Methods executed by computers, computer systems, computer programs (updating object model data of interface elements based on emission scores)

Abstract

Updating the object model data of interface elements based on emission scores. [Solution] Includes receiving first interaction data associated with an interaction session between one or more interface elements of an operation of a specific user and application. Based on the interaction data, a user emission score associated with the interaction session is generated. Based on the user emission score and cluster data, a first cluster is identified for a specific user. A cluster emission score associated with the first cluster is generated based on the user emission score and the cluster data associated with the first cluster. Object model data associated with each interface element is received. The object model data associated with the interface elements is updated and output.

Description

Technical Field

[0001] The present disclosure relates to updating object model data of interface elements, and more specifically, to updating object model data of interface elements based on a user's emission score.

[0002] As awareness of climate change increases, there is growing interest in tools that assist individuals and organizations in monitoring and reducing their carbon footprint. While much attention has been focused on emissions from transportation and industrial processes, carbon emissions generated by digital activities, particularly application usage, have remained relatively unexamined.

Summary of the Invention

Problems to be Solved by the Invention

[0003] As more users come to utilize digital platforms for various tasks, the energy consumption associated with these applications has been significantly increasing. This energy usage is mainly caused by data processing and cloud services, and in particular, the use of non-renewable power sources for power supply to data centers contributes to carbon emissions.

Means for Solving the Problems

[0004] This disclosure describes a computer implementation method for updating object model data of interface elements based on emission scores, according to one embodiment of this disclosure. The computer implementation method includes the computer receiving first interaction data associated with an interaction session between a particular user and one or more interface elements of an application operation. The computer implementation method further includes the computer generating a user emission score associated with the interaction session of the particular user based on the first interaction data. The computer implementation method further includes the computer identifying a first cluster from a plurality of clusters for a particular user based on the user emission score and cluster data. The cluster data is associated with each cluster of the plurality of clusters. Furthermore, the computer implementation method includes the computer generating a cluster emission score associated with the first cluster based on the user emission score and the cluster data associated with the first cluster. The computer implementation method further includes the computer receiving object model data associated with each interface element of one or more interface elements. Furthermore, the computer implementation method includes the computer updating object model data associated with at least one interface element of one or more interface elements based on the cluster emission score. The computer implementation method further includes the computer outputting the updated object model data.

[0005] A computer system for updating object model data of interface elements based on emission scores is disclosed according to one or more embodiments. The computer system includes a set of processors, one or more computer-readable storage media, and program instructions stored on the one or more computer-readable storage media. The program instructions are executable by the set of processors and cause the set of processors to receive first interaction data associated with an interaction session between a particular user and one or more interface elements of an application operation. The program instructions cause the set of processors to generate a user emission score associated with the interaction session of a particular user based on the first interaction data. The program instructions cause the set of processors to identify a first cluster from a plurality of clusters for a particular user based on the user emission score and cluster data. The cluster data is associated with each of the plurality of clusters. The program instructions cause the set of processors to generate a cluster emission score associated with the first cluster based on the user emission score and the cluster data associated with the first cluster. The program instructions cause the set of processors to receive object model data associated with each of the one or more interface elements. The program instructions cause the processor set to update object model data associated with at least one of one or more interface elements based on the cluster emission score. The program instructions cause the processor set to render at least one of the one or more interface elements on the user device based on the updated object model data. At least one interface element is rendered concurrently with the interaction session.

[0006] A computer program product for updating object model data of interface elements based on emission scores is disclosed according to one or more embodiments of this disclosure. The computer program product includes one or more computer-readable storage media and program instructions stored on the one or more computer-readable storage media, and performs an operation. The operation includes receiving interaction data associated with an interaction session between a particular user and one or more interface elements of an application operation. The operation further includes generating a user emission score associated with the interaction session of a particular user based on the first interaction data. The operation further includes identifying a first cluster from a plurality of clusters for a particular user based on the user emission score and cluster data. The cluster data is associated with each cluster of the plurality of clusters. The operation further includes generating a cluster emission score associated with the first cluster based on the user emission score and cluster data associated with the first cluster. The operation further includes receiving object model data associated with each interface element of one or more interface elements. Furthermore, the operation further includes updating the object model data associated with at least one interface element of one or more interface elements based on the cluster emission score. The operation further includes outputting updated object model data. [Brief explanation of the drawing]

[0007] The following description provides details of preferred embodiments with reference to the drawings below.

[0008] [Figure 1] This figure shows a computing environment for updating object model data of interface elements based on emissions scores, in accordance with one embodiment of the present disclosure.

[0009] [Figure 2] This figure illustrates, in accordance with one embodiment of the present disclosure, an environment in which a system is implemented to update the object model data of interface elements based on emission scores.

[0010] [Figure 3] This figure shows one or more operations performed by a system for updating object model data based on emissions scores, in accordance with one embodiment of the present disclosure.

[0011] [Figure 4] This figure shows a flowchart illustrating a method for updating object model data associated with at least one interface element, in accordance with one embodiment of the present disclosure.

[0012] [Figure 5A] This diagram collectively shows flowcharts illustrating the reassignment of a specific user from one cluster to an updated cluster, in accordance with one embodiment of this disclosure. [Figure 5B] This diagram collectively shows flowcharts illustrating the reassignment of a specific user from one cluster to an updated cluster, in accordance with one embodiment of this disclosure.

[0013] [Figure 6] This figure shows a flowchart associated with training a first AI model to make specific users a group in a first cluster, in accordance with one embodiment of the present disclosure.

[0014] [Figure 7] This figure shows a comparison of a user emission score and several thresholds for determining a cluster of specific users, in accordance with one embodiment of the present disclosure.

[0015] [Figure 8]A flowchart illustrating an exemplary method for updating object model data of an interface element based on an emission score, in accordance with one embodiment of the present disclosure, is provided. [Modes for carrying out the invention]

[0016] In today's digital environment, users are often unaware of the environmental impact associated with their online activities, such as when using administrative applications, e-commerce applications, and social media applications. When users interact with various interface elements, such as images, videos, buttons, and filters, they unconsciously contribute to carbon emissions through data consumption and energy use. This lack of awareness can hinder efforts to promote sustainable practices and make informed choices, leading to an increase in the carbon footprint associated with everyday digital interactions.

[0017] Furthermore, existing applications may be unable to dynamically evaluate user behavior, resulting in a uniform approach that does not consider individual emission patterns. Without personalized insights, users may continue to engage with high-emission content, such as high-quality videos or complex user interfaces, without recognizing the cumulative impact of their actions. This creates a gap in user engagement and sustainable behavior because users are not provided with real-time feedback to make eco-friendly choices.

[0018] The disclosed system addresses these challenges by tracking user interactions within an application and evaluating associated carbon emissions. By collecting interaction data and generating user emission scores, the system effectively quantifies the environmental impact of individual user sessions. This enables the application to tailor its responses and recommendations to specific user behaviors by allowing the identification of user clusters based on emission scores.

[0019] By updating the Document Object Model (DOM) associated with interface elements based on the user cluster's emission score, the disclosed system not only enhances user awareness but also encourages more sustainable practices. For example, if a user contributes to high emissions through their interaction, the application may modify visual elements to highlight eco-friendly options or reduce data consumption. This adaptive approach not only promotes responsible user behavior but also fosters a culture of sustainability within digital platforms, ultimately reducing the overall carbon footprint associated with online activities.

[0020] A computer-implemented method for updating object model data of an interface element based on a user's emission score will be described according to an embodiment of the present disclosure. The computer-implemented method includes receiving, by a computer, interaction data associated with an interaction session between a particular user and one or more interface elements of an application operation. Further, the computer-implemented method includes generating, by the computer, a user emission score associated with the interaction session of the particular user based on the first interaction data. The computer-implemented method further includes identifying, by the computer, a first cluster from a plurality of clusters for a particular user based on the user emission score and cluster data. The cluster data is associated with each of the plurality of clusters. The computer-implemented method further includes generating, by the computer, a cluster emission score associated with the first cluster based on the user emission score and the cluster data associated with the first cluster. The computer-implemented method further includes receiving, by the computer, object model data associated with each of the one or more interface elements of the one or more interface elements. Further, the computer-implemented method includes updating, by the computer, the object model data associated with at least one of the one or more interface elements based on the cluster emission score. The computer-implemented method further includes outputting, by the computer, the updated object model data.

[0021] In various embodiments of the present disclosure, the computer-implemented method further includes a computer comparing a user emission score with a plurality of threshold values. Each of the plurality of threshold values corresponds to a respective one of a plurality of clusters. Further, the computer-implemented method includes a computer determining whether the user emission score is equal to or greater than a first threshold value of the plurality of threshold values. The first threshold value is associated with a first cluster. Further, the computer-implemented method includes a computer identifying the first cluster from the plurality of clusters for a particular user based on the determination.

[0022] In the disclosure of various embodiments, the computer-implemented method further includes a computer rendering at least one of one or more interface elements on a user device based on updated object model data. Also, the computer-implemented method further includes a computer performing an operation based on the rendering.

[0023] In various embodiments of the present disclosure, the computer-implemented method further includes a computer rendering at least one interface element concurrently with an interaction session based on updated object model data.

[0024] In various embodiments of this disclosure, the computer implementation method further includes the computer obtaining second interaction data associated with an interaction session. The second interaction data is associated with an interaction between a user and at least one interface element. The at least one interface element is updated based on updated object model data. The computer implementation method further includes the computer dynamically updating a user emission score based on the second interaction data. The computer implementation method further includes the computer determining whether the updated user emission score is equal to or greater than a second threshold. The second threshold is associated with a second cluster of a plurality of clusters. The computer implementation method further includes, based on the determination, the computer reassigning a particular user from a first cluster to a second cluster of the plurality of clusters.

[0025] In various embodiments of this disclosure, the computer implementation method further includes updating the user interface associated with the operation of the application by the computer. The user interface update is performed based on updated object model data. The computer implementation method further includes rendering the updated user interface by the computer.

[0026] In various embodiments of the present disclosure, the computer implementation method further includes the computer receiving emission value data associated with each interface element of one or more interface elements. The computer implementation method further includes the computer generating a user emission score based on the emission value data and first interaction data.

[0027] In various embodiments of the present disclosure, the computer implementation method further includes the computer determining, based on first interaction data and emission value data, an element emission score associated with each interface element of one or more interface elements. The computer implementation method further includes the computer identifying, based on the element emission score, at least one interface element of one or more interface elements. The element emission score of at least one interface element is greater than an emission threshold. The computer implementation method further includes the computer updating, based on cluster data associated with a first cluster, object model data associated with the identified at least one interface element.

[0028] In various embodiments of this disclosure, a first size associated with the updated object model data of at least one interface element is smaller than a second size associated with the object model data of at least one interface element.

[0029] In various embodiments of this disclosure, the operation of an application is associated with a plurality of interface elements. A user interacts with one or more interface elements of the plurality of interface elements during an interaction session. The computer implementation method further includes the computer receiving user input from the user that is associated with the plurality of interface elements. The computer implementation method further includes the computer updating object model data associated with at least one interface element of the one or more interface elements based on the user input.

[0030] In various embodiments of this disclosure, the first cluster includes multiple users performing application operations. The multiple users exclude certain users. The computer implementation method further includes the computer generating a cluster emission score associated with the first cluster based on user emission scores and the multiple user emission scores. The cluster data includes the multiple user emission scores associated with the multiple users.

[0031] In various embodiments of this disclosure, the computer implementation method further includes training a first artificial intelligence (AI) model on historical interaction data by a computer. The historical interaction data is associated with multiple training interaction sessions between multiple training users and application operations. The computer implementation method further includes applying the trained first AI model to user emission scores and cluster data by a computer. Furthermore, the computer implementation method includes identifying a first cluster from multiple clusters for a particular user based on the application by a computer. The computer implementation method further includes grouping a particular user within the first cluster based on the identification by a computer.

[0032] A computer system for updating object model data of interface elements based on emission scores is disclosed according to one or more embodiments of the present disclosure. The computer system includes a set of processors, one or more computer-readable storage media, and program instructions stored in the one or more computer-readable storage media, the program instructions being executable by the set of processors, which cause the set of processors to receive interaction data associated with an interaction session between a particular user and one or more interface elements of an application's operation. The program instructions cause the set of processors to generate a user emission score associated with the interaction session of a particular user, based on the first interaction data. The program instructions cause the set of processors to identify a first cluster from a plurality of clusters for a particular user, based on the user emission score and cluster data. The cluster data is associated with each of the plurality of clusters. The program instructions cause the set of processors to generate a cluster emission score associated with the first cluster, based on the user emission score and the cluster data associated with the first cluster. The program instructions cause the set of processors to receive object model data associated with each of the one or more interface elements. The program instructions cause the processor set to update the object model data associated with at least one of the one or more interface elements based on the cluster emission score. The program instructions cause the processor set to render at least one of the one or more interface elements on the user device based on the updated object model data. At least one interface element is rendered concurrently with the interaction session.

[0033] In various embodiments of this disclosure, a program instruction causes a set of processors to compare a user emission score with a set of thresholds, each of which corresponds to a cluster of a set of clusters. The program instruction causes the set of processors to determine whether the user emission score is equal to or greater than a first threshold of the set of thresholds, the first threshold being associated with a first cluster. Based on the determination, the program instruction causes the set of processors to identify a first cluster from the set of clusters for a particular user.

[0034] In various embodiments of this disclosure, a program instruction causes a set of processors to acquire second interaction data associated with an interaction session. The second interaction data is associated with an interaction with a user and at least one interface element. The at least one interface element is updated based on updated object model data. The program instruction causes the set of processors to dynamically update a user emission score based on the second interaction data. The program instruction causes the set of processors to determine whether the updated user emission score is equal to or greater than a second threshold. The second threshold is associated with a second cluster of a plurality of clusters. Based on the determination, the program instruction causes the set of processors to reassign a particular user from a first cluster to a second cluster of the plurality of clusters.

[0035] In various embodiments of this disclosure, program instructions cause a set of processors to update the user interface associated with the operation of the application. The user interface update is performed based on updated object model data. The program instructions cause the set of processors to render the updated user interface on the user device.

[0036] In various embodiments of the disclosure, a program instruction causes a processor set to receive emission value data associated with each interface element of one or more interface elements. The program instruction causes the processor set to generate a user emission score based on the emission value data and interaction data.

[0037] In various embodiments of this disclosure, a program instruction causes a set of processors to determine the element emission score associated with each of one or more interface elements based on first interaction data and emission value data. The program instruction causes the set of processors to identify at least one of the one or more interface elements based on the element emission score. The element emission score of at least one interface element is greater than the emission threshold. The program instruction causes the set of processors to update the object model data associated with the identified at least one interface element based on cluster data associated with a first cluster.

[0038] A computer program product is disclosed for updating object model data of interface elements based on emission scores, according to one or more embodiments of this disclosure. The computer program product includes one or more computer-readable storage media and program instructions stored on one or more computer-readable storage media, which perform an operation. The operation includes receiving interaction data associated with an interaction session between a particular user and one or more interface elements of an application operation. The operation further includes generating a user emission score associated with the interaction session of a particular user based on the first interaction data. The operation further includes identifying a first cluster from a plurality of clusters for a particular user based on the user emission score and cluster data. The cluster data is associated with each cluster of the plurality of clusters. The operation further includes generating a cluster emission score associated with the first cluster based on the user emission score and the cluster data associated with the first cluster. The operation further includes receiving object model data associated with each interface element of one or more interface elements. The operation further includes updating object model data associated with at least one interface element of one or more interface elements based on the cluster emission score. The operation further includes outputting updated object model data.

[0039] In various embodiments of this disclosure, the operation further includes rendering at least one interface element of one or more interface elements on the user device concurrently with the interaction session, based on updated object model data. The operation further includes performing an operation based on the rendering.

[0040] Various aspects of this disclosure are described by explanatory text, flowcharts, block diagrams of computer systems, and / or block diagrams of mechanical logic included in embodiments of computer program products (CPPs). With respect to any flowchart, operations may be performed in a different order than those shown in a given flowchart, depending on the technology involved. For example, again, depending on the technology involved, two operations shown in consecutive flowchart blocks may be performed in reverse order, as a single integrated step, simultaneously, or with at least partial time overlap.

[0041] Embodiments of a computer program product ("CPP Embodiment" or "CPP") are terms used in this disclosure to describe any set of one or more storage media ("mediums") that are collectively comprised of one or more storage devices that collectively contain machine-readable code corresponding to instructions and / or data for performing computer operations specified in a given CPP claim. A "storage device" is any tangible device capable of holding and storing instructions for use by a computer processor. Computer-readable storage media may be, but are not limited to, electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, mechanical storage media, or any suitable combination thereof. Some known types of storage devices, including these media, include diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanically encoded devices (such as pits / lands formed on the main surface of a punch card or disk), or any suitable combination of the foregoing. The term “computer-readable storage medium” as used in this disclosure is not to be interpreted as storage in the form of transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides, optical pulses passing through optical fiber cables, electrical signals communicated through wires and / or other transmission media.As those skilled in the art will understand, data is typically moved at several intermittent points during the normal operation of a storage device, such as during access, defragmentation, or garbage collection; however, data is not transient while it is stored, and therefore the storage device is not transient.

[0042] Figure 1 shows a computing environment for updating object model data based on a user's emission score, according to one embodiment of the present disclosure. Referring to Figure 1, a computing environment 100 is shown, which includes an example of an environment for executing at least a portion of the computer code involved in performing the method, such as the object model data update module 120B. In addition to the object model data update module 120B, the computing environment 100 includes, for example, a computer 102, a wide area network (WAN) 104, an end-user device (EUD) 106, a remote server 108, a public cloud 110, and a private cloud 112. In this embodiment of the Disclosure, the computer 102 includes a processor set 114 (including processing circuits 114A and cache 114B), a communication fabric 116, volatile memory 118, persistent storage 120 (including an operating system 120A and object model data update module 120B as identified above), a peripheral device set 122 (including a user interface (UI) device set 122A, storage 122B, and an Internet of Things (IoT) sensor set 122C), and a network module 124. The remote server 108 includes a remote database 108A. The public cloud 110 includes a gateway 110A, a cloud orchestration module 110B, a host physical machine set 110C, a virtual machine set 110D, and a container set 110E.

[0043] Computer 102 may take the form of a desktop computer, laptop computer, tablet computer, smartphone, smartwatch or other wearable computer, mainframe computer, quantum computer, or any other form of computer or mobile device currently known or to be developed in the future that has the ability to run programs and access a network or query a database such as remote database 108A. As is well understood in the field of computer technology, depending on the technology, the execution of a computer implementation may be distributed across multiple computers and / or multiple locations. On the other hand, in this presentation of computing environment 100, in order to keep the presentation as simple as possible, the detailed discussion focuses on a single computer, specifically computer 102. Computer 102 may be located in the cloud, although it is not shown in the cloud in Figure 1. On the other hand, computer 102 does not need to be located in the cloud, except in any extent that it may be shown positively.

[0044] The processor set 114 includes one or more computer processors of any type currently known or to be developed in the future. The processing circuitry 114A may be distributed across multiple packages, for example, multiple coordinated integrated circuit chips. The processing circuitry 114A may implement multiple processor threads and / or multiple processor cores. The cache 114B may be memory located within the processor chip package and is typically used for data or code that should be available for high-speed access by threads or cores running on the processor set 114. The cache memory is typically organized into multiple levels depending on its relative proximity to the processing circuitry 114A. Alternatively, some or all of the cache 114B for the processor set 114 may be located "off-chip". In some computing environments, the processor set 114 may operate using qubits and be designed to perform quantum computing.

[0045] Computer-readable program instructions are typically loaded onto computer 102 and cause the processor set 114 of computer 102 to execute a series of operations, thereby realizing a computer implementation method. The instructions thus executed instantiate the method (collectively referred to as the "method") defined in the flowcharts and / or descriptive descriptions of the computer implementation methods contained in this document. These computer-readable program instructions are stored in various types of computer-readable storage media, such as the cache 114B and other storage media discussed below. The program instructions and associated data are accessed by the processor set 114 to control and direct the execution of the method. In computing environment 100, at least some of the instructions for executing the method may be stored in the dynamic modification of the object model data update module 120B in persistent storage 120.

[0046] The communication fabric 116 is a signal conduction path that enables various components of the computer 102 to communicate with one another. Typically, this fabric is made up of switches and conductive paths, such as buses, bridges, physical input / output ports, and similar switches and conductive paths. Other types of signal communication paths, such as optical fiber communication paths and / or wireless communication paths, may be used.

[0047] The volatile memory 118 is any type of volatile memory that is currently known or may be developed in the future. Examples include dynamic random-access memory (RAM) or static RAM. Typically, the volatile memory 118 is characterized by random access, but this is not required unless otherwise indicated. In computer 102, the volatile memory 118 is located in a single package and is internal to computer 102, but alternatively or further, the volatile memory 118 may be distributed across multiple packages and / or located externally to computer 102.

[0048] Persistent storage 120 is any form of non-volatile storage for a computer, currently known or to be developed in the future. The non-volatility of this storage means that the stored data is maintained regardless of whether power is supplied to the computer 102 and / or directly to the persistent storage 120. Persistent storage 120 may be read-only memory (ROM), but typically at least a portion of persistent storage 120 allows for writing, deleting, and rewriting of data. Some well-known forms of persistent storage 120 include magnetic disks and solid-state storage devices. Operating system 120A can take several forms, including various known proprietary operating systems or open-source portable operating system interface (CSI) type operating systems that employ a kernel. The code contained in object model data update module 120B typically includes at least a portion of computer code involved in the execution of the disclosed method.

[0049] The peripheral device set 122 includes a set of peripheral devices for the computer 102. Data communication connections between the peripheral devices and other components of the computer 102 may be implemented in various ways, such as Bluetooth® connections, near-field communication (NFC) connections, connections made by cables (e.g., Universal Serial Bus (USB) type cables), insertable connections (e.g., Secure Digital (SD) cards), connections made via local area communication networks, and further, connections made via wide area networks such as the Internet. In various embodiments of this disclosure, the UI device set 122A may include components such as a display screen, speakers, microphones, wearable devices (e.g., goggles and smartwatches), keyboards, mice, printers, touchpads, game controllers, and haptic devices. Storage 122B is external storage such as an external hard drive, or insertable storage such as an SD card. Storage 122B may be persistent and / or volatile. In some embodiments of this disclosure, storage 122B may take the form of a quantum computing storage device for storing data in the form of qubits. In embodiments of this disclosure, if computer 102 requires a large amount of storage (for example, if computer 102 locally stores and manages a large database), this storage may be provided by peripheral storage devices designed to store very large amounts of data, such as a storage area network (SAN) shared by multiple geographically distributed computers. The IoT sensor set 122C consists of sensors that can be used in Internet of Things applications. For example, one sensor may be a thermometer and the other may be a motion detector.

[0050] The network module 124 is a collection of computer software, hardware, and firmware that enables computer 102 to communicate with other computers via the WAN 104. The network module 124 may include hardware such as a modem or Wi-Fi® signal transceiver, software for packetizing and / or depacketizing data for communication network transmission, and / or web browser software for communicating data over the Internet. In some embodiments of this disclosure, the network control function and the network forwarding function of the network module 124 are performed on the same physical hardware device. In one embodiment of this disclosure (e.g., an embodiment utilizing a software-defined network (SDN)), the control and forwarding functions of the network module 124 are performed on physically separate devices, and the control function manages several different network hardware devices. Computer-readable program instructions for performing the disclosed method can typically be downloaded to computer 102 from an external computer or external storage device via a network adapter card or network interface included in the network module 124.

[0051] WAN104 is any wide area network (e.g., the Internet) capable of transmitting computer data over non-local distances by any currently known or future-developed technology for transmitting computer data. In some embodiments of this disclosure, WAN104 may be replaced and / or complemented by a local area network (LAN), such as a Wi-Fi® network, designed to transmit data between devices located within a local area. WAN104 and / or LAN typically include computer hardware such as copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and edge servers.

[0052] EUD106 is any computer system used and controlled by an end user (e.g., a customer of the company operating computer 102), and may take any of the forms described above with respect to computer 102. EUD106 typically receives useful and valuable data from the operation of computer 102. For example, in a hypothetical case where computer 102 is designed to provide recommendations to an end user, these recommendations would typically be communicated from the network module 124 of computer 102 to EUD106 via the WAN 104. In this way, EUD106 can display or otherwise present the recommendations to the end user. In some embodiments of this disclosure, EUD106 may be a client device, such as a thin client, a heavy client, a mainframe computer, or a desktop computer.

[0053] The remote server 108 is any computer system that provides at least some data and / or functionality to computer 102. The remote server 108 may be controlled and used by the same entity that operates computer 102. The remote server 108 represents a machine that collects and stores useful and valuable data for use by other computers, such as computer 102. For example, in a hypothetical case where computer 102 is designed and programmed to provide recommendations based on historical data, this historical data may be provided to computer 102 from the remote database 108A of the remote server 108.

[0054] The public cloud 110 is any computer system available for use by multiple entities, providing on-demand availability of computer system resources and / or other computing capabilities, particularly data storage (cloud storage) and computing power, without direct active management by the user. Cloud computing typically leverages resource sharing to achieve coherence and economies of scale. Direct active management of the computing resources of the public cloud 110 is performed by the computer hardware and / or software of the cloud orchestration module 110B. The computing resources provided by the public cloud 110 are typically implemented by virtual computing environments running on various computers that make up the host physical machine set 110C, which is a universe of physical computers within and / or available to the public cloud 110. The virtual computing environment (VCE) typically takes the form of virtual machines from the virtual machine set 110D and / or containers from the container set 110E. These VCEs can be stored as images and transferred between various physical machine hosts, either as images or after VCE instantiation. The cloud orchestration module 110B manages the transfer and storage of images, deploys new VCE instantiations, and manages active instantiations of VCE deployments. The gateway 110A is a collection of computer software, hardware, and firmware that enables the public cloud 110 to communicate through the WAN 104.

[0055] Here, some further explanation of virtualized computing environments (VCEs) is provided. A VCE can be stored as an "image." A new active instance of a VCE can be instantiated from an image. Two well-known types of VCEs are virtual machines and containers. A container is a VCE that uses operating system-level virtualization. This refers to an operating system feature in which the kernel allows for the existence of multiple isolated user-space instances called containers. These isolated user-space instances typically behave like actual computers in terms of the programs running within them. Computer programs running on a normal operating system can utilize all the resources of that computer, such as connected devices, files and folders, network shares, CPU power, and quantifiable hardware capabilities. However, programs running inside a container can only use the contents of the container and the devices allocated to the container; this feature is known as containerization.

[0056] Private Cloud 112 is similar to Public Cloud 110, except that its computing resources are available only for use by a single enterprise. Although Private Cloud 112 is illustrated to communicate with WAN 104, in various embodiments of this disclosure, the private cloud may be completely isolated from the internet and accessible only through a local or private network. A hybrid cloud is a combination of multiple clouds of different types (e.g., private, community, or public cloud types), often implemented by different vendors. Each of the multiple clouds remains a separate discrete entity, but the larger hybrid cloud architecture is brought together by standardized or proprietary technologies that enable orchestration, management, and / or data / application portability between the multiple configuration clouds. In embodiments of this disclosure, both Public Cloud 110 and Private Cloud 112 are parts of a larger hybrid cloud.

[0057] Figure 2 shows an environment for updating object model data based on a user's emissions score, according to one embodiment of the disclosure. Figure 2 is described in conjunction with elements from Figure 1. Referring to Figure 2, a diagram of the network environment 200 is shown. The network environment 200 includes a system 202, a user device 204, and an application 208. Furthermore, it is shown that an operation 210 associated with application 208 is being performed. Operation 210 further includes one or more interface elements 212. One or more interface elements 212 include a first interface element 212A, a second interface element 212B, a third interface element 212C, and up to N interface elements 212N. The system 202 further includes a first artificial intelligence (AI) model 202A and multiple clusters 202B. The network environment 200 further includes a database 206. The database 206 may store emissions value data 206A. The network environment 200 further includes the WAN 104 in Figure 1.

[0058] System 202 may include suitable logic, circuitry, interfaces, and / or code configured to update the object model data of interface elements based on emission scores. System 202 is configured to dynamically update the object model data of interface elements by integrating emission scores. By evaluating the carbon impact of various actions and interactions, System 202 ensures that the displayed information reflects real-time environmental metrics. If the emission score changes due to the actions of a particular user 214, System 202 automatically adjusts the relevant one or more interface elements 212 to increase the particular user 214's awareness of their own environmental footprint. Actions may represent, for example, multiple interactions by a particular user 214 with at least one of the one or more interface elements 212 that have high emission values. In exemplary embodiments, high emission values ​​may correspond to emission values ​​greater than a first emission level, e.g., emissions of carbon dioxide equivalent (gCO2e) greater than 10 grams. This approach promotes sustainable practices and ultimately leads to more environmentally conscious digital experiences by providing users with actionable insights and encouraging informed decision-making.

[0059] In one embodiment, the computer system 202 (hereinafter referred to as System 202) includes a first artificial intelligence (AI) model 202A. The first AI model 202A may be an algorithm designed to analyze data, learn patterns, and make predictions or decisions based on historical or current data. The first AI model 202A may be implemented using, for example, linear regression techniques, neural networks, etc. The first AI model 202A may be configured to process natural language or recognize images. By training the first AI model 202A on large datasets, System 202 improves the accuracy and efficiency of the first AI model 202A over time, enabling applications in various fields such as healthcare, finance, and entertainment.

[0060] In one embodiment, system 202 trains a first AI model 202A with historical interaction data associated with a plurality of training interaction sessions. Leveraging the first AI model 202A, system 202 identifies at least one cluster from the plurality of clusters 202B for a particular user 214, based on the user emission score associated with that user 214 and the cluster data associated with a plurality of clusters 202B. Each cluster from the plurality of clusters 202B contains a plurality of users with similar user emission scores. When a particular user 214 accesses application 208, system 202, by leveraging the first AI model 202A, assigns the particular user 214 to a cluster in the plurality of clusters 202B. In an exemplary embodiment, the cluster may correspond to a first cluster.

[0061] In one embodiment, application 208 may be a software program designed to facilitate user interaction while performing specific tasks, such as browsing, purchasing, project management, customer management, or content consumption. Examples of application 208 may include, but are not limited to, an e-commerce platform in which a specific user 214 may perform operations 210, such as exploring and purchasing products; a social media application that enables users to share and engage with content; and an educational tool that provides an interactive learning experience. Furthermore, a streaming service may enable users to access multimedia content such as movies and music, and a productivity application may help a specific user 214 create and manage projects, documents, spreadsheets, and presentations. Each of these applications may leverage the disclosed system, namely system 202, to improve user engagement, optimize resource consumption, and promote environmentally responsible behavior through real-time evaluation of user interactions and corresponding emissions.

[0062] In one embodiment, the database 206 is an organized collection of structured information that enables efficient storage, retrieval, and management of data. In an exemplary embodiment, the database 206 stores emission data 206A associated with each interface element of one or more interface elements 212 within the application 208. Various types of databases may include, but are not limited to, relational databases that organize data into tables in predetermined relationships; NoSQL (No-Structured Query Language) databases that store unstructured or semi-structured data and allow for flexible schema design; and graph databases that focus on the relationships between data points and are particularly useful for complex networks. Furthermore, there are object-oriented databases that store data in the form of objects, thereby aligning with object-oriented programming principles. By utilizing a suitable database type, the system 202 can efficiently manage the emission data 206A, enabling rapid access and analysis, thereby supporting user interaction within the application 208 and promoting sustainability.

[0063] The user device 204 may include preferred logic, circuitry, interfaces, and / or code that may be configured to render at least one interface element of one or more interface elements 212. Examples of the user device 204 may include, but are not limited to, computing devices, servers, computer workstations, smartphones, desktops, laptops, augmented reality (AR) devices, virtual reality (VR) devices, cellular phones, mobile phones, mainframe machines, gaming devices, consumer electronics (CE) devices, head-mounted devices, projection-based systems, and / or any other devices having computer vision display capabilities.

[0064] In the operation, a specific user 214 accesses an application 208 on a user device 204. The application 208 may be, but is not limited to, an e-commerce application, a productivity application, a social media application, or an entertainment application. When accessing the application 208, the specific user 214 performs an operation 210 associated with the application 208 on the user device 204. In a scenario where the application 208 corresponds to an e-commerce application, the operation 210 associated with the application 208 may correspond to placing an order for a product on the application 208. In one embodiment, the operation 210 is performed by a specific user 214 associated with the user device 204. Furthermore, while the specific user 214 is performing the operation 210, the specific user 214 interacts with one or more interface elements 212 associated with the operation 210. The one or more interface elements 212 may be user interface elements rendered on the user device 204 that can be used to successfully perform the operation 210 associated with the application 208. One or more interface elements 212 may be, for example, one or more buttons, one or more text fields, one or more dropdown menus, checkboxes, one or more sliders, navigation bars, icons, images, and videos.

[0065] Furthermore, system 202 is configured to receive interaction data associated with an interaction session between a specific user 214 and one or more interface elements 212 of operation 210 of application 208. In one embodiment, data associated with the interaction between a specific user 214 and one or more interface elements 212 while operation 210 is being performed corresponds to interaction data. In an exemplary embodiment, if a specific user 214 starts an interaction with one or more interface elements 212 associated with operation 210 at a first timestamp "T1" and ends the interaction with one or more interface elements associated with operation 210 at a second timestamp "T2", the duration of the interaction between the specific user 214 and one or more interface elements from the first timestamp "T1" to the second timestamp "T2" corresponds to the interaction session. Furthermore, data associated with the interaction session, such as the number of clicks on interface elements, the number of clicks on one or more interface elements, and the amount of time the interaction session lasts, corresponds to interaction data. Interaction data indicates one or more interface elements 212 that a specific user 214 interacted with during an interaction session. In this scenario, a specific user 214 interacted with a first interface element 212A, a second interface element 212B, and a third interface element 212C during the interaction session. In this case, the system 202 receives interaction data indicating that the specific user 214 interacted with the first interface element 212A, the second interface element 212B, and the third interface element 212C during the interaction session.

[0066] In one embodiment, system 202 is configured to generate a user emissions score associated with a particular user 214's interaction session based on first interaction data. In an exemplary embodiment, when a particular user 214 interacts with one or more interface elements 212 while performing operation 210 within application 208, each interaction between the particular user 214 and one or more interface elements 212 generates carbon emissions based on the energy consumed by the user device 204 and other computing entities such as servers, data centers, and other computing entities hosting the application. For example, clicking one or more buttons, browsing the internet, streaming video content, typing into one or more text fields, or navigating a menu may all require energy use and therefore may result in carbon emissions. System 202 receives interaction data associated with the interaction session and generates a user emissions score that reflects the overall environmental impact of the interaction session. A higher user emissions score indicates greater carbon emissions due to more intensive interactions, while a lower score indicates more efficient use. For example, a user emission score may be high if it is greater than the first emission level, e.g., greater than 10 gCO2e. Alternatively, a user emission score may be low if it is less than the second emission level, e.g., less than 5 gCO2e. Also, a user emission score may be moderate if it is greater than the second emission level but less than the first emission level, e.g., greater than 5 gCO2e and less than 10 gCO2e.

[0067] In one embodiment, the system 202 further identifies a first cluster from a plurality of clusters 202B for a particular user 214 based on user emission scores and cluster data. Cluster data is associated with each cluster in the plurality of clusters. In one embodiment, the cluster data includes a plurality of user emission scores associated with a plurality of users in each cluster of the plurality of clusters 202B. For example, the cluster data associated with the first cluster of the plurality of clusters 202B includes the user emission score for each user present in the first cluster. Similarly, the cluster data associated with the second and third clusters of the plurality of clusters 202B includes the user emission score for each of the plurality of users associated with the second and third clusters, respectively. Each cluster in the plurality of clusters includes a plurality of users. Furthermore, each cluster in the plurality of clusters represents a group of users characterized by different carbon emission scores.

[0068] In one embodiment, system 202 is further configured to generate a cluster emission score associated with the first cluster based on user emission scores and cluster data associated with the first cluster. The cluster emission score associated with the first cluster is generated by analyzing the individual user emission scores of each of the multiple users within the first cluster of multiple clusters 202B. By aggregating the individual user emission scores, system 202 derives an average score associated with the first cluster (called the cluster emission score). The cluster emission score highlights the collective carbon emissions associated with the multiple users within the first cluster.

[0069] In one embodiment, the system 202 is further configured to receive object model data associated with each of the one or more interface elements. The object model data associated with each of the one or more interface elements represents data associated with each of the one or more interface elements rendered on the user device 204. For example, if a first interface element 212A corresponds to a text field and a second interface element 212B corresponds to a submit button, the object model data for the first interface element 212A represents a plurality of attributes associated with the first interface element 212A, and the object model data for the second interface element 212B represents a plurality of attributes associated with the second interface element 212B. The plurality of attributes may be, for example, but are not limited to, the position of one or more interface elements 212, the color of one or more interface elements 212, the size of one or more interface elements 212, and the font of one or more interface elements 212.

[0070] Furthermore, system 202 is configured to update object model data associated with at least one interface element of one or more interface elements based on the cluster emission score. For example, system 202 identifies a first cluster of multiple clusters 202B as suitable for a particular user 214 based on the user emission score associated with that user 214. In this example, the first cluster includes multiple users with high user emission scores, in which case system 202 updates object model data associated with at least one interface element of one or more interface elements based on the cluster emission score of the first cluster. In an exemplary embodiment, a user emission score may be considered high if it is equal to or greater than, for example, 75 gCO2e. A user emission score may be considered medium if it is equal to or greater than, for example, 51 gCO2e and less than 75 gCO2e. Furthermore, a user emission score may be considered low if it is less than, for example, 50 gCO2e.

[0071] In one embodiment, system 202 is configured to output updated object model data. In an exemplary embodiment, if system 202 determines that interaction with a particular user 214's first interface element 212A results in high carbon emissions, system 202 updates the object model data associated with the first interface element 212A. Intensive interaction with a particular user 214's first interface element 212A (which may have high emission data 206A) may result in high carbon emissions. In an exemplary embodiment, high emission data 206A may be, for example, 5 gCO2e, for example, medium emission data 206A may be, for example, 3 gCO2e, and low emission data 206A may be, for example, 1 gCO2e. For example, if the first interface element 212A corresponds to a text field and the default size in the object model of the first interface element 212A is "16", the system 202 updates the object model data and changes the default size to, for example, "12". Updating the object model data of the first interface element 212A can reduce carbon emissions and make the interaction session more eco-friendly. Similarly, the system 202 may update the object model data of each interface element of one or more interface elements 212 whose element emission score is greater than the emission threshold.

[0072] Figure 3 is a diagram illustrating one or more operations performed by system 202 to update object model data based on emission scores, according to one embodiment of the present disclosure. Figure 3 will be described in relation to elements from Figures 1 and 2. Referring to Figure 3, the operation may be initiated in 302.

[0073] In 302, an interaction data reception operation is performed. In one embodiment, system 202 is configured to perform an interaction data reception operation. In the interaction data reception operation, system 202 is configured to receive first interaction data associated with an interaction session between one or more interface elements 212 of an operation 210 of a specific user 214 and application 208.

[0074] In an exemplary embodiment, system 202 is configured to receive first interaction data related to an ongoing session (such as an interaction session) between a specific user 214 and one or more interface elements 212 of application 208. In the exemplary scenario, the specific user 214 is using application 208, which corresponds to a website used to track and reduce carbon emissions. During this interaction session, the specific user 214 interacts with one or more interface elements 212, including, but not limited to, buttons for recording activity, sliders for adjusting their daily carbon targets, and graphs displaying the specific user 214's progress over time.

[0075] Furthermore, a specific user 214 interacts with each of the one or more interface elements 212, for example, by clicking a button to record a new activity or adjusting a slider to set a new goal. The system 202 is configured to receive real-time data associated with the specific user 214 interacting with one or more interface elements 212 and performing the operation 210 during the interaction session.

[0076] In 304, a user emission score generation operation is performed. In one embodiment, system 202 is configured to perform a user emission score generation operation. In the user emission score generation operation, system 202 is configured to generate a user emission score associated with a specific user 214's interaction session based on first interaction data.

[0077] In an exemplary embodiment, system 202 performs a user emissions score generation operation that utilizes first interaction data collected in real time during an interaction session. System 202 is further configured to analyze the first interaction data to generate a user emissions score that indicates the environmental impact associated with the interactions that a particular user 214 has with one or more interface elements 212 while performing operation 210 within application 208.

[0078] In the exemplary scenario, a specific user 214 uses an application 208 that corresponds to an e-commerce application. The specific user 214 performs an operation 210 that corresponds to ordering a product such as a pair of shoes. In this example, during the interaction session, the specific user 214 will engage with one or more interface elements 212 associated with operation 210, which include clicking a button to view product details, scrolling through customer reviews, and viewing images of the shoes.

[0079] Furthermore, when a particular user 214 interacts with one or more interface elements 212, the system 202 captures first interaction data, including the timestamp of each action such as each click and / or input, and the one or more interface elements 212 involved in the execution of operation 210. For example, a particular user 214 searches for multiple items, selects a pair of shoes, reads reviews, clicks a button to add the shoes to a cart, and then selects a shipping option. The system 202 then accesses a database 206 containing emission value data 206A associated with each of the one or more interface elements 212.

[0080] In one embodiment, interactions with one or more interface elements 212, such as clicking to add a product to a cart, viewing images and / or videos, or reading reviews, have associated emission data stored in a database. For example, clicking a button to add a pair of shoes to a cart may contribute 2 grams of carbon dioxide equivalent (gCO2e), viewing an image of a pair of shoes may have emission data of 4 gCO2e, viewing a video of a pair of shoes may have emission data of 10 gCO2e, and engaging with a customer review may have emission data of 1 gCO2e.

[0081] Furthermore, system 202 is configured to generate a user emission score associated with a particular user 214's interaction session or a portion of an ongoing interaction session. In an exemplary embodiment, considering that a particular user 214 performed each action only once, the user emission score could be (2+4+10+1=4)gCO2e.

[0082] In 306, a first cluster identification operation is performed. In one embodiment, system 202 is configured to perform a first cluster identification operation. In the first cluster identification operation, system 202 is configured to identify a first cluster from a plurality of clusters 202B for a particular user 214 based on the user emission score and cluster data. The cluster data is associated with each cluster of the plurality of clusters 202B. For example, once system 202 generates a user emission score in 304, system 202 accesses the plurality of clusters 202B, and each cluster of the plurality of clusters 202B is associated with a specific threshold score.

[0083] In an exemplary scenario, system 202 leverages a trained first AI model 202A to generate three clusters. The three clusters include the first, second, and third clusters. The first cluster includes multiple users whose user emission scores range from 0 gCO2e to 50 gCO2e. The first cluster may be characterized as low-emission users. Furthermore, the second cluster covers another group of users with user emission scores ranging from 51 gCO2e to 75 gCO2e and is classified as medium-emission users. Furthermore, the third cluster includes multiple users with scores exceeding 75 gCO2e and is characterized as high-emission users.

[0084] In this scenario, if a particular user 214 has a user emission score of 70 gCO2e, the system 202 refers to the cluster data associated with each of the multiple clusters 202B to identify a cluster for the particular user 214. The system 202 compares the user emission score of the particular user 214 to multiple thresholds for each of the multiple clusters. Since 70 gCO2e falls within the range of the second cluster (51 gCO2e to 75 gCO2e), the system 202 identifies that the particular user 214 should be assigned to the medium emission cluster. It should be noted that the fact that the first cluster is associated with low emissions and the third cluster is associated with high emissions is merely illustrative and should not be interpreted as limiting. In an alternative embodiment, the first cluster may correspond to high emissions and may include users with high user emission scores, and the third cluster may correspond to low emissions and may include users with low user emission scores. Furthermore, the generation of three clusters corresponding to high, medium, and low emissions is also exemplary. In an alternative embodiment, any number of clusters, e.g., 2, 4, 5, 10, etc., may be generated.

[0085] In 308, a cluster emission score generation operation is performed. In one embodiment, system 202 is configured to perform a cluster emission score generation operation. In the cluster emission score generation operation, system 202 is configured to generate a cluster emission score associated with a first cluster based on the user emission score and cluster data associated with the first cluster of a plurality of clusters 202B. For example, if a particular user 214 is assigned to an appropriate cluster, system 202 utilizes the user emission score, such as 70 gCO2e but not limited to 70 gCO2e, and the cluster data associated with the first cluster.

[0086] For example, the first cluster represents high-emission users, defined as those with emission scores ranging from 51 gCO2e to 75 gCO2e. System 202 accesses cluster data, which may include the average emission score, the total number of users in the cluster, and other relevant metrics characterizing the environmental impact of multiple users in the corresponding cluster.

[0087] Furthermore, to generate a cluster emissions score, system 202 calculates the average user emissions score of multiple users within the first cluster. In an exemplary embodiment, the cluster data includes that the average user emissions score of the high-emission cluster is 65 gCO2e and the total number of users assigned to the first cluster is 10. System 202 then combines this average with the user emissions score (e.g., 70 gCO2e) associated with a particular user 214 of 70 gCO2e to derive a new cluster emissions score.

[0088] In 310, an object model data reception operation is performed. In one embodiment, system 202 is configured to perform an object model data reception operation. In the object model data reception operation, system 202 is configured to receive object model data associated with each interface element of one or more interface elements 212. System 202 is configured to receive object model data associated with each interface element of one or more interface elements associated with operation 210 in application 208. For example, system 202 collects object model data corresponding to the Document Object Model (DOM) structure of each interface element of one or more interface elements 212 in application 208.

[0089] Furthermore, a specific user 214 interacts with at least one element of one or more interface elements 212 of an operation 210 associated with application 208. In an exemplary embodiment, if a specific user 214 is browsing products on an e-commerce website, the one or more interface elements 212 may be a product list, an "Add to Cart" button, and a filter, etc. Furthermore, system 202 retrieves object model data that reflects the DOM structure of the one or more interface elements 212. This data includes details about the hierarchy, attributes, and behaviors associated with each interface element of the one or more interface elements 212.

[0090] For example, when a user clicks on a product image, system 202 accesses DOM structure data that shows how that image is nested within the product card. This may include attributes such as the image source, alternative text for accessibility, and event listeners that trigger the display of additional product details. Similarly, if a particular user 214 applies a filter to view only low-emission products, system 202 captures DOM data corresponding to the filter dropdown. This data includes information about the filter options, their default state, and how it interacts with the product list displayed on the page.

[0091] Furthermore, if a specific user 214 clicks the "Add to Cart" button, system 202 receives object model data specifying its position in the DOM hierarchy, any associated styles, and specific functions it performs when clicked (e.g., update cart total and calculate associated carbon emissions).

[0092] In 312, an object model data update operation is performed. In one embodiment, system 202 is configured to perform an object model data update operation. In an object model data reception operation, system 202 is configured to update object model data associated with at least one interface of one or more interface elements 212 based on cluster discharge data.

[0093] In an exemplary embodiment, if the system 202 determines that a particular user 214 is assigned to a cluster (e.g., the first cluster) from among several clusters 202B with higher cluster emission scores, it identifies which of one or more interface elements contributes most to the carbon emissions of that particular user 214. For example, if a particular user 214 is viewing high-quality images of products in an e-commerce application, and the system determines that high-resolution images, such as 1080-pixel or higher resolution images, consume a large amount of data and energy, the system 202 updates the object model data of the interface elements associated with the product images. The system 202 converts the high-resolution product images to lower-resolution images of the products, significantly reducing carbon emissions. For example, instead of displaying a 4K image that uses a considerable amount of data and energy, the system 202 converts it to a more energy-efficient 720-pixel image.

[0094] In step 314, an object model data output operation is performed. In one embodiment, system 202 is configured to perform an object model data output operation. In the object model data output operation, system 202 is configured to output updated object model data. In one embodiment, system 202 is further configured to render at least one interface element of one or more interface elements on the user device 204 based on the updated object model data. For example, system 202 renders an updated image of the product in 720p on the user device 204 based on the updated object model data. Furthermore, system 202 is configured to perform operation 210 based on the rendering.

[0095] Figure 4 is a flowchart 400 of a method demonstrating an embodiment of the present disclosure for updating object model data associated with at least one interface element. Figure 4 is described in relation to elements from Figures 1, 2, and 3.

[0096] In 402, emission data 206A associated with each interface element of one or more interface elements 212 is received. In an exemplary embodiment, system 202 receives emission data from database 206. In an exemplary embodiment, emission data 206A associated with each interface element of one or more interface elements 212 indicates the amount of carbon emitted per interaction or while interacting with the corresponding interface element within a given time.

[0097] For example, when a particular user 214 interacts with a first interface element 212A of one or more interface elements 212 while performing an operation 210 on an application 208, the system 202 receives emission data 206A associated with the first interface element 212A from the database 206. The emission data 206A associated with the first interface element 212A indicates the amount of carbon emitted as a result of the interaction with the first interface element 212A. In an exemplary embodiment, the first interface element 212A corresponds to a product image on an e-commerce application. The system 202 processes the product image and renders it on the user device 204. If the resolution of the product image is low (e.g., 480 pixels or less), the system 202 consumes less energy to process and render the image compared to if the resolution of the product image is high (e.g., 1080 pixels).

[0098] In an exemplary scenario, emission data 206A stored in database 206 associated with a low-resolution product image corresponds, for example, to 5 grams of carbon dioxide equivalent (5 gCO2e), and emission data 206A stored in database 206 associated with a high-resolution product image corresponds, for example, to 10 grams of CO2 equivalent (10 gCO2e). This indicates that when a particular user 214 interacts with a low-resolution product image, system 202 receives emission data 206A corresponding to 5 grams of CO2 equivalent (gCO2e). Furthermore, when a particular user 214 interacts with a high-resolution product image, system 202 receives emission data 206A corresponding to 10 gCO2e from database 206.

[0099] In 404, the user emission score is generated based on emission value data 206A and interaction data. In an exemplary embodiment, the interaction data received by the system 202 indicates that a particular user 214 interacted with the first interface element 212A and the second interface element 212B during an interaction session.

[0100] Furthermore, system 202 receives emission data 206A associated with the first interface element 212A and emission data 206A associated with the second interface element 212B. The emission data 206A associated with the first interface element corresponds to 3 gCO2e per interaction, and the emission data 206A associated with the second interface element corresponds to 5 gCO2e per interaction. In addition, the interaction data indicates that a particular user 214 interacted with the first interface element 212A 5 times during the interaction session, and that a particular user 214 interacted with the second interface element 212B 2 times during the interaction session. Furthermore, system 202 generates a user emission score based on the emission data 206A and the interaction data. The user emission score corresponds to (3*5+5*2=25) gCO2e.

[0101] In 406, the system 202 is configured to determine the element emission score associated with each interface element of one or more interface elements 212 based on the first interaction data and emission value data 206A. In an exemplary embodiment, as described in 404, the emission value data 206A associated with the first interface element 212A corresponds to 3 gCO2e, and the emission value data 206A associated with the second interface element 212B corresponds to 5 gCO2e. Furthermore, the interaction data indicates that a particular user 214 interacts with the first interface element 212A for 5 seconds, resulting in an element emission score of (3 * 5 = 15) gCO2e.

[0102] Similarly, the interaction data shows that a specific user 214 interacted with the second interface element 212B for 2 seconds, and that the emission value data 206A associated with the second interface element corresponds to 5 gCO2e, resulting in an element emission score of (5*2=10) gCO2e associated with the second interface element 212B.

[0103] In 408, the system 202 is further configured to identify at least one interface element of one or more interface elements 212 based on an element emission score. The element emission score associated with each interface element of one or more interface elements corresponds to the amount of carbon emissions caused when a particular user 214 interacts with the corresponding interface element during an interaction session. In one embodiment, at least one interface element is identified if its element emission score is greater than an emission threshold.

[0104] In an exemplary embodiment, if the emission threshold corresponds to, for example, 20 gCO2e (but not limited to), the system 202 is configured to compare the element emission score of each interface element of one or more interface elements 212 with the emission threshold. In an exemplary embodiment, the emission threshold indicates the allowable amount of carbon emissions associated with the interaction between a particular user 214 and a first interface element 212A of one or more interface elements 212. In the exemplary scenario, the first interface element 212A corresponds to a high-resolution image, and the emission threshold associated with the first interface element 212A corresponds to 20 gCO2e. Furthermore, the emission value data 206A associated with the first interface element 212A corresponds to 5 gCO2e per interaction. The system 202 determines that the particular user 214 interacted with the first interface element 212A five times. Furthermore, the system 202 determines that the element emission score corresponds to (5*5=25) gCO2e. System 202 further compares the element emission score of the first interface element 212A with the emission threshold. In the exemplary scenario described, the element emission score is greater than the emission threshold. Furthermore, System 202 identifies the first interface element 212A as a high-carbon emission interface element of one or more interface elements 212.

[0105] In 410, the system 202 is configured to update object model data associated with at least one identified interface element based on cluster data associated with a first cluster of multiple clusters 202B. In an exemplary embodiment, the system 202 assigns a particular user 214 to the first cluster based on the user emission score associated with that particular user 214. Furthermore, the system 202 identifies at least one interface element from one or more interface elements 212 based on the element emission score. For example, the identified interface element corresponds to a first interface element 212A. Furthermore, the system 202 updates the object model data associated with the first interface element 212A. In one embodiment, the first size associated with the updated object model data associated with the first interface element 212A is smaller than the second size associated with the object model data associated with the first interface element 212A. For example, the first interface element 212A corresponds to a high-resolution product image, and the object model data associated with the first interface element 212A indicates that the second size associated with the first interface element 212A corresponds to, for example, 1080 pixels or more. Furthermore, if the first interface element 212A is identified as a high-carbon emission interface element, the system 202 updates the object model data associated with the first interface element 212A to a low-resolution product image. The first size associated with the updated object model data corresponds to, for example, 720 pixels or less. This indicates that the system 202 has reduced the quality of the product image from 1080 pixels or more to 720 pixels or less.

[0106] Figures 5A and 5B are diagrams that collectively show a flowchart 500 illustrating the reassignment of a specific user 214 from one cluster to an updated cluster, according to one embodiment of the present disclosure. Figures 5A and 5B are described in relation to elements from Figures 1, 2, 3, and 4.

[0107] In one embodiment, a specific user 214 performs an operation 210 within an application 208 on a user device 204. To perform the operation 210, the specific user 214 interacts with one or more interface elements 212 associated with the operation 210. The period during which the specific user 214 interacts with one or more interface elements 212 is called a first interaction session.

[0108] In 502, first interaction data associated with a first interaction session may be generated on the user device 204. The first interaction session may be between a specific user 214 and one or more interface elements 212 associated with an operation 210 of an application 208 on the user device 204. For example, a specific user 214 performs an operation 210 corresponding to purchasing a product on application 208 (such as an e-commerce application). To perform operation 210, i.e., to purchase a product, the specific user 214 interacts with one or more interface elements 212 such as a search bar, product description, product image, review, and payment interface. One or more interface elements 212 may correspond to buttons such as "Add to Cart" and "Proceed to Checkout," images associated with one or more products, a text field for searching for a desired product, and so on. Based on the specific user 214's interaction with one or more interface elements 212, first interaction data is generated.

[0109] In 504, the user device 204 transmits first interaction data to the system 202. The system 202 is configured to receive first interaction data associated with a first interaction session between a specific user 214 and one or more interface elements 212. In an exemplary embodiment, the first interaction session ends at time T1, and thereafter, the user device 204 transmits the first interaction data at time T1. The first interaction data may include a pattern of interactions between a specific user 214 and one or more interface elements 212 while performing operation 210 during the first interaction session. For example, the first interaction data associated with the first interaction session may indicate that a specific user 214 interacted with a button labeled "Add to Cart" five times, with a text field for searching for a desired product ten times, and with a button labeled "Proceed to Checkout" once. The first interaction data may also include the number of interactions between a specific user 214 and the same or different images of one or more products that the specific user 214 has searched for and / or that the application 208 has offered.

[0110] In 506, the system 202 is configured to generate a first user emissions score for a specific user 214 based on first interaction data. In one embodiment, the system 202 may generate a first user emissions score for a specific user 214 with respect to a first interaction session. In an example, to generate a first user emissions score, the system 202 receives emission value data 206A associated with each interface element of one or more interface elements 212 from the database 206. Furthermore, upon receiving the emission value data 206A, the system 202 generates a first user emissions score for a specific user 214. The first user emissions score may correspond to, for example, 60 gCO2e.

[0111] In 508, system 202 is configured to determine the cluster of a particular user 214 based on the generated first user emission score. In one embodiment, system 202 may use a first AI model 202A to determine the cluster of a particular user 214. System 202 may provide first interaction data and cluster data as input to the first AI model 202A to determine the cluster of a particular user 214. The cluster data may include user emission scores associated with each user of multiple users associated with each cluster of multiple clusters.

[0112] System 202 may further generate cluster emission scores associated with each of the multiple clusters 202B. According to this example, based on interaction data and emission value data, System 202 determines a first user emission score for a specific user 214. Based on the first user emission score, System 202 may identify a cluster, for example, a first cluster to which the specific user 214 may subsequently be assigned. System 202 then determines a cluster emission score. In this regard, System 202 may obtain cluster data for that cluster. The cluster data may include user emission scores for multiple users grouped within the cluster. With the addition of a specific user 214, cluster emission scores for the cluster, such as the average of the user emission scores for multiple users and the first emission score, may be regenerated. For example, a generated cluster emission score associated with a cluster, such as the first cluster, corresponds to 75 gCO2e. It should be noted that, for example, grouping a specific user 214 into a first cluster, i.e., a cluster containing high-emission users, is merely illustrative.

[0113] In an exemplary embodiment, the first AI model 202A analyzes the first interaction data and the generated cluster data associated with each of the multiple clusters 202B to determine the cluster of a particular user 214.

[0114] In 510, the system 202 is configured to determine object model data based on the determined cluster. In an exemplary embodiment, if the system 202 determines that a cluster assigned to a particular user 214 corresponds to a cluster that may be a high-emission cluster, the system 202 determines the object model data such that the object model data can reduce the emission score of the particular user 214. For example, the generated object model data may result in a reduction in the resolution, font, size, etc., of one or more interface elements 212 associated with operation 210 in order to reduce the emissions of the particular user 214.

[0115] For example, system 202 may use a first AI model 202A, trained based on historical training sessions, to determine if a high-resolution video of a product may be displayed to a particular user 214 if users in a cluster to which the user is grouped are performing an operation 210 corresponding to purchasing a product on application 208. Furthermore, once system 202 assigns a particular user 214 to the cluster under consideration, system 202 determines that the object model data associated with the first interface element 212A (e.g., the product video) has a resolution lower than 720 pixels.

[0116] In 512, the system 202 is further configured to transmit the determined object model data to the user device 204. In an exemplary embodiment, the user device 204 may receive the determined object model data from the system 202 at time "T2".

[0117] In 514, the user device 204 is configured to render one or more interface elements based on the received object model data. In an exemplary embodiment, based on the fact that a particular user 214 is grouped in a cluster, the user device 204 may render a low-resolution video (720 pixels) of the product on the user device 204. Similarly, the user device 204 may render each of the one or more interface elements 212 associated with operation 210 in accordance with the object model data associated with the cluster of multiple clusters 202B.

[0118] In 516, second interaction data is generated for a second interaction session of a specific user 214. The process for generating the second interaction data associated with the second interaction session may be similar to the process for generating the first interaction data associated with the first interaction session, as described in 502. The second interaction session may start at or after time "T2".

[0119] In 518, the generated second interaction data associated with the second interaction session is sent to system 202 at time "T3" which occurs after time "T2". System 202 is configured to receive second interaction data associated with a specific user 214's second interaction from user device 204.

[0120] In 520, the system 202 is configured to generate a second user emissions score associated with a particular user 214 based on the received second interaction data. In one embodiment, the process of generating the second user emissions score associated with a particular user 214 is similar to the process of generating the first user emissions score associated with a particular user 214 as described in 506. In an exemplary embodiment, the second user emissions score corresponds to 85 gCO2e. In one embodiment, the second user emissions score associated with a particular user 214 may indicate that the particular user 214's interaction with one or more interface elements 212 associated with operation 210 increased after time "T2". This may further indicate that the particular user 214 emitted more carbon at or after time "T2" compared to time "T1".

[0121] In an exemplary embodiment, system 202 is configured to dynamically update the user emissions score based on second interaction data indicating the current interaction of a particular user 214 with at least one interface element of one or more interface elements 212 associated with operation 210 of application 208. When a particular user 214 interacts with one or more interface elements 212, such as viewing additional product images, clicking a new category, or applying a filter, system 202 receives the second interaction data and may recalibrate the user emissions score associated with the particular user 214 as appropriate, for example, in real time or near real time. Dynamic adjustment ensures that the user emissions score accurately reflects the current carbon impact of the particular user 214 based on the most recent interaction (second interaction session). For example, the second interaction session may be near real time relative to the first interaction session.

[0122] In 522, the system 202 is configured to determine an updated cluster for a particular user 214 based on a second user emission score. In an exemplary embodiment, the process of determining an updated cluster for a particular user 214 is similar to the process of determining a cluster for a particular user 214 described in 508.

[0123] In an exemplary embodiment, based on a second user emissions score associated with a particular user 214, the system 202 determines an updated cluster, such as a second cluster, to which the particular user may be grouped. For example, the system 202 may analyze cluster data associated with each of a plurality of clusters to identify an updated cluster for a particular user 214. Furthermore, the cluster data of the updated cluster, which is, for example, a second cluster, may then be used to determine the cluster emissions score of the second cluster to which the particular user 214 is grouped. In this example, the second cluster includes users with higher emissions than the users in the first cluster. In other words, the second cluster may correspond to higher emissions scores than the first cluster. For this purpose, such statements that the second cluster is associated with higher emissions scores and the first cluster is associated with lower emissions scores are merely illustrative and should not be interpreted as limiting. In one example, the first cluster may correspond to high emissions scores and the second cluster may correspond to low emissions scores.

[0124] In 524, the system 202 is configured to determine updated object model data based on the determined updated cluster. In one embodiment, the process for determining updated object model data is similar to the process for determining object model data described in 510.

[0125] In an exemplary embodiment, the system 202 determines that the frequency of interaction between a particular user 214 and the first interface element 212A (high-resolution video of the product) has increased after time T2 during the second interaction session. The system 202 identifies the first interface element 212A and determines the updated object model data associated with the first interface element 212A. In one embodiment, the updated object model data associated with the first interface element 212A may further correspond to a reduced resolution, such as a resolution of less than 480 pixels for the first interface element 212A.

[0126] In an exemplary embodiment, system 202 receives user input from a specific user 214 associated with a plurality of interface elements. Furthermore, system 202 may update object model data associated with at least one of the interface elements of one or more interface elements based on the user input. For example, if the user input indicates that a specific user 214 does not want to reduce the resolution of the first interface element 212A, i.e., the video resolution, system 202 may identify other interface elements that may be modified to reduce the emission score of that specific user 214.

[0127] At 526, the system 202 transmits the determined updated object model data to the user device 204. In an exemplary embodiment, the user device 204 associated with a particular user 214 receives the transmitted updated object model data at time T4.

[0128] In 528, a user device 204 associated with a particular user 214 is configured to render one or more interface elements 212 based on the transmitted updated object model data 528. In the first example, while a particular user 214 may be in a first cluster (medium emission cluster), the user device 204 may render a high-resolution image of the product at time "T1". Furthermore, while a particular user 214 may be in a second cluster (high emission cluster), the user device 204 may render a low-resolution image of the product at time "T4". In the second example, if a specific user 214 may be in the first cluster and the system 202 determines that the third interface element 212C may be a high-carbon emission element, then the user device 204 may render the first interface element 212A, the second interface element 212B, and the third interface element 212C at time "T1", then the user device 204 may render the first interface element 212A and the second interface element 212B for the specific user 214 at time "T4".

[0129] Figure 6 shows a flowchart 600 associated with training a first AI model 202A to group a specific user 214 with a first cluster, according to one embodiment of the present disclosure. Figure 6 is described in relation to elements from Figures 1, 2, 3, 4, 5A, and 5B.

[0130] In 602, system 202 is configured to train a first AI model 202A based on historical interaction data. In an exemplary embodiment, historical interaction data is associated with historical interaction events. Historical interaction events may occur, for example, one day before the operation, two days before the operation, one week before the operation, one month before the operation, and one year before the operation. The day of the operation may correspond to the current interaction session between a specific user 214 and one or more interface elements 212 associated with operation 210 of application 208.

[0131] In an exemplary embodiment, historical interaction data associated with multiple training interaction sessions between multiple training users and operation 210 of application 208 includes, for example, multiple historical user emission scores, but is not limited to these. In one embodiment, a first AI model 202A is trained on the historical interaction data to classify multiple users into multiple clusters based on their corresponding user emission scores and cluster data associated with each of multiple clusters 202B. In an exemplary embodiment, the first AI model 202A may use algorithms such as k-means or hierarchical clustering to identify patterns in the interaction behavior of multiple users and their corresponding user emission scores. By processing a large dataset containing user demographic features, behavioral characteristics, and emission metrics, the first AI model 202A identifies different user segments, each segment associated with a unique emission profile.

[0132] In the exemplary scenario, system 202 segments multiple training users into multiple clusters 202B. The multiple training users are clustered into categories such as "low emitters," "medium emitters," and "high emitters" based on the user emission score associated with the corresponding training interaction session.

[0133] In 604, system 202 is configured to apply the first AI model 202A to the user emission score and cluster data. In one embodiment, system 202 generates a user emission score for a specific user 214. Furthermore, system 202 determines the cluster data associated with each of the multiple clusters 202B.

[0134] In an exemplary embodiment, the user emission score associated with a particular user 214 corresponds to 60 gCO2e. Furthermore, the system 202 determines cluster data that includes multiple user emission scores associated with multiple users. For example, the first user emission score associated with the first user corresponds to 30 gCO2e, the second user emission score associated with the second user corresponds to 35 gCO2e, and the third user emission score associated with the third user corresponds to 55 gCO2e. Furthermore, the system 202 applies the first AI model 202A to the user emission score associated with a particular user 214 and the cluster data associated with each of the multiple clusters 202B.

[0135] In 606, system 202 is configured to identify a first cluster from a plurality of clusters 202B for a particular user 214, based on the application of a trained first AI model 202A to user emission scores and cluster data associated with that particular user 214. In one embodiment, system 202 leverages the trained first AI model 202A to identify the cluster for a particular user 214.

[0136] As a first example, system 202 segments multiple users into multiple clusters 202B. As described in 604, the first user emissions score associated with the first user among the multiple users corresponds to 30 gCO2e, and the second user emissions score associated with the second user among the multiple users corresponds to 35 gCO2e. Furthermore, the third user emissions score associated with the third user corresponds to 55 gCO2e. Based on the first user emissions score, the second user emissions score, and the third user emissions score, system 202 utilizes the trained first AI model 202A to determine that the first user (30 gCO2e) and the second user have similar carbon emissions scores (35 gCO2e). Furthermore, system 202 assigns the first user and the second user to the same cluster (e.g., the second cluster) in the multiple clusters 202B. Similarly, system 202 utilizes the trained AI model 202A to assign clusters to a specific user 214. System 202 determines that the user emission scores of a specific user 214 (60 g CO2e) and a third user (55 g CO2e) are closely similar.

[0137] Furthermore, when calculating centroids, system 202 evaluates the user emission scores associated with each of the multiple users, for example, a specific user 214 with a user emission score of 60 gCO2e, along with the first user with a user emission score of 30 gCO2e, the second user with a user emission score of 35 gCO2e, and the third user with a user emission score of 55 gCO2e. System 202 then uses Euclidean distance as a metric to assign each user to the cluster whose centroid is closest.

[0138] As a second example, system 202 is configured to utilize cluster data associated with the first cluster to determine the user emission score associated with each of the multiple users within the first cluster. Similarly, system 202 is configured to utilize cluster data associated with the second cluster and the third cluster of the multiple clusters to determine the user emission score for each of the multiple users within the second cluster and each of the multiple users within the third cluster. Furthermore, once the user emission scores associated with each of the multiple users associated with the first, second, and third clusters have been determined, system 202 is configured to determine the cluster emission score (which may be the average of the user emission scores of the multiple users) for each of the multiple clusters 202B. For example, the cluster emission score associated with the first cluster corresponds to 80 gCO2e, the cluster emission score associated with the second cluster corresponds to 60 gCO2e, and the cluster emission score associated with the first cluster corresponds to 40 gCO2e. In this scenario, if the user emission score associated with a particular user 214 corresponds to 65 gCO2e, system 202 may assign that particular user 214 to the second cluster of the multiple clusters 202B.

[0139] In an exemplary embodiment, system 202 applies a k-means approach and initializes a predetermined number of clusters (e.g., 3 clusters) based on the user emissions score of each of a plurality of users. Each of the plurality of users is represented as a point in a multidimensional space where the dimensions correspond to their carbon emissions metrics. Initially, system 202 randomly selects three centroids, each representing a potential cluster.

[0140] In 608, system 202 is configured to group specific users 214 within a first cluster. For example, system 202 groups the first and second users together based on their proximity in user emission scores. Furthermore, system 202 clusters specific users 214 and a third user based on their closer user emission scores, which are 60 gCO2e and 55 gCO2e, respectively.

[0141] Furthermore, once each user of multiple users has been assigned to their corresponding cluster, system 202 recalculates the centroid by computing the average user emission score within each cluster. The assignment and centroid recalculation processes continue iteratively until convergence is reached.

[0142] Figure 7 is a diagram illustrating, according to one embodiment of the present disclosure, that a user emission score is compared to several thresholds in order to determine the cluster of a particular user 214. Figure 7 is described in relation to elements from Figures 1, 2, 3, 4, 5A, 5B and 6.

[0143] In 702, system 202 generates a user emission score associated with the interaction session of a specific user 214, based on the first interaction data.

[0144] In 704, the system 202 is further configured to determine whether the user emission score generated in 702 for a particular user 214 is equal to or greater than a first threshold associated with a first cluster of a plurality of clusters 202B. In one embodiment, the system 202 compares the user emission score to a plurality of thresholds, each of which corresponds to a respective cluster of the plurality of clusters 202B.

[0145] In an exemplary embodiment, system 202 is designed to evaluate the user emission score of a particular user 214 to determine whether it is equal to or higher than a first threshold corresponding to a first cluster of multiple clusters 202B. In an exemplary embodiment, system 202 obtains the first threshold from database 206. In an alternative exemplary embodiment, system 202 receives the first threshold from the administrator of application 208.

[0146] Furthermore, system 202 determines that the first cluster is defined for users whose user emission score is higher than or equal to 75 gCO2e, and classifies them as high-carbon emission users. System 202 generates a user emission score for a specific user 214. The generated user emission score is 80 gCO2e. System 202 further compares this score to a threshold of 75 gCO2e. Since 80 gCO2e is greater than the first threshold, system 202 determines that the specific user 214 is eligible for inclusion in the first cluster of multiple clusters 202B, and control may proceed to 704A.

[0147] In 704A, the system 202 assigns a specific user 214 to a first cluster, which may be a high-emission cluster. In an exemplary embodiment, if the system 202 determines, based on a comparison, that the user emission score associated with a specific user 214 is less than a first threshold associated with the first cluster, control may proceed to 704B.

[0148] In 704B, system 202 is further configured to determine whether a particular user 214's user emission score is equal to or higher than a second threshold associated with a second cluster of multiple clusters 202B. In one embodiment, system 202 compares the user emission score to multiple thresholds, each of which corresponds to a respective cluster of multiple clusters 202B.

[0149] In an exemplary embodiment, system 202 is designed to evaluate the user emission score of a particular user 214 and determine whether it is equal to or higher than a second threshold corresponding to a second cluster of multiple clusters 202B. In an exemplary embodiment, system 202 obtains the second threshold from database 206. In an alternative exemplary embodiment, system 202 receives the second threshold from the administrator of application 208.

[0150] Furthermore, system 202 determines that the second cluster is defined for users whose user emissions score is higher than or equal to 51 gCO2e and lower than the first threshold (e.g., 75 gCO2e) associated with the first cluster, and classifies them as moderate carbon emission users. System 202 generates a user emissions score for a specific user 214, which is 57 gCO2e. System 202 further compares the user emissions score to the second threshold. Since 57 gCO2e is greater than the second threshold and less than the first threshold, system 202 determines that the specific user 214 is eligible for inclusion in the second cluster of multiple clusters 202B, and control may proceed to 706A.

[0151] In 706A, system 202 assigns a specific user 214 to an intermediate emission cluster (such as a second cluster). In an exemplary embodiment, if system 202 determines, based on comparison, that the user emission score associated with a specific user 214 is lower than a second threshold associated with the second cluster, control may proceed to 706B.

[0152] In 706B, system 202 is configured to assign a specific user 214 to a third cluster of multiple clusters 202B. In one embodiment, system 202 determines that the third cluster is defined for users whose user emission score is greater than 0 gCO2e and less than a second threshold (e.g., 51 gCO2e) associated with the second cluster, and classifies them as low-carbon emission users.

[0153] Figure 8 illustrates a flowchart 800 of an exemplary method for updating object model data based on emissions scores, according to one embodiment of the present disclosure. Figure 8 is described in relation to the elements of Figures 1, 2, 3, 4, 5A, 5B, 6, and 7. Referring to Figure 8, the flowchart 800 is shown therein. The operation of the exemplary method may be performed by any computing system, for example, computer 102 in Figure 1 or system 202 in Figure 2. The operation of flowchart 800 may be initiated on 802.

[0154] In 802, first interaction data is received that is associated with an interaction session between a specific user 214 and one or more interface elements of operation 210 of application 208. In one embodiment, system 202 is configured to receive interaction data associated with an interaction session between a specific user 214 and one or more interface elements of operation 210 of application 208.

[0155] In 804, the user emission score associated with a particular user 214's interaction session is generated based on the first interaction data. In one embodiment, the system 202 is configured to generate a user emission score associated with a particular user 214's interaction session based on the first interaction data.

[0156] In 806, a first cluster is identified for the user from among multiple clusters 202B based on the user emission score and cluster data. In one embodiment, system 202 is configured to identify a first cluster for the user from among multiple clusters 202B based on the user emission score and cluster data.

[0157] In 808, the cluster emission score associated with the first cluster is generated based on the user emission score and the cluster data associated with the first cluster. In one embodiment, the system 202 is configured to generate a cluster emission score associated with the first cluster based on the user emission score and the cluster data associated with the first cluster.

[0158] In 810, object model data associated with each interface element of one or more interface elements 212 is received. In one embodiment, the system 202 is configured to receive object model data associated with each interface element of one or more interface elements 212.

[0159] In 812, the object model data associated with at least one interface element of one or more interface elements 212 is updated based on the cluster emission score. In one embodiment, the system 202 is configured to update the object model data associated with at least one interface element of one or more interface elements 212 based on the cluster emission score.

[0160] At 814, the updated object model is output. In one embodiment, system 202 is configured to output the updated object model.

[0161] According to one or more embodiments of this disclosure, a computer program product for updating object model data is disclosed. The computer program product includes one or more computer-readable storage media and program instructions stored on one or more computer-readable storage media for performing an operation. The operation includes receiving interaction data associated with an interaction session between one or more interface elements of an operation for a particular user and application. The operation further includes generating a user emission score associated with the interaction session for a particular user based on the interaction data. The operation further includes identifying a first cluster from a plurality of clusters for a particular user based on the user emission score and cluster data. Cluster data is associated with each cluster of the plurality of clusters. The operation further includes generating a cluster emission score associated with the first cluster based on the user emission score and the cluster data associated with the first cluster. The operation further includes receiving object model data associated with each interface element of one or more interface elements. The operation further includes updating object model data associated with at least one interface element of one or more interface elements based on the cluster emission score. The operation further includes outputting the updated object model data.

[0162] The various embodiments described herein are presented for illustrative purposes only and are not intended to be exhaustive or limitful to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terms used herein have been selected to best describe the principles, practical applications, or technological improvements over commercially available technologies of the embodiments, or to enable other those skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method performed by a computer, The computer receives first interaction data associated with an interaction session between a specific user and one or more interface elements of the application's operation; The computer generates a user emission score associated with the interaction session of the particular user, based on the first interaction data; The computer then identifies a first cluster from a plurality of clusters for a particular user based on the user emission score and cluster data, where the cluster data is associated with each cluster of the plurality of clusters; The step of generating a cluster emission score associated with the first cluster using the computer, based on the user emission score and the cluster data associated with the first cluster; The computer receives object model data associated with each of the one or more interface elements; The steps include: updating the object model data associated with at least one interface element of the one or more interface elements based on the cluster emission score using the computer; and The computer outputs the updated object model data. A method performed by a computer, comprising the following:

2. The computer then compares the user emission score with a plurality of thresholds, where each of the plurality of thresholds corresponds to each of the plurality of clusters; The computer determines whether the user emission score is equal to or greater than a first threshold of the plurality of thresholds, where the first threshold is associated with the first cluster; and The computer, based on the determination, identifies the first cluster from the plurality of clusters for the specific user. A computer-based method according to claim 1, further comprising the following:

3. The steps include: rendering at least one interface element of the one or more interface elements on the user device based on the updated object model data using the computer; and The computer performs the operation based on the rendering. A computer-based method according to claim 1, further comprising the following:

4. The method performed by a computer according to claim 3, further comprising the step of rendering the at least one interface element simultaneously with the interaction session based on the updated object model data by the computer.

5. The computer obtains second interaction data associated with the interaction session, wherein the second interaction data is associated with an interaction between the particular user and the at least one interface element, wherein the at least one interface element is updated based on the updated object model data; The computer dynamically updates the user emission score based on the second interaction data; The computer determines whether the updated user emission score is equal to or greater than a second threshold, wherein the second threshold is associated with the second cluster of the plurality of clusters; and The computer, based on the determination, reassigns the specific user from the first cluster to the second cluster from the plurality of clusters. A computer-based method according to claim 3, further comprising the following:

6. The computer updates the user interface associated with the operation of the application, wherein the step of updating the user interface is based on the updated object model data; and The computer renders the updated user interface. The computer-based method according to claim 2, further comprising the following:

7. The steps include: receiving emission value data associated with each interface element of the one or more interface elements by the computer; and The computer generates the user emission score based on the emission value data and the first interaction data. A computer-based method according to claim 1, further comprising the following:

8. A step in which the computer determines, based on the first interaction data and the emission value data, the element emission score associated with each interface element of the one or more interface elements; The computer identifies at least one interface element of the one or more interface elements based on the element emission score, wherein the element emission score of the at least one interface element is greater than the emission threshold; and The computer updates the object model data associated with the identified at least one interface element based on the cluster data associated with the first cluster. The computer-based method according to claim 7, further comprising the following:

9. The computer-based method according to claim 1, wherein the first size associated with the updated object model data of the at least one interface element is smaller than the second size associated with the object model data of the at least one interface element.

10. The operation of the application is associated with a plurality of interface elements, and the particular user interacts with one or more of the plurality of interface elements in the interaction session, and the method is The steps include: the computer receiving user input associated with the plurality of interface elements from the specific user; and The computer updates the object model data associated with at least one interface element of the one or more interface elements based on the user input. A computer-based method according to any one of claims 1 to 9, further comprising the following:

11. The first cluster includes a plurality of users who perform the operations of the application, the plurality of users excluding the specific user, and the method is The computer generates the cluster emission score associated with the first cluster based on the user emission score and the plurality of user emission scores, wherein the cluster data includes the plurality of user emission scores associated with the plurality of users. A computer-based method according to claim 10, further comprising the following:

12. The computer trains a first artificial intelligence (AI) model based on historical interaction data, wherein the historical interaction data is associated with multiple training interaction sessions between multiple training users and the operations of the application; The step of applying the trained first AI model to the user emission score and the cluster data using the computer; The steps include: the computer identifying the first cluster from the plurality of clusters for the specific user based on the application; and The step of the computer grouping the specific user within the first cluster based on the identification. A computer-based method according to any one of claims 1 to 9, further comprising the following:

13. Processor set; One or more computer-readable storage media; and The processor set comprises program instructions stored in one or more computer-readable storage media, and the program instructions are transmitted to the processor set. To receive first interaction data associated with an interaction session between a specific user and one or more interface elements of the application's operation; Based on the first interaction data, a user emission score associated with the interaction session of the specific user is generated; Based on the user emission score and cluster data, a first cluster is identified from a plurality of clusters for the specific user, where the cluster data is associated with each of the plurality of clusters; Based on the user emission score and the cluster data associated with the first cluster, a cluster emission score associated with the first cluster is generated; The object model data associated with each of the one or more interface elements is received; Based on the cluster emission score, update the object model data associated with at least one interface element of the one or more interface elements; Based on the updated object model data, at least one of the one or more interface elements is rendered on the user device, where the at least one interface element is rendered simultaneously with the interaction session. A computer system that can be executed by the aforementioned set of processors.

14. The aforementioned program instructions further include the following in the processor set: The user emission score is compared with a plurality of thresholds, where each of the plurality of thresholds corresponds to each of the plurality of clusters; The system determines whether the user emission score is equal to or higher than a first threshold among the plurality of thresholds, where the first threshold is associated with the first cluster; Based on the above determination, the first cluster is identified from the multiple clusters for the specific user. The computer system according to claim 13.

15. The aforementioned program instructions further include the following in the processor set: The system retrieves second interaction data associated with the interaction session, where the second interaction data is associated with an interaction between the specific user and the at least one interface element, where the at least one interface element is updated based on the updated object model data; The user emission score is dynamically updated based on the second interaction data; Determine whether the updated user emission score is equal to or greater than a second threshold, where the second threshold is associated with the second cluster of the plurality of clusters; Based on the above determination, the specific user is reassigned from the first cluster to the second cluster among the multiple clusters. The computer system according to claim 13.

16. The aforementioned program instructions further include the following in the processor set: The user interface associated with the operation of the application is updated, wherein the update of the user interface is based on the updated object model data; The updated user interface is rendered on the user device. The computer system according to claim 13.

17. The aforementioned program instructions further include the following in the processor set: The system receives emission value data associated with each of the one or more interface elements; The user emission score is generated based on the emission value data and the first interaction data. The computer system according to any one of claims 13 to 16.

18. The aforementioned program instructions further include the following in the processor set: Based on the first interaction data and the emission value data, the element emission score associated with each interface element of the one or more interface elements is determined; Based on the element emission score, at least one interface element of the one or more interface elements is identified, wherein the element emission score of the at least one interface element is greater than the emission threshold; The object model data associated with the identified at least one interface element is updated based on the cluster data associated with the first cluster. The computer system according to claim 17.

19. A computer program for updating object model data, wherein the computer program is A procedure for receiving first interaction data associated with an interaction session between one or more interface elements of a specific user and application operation; A procedure for generating a user emission score associated with the interaction session of a particular user, based on the first interaction data; A procedure for identifying a first cluster from a plurality of clusters for a particular user, based on the user emission score and cluster data, wherein the cluster data is associated with each cluster of the plurality of clusters; A procedure for generating a cluster emission score associated with a first cluster based on the user emission score and the cluster data associated with the first cluster; A procedure for receiving object model data associated with each of the one or more interface elements; A procedure for updating the object model data associated with at least one interface element of the one or more interface elements based on the cluster emission score; and Procedure for outputting the updated object model data Program instructions that cause a set of processors to perform an operation including A computer program equipped with [a specific feature / ability].

20. The operation includes the steps of rendering at least one interface element of the one or more interface elements on the user device simultaneously with the interaction session based on the updated object model data; and Procedure for performing the operation based on the rendering The computer program according to claim 19, further comprising:

Images