WO2025108123A1 - Method and apparatus for processing messages, device, and storage medium - Google Patents

Abstract

According to embodiments of the present disclosure, a method and apparatus for processing messages, a device, and a storage medium are provided. In one method, recommendation information associated with a target object is acquired, and recommended content is provided to the target object on the basis of the recommendation information. The recommended content at least comprises a first part corresponding to a first message classification, and the first part comprises a first group of content items corresponding to the first message classification. The first group of content items indicates a first group of sessions corresponding to the first message classification, and first description information relating to unread messages in the first group of sessions. The first message classification is determined on the basis of a first degree of correlation between the unread messages in the first group of sessions and the current work of the target object, or the first message classification is determined on the basis of time information of the unread messages in the first group of sessions. Therefore, according to embodiments of the present disclosure, unread messages can be sorted on the basis of the degree of relevance to work or time information, thereby improving message acquisition efficiency.

Description

Method, device, equipment and storage medium for message processing

This application claims priority to the Chinese invention patent application entitled “Method, apparatus, device and storage medium for message processing” filed on November 21, 2023, with application number: 202311560899.8. The entire contents of this application are incorporated by reference into this application.

Technical Field

Example embodiments of the present disclosure generally relate to the field of computers, and more particularly, to methods, devices, apparatuses, and computer-readable storage media for message processing.

Background Art

With the rapid development of Internet technology, the Internet has become an important platform for people to obtain and share content. Users can access the Internet through terminal devices to enjoy various Internet services. On the Internet platform, people can obtain various types of messages, and how to effectively process messages has become the focus of people's attention.

Summary of the invention

In a first aspect of the present disclosure, a method for message processing is provided. The method includes: obtaining recommendation information associated with a target object; and providing recommended content to the target object based on the recommendation information, wherein the recommended content includes at least a first part corresponding to a first message classification, the first part includes a first group of content items corresponding to the first message classification, wherein the first group of content items indicates: a first group of conversations corresponding to the first message classification, and first description information about unread messages in the first group of conversations, wherein the first message classification is determined based on a first degree of association between the unread messages in the first group of conversations and the current work of the target object, or the first message classification is determined based on time information of the unread messages in the first group of conversations.

In a second aspect of the present disclosure, a method for message processing is provided. The method includes: providing at least one scene in an interaction window between a target object and a digital assistant, wherein the at least one scene includes a first scene; wherein the first scene is configured with corresponding configuration information to perform tasks related to processing unread messages, and the configuration information includes at least one of the following: scene setting information, plug-in information, wherein the scene setting information is used to describe information related to processing unread messages, and the plug-in information indicates at least one plug-in for performing tasks related to processing unread messages; and in response to a preset operation of the target object on the first scene, performing interaction between the target object and the digital assistant based at least on the configuration information of the first scene.

In a third aspect of the present disclosure, a device for message processing is provided. The device comprises: information acquisition A module is configured to obtain recommendation information associated with a target object; and a content providing module is configured to provide recommended content to the target object based on the recommendation information, wherein the recommended content includes at least a first part corresponding to a first message classification, the first part includes a first group of content items corresponding to the first message classification, wherein the first group of content items indicates: a first group of conversations corresponding to the first message classification, and first descriptive information about unread messages in the first group of conversations, wherein the first message classification is determined based on a first degree of association between the unread messages in the first group of conversations and the current work of the target object, or the first message classification is determined based on time information of the unread messages in the first group of conversations.

In the fourth aspect of the present disclosure, a device for message processing is provided. The device includes: a scene providing module, configured to provide at least one scene in the interaction window between the target object and the digital assistant, at least one scene including a first scene; wherein the first scene is configured with corresponding configuration information to perform tasks related to processing unread messages, and the configuration information includes at least one of the following: scene setting information, plug-in information, wherein the scene setting information is used to describe information related to unread message processing, and the plug-in information indicates at least one plug-in for performing tasks related to unread message processing; and an interaction module, configured to respond to the preset operation of the target object on the first scene, and perform the interaction between the target object and the digital assistant at least based on the configuration information of the first scene.

In a fifth aspect of the present disclosure, an electronic device is provided. The device includes at least one processing unit; and at least one memory, the at least one memory is coupled to the at least one processing unit and stores instructions for execution by the at least one processing unit. When the instructions are executed by the at least one processing unit, the device executes the method of the first aspect or the second aspect.

In a sixth 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 or the second aspect.

It should be understood that the contents described in this content section are not intended to limit the key features or important features of the embodiments of the present disclosure, nor are they 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

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:

FIG1 is a schematic diagram showing an example environment in which embodiments of the present disclosure can be implemented;

FIG2 shows a flow chart of a method for message processing according to some embodiments of the present disclosure;

3A to 3C illustrate example interfaces according to some embodiments of the present disclosure;

FIG4 illustrates an example interface according to some embodiments of the present disclosure;

FIG5 shows a flow chart of a method for message processing according to some embodiments of the present disclosure;

6A to 6C illustrate example interfaces according to some embodiments of the present disclosure;

7A and 7B show a block diagram of an apparatus for message processing according to some embodiments of the present disclosure; and

FIG8 shows a block diagram of a device capable of implementing various embodiments of the present disclosure.

DETAILED DESCRIPTION

It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, and usage scenarios of the information involved in the present disclosure should be informed to the relevant users in an appropriate manner in accordance with relevant laws and regulations, and the authorization of the relevant users should be obtained. The relevant users may include any type of right holders, such as individuals, enterprises, and groups.

For example, in response to receiving an active request from a user, a prompt message is sent to the relevant user to clearly prompt the relevant user that the operation requested to be performed will require obtaining and using the relevant user's information. Thus, the relevant user can autonomously choose whether to provide information to the electronic device, application, server, storage medium, or other software or hardware that performs the operation of the technical solution of the present disclosure according to the prompt message.

As an optional but non-limiting implementation, in response to receiving an active request from a relevant user, the method of sending a prompt message to the relevant user may be, for example, a pop-up window, in which the prompt message may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to select "agree" or "disagree" to provide personal information to the electronic device.

It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet the relevant laws and regulations may also be applied to the implementation of the present disclosure.

It is understandable that when adopting this technical solution, the data involved (including but not limited to the data itself, data acquisition, use, storage, and transmission) shall comply with the requirements of relevant laws, regulations and relevant provisions.

The term "in response to" as used herein refers to a state in which a corresponding event occurs or a condition is satisfied. It will be understood that the timing of executing a subsequent action executed in response to the event or condition is not necessarily strongly related to the time when the event occurs or the condition is satisfied. For example, in some cases, the subsequent action may be executed immediately when the event occurs or the condition is satisfied; while in other cases, the subsequent action may be executed some time after the event occurs or the condition is satisfied.

Embodiments of the present disclosure will be described in more detail below 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 being limited to the embodiments set forth herein. On the contrary, 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 only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

It should be noted that the titles of any sections/subsections provided herein are not restrictive. Various embodiments are described throughout this article, and any type of embodiment may be included under any section/subsection. In addition, 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 different sections/subsections.

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.

As used herein, the term "model" can learn the association between the corresponding input and output from the training data, so that the corresponding output can be generated for a given input after the training is completed. The generation and use of the model can be based on the technology permitted by laws and regulations such as machine learning, referred to as available technology. For example, deep learning is a machine learning algorithm that processes inputs and provides corresponding outputs by using multiple layers of processing units. In this article, "model" may also be referred to as "machine learning model", "machine learning network" or "network", and these terms are used interchangeably in this article. A model can also include different types of processing units or networks.

As briefly mentioned above, on the Internet platform, people can obtain various types of messages, which may cause people to need to process a large number of unread messages. Therefore, how to improve the processing efficiency of unread messages has become the focus of people's attention.

The embodiments of the present disclosure provide a scheme for message processing. Specifically, recommendation information associated with a target object can be obtained. Further, based on the recommendation information, recommended content can be provided to the target object, wherein the recommended content includes at least a first part corresponding to a first message classification, and the first part includes a first group of content items corresponding to the first message classification, wherein the first group of content items indicates: a first group of conversations corresponding to the first message classification, and first description information about unread messages in the first group of conversations, wherein the first message classification is determined based on a first degree of association between the unread messages in the first group of conversations and the current work of the target object, or the first message classification is determined based on the time information of the unread messages in the first group of conversations. Thus, the embodiments of the present disclosure can sort out unread messages according to the degree of relevance to the work or the time information, thereby improving the efficiency of message acquisition.

Example embodiments of the present disclosure are described below with reference to the accompanying drawings.

Example Environment

1 shows a schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented. In the example environment 100, a digital assistant 120 and an application 125 are installed in a terminal device 110. A target object 140 can interact with the digital assistant 120 and the application 125 via the terminal device 110 and/or an attached device of the terminal device 110.

In some embodiments, the digital assistant 120 and the application 125 can be downloaded and installed on the terminal device 110. In some embodiments, the digital assistant 120 and the application 125 can also be accessed in other ways, such as through a web page. In the environment 100 of FIG. 1, in response to the application 125 being started, the terminal device 110 can present an interface 150 of the digital assistant 120 and the application 125.

Applications 125 include, but are not limited to, one or more of the following: chat applications (also known as instant messaging applications), document applications, audio and video conferencing applications, email applications, task applications, calendar applications, objectives and key results (OKRs) Applications, etc. Although a single application is shown in FIG. 1 , in fact, multiple applications may be installed on the terminal device 110. In some embodiments, the application 125 may include a multi-functional collaboration platform, such as an office collaboration platform (also referred to as an office suite) that can provide integration of various types of business components, such as a chat component, a document component, a calendar component, an audio and video conference component, etc., to facilitate people to carry out office work, communication and other activities. In the multi-functional collaboration platform, people can start different business components as needed to complete corresponding information processing, sharing, communication, etc.

The application 125 may provide a content entity 126. The content entity 126 may be a content instance created by the target object 140 or other users on the application 125. For example, depending on the type of the application 125, the content entity 126 may 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 application), a calendar, a schedule, a task, an audio, a video, an image, etc.

In some embodiments, the digital assistant 120 can be provided by a separate application, or can be integrated into an application 120 that can provide content entities. The application for providing the client interface of the digital assistant can correspond to a single-function application or a multi-function collaboration platform, such as an office suite or other collaboration platform that can integrate multiple components. In some embodiments, the digital assistant 120 supports the use of plug-ins. Each plug-in can provide one or more functions of an application or business component. Such plug-ins include, but are not limited to, one or more of the following: search plug-ins, contact plug-ins, message plug-ins, document plug-ins, table plug-ins, mail plug-ins, calendar plug-ins, schedule plug-ins, task plug-ins, and the like.

The digital assistant 120 is an intelligent assistant of the user, which has intelligent dialogue and information processing capabilities. In an embodiment of the present disclosure, the digital assistant 120 is used to interact with the target object 140 to assist the target object 140 in using a terminal device or application. An interaction window with the digital assistant 120 may be presented in the client interface. In the interaction window, the target object 140 can communicate with the digital assistant 120 by inputting natural language to instruct the digital assistant to assist in completing various tasks, including operations on the content entity 126.

In some embodiments, the digital assistant 120 can be included in the contact list of the current target object 140 in the office suite, or included in the information flow of the chat component as a contact of the target object 140. In some embodiments, the target object 140 has a corresponding relationship with the digital assistant 120. For example, the first digital assistant corresponds to the first user, the second digital assistant corresponds to the second user, and so on. In some embodiments, the first digital assistant can correspond uniquely to the first user, the second digital assistant can correspond uniquely to the second user, and so on. That is, 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 service to the first user, the first digital assistant can use its historical interaction information with the first user, the data authorized by the first user that it can access, and the current interaction context with the first user. If the first user is an individual or an individual, the first digital assistant can be regarded as a personal digital assistant. It can be understood that in the disclosed embodiments, the first digital assistant is based on the authorized access to the data granted permission by the first user. It should be understood that "uniquely corresponding" or similar expressions in the present 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 application needs, the digital assistant 120 does not have to be specific to the current target object 140, but can be a general digital assistant.

In some embodiments, multiple interaction modes between the target object 140 and the digital assistant 120 can be provided, and the multiple interaction modes can be flexibly switched. When a certain interaction mode is triggered, the corresponding interaction area is presented to facilitate the interaction between the target object 140 and the digital assistant 120. The target object 140 and the digital assistant 120 interact in different ways in different interaction modes, so that it can be flexibly adapted to the interaction needs in different application scenarios.

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

The digital assistant 120 can be called or awakened by an appropriate method (e.g., shortcut keys, buttons, or voice) to present an interaction window with the target object 140. By selecting the digital assistant 1201, 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 the like. 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.

The interactive window between the digital assistant 120 and the target object 140 may include a conversation window, such as a conversation window in an instant messaging application or an instant messaging module of the target application. In some embodiments, the interactive window between the digital assistant 120 and the target object 140 may include a floating window corresponding to the digital assistant.

In some embodiments, the digital assistant 120 may support an interactive mode of a conversation window, also referred to as a conversation mode. In this interactive mode, a conversation window between the target object 140 and the digital assistant 120 is presented, and in the conversation window, the target object 140 and the digital assistant 120 interact through conversation messages. In the conversation mode, the digital assistant 120 may perform tasks according to the conversation messages in the conversation window.

In some embodiments, the conversation mode between the target object 140 and the digital assistant 120 can be called or awakened by an appropriate method (e.g., a shortcut key, a button, or voice) to present a conversation window. By selecting the digital assistant 120, a conversation window with the 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.

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, an operation panel (also referred to as a floating window) corresponding to the digital assistant 120 is presented, and the target object 140 may 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 target object 140 through the operation panel.

In some embodiments, the floating window mode of the target object 140 and the digital assistant 120 can also be called or awakened by an appropriate method (for example, a shortcut key, a button, or voice) to present a corresponding operation panel. In an embodiment, the wake-up of the digital assistant 120 can be supported in a specific application, such as in a document business component, to provide interaction in a 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 application interface. In response to detecting a trigger 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.

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 of a conversation window.

In some embodiments, the terminal device 110 communicates with the server 130 to provide services for the digital assistant 120 and the application 125. The terminal device 110 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 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. In some embodiments, the terminal device 110 can also support any type of interface for the user (such as a "wearable" circuit, etc.). The application 130 can be various types of computing systems/servers that can provide computing capabilities, including but not limited to mainframes, edge computing nodes, computing devices in cloud environments, and the like.

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

Example Process

FIG2 shows a flow chart of a process 200 for message processing according to some embodiments of the present disclosure. The process 200 may be implemented by an appropriate electronic device or a combination of electronic devices (e.g., the server 130, the terminal device 110, or a combination of the server 130 and the terminal device 110 in FIG1 ). For ease of description, the process 200 is described below using the terminal device 110 as an example and referring to FIG1 .

As shown in the figure, in block 210, the terminal device 110 obtains recommendation information associated with the target object 140. In some embodiments, after generating the recommended content, the server 130 may send the recommendation information associated with the recommended content to the target object 140. Such recommendation information may include the recommended content, or may be used for the terminal device 110 to present the corresponding recommended content.

In box 220, the terminal device 110 provides recommended content to the target object based on the recommendation information, wherein the recommended content includes at least a first part corresponding to the first message category, the first part includes a first group of content items corresponding to the first message category, wherein the first group of content items indicates: a first group of conversations corresponding to the first message category, and first descriptive information about unread messages in the first group of conversations.

In some embodiments, the first message classification is based on the unread messages in the first group of conversations and the current The first message classification is determined based on the first correlation degree of the previous work, or the first message classification is determined based on the time information of the unread messages in the first group of conversations.

The specific process of block 220 will be described below with reference to Fig. 3A. Fig. 3A shows an example interface 300A according to some embodiments of the present disclosure. The interface 300 may be provided by the terminal device 110, for example.

As shown in Fig. 3, the terminal device 110 can provide recommended content 305, and the recommended content 305 can include, for example, multiple parts corresponding to different message categories. Further, each message category can include a corresponding set of content items, for example, the first category 310-1 can correspond to content item 315-1, the second category 310-2 can correspond to content item 315-2, and the third category 310-3 can correspond to content item 315-3.

In some embodiments, such content items may be generated based on one or more unread messages in the corresponding conversation.

Specifically, content item 315 - 1 may correspond to session 320 , content item 315 - 2 may correspond to session 330 , and content item 315 - 3 may correspond to session 340 , for example.

Taking content item 315-1 as an example, content item 315-1 may indicate a conversation 320 corresponding to first category 310-1. Additionally, terminal device 110 may also present the number of unread messages in conversation 320. Additionally, content item 315-1 may also indicate description information 325 of unread messages in conversation 320.

In some embodiments, the description information 325 may be used to indicate summary contents of the unread messages in the conversation 320. Such summary contents may include, for example, the subject of the unread message, or a summary of the unread message, etc. In some embodiments, the server 130 may, for example, use a language model to process the unread messages in the conversation 320 to generate summary contents about the unread messages.

In some embodiments, the description information 320 may also indicate to-do content generated based on unread messages in the conversation 320. As an example, the server 130 may also process the unread messages in the conversation 320 using a language model to determine one or more to-do events for the target object 140.

It should be understood that such a language model may be implemented by any appropriate machine learning technology, and the present disclosure is not intended to be limited thereto.

Furthermore, the terminal device 110 may also provide one or more operation portals in association with the content item 315 - 1 . For example, the terminal device 110 may provide a portal “clear unread” for quickly marking unread messages in the conversation 320 as read.

As another example, the terminal device 110 may provide an entry “Convert to Task” to generate a corresponding reminder (eg, task) based on the to-do content in the content item 315 - 1 .

As another example, the terminal device 110 provides a portal associated with the session 320 to support the user to access the session window of the session 320. For example, the user can jump to the session window of the session 320 by clicking "X group".

As described above, such recommended content 320 may also include content (eg, content item 315-2 and content item 315-3) corresponding to other categories (eg, second category 310-2 and third category 310-3, etc.). By way of example, such different message classifications may include a first classification 310-1 (eg, "high relevance"), a second classification 310-2 (eg, "certain relevance"), and a third classification 310-3 (eg, "low relevance") corresponding to different work relevances.

In some embodiments, the server 130 may utilize the historical interaction information of the target object 140 to determine the relevance of the message or conversation to the current work of the target object 140 .

The following will briefly introduce the generation process of historical interaction information. In some embodiments, when the target object 140 interacts with the business component, the business component can generate a log record and send the log record to a recording module. Such a recording module can run at a suitable electronic device such as the server 130.

Further, the recording module can generate corresponding record entries based on the received log records and construct a record library. In some embodiments, such record entries may include knowledge elements (Knowledge) to describe the business objects corresponding to the historical interaction events. In some embodiments, the record library can be maintained in an appropriate electronic device, which can be stored at the terminal device 110, or stored at the server 130.

In some embodiments, such business objects may include business objects generated, edited, referenced, shared, etc. by the target object 140 during the interaction with the business component. Taking the business component 115 as a document component as an example, the historical interaction event may include a creation event of a specific document in the document component by the target object 140. Accordingly, the business object corresponding to the document creation event is the specific document.

In some embodiments, the knowledge element may be a natural language description of the business object, which is intended to abstract and/or compress the content of the business object. For example, taking a document object as an example of a business object, the knowledge element may be used to describe the subject, completion status, audience, language, expression style, etc. of the document object.

It should be understood that information of different dimensions can be selected according to the type of business object to generate knowledge elements for describing the business object. For example, taking a conversation as an example of a business object, the knowledge element can be used to describe the type of conversation (e.g., whether it is a one-on-one chat), the overview of the conversation, etc.

Therefore, by maintaining knowledge elements in record entries, embodiments of the present disclosure can describe or characterize the business objects involved in the corresponding historical interaction events through limited content length.

In some embodiments, the record entry may also include a time element for indicating the time of occurrence of the historical interaction event. For example, continuing to use the creation of a document as an example of a historical interaction event, such a time element may, for example, indicate the creation time of the document.

In some other embodiments, the record entry may also include an action element for indicating the event type of the historical interaction event. Continuing to use the creation of a document as an example of a historical interaction event, such an action element may, for example, indicate that the type of the historical interaction event is a "create" type.

In some embodiments, the record entry may also include a payload element for indexing a business object corresponding to the corresponding historical interaction event. Taking a document as an example of a business object, the payload element may include, for example, a document number or a document identifier for indexing the document.

Thus, in some scenarios, after the target object 140 interacts with the business component, the recording module can generate a corresponding record entry. Such a record entry can be expressed as {time element, action element, knowledge element, load element}, for example, to describe the historical interaction event from multiple preset dimensions. Such a record entry can also be called historical interaction information corresponding to the interaction event.

Further, the server 130 may associate a set of working topics with the group of interaction events based on the historical interaction information.

In some embodiments, the server 130 may use the target processing entity to determine the topic set. Specifically, the server 130 may provide at least the knowledge elements and action elements in the historical interaction information to the target processing entity so that the target processing entity can perform clustering processing on the historical interaction information.

It should be understood that the target processing entity can be a processing entity based on appropriate information processing technology, and can implement one or more functions such as text generation, image generation, summarization, encoding, translation, chatbot, etc. The target processing entity can also be any other appropriate entity form. In some examples, the target processing entity can include a language model, for example.

Exemplarily, the target processing entity (eg, language model) may be based on a semantic analysis of each action element and knowledge element in the historical interaction information to obtain a plurality of topics through clustering.

In some embodiments, at least one business component may include, for example, an office component used in the work of the target object 140. Accordingly, the subject determined by the target processing entity may also be, for example, the subject of the work content (eg, project name, etc.).

Further, the server 130 may obtain a topic set determined by the target processing entity based on the knowledge element and the action element. Exemplarily, continuing to take the topic of the work content as an example, such a topic set may include, for example, "Project X", "Project Y", etc. In some embodiments, if the historical interaction information corresponding to some interaction events cannot be clustered into such a topic, it may also be clustered into a preset category, for example, "others".

In some embodiments, the server 130 may, for example, periodically trigger analysis of historical interaction information to determine a corresponding set of topics. For example, the server 130 may trigger full analysis of historical interaction information every two weeks to determine a corresponding set of topics, for example, using a target processing entity.

In some embodiments, the server 130 may also process the newly acquired historical interaction information by merging clusters. For example, when a topic set has been determined based on the first historical interaction information within a first time period (e.g., before yesterday), the server 130 may also obtain the second historical interaction information of the target object 140. Such second historical interaction information may be generated based on a group of interaction events (also referred to as a second group of interaction events) within a second time period (e.g., yesterday).

Accordingly, the server 130 may, for example, determine the matching degree between the second group of interaction events and the topics in the topic set in the topic set. Continuing with the example that the topic set includes "Project X", "Project Y" and "Others", the server 130 may, for example, determine the matching degree between the second group of interaction events and the topics in the topic set based on the historical interaction information corresponding to the second group of interaction events. The degree to which the event matches "Project X" or "Project Y".

Exemplarily, the server 130 may determine the degree of matching by providing the target processing entity with information such as knowledge elements and action elements of historical interaction information corresponding to the second group of interaction events.

Further, the server 130 may determine the association between the second group of interaction events and the topic set based on the matching degree.

Specifically, in response to the first interaction event in the second group of interaction events having a matching degree with the target topic in the topic set reaching a threshold, the server 130 may associate the first interaction event with the target topic. For example, if a certain event in the second group of interaction events has a matching degree with "Project X" reaching a threshold, the historical interaction information corresponding to the interaction event may be marked as being associated with "Project X".

In response to the second interaction event in the second group of interaction events having a matching degree with a theme other than the preset theme in the theme set being less than a threshold, the second interaction event is associated with the preset theme. For example, if a certain event in the second group of interaction events has a matching degree with both "Project X" and "Project Y" being less than a threshold, the historical interaction information corresponding to the interaction event may be marked as being associated with the preset theme "Others".

In some embodiments, in response to the number of multiple interaction events associated with a preset topic (e.g., "other") reaching a preset number, the server 130 may trigger a re-clustering process. For example, the server 130 may determine at least one topic based on the historical interaction information corresponding to the multiple interaction events associated with the preset topic. For example, if the number of events included in the topic "other" exceeds a threshold number, the server 130 may, for example, use the target processing entity to re-cluster the events in the topic to determine one or more new topics. Further, the server 130 may update the topic set using at least one determined topic.

Based on this approach, the embodiments of the present disclosure can cluster corresponding topics according to the historical interactions between the target object and the business component, thereby facilitating the sorting out of the historical interactions of the target object.

Furthermore, the server 130 may determine the relevance of the conversation to the current work of the target object based on the relevance between the unread messages in each conversation and the work topic set. For example, the server 130 may use a language model to determine the relevance between the unread messages in the conversation and the aggregated work topics, such as the matching degree.

In some embodiments, the server 130 may also provide a set of description items about unread messages to the target model and obtain the relevance determined by the target model. As an example, the server 130 may provide the description items of the unread messages and the aggregated work topics to the target model, so that the target model determines the relevance between the description items and the target work topics.

In some embodiments, the description item of the unread message may also include, for example, a description item associated with a user associated with the unread message. For example, such a description item may indicate the relationship between the user who sent the unread message and the current user. Alternatively, such a description item may also include, for example, other users mentioned in the unread message.

In some embodiments, the description item of the unread message may also include, for example, an attribute of the session in which the unread message is located. For example, the attribute may include the session type of the session, such as a conference group, a project group, and the like.

Furthermore, the server 130 may be based on the relationship between each unread message in the conversation and the current work of the target object 140. The degree of association between the conversation and the target object 140 can be determined based on the degree of association between the conversation and the target object 140, and the corresponding classification can be determined based on the comparison between the degree of association and the preset range. For example, when the degree of association is higher than a specific threshold, the conversation can be determined as having a high relevance to the work.

3A , the third category 310-3 may, for example, indicate that the relevance of the corresponding conversation (e.g., conversation 340) to the current work of the target object 140 is lower than a threshold level. In this case, the content item 315-3 corresponding to the conversation 340 may, for example, provide description information 345 to indicate that the relevance of the conversation 340 to the target object 140 is lower than a threshold level. For example, the description information 345 describes the reason why the conversation 340 is determined to be not highly relevant.

In some embodiments, the recommended content 305 may also include a content item 315-4 corresponding to the fourth category 310-4. The fourth category 310-4 may, for example, indicate that unread messages in the corresponding conversations (e.g., "Group A1", "Group A2", and "Group A3") are determined to be expired messages or invalid messages based on the conversation attributes of the conversations. For example, the conversation "Group A1" may be a schedule conversation created based on a meeting schedule, and after the schedule ends for a predetermined period of time, the unread messages in the schedule conversation may be determined to be expired messages or invalid messages.

As shown in Fig. 3, in some embodiments, the terminal device 110 may also present a subscription entry 350 in association with the recommended content 305. Further, based on the selection of the subscription entry 350, the terminal device 110 may periodically provide the target object with recommended content corresponding to the corresponding time period.

For example, when the target object 140 subscribes to recommended content (e.g., daily unread message summary) through the subscription portal 350, the terminal device 110 can, for example, periodically (e.g., at a predetermined time every morning) provide the target object 140 with a summary of the previous day's unread messages.

Further, in the case where the target object has subscribed to the recommended content, the terminal device 110 can present an unsubscribe entry in association with the recommended content corresponding to the corresponding time period. Accordingly, based on the selection of the unsubscribe entry, the terminal device 110 can stop providing the target object 140 with the recommended content corresponding to the subsequent time period.

Continuing with the unread message summary as an example, for the unread message summary received subsequently, the target object can, for example, trigger the terminal device 110 to stop providing such daily unread message summary by clicking on the corresponding unsubscribe entry.

In some embodiments, the recommended content 305 described above may be automatically provided by the terminal device 110. For example, the digital assistant 120 may periodically provide recommended content to the target object 140 for organizing the unread messages of the target object 140.

In some embodiments, the recommended content 305 may also be provided when the number of unread messages associated with the target object 140 reaches a threshold. Accordingly, in response to the number of unread messages associated with the target object 140 reaching the threshold, the server 120 may automatically send the recommended information associated with the target object 140 to the terminal device 110.

In some embodiments, the terminal device 110 can also provide the target object 140 with an acquisition entry for obtaining the recommended content 305, and can obtain recommendation information associated with the target object 140 from the server 120 based on the target object 140's selection of the acquisition entry, and present the corresponding recommended content 305.

As an example, as shown in FIG3B , the terminal device 110 may provide an acquisition entry 360-1 in association with a message tag 355 in a navigation bar of a conversation application. For example, when the target object 140 has unread messages, or when the number of unread messages of the target object 140 is greater than a threshold number, the terminal device 110 may present the acquisition entry 360-1 based on a preset operation (e.g., a right-click operation) of the target object 140 for the message tag 355. As an example, the terminal device 110 may also provide an entry 360-2 for marking all unread messages as read.

As another example, as shown in FIG3C , the navigation bar may have a greater width than the navigation bar shown in FIG3B , and accordingly, the terminal device 110 may present the acquisition entry 370 in response to a preset operation (e.g., a hover operation) of the target object 140 for the message tag 365. Accordingly, upon receiving a selection for the acquisition entry 370, the terminal device 110 may present the recommended content 305 discussed above.

In some embodiments, the terminal device 110 may also provide an acquisition entry in the conversation between the target object 140 and the digital assistant 120. The target object 140 may trigger the presentation of the recommended content 305 by clicking on the acquisition entry, for example.

In some other embodiments, the terminal device 110 may also receive input from the target object 140 regarding organizing messages in a conversation between the target object 140 and the digital assistant 120, and may present corresponding recommended content 305 accordingly.

In some other embodiments, the terminal device 110 may also support the target object 140 to configure the range of conversations to be sorted. Specifically, the terminal device 110 may determine a group of target conversations to be processed based on the configuration operation of the target object 140. Accordingly, the recommendation information generated based on the unread messages in the group of target conversations and the corresponding recommended content 305 are presented accordingly.

For example, the target object 140 may specify one or more conversations to be sorted. Accordingly, the generated recommended content 305 will only involve sorting out the unread messages in the specified one or more conversations.

Therefore, the embodiments of the present disclosure can automatically sort out unread messages according to their relevance to work, thereby improving the efficiency of message acquisition.

In some embodiments, different message categories in the recommended content may also be determined based on time information of unread messages in the corresponding conversation. FIG4 shows an example interface 400D according to some embodiments of the present disclosure.

4 , terminal device 110 may provide recommended content 405. In recommended content 405, terminal device 110 may provide different content items (e.g., content item 415-1, content item 415-2, content item 415-3, and content item 415-4) corresponding to different categories (e.g., category 410-1, category 410-2, and category 410-3).

Different from the above-mentioned categories 310-1 to 310-4, categories 410-1 to 410-3 can be determined based on the time information (sending time or receiving time) of the unread messages. For example, category 410-1 can correspond to a first time range (e.g., within 7 days); category 410-2 can correspond to a second time range (7 to 30 days); and category 410-3 can correspond to a third time range (e.g., more than 30 days).

Similar to the recommended content 305 discussed above, the content items corresponding to each category may indicate the corresponding conversation and description information about the conversation. For example, content item 415-1 may indicate conversation 420 and description information 425 of unread messages in conversation 420; content item 415-2 may indicate conversation 430 and description information 425 of unread messages in conversation 430; The content item 415-3 may indicate the conversation 440 and the description information 445 of the unread messages in the conversation 440. As another example, the content item 415-4 may also indicate a group of conversations corresponding to .

Therefore, the embodiments of the present disclosure can automatically sort out unread messages according to the time range, thereby improving the efficiency of message acquisition.

FIG5 shows a flowchart of a process 500 for message processing according to some embodiments of the present disclosure. The process 500 may be implemented by an appropriate electronic device or a combination of electronic devices (e.g., the server 130, the terminal device 110, or a combination of the server 130 and the terminal device 110 in FIG1 ). For ease of description, the process 500 is described below using the terminal device 110 as an example and referring to FIG1 .

As shown in the figure, in box 510, the terminal device 110 provides at least one scene in the interaction window between the target object and the digital assistant, and the at least one scene includes a first scene; wherein the first scene is configured with corresponding configuration information to perform tasks related to processing unread messages, and the configuration information includes at least one of the following: scene setting information, plug-in information, wherein the scene setting information is used to describe information related to unread message processing, and the plug-in information indicates at least one plug-in for performing tasks related to unread message processing.

In this article, in the context of the interaction between the user and the digital assistant, "scenario" refers to a collection of tasks of the same type, that is, one scenario corresponds to multiple tasks of the same type. One or more scenarios can be configured with corresponding configuration information to perform tasks of the corresponding type. For ease of understanding, we first briefly introduce the scenarios used in the interaction between the user and the digital assistant.

The configuration information of the scene includes at least one of the following: scene setting information, plug-in information. The scene setting information is used to describe information related to the corresponding scene. The plug-in information indicates at least one plug-in used to perform the task in the corresponding scene. As will be discussed below, the configuration information of the scene may also include, for example, an indication of the selected model (the model here is called to determine the response to the user in the corresponding scene), scene guidance information (the scene guidance information is presented to the user after the corresponding scene is selected), at least one recommended question for the digital assistant (at least one recommended question is presented to the user for selection after the corresponding scene is selected), and so on. In some embodiments, the configuration of the scene setting information and configuration information of the scene can be completed, for example, in a natural language manner, so that the scene creator can easily constrain the output of the model and configure a variety of scenes.

The configuration information of the scene includes at least one of the following: scene setting information, plug-in information. The scene setting information is used to describe information related to the corresponding scene. The scene setting information of the scene can affect the digital assistant's response to the user to a certain extent, or be used to determine the digital assistant's response to the user. In some embodiments, the scene setting information is used to construct a prompt input to provide to the model used in the corresponding scene. The digital assistant's response to the user is based on the output of the model. The scene setting information of the scene may, for example, include a description of the corresponding type of task, the digital assistant's response style in the scene, the definition of the workflow to be executed in the corresponding scene, the definition of the digital assistant's response format in the corresponding scene, and so on. In some embodiments, the digital assistant will use the model to understand the user input and provide a response to the user based on the output of the model. The digital assistant used The model can be run locally on the terminal device 110 or on a remote server. By using the scene setting information to construct a part of the prompt word input of the model, the model can be guided to complete the task to be achieved in the corresponding scene. In some embodiments, the model 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). The language model can have question-answering capabilities by learning from a large amount of corpus. The model can also be based on other appropriate models.

The plug-in information indicates at least one plug-in used to perform the task in the corresponding scenario. Through the plug-in information of the scenario, the plug-in to be used in the corresponding scenario can be configured. In some embodiments, in the corresponding scenario, during the operation of the plug-in, the plug-in can also call the model to complete the corresponding task. In some embodiments, a plug-in can also call the open interface provided by other business components (for example, business components such as documents, calendars, and meetings) to complete the corresponding task, such as modifying documents, creating schedules, summarizing meetings, etc.

In some embodiments, the configuration information of the scene may also include a scene name, description information of the scene, and the like. In some embodiments, the terminal device 110 may provide a message card to the user in the conversation window, and at least part of a set of scenes may be presented in the message card. The terminal device 110 may present the scene name and/or description information of the scene of the corresponding scene in association with the scene in the message card. For example, the user may select a scene that meets his or her needs based on the scene name and/or description information of the scene presented in the message card.

In some embodiments, the configuration information of the scene may also include, but is not limited to: the selected model (the model here is called to determine the response to the user in the corresponding scene), scene guidance information (the scene guidance information is presented to the user after the corresponding scene is selected), at least one recommended question for the digital assistant (at least one recommended question is presented to the user for selection after the corresponding scene is selected), any combination of one or more of the foregoing, and the like. The scene guidance information may, for example, be descriptive information about a task instance that can be performed in the scene. In some embodiments, the scene setting information and configuration information of the scene may be configured, for example, in a natural language manner, so that the scene creator can easily constrain the output of the model and configure a variety of scenes.

In some embodiments, the configuration information of the scene may also indicate at least one operation control associated with the scene. As will be described in detail below, at least one operation control associated with the scene may be presented to the user when the scene is selected for interaction, so as to facilitate the user to perform interaction with the digital assistant in the corresponding scene. That is to say, during the scene creation process, at least one operation control associated with the corresponding scene may be configured by the scene creator. In some embodiments, the scene setting information and configuration information of the scene may be configured by the scene creator in a natural language manner, for example. In this way, the scene creator can easily constrain the output of the model and configure a variety of scenes.

In some embodiments, if a certain scene is selected for the interaction between the first user and the first digital assistant, in the conversation window, the interaction between the user and the digital assistant is performed based at least on the configuration information of the selected scene. The scene setting information, plug-in information, selected model, etc. of the selected scene are all used to guide the interaction in the scene.

The specific process of block 510 will be described below with reference to FIG. 6A. FIG. 6A shows some embodiments of the present disclosure. Example interface 600A of the embodiment. As shown in FIG6A , the terminal device 110 provides content 605 in the interactive window between the target object 140 and the digital assistant 120. The content 605 may include at least one scene, for example, scene 610 and scene 615. Scene 610 may correspond to a first scene for “unread message summary”, which may be configured with corresponding configuration information to perform tasks related to processing unread messages. Scene 615 may correspond to a second scene for “content understanding”, for example. The user may also select other more scenes through control 620, for example.

Continuing with reference to FIG. 5 , in box 520 , in response to the preset operation of the target object on the first scene, the terminal device 110 performs interaction between the target object and the digital assistant based at least on the configuration information of the first scene.

As an example, when the scenario 610 is selected, the terminal device 110 may present an interface 600B as shown in FIG6B . In the interface 600B, the digital assistant 120 may provide content 640, which may include a set of recommended questions 645, 650, and 655. Such different recommended questions may correspond to different interaction requests.

For example, the recommended question 645 can be used to trigger the acquisition of organized content for all unread messages. The recommended question 650 can trigger the acquisition of organized content for unread messages in a specified session. The recommended question 655 can trigger the subscription to organized content for unread messages.

As an example, when the user selects the recommendation question 645, the corresponding input content 660 may be sent to the digital assistant. Further, as shown in FIG6C, the terminal device 110 may provide the recommendation content 665 generated for all unread messages.

For the specific content and generation process of the recommended content 665, please refer to the contents described above with reference to Figures 2 to 4, which will not be repeated here.

Based on the process discussed above, the embodiments of the present disclosure can provide the user with a scenario for sorting unread messages, thereby improving the efficiency of sorting unread messages for the user.

Example devices and equipment

7A shows a schematic structural block diagram of an apparatus 700A for message processing according to certain embodiments of the present disclosure. The apparatus 700A may be implemented as or included in the server 130, the terminal device 110, or a combination of the server 130 and the terminal device 110 in FIG. 1. Each module/component in the apparatus 700A may be implemented by hardware, software, firmware, or any combination thereof.

As shown in the figure, the device 700A includes an information acquisition module 710, which is configured to acquire recommendation information associated with the target object; and a content provision module 720, which is configured to provide recommended content to the target object based on the recommendation information, wherein the recommended content includes at least a first part corresponding to a first message classification, and the first part includes a first group of content items corresponding to the first message classification, wherein the first group of content items indicates: a first group of conversations corresponding to the first message classification, and first descriptive information about unread messages in the first group of conversations, wherein the first message classification is determined based on a first degree of association between the unread messages in the first group of conversations and the current work of the target object, or the first message classification is determined based on the time information of the unread messages in the first group of conversations.

In some embodiments, the recommended content also includes a second part corresponding to the second message classification, the second part including The content item includes a second group of content items corresponding to the second message classification, and the second group of content items is generated based on a second group of conversations associated with the second message classification.

In some embodiments, the second group of content items and the second group of conversations corresponding to the second message classification have a second degree of association with the current work of the target object that is lower than a threshold degree, and the second group of content items includes second descriptive information about the second degree of association between the second group of conversations and the target object that is lower than the threshold degree.

In some embodiments, the second message classification indicates that the unread messages in the second group of conversations are determined to be expired messages or invalid messages based on conversation attributes of the second group of conversations.

In some embodiments, the first degree of association is determined based on the following process: obtaining historical interaction information of the target object, the historical interaction information being generated based on a set of interaction events between the target object and at least one business component; determining a set of work topics associated with a set of interaction events based on the historical interaction information; and determining a first degree of association between the first group of conversations and the current work of the target object based on the correlation between the unread messages in the first group of conversations and the set of work topics.

In some embodiments, the first degree of association is further determined based on providing a set of descriptive items about the unread messages to a target model to obtain the relevance determined by the target model.

In some embodiments, the description information indicates at least one of the following: summary content of a group of unread messages in a corresponding conversation of the first group of conversations; to-do content generated based on the group of unread messages.

In some embodiments, the device 700A also includes an entry providing module, which is configured to: provide a first entry, the first entry is used to mark a group of unread messages in the corresponding conversation as read; provide a second entry, the second entry is used to generate a reminder corresponding to the to-do content; or provide a third entry, the third entry is used to display the conversation window of the corresponding conversation.

In some embodiments, the device 700A further includes a subscription module configured to: present a subscription entry in association with the recommended content; and periodically provide the target object with the recommended content corresponding to the corresponding time period based on a selection of the subscription entry.

In some embodiments, the device 700A also includes a cancellation module configured to: present a cancellation entry in association with the recommended content corresponding to the corresponding time period; and stop providing the recommended content corresponding to the subsequent time period to the target object based on the selection of the cancellation entry.

In some embodiments, the information acquisition module 710 is further configured to: in response to the number of unread messages associated with the target object reaching a threshold, acquire recommendation information associated with the target object.

In some embodiments, the information acquisition module 710 is further configured to: provide an acquisition entry for acquiring recommended content; and acquire recommended information associated with the target object based on a preset operation for the acquisition entry.

In some embodiments, the information acquisition module 710 is further configured to: provide an acquisition entry in association with a message tag in a navigation bar of a conversation application; or provide an acquisition entry in a conversation between a target object and a digital assistant.

In some embodiments, the information acquisition module 710 is further configured to: determine a set of target sessions to be processed based on the configuration operation of the target object; and obtain recommendation information associated with the target object, wherein the recommendation information is Generated based on a set of unread messages in a target conversation.

7B shows a schematic structural block diagram of an apparatus 700B for message processing according to certain embodiments of the present disclosure. The apparatus 700B may be implemented as or included in the server 130, the terminal device 110, or a combination of the server 130 and the terminal device 110 in FIG. 1. Each module/component in the apparatus 700B may be implemented by hardware, software, firmware, or any combination thereof.

As shown in the figure, the device 700B includes a scene providing module 730, which is configured to provide at least one scene in the interaction window between the target object and the digital assistant, and the at least one scene includes a first scene; wherein the first scene is configured with corresponding configuration information to perform tasks related to processing unread messages, and the configuration information includes at least one of the following: scene setting information, plug-in information, wherein the scene setting information is used to describe information related to unread message processing, and the plug-in information indicates at least one plug-in for performing tasks related to unread message processing; and an interaction module 740, which is configured to respond to the preset operation of the target object on the first scene, and perform interaction between the target object and the digital assistant at least based on the configuration information of the first scene.

In some embodiments, the configuration information also includes at least one of the following: an indication of the selected model, the model is called to determine a response to the target object in the first scenario; scene guidance information, the scene guidance information is presented to the target object after the first scenario is selected; or at least one recommended question for the digital assistant, at least one recommended question is presented to the target object for selection after the first scenario is selected.

In some embodiments, the context setting information is used to construct a prompt word input to provide to a model used in a first context, and the response to the target is based on the output of the model.

In some embodiments, the interaction module 740 is also configured to: provide recommended content by the digital assistant, wherein the recommended content includes at least a first part corresponding to the first message category, the first part includes a first group of content items corresponding to the first message category, wherein the first group of content items indicates: a first group of conversations corresponding to the first message category, and first descriptive information about unread messages in the first group of conversations.

In some embodiments, the first message classification is determined based on a first degree of association between unread messages in the first group of conversations and the current work of the target object; or the first message classification is determined based on time information of the unread messages in the first group of conversations.

The electronic device 800 typically includes a plurality of computer storage media. Such media can be any accessible media that can be obtained by the electronic device 800, including but not limited to volatile and non-volatile media, removable and non-removable media. The memory 820 can be a volatile memory (e.g., a register, a cache, a random access memory (RAM)), a non-volatile memory (e.g., a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. The storage device 830 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, which can be used to store information and/or data and can be accessed within the electronic device 800.

The electronic device 800 may further include additional removable/non-removable, volatile/non-volatile storage media. Although not shown in FIG. 8 , a memory device for transferring data from a removable, non-volatile disk (e.g., a “floppy disk”) may be provided. A disk drive for reading or writing and an optical drive for reading or writing from a removable, non-volatile optical disk. In these cases, each drive can be connected to a bus (not shown) by one or more data media interfaces. Memory 820 can include a computer program product 825 having one or more program modules that are configured to perform various methods or actions of various embodiments of the present disclosure.

The communication unit 840 implements communication with other electronic devices through a communication medium. Additionally, the functions of the components of the electronic device 800 can be implemented with a single computing cluster or multiple computing machines that can communicate through a communication connection. Therefore, the electronic device 800 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.

The input device 850 may be one or more input devices, such as a mouse, a keyboard, a tracking ball, etc. The output device 860 may be one or more output devices, such as a display, a speaker, a printer, etc. The electronic device 800 may also communicate with one or more external devices (not shown) through the communication unit 840 as needed, such as a storage device, a display device, etc., communicate with one or more devices that allow a user to interact with the electronic device 800, or communicate with any device that allows the electronic device 800 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).

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.

Various aspects of the present disclosure are described herein with reference to the flowcharts and/or block diagrams of the methods, devices, equipment, and computer program products implemented according to the present disclosure. It should be understood that each box in the flowchart and/or block diagram and the combination of each box in the flowchart and/or block diagram can be implemented by computer-readable program instructions.

These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device that implements the functions/actions specified in one or more boxes in the flowchart and/or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and/or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions/actions specified in one or more boxes in the flowchart and/or block diagram.

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, so that the instructions executed on the computer, other programmable data processing apparatus, or other device implement the functions/actions specified in one or more boxes in the flowchart and/or block diagram.

The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple implementations of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of a module, program segment or instruction includes one or more executable instructions for realizing the logical function of the specification. In some implementations as replacements, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and/or flow chart, and the combination of the square 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.

The above descriptions of various implementations of the present disclosure are exemplary, non-exhaustive, and not limited to the disclosed implementations. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The selection of terms used herein is intended to best explain the principles of the implementations, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the various implementations disclosed herein.

Claims (23)

A message processing method, comprising: Obtaining recommendation information associated with the target object; and Based on the recommendation information, recommended content is provided to the target object, wherein the recommended content at least includes a first part corresponding to a first message classification, the first part includes a first group of content items corresponding to the first message classification, wherein the first group of content items indicates: a first group of conversations corresponding to the first message classification, and first description information about unread messages in the first group of conversations, The first message classification is determined based on a first degree of association between unread messages in the first group of conversations and the current work of the target object, or the first message classification is determined based on time information of the unread messages in the first group of conversations. The method according to claim 1, wherein the recommended content also includes a second part corresponding to a second message classification, the second part includes a second group of content items corresponding to the second message classification, and the second group of content items is generated based on a second group of conversations associated with the second message classification. The method according to claim 2, wherein the second group of content items and the second group of conversations corresponding to the second message classification have a second degree of association with the current work of the target object lower than a threshold degree, and the second group of content items includes second descriptive information about the second degree of association of the second group of conversations with the target object being lower than the threshold degree. The method according to claim 2, wherein the second message classification indicates that the unread messages in the second group of conversations are determined to be expired messages or invalid messages based on conversation attributes of the second group of conversations. The method of claim 1, wherein the first degree of association is determined based on: Acquire historical interaction information of a target object, where the historical interaction information is generated based on a set of interaction events between the target object and at least one business component; Based on the historical interaction information, determining a set of working topics associated with the set of interaction events; and Based on the correlation between the unread messages in the first group of conversations and the work topic set, the first degree of association between the first group of conversations and the current work of the target object is determined. The method according to claim 5, wherein the first degree of association is further determined based on the following process: A set of description items about the unread messages is provided to a target model to obtain the relevance determined by the target model. The method according to claim 1, wherein the description information indicates at least one of the following: a summary content of a group of unread messages in a corresponding conversation of the first group of conversations; To-do content generated based on the set of unread messages. The method according to claim 7, further comprising: providing a first entry, the first entry being used to mark the group of unread messages in the corresponding conversation as read; providing a second entry, the second entry being used to generate a reminder corresponding to the to-do content; or A third entrance is provided, and the third entrance is used to display a session window of the corresponding session. The method according to claim 1, further comprising: In association with the recommended content, presenting a subscription entry; and Based on the selection of the subscription entry, recommended content corresponding to a corresponding time period is periodically provided to the target object. The method according to claim 9, further comprising: In association with the recommended content corresponding to the corresponding time period, presenting an unsubscribe entry; and Based on the selection of the unsubscribe entry, the provision of recommended content corresponding to the subsequent time period to the target object is stopped. The method according to claim 1, wherein obtaining recommendation information associated with the target object comprises: In response to the number of unread messages associated with the target object reaching a threshold, recommendation information associated with the target object is obtained. The method according to claim 1, wherein obtaining recommendation information associated with the target object comprises: Providing an access point for obtaining the recommended content; and Based on a preset operation for the acquisition entry, the recommendation information associated with the target object is acquired. The method according to claim 12, wherein providing an acquisition entry for acquiring the recommended content comprises: A message tag in a navigation bar associated with the conversation application provides the acquisition entry; or The acquisition entry is provided in the conversation between the target object and the digital assistant. The method according to claim 1, wherein obtaining recommendation information associated with the target object comprises: Determining a set of target sessions to be processed based on the configuration operation of the target object; and The recommendation information associated with the target object is obtained, wherein the recommendation information is generated based on unread messages in the group of target conversations. A message processing method, comprising: Providing at least one scene in the interaction window between the target object and the digital assistant, wherein the at least one scene includes a first scene; wherein the first scene is configured with corresponding configuration information to perform tasks related to processing unread messages, wherein the configuration information includes at least one of the following: scene setting information and plug-in information, wherein the scene setting information is used to describe information related to processing unread messages, and the plug-in information indicates at least one plug-in used to perform tasks related to processing unread messages; and In response to a preset operation of the target object on the first scene, at least based on the configuration of the first scene information to perform interaction between the target object and the digital assistant. The method according to claim 15, wherein the configuration information further includes at least one of the following: an indication of a selected model, the model being invoked to determine a response to the target object in a first scenario; Scene guidance information, which is presented to the target object after the first scene is selected; or At least one recommended question for the digital assistant is presented to the target object for selection after the first scenario is selected. The method of claim 15, wherein the scenario setting information is used to construct a prompt word input to provide to a model used in the first scenario, and the response to the target is based on the output of the model. According to the method of claim 15, performing the interaction between the target object and the digital assistant based at least on the configuration information of the first scene comprises: Recommended content is provided by the digital assistant, wherein the recommended content includes at least a first part corresponding to a first message category, the first part includes a first group of content items corresponding to the first message category, wherein the first group of content items indicates: a first group of conversations corresponding to the first message category, and first descriptive information about unread messages in the first group of conversations. The method according to claim 18, wherein: The first message classification is determined based on a first degree of relevance between the unread messages in the first group of conversations and the current work of the target object; or The first message classification is determined based on time information of unread messages in the first group of conversations. A device for message processing, comprising: an information acquisition module, configured to acquire recommendation information associated with the target object; and a content providing module configured to provide recommended content to the target object based on the recommendation information, wherein the recommended content at least includes a first part corresponding to a first message classification, the first part includes a first group of content items corresponding to the first message classification, wherein the first group of content items indicates: a first group of conversations corresponding to the first message classification, and first description information about unread messages in the first group of conversations, The first message classification is determined based on a first degree of association between unread messages in the first group of conversations and the current work of the target object, or the first message classification is determined based on time information of the unread messages in the first group of conversations. A device for message processing, comprising: A scenario providing module, configured to provide at least one scenario in an interaction window between a target object and a digital assistant, wherein the at least one scenario includes a first scenario; wherein the first scenario is configured with corresponding configuration information to perform tasks related to processing unread messages, wherein the configuration information includes at least one of the following: scenario setting information and plug-in information, wherein the scenario setting information is used to describe information related to processing unread messages, and the plug-in information indicates at least one plug-in used to perform tasks related to processing unread messages; and An interaction module is configured to perform interaction between the target object and the digital assistant in response to a preset operation of the target object on the first scene, at least based on configuration information of the first scene. An electronic device, comprising: at least one processing unit; and At least one memory, the at least one memory being coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions, when executed by the at least one processing unit, causing the electronic device to perform a method according to any one of claims 1 to 14 or claims 15 to 19. A computer-readable storage medium having a computer program stored thereon, wherein the computer program can be executed by a processor to implement the method according to any one of claims 1 to 14 or claims 15 to 19.