Item processing method and device, equipment and storage medium

Abstract

According to the embodiment of the invention, an item processing method and device, equipment and a storage medium are provided. The method comprises the following steps: identifying at least one item to be processed for a target user from at least one data source associated with a user interaction activity; using a machine learning model to determine corresponding context information of one or more items in the at least one item, the context information indicating available elements for processing the corresponding items; and generating a processing suggestion for the one or more items based on the corresponding context information of the one or more items.

Description

Technical Field

[0001] Example embodiments of the present disclosure generally relate to the field of computers, and more particularly, to methods, apparatuses, devices, and computer-readable storage media for transaction processing. Background Art

[0002] With the development of information technology, various terminal devices can provide a variety of services to people in work and life. Applications that provide these services can be deployed on these terminal devices. Applications can be designed to provide users with digital assistants and task management services. Digital assistants can help users manage various tasks. Summary of the Invention

[0003] In a first aspect of the present disclosure, a method for item processing is provided. The method comprises: identifying at least one item to be processed for a target user from at least one data source associated with a user interaction activity; determining, using a machine learning model, corresponding context information for one or more items of the at least one item, the context information indicating available elements for processing the corresponding item; and generating processing suggestions for the one or more items based on the corresponding context information of the one or more items.

[0004] In a second aspect of the present disclosure, a device for item processing is provided. The device includes: an item identification module configured to identify at least one item to be processed for a target user from at least one data source associated with a user interaction activity; an information determination module configured to determine, using a machine learning model, corresponding context information for one or more items in the at least one item, the context information indicating available elements for processing the corresponding item; and an item processing module configured to generate processing suggestions for the one or more items based on the corresponding context information of the one or more items.

[0005] In a third aspect of the present disclosure, an electronic device is provided. The device includes at least one processor; and at least one memory coupled to the at least one processor and storing instructions for execution by the at least one processor. When executed by the at least one processor, the instructions cause the device to perform the method of the first aspect.

[0006] In a fourth aspect of the present disclosure, a computer-readable storage medium is provided, wherein a computer program is stored on the computer-readable storage medium, and the computer program can be executed by a processor to implement the method of the first aspect.

[0007] In a fifth aspect of the present disclosure, a computer program product is provided, which is tangibly stored in a computer storage medium and includes computer-executable instructions, which, when executed by a device, cause the device to perform the method of the first aspect.

[0008] It should be understood that the content described in this summary section is not intended to limit the key features or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0010] Figure 1 A schematic diagram illustrating an example environment in which embodiments according to the present disclosure may be implemented;

[0011] Figure 2 A flowchart illustrating an example architecture for transaction processing according to some embodiments of the present disclosure;

[0012] Figures 3A to 3D An example interface for transaction processing according to some embodiments of the present disclosure is shown;

[0013] Figure 4 A flowchart illustrating an example process for transaction processing according to some embodiments of the present disclosure;

[0014] Figure 5 A schematic structural block diagram showing an example apparatus for transaction processing according to some embodiments of the present disclosure; and

[0015] Figure 6 A block diagram of an electronic device capable of implementing various embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0016] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0017] It should be noted that the titles of any section / subsection provided herein are not limiting. Various embodiments are described throughout this document, and any type of embodiment may be included under any section / subsection. Furthermore, the embodiments described in any section / subsection may be combined in any manner with any other embodiments described in the same section / subsection and / or in different sections / subsections.

[0018] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may be included below. The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may be included below.

[0019] The embodiments of the present disclosure may involve user data, data acquisition and / or use, etc. These aspects shall comply with the corresponding laws, regulations and relevant provisions. In the embodiments of the present disclosure, all data collection, acquisition, processing, processing, forwarding, use, etc. are carried out on the premise that the user is aware of and confirms them. Accordingly, when implementing the various embodiments of the present disclosure, the types, scope of use, and usage scenarios of the data or information that may be involved should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with the relevant laws and regulations. The specific notification and / or authorization method may vary according to the actual situation and application scenario, and the scope of the present disclosure is not limited in this respect.

[0020] If this specification and the solutions in the examples involve the processing of personal information, such processing will be done only with a legitimate basis (such as with the consent of the subject of personal information or as necessary for the performance of a contract) and only within the prescribed or agreed scope. A user's refusal to process personal information other than that required for basic functions will not affect the user's use of basic functions.

[0021] As used herein, the term "model" can learn the association between corresponding inputs and outputs from training data, so that after training is completed, corresponding outputs can be generated for given inputs. The generation of the model can be based on machine learning technology. Deep learning is a machine learning algorithm that processes inputs and provides corresponding outputs by using multiple layers of processing units. A neural network model is an example of a model based on deep learning. In this article, "model" may also be referred to as "machine learning model", "learning model", "machine learning network" or "learning network", and these terms are used interchangeably in this article.

[0022] Generally speaking, machine learning can be roughly divided into three stages, namely the training stage, the testing stage, and the application stage (also called the inference stage). In the training stage, a given model can be trained using a large amount of training data, and the parameter values ​​are continuously updated iteratively until the model can obtain consistent inferences that meet the expected goals from the training data. Through training, the model can be considered to be able to learn the association between input and output (also called input-to-output mapping) from the training data. The parameter values ​​of the trained model are determined. In the testing stage, the test input is applied to the trained model to test whether the model can provide the correct output, thereby determining the performance of the model. The testing stage can sometimes be integrated into the training stage. In the application or inference stage, the trained model can be used to process the actual model input based on the parameter values ​​obtained through training to determine the corresponding model output.

[0023] Figure 1 FIG2 is a schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented. In the example environment 100 , a component execution platform 110 can support the execution of a business component 125 . A user 140 can interact with the business component 125 via a client of the component execution platform 110 .

[0024] In some embodiments, the business component 125 can be downloaded and installed on the terminal device of the user 140. In some embodiments, the business component 125 can also be accessed through other means, such as through a web page. Figure 1 In the environment 100 , in response to the business component 125 being started, the client of the component execution platform 110 may present the interface 150 of the business component 125 .

[0025] The business component 125 includes but is not limited to one or more of the following: chat business component (also known as instant messaging business IM component), document business component, audio and video conference business component, email business component, task business component, calendar business component, objectives and key results (OKR) business component, etc. It can be understood that although Figure 1Although a single business component is shown in the figure, multiple business components can actually be installed on the component runtime platform 110. Multiple business components can be integrated on the component runtime platform 110, and such a component runtime platform 110 can be considered a multifunctional collaboration platform. When multiple business components are installed on a terminal device, these multiple business components can be integrated on one or more component runtime platforms 110. On the component runtime platform 110, people can launch different business components as needed to complete corresponding information processing, sharing, communication, etc. Business component 125 can provide content entity 126. Content entity 126 can be a content instance created by user 140 or other users on business component 125. For example, depending on the type of business component 125, content entity 126 can be a document (e.g., a Word document, a PDF document, a presentation, a spreadsheet document, etc.), an email, a message (e.g., a conversation message on an instant messaging business component), a calendar, a schedule, a task, audio, video, an image, etc.

[0026] In some embodiments, the component execution platform 110 may provide a digital assistant 120. The digital assistant 120 may be provided by a separate business component, or may be integrated into a business component 125 that can provide a content entity. The business component for providing a client interface for the digital assistant may correspond to a single-function business component or a multi-function collaboration platform, such as an office suite or other collaboration platform that can integrate multiple components. It is understood that similar to the business component, although Figure 1 A single digital assistant is shown in the figure, but there can actually be multiple digital assistants.

[0027] In some embodiments, the digital assistant 120 supports the use of plug-ins. Each plug-in can provide one or more functions of a business component. Such plug-ins include, but are not limited to, one or more of the following: a search plug-in, a contact plug-in, a message plug-in, a document plug-in, a spreadsheet plug-in, an email plug-in, a calendar plug-in, a schedule plug-in, a task plug-in, and the like.

[0028] The digital assistant 120 can be an intelligent assistant for the user, having intelligent dialogue and information processing capabilities. In an embodiment of the present disclosure, the digital assistant 120 is used to interact with the user 140 to assist the user 140 in using a terminal device or a service component. An interaction window with the digital assistant 120 can be presented in the client interface. In the interaction window, the user 140 can communicate with the digital assistant 120 by inputting natural language, pictures, audio files, video files, web page files, etc., to instruct the digital assistant to assist in completing various tasks, including operations on the content entity 126.

[0029] In some embodiments, user 140 has a corresponding relationship with digital assistant 120. For example, a first digital assistant corresponds to the first user, a second digital assistant corresponds to the second user, and so on. In some embodiments, the first digital assistant can uniquely correspond to the first user, the second digital assistant can uniquely correspond to the second user, and so on. In other words, the first digital assistant of the first user can be specific to or exclusive to the first user. For example, in the process of the first digital assistant providing assistance or services to the first user, the first digital assistant can utilize its historical interaction information with the first user, the data authorized by the first user that it can access, the current interaction context with the first user, and so on. If the first user is an individual or a person, the first digital assistant can be considered a personal digital assistant. It will be understood that in the disclosed embodiments, the first digital assistant accesses authorized data based on the authorization of the first user. In some embodiments, digital assistant 120 can access authorized data associated with user 140 with the authorization of user 140. Digital assistant 120 can assist user 140 in processing the data associated with user 140 that it can access. For example, user 140 can grant permission to the digital assistant 120 for the phone deployed on user 140's terminal device. The digital assistant 120 can access and process various call data of the user 140. For example, when a new call comes in on the terminal device of the user 140, the digital assistant 120 can identify whether the contact of the call is a stranger and can automatically answer or hang up the call. It should be understood that "uniquely corresponding" or similar expressions in this disclosure are not intended to limit the first digital assistant to be updated accordingly based on the interaction process between the first user and the first digital assistant. Of course, depending on actual needs, the digital assistant 120 does not have to be specific to the current user 140, but can be a general digital assistant.

[0030] In some embodiments, multiple interaction modes can be provided between user 140 and digital assistant 120, and flexible switching between the multiple interaction modes can be achieved. When a certain interaction mode is triggered, a corresponding interaction area is presented to facilitate interaction between user 140 and digital assistant 120. In different interaction modes, the interaction methods between user 140 and digital assistant 120 are different, which can flexibly adapt to the interaction needs in different scenarios.

[0031] In some embodiments, information processing services specific to user 140 can be provided based on historical interaction information between user 140 and digital assistant 120 and / or data ranges specific to user 140. In some embodiments, historical interaction information of user 140 interacting with digital assistant 120 in multiple interaction modes can be stored in association with user 140. In this way, in one of the multiple interaction modes (any or a designated one), digital assistant 120 can provide services to user 140 based on the historical interaction information stored in association with user 140.

[0032] The digital assistant 120 can be called or awakened by an appropriate means (e.g., a shortcut key, a button, or voice) to present an interaction window with the user 140. By selecting the digital assistant 120, the interaction window with the digital assistant 120 can be opened. The interaction window may include interface elements for information interaction, such as an input box, a message list, a message bubble, and so on. In other embodiments, the digital assistant 120 can be awakened through an entry control or menu provided in a page, or by inputting a preset instruction.

[0033] The interaction window between the digital assistant 120 and the user 140 may include a conversation window, such as a conversation window in an instant messaging service component or an instant messaging module of a target service component. In the conversation window, the interaction between the digital assistant 120 and the user 140 may be presented in the form of conversation messages. Alternatively or additionally, the interaction window between the digital assistant 120 and the user 140 may also include other types of windows, such as a window in a floating window mode, in which the user 140 can trigger the digital assistant 120 to perform corresponding operations by inputting instructions, selecting shortcut instructions, etc.

[0034] In some embodiments, digital assistant 120 may support a conversation window interaction mode, also referred to as conversation mode. In this interaction mode, a conversation window is presented between user 140 and digital assistant 120, in which user 140 and digital assistant 120 interact through conversation messages. In conversation mode, digital assistant 120 can perform tasks based on the conversation messages in the conversation window. In the interaction window, user 140 enters interaction messages, and digital assistant 120 provides reply messages in response to the user input.

[0035] In some embodiments, the conversation mode between user 140 and digital assistant 120 can be invoked or awakened by an appropriate means (e.g., a shortcut key, button, or voice) to present a conversation window. By selecting digital assistant 120, a conversation window with digital assistant 120 can be opened. The conversation window may include interface elements for information interaction, such as an input box, a message list, a message bubble, and the like.

[0036] In some embodiments, the digital assistant 120 may support an interactive mode of a floating window (or floating window), also referred to as a floating window mode. When the floating window mode is triggered, the operation panel (also referred to as a floating window) corresponding to the digital assistant 120 is presented, and the user 140 can issue instructions to the digital assistant 120 based on the operation panel. In some embodiments, the operation panel may include at least one candidate shortcut instruction. Alternatively or additionally, the operation panel may include an input control for receiving instructions. In floating window mode, the digital assistant 120 can perform tasks according to instructions issued by the user 140 through the operation panel.

[0037] In some embodiments, the floating window mode of the user 140 and the digital assistant 120 can also be called or awakened by an appropriate means (for example, a shortcut key, a button, or voice) to present the corresponding operation panel. In some embodiments, the awakening of the digital assistant 120 can be supported in a specific business component, such as a document business component, to provide interaction in the floating window mode. In some embodiments, in order to trigger the floating window mode to present the operation panel corresponding to the digital assistant 120, an entry control for the digital assistant 120 can be presented in the business component interface. In response to detecting a triggering operation for the entry control, it can be determined that the floating window mode is triggered, and the operation panel corresponding to the digital assistant 120 is presented in the target interface area.

[0038] In some embodiments described below, for ease of discussion, the interaction window between the user and the digital assistant is mainly taken as an example, which is a conversation window.

[0039] The component operation platform 110 can be deployed locally on the terminal device of each user 140, and / or can be supported by a server-side device. For example, the terminal device of user 140 can run a client of the component operation platform 110, which can support the interaction between the user 140 and the component operation platform 110 provided by the server. In the case where the component operation platform 110 runs locally on the user's terminal device, the user 145 can directly use the terminal device to interact with the local component operation platform 110. In the case where the component operation platform 110 runs on the server-side device, the server-side device can realize the service provision of the client running in the terminal device based on the communication connection between the terminal device. The component operation platform 110 can present a corresponding interface 150 to the user 140 based on the operation of the user 140, so as to output and / or receive information related to the use of the component to the user 140 and / or from the user 140.

[0040] In some embodiments, at least some functionality of business component 125 and / or at least some functionality of digital assistant 120 can be implemented based on models. During the operation of business component 125, one or more models 155 can be invoked. Models 155 can be used to understand user input and provide services based on the output of model 155, such as providing responses to the user.

[0041] Although shown as being independent of the component execution platform 110, one or more models 155 can run on the component execution platform 110 or other remote servers. In some embodiments, the model 155 can be a machine learning model, a deep learning model, a learning model, a neural network, etc. In some embodiments, the model can be based on a language model (LM). A language model can have question-answering capabilities by learning from a large amount of corpus. The model 155 can also be based on other appropriate models.

[0042] The component operation platform 110 can run on an appropriate electronic device. The electronic device here can be any type of device with computing capabilities, including a terminal device or a server device. The terminal device can be any type of mobile terminal, fixed terminal or portable terminal, including a mobile phone, a desktop computer, a laptop computer, a notebook computer, a netbook computer, a tablet computer, a media computer, a multimedia tablet, a personal communication system (PCS) device, a personal navigation device, a personal digital assistant (PDA), an audio / video player, a digital camera / camcorder, a positioning device, a television receiver, a radio broadcast receiver, an e-book device, a gaming device or any combination of the foregoing, including accessories and peripherals of these devices or any combination thereof. The server device can, for example, include a computing system / server, such as a mainframe, an edge computing node, a computing device in a cloud environment, and the like. In some embodiments, the component operation platform 110 can be implemented based on a cloud service.

[0043] It should be understood that the structure and function of the various elements in the environment 100 are described for illustrative purposes only and do not imply any limitation on the scope of the present disclosure.

[0044] As mentioned above, the application in the terminal device can provide event management services for users. However, the traditional event management method is for users to manually manage events through applications. The management of events may include but is not limited to: users actively recording pending events in the application, users manually selecting or entering the planned processing time for events, users manually changing the status of events after the events are completed, etc. It can be seen that this method relies on user operations, resulting in poor flexibility and low efficiency in event management. In addition, some event management methods can use models to automatically identify pending events associated with users and determine descriptive information for events. However, these methods cannot associate the identified events with the user's schedule information. These methods are also unable to help users handle events quickly and efficiently. These defects lead to low event management efficiency and poor user experience.

[0045] In light of this, embodiments of the present disclosure propose a solution for item handling. This solution first identifies items to be handled for a target user from data sources associated with user interaction activities. Then, using a machine learning model, it determines the corresponding contextual information for one or more of the identified items. This contextual information may indicate the available elements for handling the corresponding item. Furthermore, based on the corresponding contextual information of the item, it may generate a handling suggestion for the item.

[0046] In this way, embodiments of the present disclosure can automatically identify pending items for a target user from data sources associated with user interaction activities. This can save users time and improve the user experience. Embodiments of the present disclosure can also utilize machine learning models to determine contextual information indicating available elements for processing items, so that contextual information can be used to provide processing suggestions for the items. This improves the convenience and efficiency of item processing.

[0047] Various example implementations of this solution are described in detail below in conjunction with the accompanying drawings.

[0048] Figure 2 Flowchart showing an example architecture 200 for transaction processing according to some embodiments of the present disclosure. The example architecture 200 may be implemented in Figure 1 The components of the operating platform 110 will refer to Figure 1The method 200 is described with reference to the environment 100 of FIG. Hereinafter, the example embodiments will be primarily described with respect to the component execution platform 110. It should be understood that the actions described with respect to the component execution platform 110 may be performed by the component execution platform 110 in collaboration with its server. For example, in a scenario where the actions are performed by the component execution platform 110 in collaboration with its server, the component execution platform 110 may request the server to provide information to be presented, or the server may proactively or in response to an operation of the component execution platform 110 provide the information to be presented to the component execution platform 110.

[0049] In box 210, the user 140 can grant the component runtime platform 110 access to multiple data sources associated with their interactive activities. User interactive activities can refer to various interactions between the user and various objects such as the operating system installed on the corresponding terminal device, various applications (such as social communication applications), various functional blocks (such as functional blocks for sending information). User interactive activities may include but are not limited to: viewing notification messages of various applications, participating in online meetings, making and receiving calls, processing documents, etc. The data sources associated with the user interactive activities may include various applications, such as calendar applications, meeting applications, task management applications, document applications, etc. The data source may be, for example, a calendar application, a meeting application, a task management application, a document application, etc. Figure 1 The business components 125 shown include, for example, IM components, document components, and the like.

[0050] In some embodiments, multiple data sources may have different granularities or levels. In other words, a data source may be further divided into multiple sub-data sources. For example, for an IM application, the IM application may be considered a high-level data source, and the group chat session within the IM application may be considered a data source at a lower level than the IM application, or a sub-data source of the IM application. For another example, for a project management application, the project management application may be considered a high-level data source, and the specific entities within the project management application used to manage a project may be considered a data source at a lower level than the project management application, or a sub-data source of the project management application.

[0051] As an example, a user interaction activity may be a user editing a document. The data source associated with the user interaction activity (e.g., a document application) may store or record the name of the edited document, the content edited by the user, etc. As another example, a user interaction activity may be a user participating in a meeting. The data source associated with the user interaction activity (e.g., a meeting application) may store or record the name of the meeting participated in, the minutes, the speeches of each party, etc. It should be noted that the activation of functions related to data acquisition, the acquired data, the processing and storage methods of the data, etc. in the embodiments of the present disclosure shall obtain the prior authorization of the user and other rights holders associated with the user, and shall comply with the provisions of relevant laws and regulations and the rules of agreement between rights holders.

[0052] It should be understood that only examples of user interaction activities and data sources associated with user interaction activities are given above. In actual applications, user interaction activities and data sources associated with user interaction activities may include more, less, or other content associated with the user.

[0053] In block 215, the component runtime platform 110 may identify matters to be processed for the target user from the data sources associated with the user interaction activities. It can be understood that the component runtime platform 110 may adopt any suitable method to identify the matters to be processed for the target user. As an example, the component runtime platform 110 may utilize a configured natural language processing model (e.g., a large language model) to perform operations such as keyword detection and semantic analysis on the data sources associated with the user interaction activities. Through these operations, the matters to be processed for the target user can be identified. For example, the data source associated with the user interaction activity may be a group chat session. Suppose the message in a certain group chat session is "@Zhang, how is the progress of Project A?". By detecting the keyword "Zhang" in this group chat message, it can be determined that the target user of this group chat message is "Zhang". Then, through semantic analysis of this group chat message, it can be determined that this group chat message indicates that the user "Zhang" needs to provide feedback on "the progress of Project A". Thus, a matter to be processed for the user "Zhang" can be identified from this group chat message. The content of the matter is to provide feedback on "the progress of Project A".

[0054] In block 215, the component runtime platform 110 may utilize the machine learning model 220 to determine the corresponding context information of the matters to be processed. The context information may indicate the available elements for processing the corresponding matters. Such available elements may include elements related to the generation of the matters (e.g., background, requirements), elements related to the processing of the matters (e.g., reference materials, required tools, etc.), and the associated parties of the matters (e.g., other users who may be interested in the progress of the matters).

[0055] In some embodiments, contextual information may include, but is not limited to: the background of the corresponding matter, demand information of the corresponding matter, reference information for handling the corresponding matter, recommended tools for handling the corresponding matter, and other users associated with the corresponding matter. The background of the corresponding matter facilitates understanding the causes and consequences of the matter. The demand information of the corresponding matter may indicate the tasks involved in the matter. Reference information for handling the corresponding matter may include relevant information on other matters similar to the matter or reference materials that are helpful in solving the matter. Reference information may provide knowledge or experience support for handling the corresponding matter. The recommended tools for handling the corresponding matter may be any suitable software tools and / or hardware tools, such as intelligent agents, digital assistants, etc. that can realize specific functions. The provided recommended tools can be used to quickly handle matters or parts of matters, thereby improving the efficiency of matter handling.

[0056] In some embodiments, the corresponding context information of the matters can be used to screen matters. Exemplarily, if the component operation platform 110 identifies two or more candidate matters from different data sources, the similarity between the two or more candidate matters can be compared based on the corresponding context information of the two or more candidate matters. For example, the similarity between the two or more candidate matters can be determined by comparing whether the background, requirement information, reference information, etc. of the two or more candidate matters are respectively consistent or matched. If the similarity between the two or more candidate matters is higher than the similarity threshold, it can be determined that the two or more candidate matters are the same matter. To avoid duplication, two or more candidate matters can be merged into one matter.

[0057] In box 225, the items identified from the data source associated with the user interaction activity can be provided or presented to the target user for confirmation. The target user can check whether the identified items are items that need to be handled or resolved by him / her. It can be understood that in some scenarios, the identified items may be for multiple target users. Since the capabilities, work scope, etc. of different users may be different, the identified items may exceed the capabilities or work scope of some target users. Therefore, the target user can further check the identified items to avoid associating irrelevant items with the target user, resulting in a poor user experience. In box 235, if the user checks and determines that the identified items are items to be handled by him / her, the items can be added to the list of items to be scheduled for the user. In box 230, if the user checks and determines that the identified items are not items to be handled by him / her, the items can be deleted.

[0058] In some embodiments, the component execution platform 110 may present the identified items to the user in a visual form. For example, the component execution platform 110 may present an interface indicating the identified items through a configured calendar plug-in. Figures 3A to 3DExample interfaces 300A to 300D for transaction processing are shown according to some embodiments of the present disclosure.

[0059] It should be understood that the interfaces shown in the accompanying drawings are merely examples, and various interface designs are possible. The various graphical elements in the interface may have different arrangements and different visual representations, one or more elements may be omitted or replaced, and one or more other elements may also be present. The embodiments of the present disclosure are not limited in this respect.

[0060] In some embodiments, the component execution platform 110 may present a schedule information request in response to the user 140's request to view the schedule information. Figure 3A Interface 300A shown. Interface 300A includes an area 310 indicating the classification of matters, an area 320 presenting a list of matters, and an area 330 presenting schedule information. Exemplarily, the component runtime platform 110 may present a list of matters to be scheduled in area 320 and a list of scheduled matters in area 330 in response to the user 140 triggering the "all items" control in area 310. Summary information of matters may be presented in the list. Exemplarily, the summary information may include the name, source, time of occurrence, etc. of the matter. The presentation of summary information can present as many matters as possible in the interface while facilitating the user's preliminary understanding of the matter. The summary information of a matter may be determined based on the contextual information of the matter. For example, part of the information may be selected from the contextual information as the summary information of the matter. For another example, summary information of a matter may be extracted or refined based on the contextual information.

[0061] Continue to refer Figure 2 .exist Figure 2 In block 240, the component execution platform 110 may generate a handling recommendation for the issue based on the determined contextual information of the issue. The component execution platform 110 may utilize a machine learning model or an agent based on the machine learning model to generate the handling recommendation. Handling the issue may include planning, scheduling, and resolving the issue. Accordingly, the handling recommendation may include suggestions related to one or more aspects of planning, scheduling, and resolving the issue, such as a recommended handling time, a recommended solution, and the like.

[0062] In some embodiments, the processing suggestion may include a recommended plan for the matter. In some embodiments, the component execution platform 110 can automatically (for example, using a machine learning model or an agent) generate a recommended plan for the matter based on the context information of the matter. For example, based on the context information of the matter, a recommended processing time for the matter is generated. The recommended processing time can be presented to the user for confirmation, or the matter can be directly scheduled to the recommended processing time in the schedule information. Alternatively or additionally, in some embodiments, the component execution platform 110 can generate a recommended plan based at least in part on user interaction (for example, user input, user selection). Refer to the following Figure 3A and Figure 3B to describe such an example embodiment.

[0063] In some embodiments, the component execution platform 110 may receive an event planning instruction for an event through a presented interface. Then, the component execution platform 110 may generate a recommended plan for the event based on the received event planning instruction.

[0064] In some embodiments, the event planning instructions may include time planning instructions for the event. In some embodiments, the time planning instructions for the event may be triggered by the user 140 in any suitable manner. For example, the user 140 may Figure 3A Item 1 indicated in the interface 300A shown is moved from position 325 to position 315 in the schedule information. In response to the movement operation of the user 140, the component operation platform 110 can determine the time indicated by position 315 (for example, the time composed of date 2 and time period 3) as the planned processing time of item 1. The component operation platform 110 can associate the time indicated by position 315 with item 1 to clearly prompt the user 140 of the planned processing time of item 1. For example, the component operation platform 110 can present item 1 at position 315. Then, the component operation platform 110 can delete item 1 from area 320 to avoid repeatedly receiving time planning instructions for item 1. In this way, users are supported to freely arrange the planned processing time of items, thereby improving the user's interactive experience.

[0065] Alternatively or additionally, in some embodiments, the time planning instructions for the matters can be automatically triggered by the digital assistant 120 in the component execution platform 110. Figure 2In the illustrated architecture 200, upon user 140 confirming that the identified item is a pending item, digital assistant 120 can determine a reasonable planned processing time for the identified item based on user 140's schedule information. Component execution platform 110 can then associate the determined planned processing time with Item 1. For example, component execution platform 110 can present Item 1 at the planned processing time determined in the schedule information to clearly indicate to user 140 the planned processing time for Item 1. This saves the user's time and improves the convenience of item processing.

[0066] In some embodiments, the matter planning instruction may include an instruction to split the matter. The instruction to split the matter may be to split the matter into two or more matters. The instruction to split the matter may be triggered in any suitable manner. In some embodiments, such as Figure 3B In interface 300B, component execution platform 110 may, in response to receiving a trigger for item 1, present an area 350 containing multiple controls for item 1. Component execution platform 110 may, in response to receiving a trigger for control 351, determine a split indication for item 1. Component execution platform 110 may present area 340 in the interface. Area 340 may present multiple recommended sub-items (e.g., sub-item 1 and sub-item 2) for item 1. These multiple recommended sub-items may be automatically generated using a machine learning model. This improves the user's convenience in splitting items and saves time. If the presented recommended sub-items do not meet the user's needs, the user can manually edit and split item 1. This increases the flexibility of item splitting. Furthermore, if component execution platform 110 receives a trigger for confirmation control 348, it may present summary information corresponding to the multiple sub-items determined in area 310. It will be appreciated that after item 1 is split, the summary information for item 1 may be removed from area 310. Alternatively, the summary information of item 1 may be replaced with the corresponding summary information of the determined multiple sub-items, so that the user can quickly locate the split sub-items.

[0067] In some embodiments, the event planning instructions may include a blocking instruction for the event. Figure 3BIn the interface 300B shown, if the component operation platform 110 receives a trigger on the control 353, it can determine a blocking indication for item 1. Assuming that item 1 is identified from data source 1, the component operation platform 110 can determine data source 1 as a data source that meets the preset conditions. The preset conditions here can indicate that the target user's attention to the data source is lower than the attention threshold. In other words, the target user may no longer pay attention to the information or events generated by data source 1. In this case, the component operation platform 110 can cancel the determination of item 1 as a pending item for user 140. In some embodiments, the component operation platform 110 can remove all items (including item 1) identified from data source 1 contained in the schedule information of user 140 from the schedule information of user 140. This can reduce the resource usage of schedule information.

[0068] In some embodiments, the component execution platform 110 can prevent items from data source 1 from being identified as pending items for the target user. For example, if an item is subsequently identified from data source 1, the identified item will no longer be determined as a pending item for the target user. In other words, information about the item will not be presented to the target user. For another example, data source 1 can be removed from the multiple data sources 210 that serve as item sources. For example, if a user issues a blocking instruction for items from a group chat session, the items in the group chat session will not be identified as the user's pending items.

[0069] In addition to the instructions described above, users can also issue other instructions for matters. Figure 3B In the illustrated interface 300B, area 350 may include a control 352 indicating a forwarding instruction for an item. If the component execution platform 110 receives a trigger for the control 352 for item 1, item 1 may be sent to users confirmed by the user 140. These users may be selected by the user 140 or automatically recommended by the digital assistant 120. Area 350 may include a control 354 indicating a deletion instruction for an item. If the component execution platform 110 receives a trigger for the control 354 for item 1, item 1 may be removed from the schedule information of the user 140.

[0070] Through the above embodiments of the matter planning instructions, the present disclosure can support users in making diversified plans for pending matters. This can meet the various needs of users for matter management. During the matter planning process, corresponding recommendation information can be provided to users (for example, recommended sub-matters to be split, recommended users to send matters, etc.). This can reduce the complexity of user operations and improve the user experience.

[0071] In some embodiments, the component execution platform 110 may present the recommended information included in the context information of the matter in response to any appropriate triggering method of the user 140. Figure 3A The triggering of event 1 in the interface 300A shown in FIG. Figure 3C Interface 300C is shown. Interface 300C may include area 360. Area 360 may present the background of item 1, demand information, reference information, and related users. In addition, interface 300C may also include a conversation area 370 between user 140 and digital assistant 120. Conversation area 370 may present some recommended tools for item 1 (for example, including tool 1 and tool 2). The recommended tool may be presented in various forms such as text, links, plug-ins, etc. In this case, interface 300C may also be referred to as a conversation interface.

[0072] In some embodiments, user 140 can use the recommended tools to handle task 1 or part of task 1 while viewing the context information of the task. Figure 3C The session area 370 presented in the interface 300C shown inputs the processing instruction "Use tool 1 to generate a solution". Then, the digital assistant 120 can use the recommended tool selected by the user 140 to generate a recommended processing result for item 1 "The solution is as follows XXX". In this way, the digital assistant can assist the user in handling matters, thereby improving the efficiency of matter handling. In some embodiments, the user 140 can directly trigger the link or plug-in of the recommended tool provided by the digital assistant 120 to handle matter 1 by himself. It should be understood that only example processing methods for matters are given here. In actual applications, any suitable method can be used to handle matters. As long as these methods can improve the efficiency of matter handling, the embodiments of the present disclosure are not limited to this.

[0073] Continue to refer Figure 2 In some embodiments, Figure 2 In the box 245 shown, the user 140 can determine whether the task is completed based on the recommended processing result generated by the digital assistant. In some embodiments, in box 255, if the component execution platform 110 receives the user 140's confirmation of the completion of the task, it can present progress feedback information for the task. Figure 3DIn the illustrated interface 300D, if component execution platform 110 receives confirmation from user 140 that item 1 has been completed, progress feedback information for item 1 may be presented in area 380. The progress feedback information may include, but is not limited to, selection of candidate users associated with item 1 as indicated in box 382 and a notification message indicating completion of item 1 as indicated in box 384. User 140 may select a user or session with whom they wish to share the progress of item 1 in area 382.

[0074] In some embodiments, the component execution platform 110 may recommend users for user 140 to share the progress of item 1. These recommended users may be other users associated with item 1 determined based on the contextual information of item 1. The recommended users may be presented as selected. For example, the component execution platform 110 may present the recommended users in a "Selected" area included in area 382.

[0075] Furthermore, the component execution platform 110 may present a notification message indicating the completion of Item 1 in area 384 based on the user or session selected by user 140. Upon receiving a trigger on the confirmation control 384, the component execution platform 110 may send a notification message indicating the completion of Item 1 to the user and / or session selected by user 140. In this manner, progress information on completed items can be quickly shared, thereby enhancing the user's interactive experience.

[0076] After the processing of the item is completed, the item can be archived. For example, the item can be added to the collection of completed items. In some embodiments, if the user wants to view the completed items, the Figure 3A The "Filed" control is shown in the interface 300A. This allows for clear management of items.

[0077] In some embodiments, Figure 2 In block 250, if user 140 determines that a particular item is incomplete based on the recommended processing results for the item, component execution platform 110 may receive an indication from user 140 as to whether further processing of the item is necessary. If user 140 indicates that further processing of the item is necessary, the scheduled processing time for the item may be redefined. The item may further be presented in association with the redefined scheduled processing time. If user 140 indicates that further processing of the item is not necessary, the item may be deleted.

[0078] In this way, the embodiments of the present disclosure can automatically identify pending items for the user from various data sources associated with user interaction activities. This reduces the time and manpower required for users to manually determine items. The embodiments of the present disclosure can also use machine learning models to determine the contextual information of identified items. The contextual information of the items can then be used to assist users in handling items. This improves the efficiency of item handling and enhances the user experience. In particular, the embodiments of the present disclosure can associate the identified items with the user's schedule information. This makes it easier for users to view and manage items, improving the efficiency of item management.

[0079] Figure 4 FIG. 4 is a flow chart illustrating an example process 400 for transaction processing according to some embodiments of the present disclosure. The process 400 may be implemented at the component execution platform 110. Figure 1 4. The process 400 is described below.

[0080] like Figure 4 As shown, in block 410 , the component execution platform 110 identifies at least one item to be processed for a target user from at least one data source associated with a user interaction activity.

[0081] In block 420 , the component execution platform 110 utilizes the machine learning model to determine corresponding context information for one or more items in the at least one item, the context information indicating available elements for processing the corresponding item.

[0082] In block 430 , the component execution platform 110 generates processing suggestions for the one or more matters based on corresponding context information of the one or more matters.

[0083] In some embodiments, the contextual information indicates at least one of the following: the background of the corresponding matter, requirement information of the corresponding matter, reference information for processing the corresponding matter, one or more recommended tools for at least partially processing the corresponding matter, or other users associated with the corresponding matter.

[0084] In some embodiments, the component execution platform 110 presents, for a first item among one or more items, recommendation information included in the context information of the first item, where the recommendation information is at least used to process at least one recommendation tool for the first item; and in response to receiving a selection of a recommendation tool among at least one recommendation tool, generates a recommendation processing result for the first item using the selected recommendation tool.

[0085] In some embodiments, the component operating platform 110 presents at least a portion of the contextual information of the first matter in a first area of ​​the user interface; and presents a conversation interface between the target user and the digital assistant in a second area of ​​the user interface, wherein the recommendation information is at least partially included in the conversation interface.

[0086] In some embodiments, the component execution platform 110 receives an issue planning indication for a second issue among the at least one issue; and generates a recommended plan for the second issue based on the issue planning indication.

[0087] In some embodiments, the matter planning indication includes a time planning indication for the second matter, and the component operation platform 110 determines the planned processing time for the second matter in response to receiving the time planning indication for the second matter; and associates the second matter with the planned processing time in the target user's schedule information to complete the time planning for the second matter.

[0088] In some embodiments, the matter planning indication includes a split indication for the second matter, and the component operation platform 110, in response to receiving the split indication for the second matter, at least determines multiple sub-matters corresponding to the second matter; and in response to receiving confirmation of the multiple sub-matters, presents corresponding summary information of the multiple sub-matters.

[0089] In some embodiments, the matter planning indication includes a blocking indication for a second matter, and the second matter is identified from a first data source among at least one data source. In response to receiving the blocking indication for the second matter, the component operation platform 110 determines the first data source as a data source that meets a preset condition, wherein the preset condition indicates that the target user's attention to the data source is lower than an attention threshold; cancels the determination of the second matter from the first data source as a pending matter for the target user; and prevents the matter from the first data source from being identified as a pending matter for the target user.

[0090] In some embodiments, the component execution platform 110 presents a recommended processing result corresponding to the first task; and in response to receiving a confirmation of the recommended processing result, determines completion of the first task.

[0091] In some embodiments, the component operating platform 110 presents at least progress feedback information for the third matter in response to the completion of a third matter among one or more matters, the progress feedback information including at least the selection of one or more candidate users associated with the third matter and a notification message indicating the completion of the third matter; and in response to receiving confirmation of the progress feedback information, sends a notification message to the selected user among the one or more candidate users.

[0092] In some embodiments, the component execution platform 110 receives user interaction indicating whether the fourth item needs further processing in response to obtaining the processing result of the fourth item in at least one item; in response to the user interaction indicating that the fourth item needs further processing, associates the updated planned processing time for the fourth item with the fourth item in the target user's schedule information; and in response to the user interaction indicating that the fourth item does not need further processing, removes the fourth item from the target user's pending item set.

[0093] In some embodiments, the component operating platform 110 identifies at least one matter from at least one data source, including: for two or more candidate matters identified from different data sources in the at least one data source, comparing the similarities between the two or more candidate matters based on corresponding context information of the two or more candidate matters; and in response to determining that the similarity between the two or more candidate matters is higher than a similarity threshold, merging the two or more candidate matters into the same matter in at least one matter.

[0094] The embodiments of the present disclosure also provide corresponding devices for implementing the above methods or processes. Figure 5 1 shows a schematic structural block diagram of an example apparatus 500 for transaction processing according to certain embodiments of the present disclosure. The apparatus 500 may be implemented as or included in the electronic device 110. Each module / component in the apparatus 500 may be implemented by hardware, software, firmware, or any combination thereof.

[0095] like Figure 5 As shown, the device 500 includes: an item identification module 510, configured to identify at least one item to be processed for a target user from at least one data source associated with a user interaction activity; an information determination module 520, configured to use a machine learning model to determine corresponding context information of one or more items in at least one item, the context information indicating available elements for processing the corresponding item; and an item processing module 530, configured to generate processing suggestions for one or more items based on the corresponding context information of the one or more items.

[0096] In some embodiments, the contextual information indicates at least one of the following: the background of the corresponding matter, requirement information of the corresponding matter, reference information for processing the corresponding matter, one or more recommended tools for at least partially processing the corresponding matter, or other users associated with the corresponding matter.

[0097] In some embodiments, the matter processing module 530 can also be configured to present, for a first matter among one or more matters, recommendation information included in the context information of the first matter, where the recommendation information is at least used to process at least one recommendation tool for the first matter; and in response to receiving a selection of a recommendation tool among at least one recommendation tool, generate a recommendation processing result for the first matter using the selected recommendation tool.

[0098] In some embodiments, the matter processing module 530 can also be configured to: present at least a portion of the context information of the first matter in a first area of ​​the user interface; and present a conversation interface between the target user and the digital assistant in a second area of ​​the user interface, wherein the recommendation information is at least partially included in the conversation interface.

[0099] In some embodiments, the matter processing module 530 may be further configured to: receive a matter planning indication for a second matter among the at least one matter; and generate a recommended plan for the second matter based on the matter planning indication.

[0100] In some embodiments, the matter planning indication includes a time planning indication for the second matter, and the information presentation module can also be configured to: determine the planned processing time for the second matter in response to receiving the time planning indication for the second matter; and associate the second matter with the planned processing time in the target user's schedule information to complete the time planning for the second matter.

[0101] In some embodiments, the matter planning indication includes a split indication for the second matter, and the information presentation module can also be configured to: in response to receiving the split indication for the second matter, at least determine multiple sub-matters corresponding to the second matter; and in response to receiving confirmation of multiple sub-matters, present corresponding summary information of the multiple sub-matters.

[0102] In some embodiments, the matter planning indication includes a blocking indication for a second matter, and the second matter is identified from a first data source among at least one data source. The information presentation module can also be configured to: in response to receiving the blocking indication for the second matter, determine the first data source as a data source that meets a preset condition, wherein the preset condition indicates that the target user's attention to the data source is lower than an attention threshold; cancel the determination of the second matter from the first data source as a pending matter for the target user; and prevent the matter from the first data source from being identified as a pending matter for the target user.

[0103] In some embodiments, the matter processing module 530 may also be configured to: present a recommended processing result corresponding to the first matter; and determine the completion of the first matter in response to receiving confirmation of the recommended processing result.

[0104] In some embodiments, the device 500 also includes: an information feedback module, configured to, in response to the completion of a third item among one or more items, present at least progress feedback information for the third item, the progress feedback information including at least a selection of one or more candidate users associated with the third item and a notification message indicating the completion of the third item; and in response to receiving confirmation of the progress feedback information, send a notification message to a selected user among the one or more candidate users.

[0105] In some embodiments, the device 500 also includes: an information interaction module, configured to receive user interaction indicating whether the fourth item needs further processing in response to obtaining the processing result of the fourth item in at least one item; in response to the user interaction indicating that the fourth item needs further processing, associate the updated planned processing time for the fourth item with the fourth item in the target user's schedule information; and in response to the user interaction indicating that the fourth item does not need further processing, remove the fourth item from the target user's pending item set.

[0106] In some embodiments, the matter identification module 510 can also be configured to: identify at least one matter from at least one data source, including: for two or more candidate matters identified from different data sources in the at least one data source, compare the similarities between the two or more candidate matters based on the corresponding context information of the two or more candidate matters; and in response to determining that the similarity between the two or more candidate matters is higher than the similarity threshold, merge the two or more candidate matters into the same matter in at least one matter.

[0107] Figure 6 1 shows a block diagram of an electronic device 600 capable of implementing various embodiments of the present disclosure. Figure 6 As shown, electronic device 600 is in the form of a general electronic device. Components of electronic device 600 may include, but are not limited to, at least one processor or processing unit 610, memory 620, storage device 630, one or more communication units 640, one or more input devices 650, and one or more output devices 660. Processor 610 may be a real or virtual processor and is capable of performing various processes according to programs stored in memory 620. In a multi-processor system, multiple processors execute computer-executable instructions in parallel to increase the parallel processing capabilities of electronic device 600.

[0108] The electronic device 600 typically includes a plurality of computer storage media. Such media can be any accessible media that can be obtained by the electronic device 600, including but not limited to volatile and non-volatile media, removable and non-removable media. The memory 620 can be a volatile memory (e.g., registers, cache, random access memory (RAM)), a non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. The storage device 630 can be a removable or non-removable medium and can include a machine-readable medium, such as a flash drive, a disk, or any other medium that can be used to store information and / or data and can be accessed within the electronic device 600.

[0109] The electronic device 600 may further include additional removable / non-removable, volatile / non-volatile storage media. Figure 6 As shown in FIG, a magnetic disk drive for reading from or writing to a removable, non-volatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, non-volatile optical disk may be provided. In these cases, each drive may be connected to a bus (not shown) by one or more data media interfaces. Memory 620 may include a computer program product 625 having one or more program modules configured to perform various methods or actions of various embodiments of the present disclosure.

[0110] The communication unit 640 enables communication with other electronic devices via a communication medium. Additionally, the functions of the components of the electronic device 600 can be implemented in a single computing cluster or multiple computing machines that can communicate via a communication connection. Thus, the electronic device 600 can operate in a networked environment using a logical connection with one or more other servers, a network personal computer (PC), or another network node.

[0111] The input device 650 may be one or more input devices, such as a mouse, keyboard, or trackball. The output device 660 may be one or more output devices, such as a display, a speaker, or a printer. The electronic device 600 may also communicate with one or more external devices (not shown) through the communication unit 640 as needed, such as a storage device, a display device, or the like, with one or more devices that allow a user to interact with the electronic device 600, or with any device that allows the electronic device 600 to communicate with one or more other electronic devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface (not shown).

[0112] According to an exemplary implementation of the present disclosure, a computer-readable storage medium is provided, on which computer-executable instructions are stored, wherein the computer-executable instructions are executed by a processor to implement the method described above. According to an exemplary implementation of the present disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, and the computer-executable instructions are executed by a processor to implement the method described above.

[0113] Various aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, apparatuses, devices, and computer program products implemented according to the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0114] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine such that when these instructions are executed by the processor of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0115] Computer-readable program instructions can be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0116] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple implementations of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a part for a module, program segment or instruction, and a part for a module, program segment or instruction comprises one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be realized by a special hardware-based system that performs the function or action of the specification, or can be realized by a combination of special hardware and computer instructions.

[0117] While various implementations of the present disclosure have been described above, the foregoing description is intended to be illustrative, not exhaustive, and not limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is selected to best explain the principles of the implementations, their practical applications, or improvements to existing technologies, or to enable others skilled in the art to understand the various implementations disclosed herein.

Claims

1. A method for handling matters, comprising: identifying at least one item to be processed for a target user from at least one data source associated with the user interaction activity; Determining, using a machine learning model, corresponding contextual information for one or more items of the at least one item, the contextual information indicating available elements for processing the corresponding item; as well as Based on the corresponding context information of the one or more matters, a processing suggestion for the one or more matters is generated.

2. The method of claim 1 , wherein the context information indicates at least one of the following: The background of the relevant matters, Requirement information for the corresponding matters, Reference information for handling the corresponding matters, one or more recommendation tools for at least partially addressing the respective matters, or Other users associated with the corresponding item.

3. The method according to claim 1, wherein generating a handling suggestion for the one or more matters comprises: For a first item among the one or more items, presenting recommendation information included in the context information of the first item, where the recommendation information is at least used to recommend at least one tool for processing the first item; as well as In response to receiving a selection of a recommendation tool from the at least one recommendation tool, a recommendation processing result for the first matter is generated using the selected recommendation tool.

4. The method according to claim 3, further comprising: presenting at least a portion of the contextual information of the first item in a first area of ​​a user interface; as well as A conversation interface between the target user and the digital assistant is presented in a second area of ​​the user interface, wherein the recommendation information is at least partially included in the conversation interface.

5. The method according to claim 1, wherein generating a handling suggestion for the one or more matters comprises: receiving an event planning instruction for a second event among the at least one event; as well as Based on the matter planning indication, a recommended plan for the second matter is generated.

6. The method according to claim 5, wherein the matter planning instruction includes a time planning instruction for the second matter, and generating a processing suggestion for the one or more matters comprises: In response to receiving the time planning indication for the second matter, determining a planned processing time for the second matter; as well as The second item is associated with the planned processing time in the schedule information of the target user to complete the time planning for the second item.

7. The method according to claim 5, wherein the matter planning instruction includes a split instruction for the second matter, and generating processing suggestions for the one or more matters comprises: In response to receiving the splitting instruction for the second item, determining at least a plurality of sub-items corresponding to the second item; as well as In response to receiving confirmation of the plurality of sub-items, corresponding summary information of the plurality of sub-items is presented.

8. The method of claim 5, wherein the matter planning instruction includes a blocking instruction for the second matter, and the second matter is identified from a first data source among the at least one data source, and generating a handling suggestion for the one or more matters comprises: In response to receiving the blocking instruction for the second matter, determining the first data source as a data source that meets a preset condition, wherein the preset condition indicates that the target user's attention to the data source is lower than an attention threshold; canceling determination of the second item from the first data source as a pending item for the target user; as well as Preventing items from the first data source from being identified as items to be processed by the target user.

9. The method according to claim 3, further comprising: Presenting the recommended processing result corresponding to the first matter; as well as In response to receiving confirmation of the recommendation processing result, completion of the first matter is determined.

10. The method according to claim 1, further comprising: In response to completion of a third item among the one or more items, presenting at least progress feedback information for the third item, the progress feedback information including at least selections of one or more candidate users associated with the third item and a notification message indicating completion of the third item; as well as In response to receiving confirmation of the progress feedback information, the notification message is sent to a selected user from the one or more candidate users.

11. The method according to claim 1 , further comprising: In response to obtaining a processing result of a fourth item among the at least one item, receiving a user interaction indicating whether the fourth item requires further processing; In response to the user interaction indicating that the fourth item requires further processing, associating the updated planned processing time for the fourth item with the fourth item in the schedule information of the target user; as well as In response to the user interaction indicating that the fourth item does not need to be further processed, the fourth item is removed from the to-be-processed item set of the target user.

12. The method of claim 1 , wherein identifying the at least one item from the at least one data source comprises: For two or more candidate items identified from different data sources among the at least one data source, comparing similarities between the two or more candidate items based on corresponding context information of the two or more candidate items; as well as In response to determining that the similarity between the two or more candidate items is higher than a similarity threshold, the two or more candidate items are merged into a same item in the at least one item.

13. A device for event processing, comprising: An issue identification module is configured to identify at least one issue to be processed for a target user from at least one data source associated with a user interaction activity; an information determination module configured to determine, using a machine learning model, corresponding context information for one or more items of the at least one item, the context information indicating available elements for processing the corresponding item; as well as The matter processing module is configured to generate processing suggestions for the one or more matters based on corresponding context information of the one or more matters.

14. An electronic device comprising: at least one processor; as well as At least one memory coupled to the at least one processor and storing instructions for execution by the at least one processor, the instructions causing the electronic device to perform the method according to any one of claims 1 to 12 when executed by the at least one processor. 15 . A computer-readable storage medium having computer-executable instructions stored thereon, wherein the computer-executable instructions can be executed by a processor to implement the method according to claim 1 .

16. A computer program product tangibly stored in a computer storage medium and comprising computer executable instructions which, when executed by a device, cause the device to perform the method according to any one of claims 1 to 12.

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