling dataset comprising a plurality of training samples, each training sample generated from a corpus of historical interaction data, each training sample of the plurality of training samples to adjust weights in the plurality of M/L models, wherein training the plurality of M/L models includes inputting different portions of the modeling dataset and comparing predictions of customer actions with target values of the training samples to adjust weights in the plurality of M/L models; receiving usage data from an application; generating a plurality of feature vectors from the usage data; inputting one or more of the plurality of feature vectors into one of the plurality of M/L models to predict one or more energy-saving policies; and generating a user interaction policy based on the one or more energy-saving policies.
20. 20 . The non-transitory computer-readable medium of claim 19 wherein the plurality of M/L models includes at least, a hybrid federated learning model, a gradient boosting machine (GBM) model, a meta-learning model, a convolutional neural network (CNN), a recurrent neural network (RNN), a Differential Privacy model, and Secure Multi-party Computation model.

Description

BACKGROUND The proliferation of the Internet and connected computing networks has greatly expanded the amount of data produced and consumed through various online platforms and services, including business-to-business (B2B) and business-to-consumer (B2C) environments. The increased presence and activity of online services significantly influences energy consumption demands and patterns, leading to a larger carbon footprint associated with such digital activities. While enhancing user experience through performance and personalization has been an industry focus, few efforts have been made to address the environmental consequences of such practices. Further, the centralization of data and user information, for the sake of efficiency and convenience, raises privacy and security concerns. There is a lack in the pursuit of sustainable digital service design and delivery that maintains user privacy and security. SUMMARY This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. According to one aspect, a method may include training a plurality of machine learning (M/L) models to predict one or more energy-saving policies using a modeling dataset comprising a plurality of training samples. Each training sample may be generated from a corpus of historical interaction data. Each training sample of the plurality of training samples may adjust weights in the plurality of M/L models. Training the plurality of M/L models may include inputting different portions of the training dataset and comparing predictions of customer actions with target values of the training samples to adjust weights in the plurality of M/L models. Usage data may be received from an application. A plurality of feature vectors may be generated from the usage data. One or more of the plurality of feature vectors may be input into one of the plurality of M/L models to predict one or more energy-saving policies. A user interaction policy may be generated based on the one or more energy-saving policies. The method may include, alone or in combination, one or more of the following features. The plurality of M/L models may include at least a federated learning model and an unsupervised learning model. The plurality of M/L models may include at least, a hybrid federated learning model, a gradient boosting machine (GBM) model, a meta-learning model, a convolutional neural network (CNN), a recurrent neural network (RNN), a Differential Privacy model, and a Secure Multi-party computation model. The usage data may include at least one of user interaction data, contextual data, and system data. The user interaction data may include at least one of device usage metrics, content preferences, and user feedback. The contextual data may include at least one of an environmental condition and a temporal dynamic. The system data may include multi-device synchronization data. The one or more energy-saving policies may include one or more of a display and interface adjustment, a content delivery optimization, a data management and processing optimization, a device and system level optimization, a services and integration optimization. An energy savings report may be generated based on the user interaction policy. According to another aspect, a system may include a memory and at least one processor that is operatively coupled to the memory. The at least one processor may be configured to perform the operations of training a plurality of M/L models to predict one or more energy-saving policies using a modeling dataset comprising a plurality of training samples. Each training sample may be generated from a corpus of historical interaction data. Each training sample of the plurality of training samples may adjust weights in the plurality of M/L models. Training the plurality of M/L models may include inputting different portions of the training dataset and comparing predictions of customer actions with target values of the training samples to adjust weights in the plurality of M/L models. Usage data may be received from an application. A plurality of feature vectors may be generated from the usage data. One or more of the plurality of feature vectors may be input into one of the plurality of M/L models to predict one or more energy-saving policies. A user interaction policy may be generated based on the one or more energy-saving policies. The system may include, alone or in combination, one or more of the following features. The plurality of M/L models may include at least a federated learning model and an unsupervised learning model. The plurality of M/L models may include at least, a hybrid federated learning model, a gradient boosting machine (GBM) model, a meta-learning model, a convolutional neural ne