Data processing method and device for message management

Abstract

The invention provides a data processing method and device for message management, and relates to the technical field of computers, in particular to natural language processing and instant messaging. According to the implementation scheme, the data processing method for message management comprises the steps that session message data are obtained, and the session message data comprise a plurality of session messages; determining the session content related to at least one theme in a character and an event in the session message data; and obtaining a content summary of the session content by using a large language model.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, in particular to natural language processing and instant messaging, and specifically to a data processing method and device for message management. Background Art

[0002] Artificial intelligence is a discipline that studies how to use computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.). It includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.

[0003] Instant messaging tools can provide users with real-time conversation messages, facilitating real-time communication in people's lives and work.

[0004] The methods described in this section are not necessarily methods that have been previously conceived or employed. Unless otherwise indicated, it should not be assumed that any method described in this section is considered to be prior art simply because it is included in this section. Similarly, unless otherwise indicated, the issues mentioned in this section should not be considered to have been recognized in any prior art. Summary of the invention

[0005] The present disclosure provides a data processing method, apparatus, electronic device, computer-readable storage medium, and computer program product for message management.

[0006] According to one aspect of the present disclosure, there is provided a data processing method for message management, comprising: acquiring conversation message data, wherein the conversation message data comprises a plurality of conversation messages; determining conversation content involving at least one subject of characters and events in the conversation message data; and acquiring a content summary of the conversation content using a large language model.

[0007] According to another aspect of the present disclosure, a data processing device for message management is provided, including: a conversation message acquisition unit, configured to acquire conversation message data, wherein the conversation message data includes multiple conversation messages; a topic determination unit, configured to determine conversation content involving at least one topic of characters and events in the conversation message data; and a content summarization unit, configured to acquire a content summary of the conversation content using a large language model.

[0008] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method according to an embodiment of the present disclosure.

[0009] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute a method according to an embodiment of the present disclosure.

[0010] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, wherein the computer program implements the method according to the embodiment of the present disclosure when being executed by a processor.

[0011] According to another aspect of the present disclosure, an instant messaging product is provided, including a computer program product according to an embodiment of the present disclosure.

[0012] According to one or more embodiments of the present disclosure, the accuracy and efficiency of content summarization can be improved, and at the same time, users can quickly obtain key content involved in conversation messages, reducing the complexity of user operations, thereby improving user experience.

[0013] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings exemplarily illustrate the embodiments and constitute a part of the specification, and together with the text description of the specification, are used to explain the exemplary implementation of the embodiments. The embodiments shown are for illustrative purposes only and do not limit the scope of the claims. In all drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0015] Figure 1 A schematic diagram showing an exemplary system in which the various methods described herein may be implemented according to an embodiment of the present disclosure;

[0016] Figure 2 An exemplary flow chart of a data processing method according to an embodiment of the present disclosure is shown;

[0017] Figure 3 An exemplary data processing process according to an embodiment of the present disclosure is shown;

[0018] Figure 4An exemplary block diagram of a data processing device according to an embodiment of the present disclosure is shown;

[0019] Figure 5 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0020] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0021] In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, temporal relationship, or importance relationship of these elements, and such terms are only used to distinguish one element from another element. In some examples, the first element and the second element may refer to the same instance of the element, and in some cases, based on the description of the context, they may also refer to different instances.

[0022] The terms used in the description of various examples in this disclosure are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element can be one or more. In addition, the term "and / or" used in this disclosure covers any one of the listed items and all possible combinations.

[0023] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0024] Figure 1 FIG. 1 is a schematic diagram of an exemplary system 100 in which various methods and apparatuses described herein may be implemented according to an embodiment of the present disclosure. Figure 1 , the system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120. The client devices 101, 102, 103, 104, 105, and 106 may be configured to execute one or more applications.

[0025] In an embodiment of the present disclosure, the server 120 may run one or more services or software applications that enable execution of the method of message management according to an embodiment of the present disclosure.

[0026] In some embodiments, server 120 may also provide other services or software applications, which may include non-virtualized environments and virtualized environments. In some embodiments, these services may be provided as web-based services or cloud services, such as provided to users of client devices 101, 102, 103, 104, 105, and / or 106 under a software as a service (SaaS) model.

[0027] exist Figure 1 In the configuration shown, the server 120 may include one or more components that implement the functions performed by the server 120. These components may include software components, hardware components, or a combination thereof that can be executed by one or more processors. Users operating client devices 101, 102, 103, 104, 105, and / or 106 may in turn utilize one or more client applications to interact with the server 120 to utilize the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from the system 100. Therefore, Figure 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

[0028] The user may use client devices 101, 102, 103, 104, 105, and / or 106 to receive user input and display output content to the user. The client device may provide an interface that enables the user of the client device to interact with the client device. The client device may also output information to the user via the interface. Figure 1 Only six client devices are depicted, but one skilled in the art will appreciate that the present disclosure may support any number of client devices.

[0029] Client devices 101, 102, 103, 104, 105 and / or 106 may include various types of computer devices, such as portable handheld devices, general-purpose computers (such as personal computers and laptop computers), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, game systems, thin clients, various messaging devices, sensors or other sensing devices, etc. These computer devices may run various types and versions of software applications and operating systems, such as MICROSOFT Windows, APPLE iOS, UNIX-like operating systems, Linux or Linux-like operating systems (such as GOOGLE Chrome OS); or include various mobile operating systems, such as MICROSOFT Windows Mobile OS, iOS, Windows Phone, Android. Portable handheld devices may include cellular phones, smart phones, tablet computers, personal digital assistants (PDAs), etc. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. Game systems may include various handheld game devices, Internet-enabled game devices, etc. Client devices are capable of executing various different applications, such as various Internet-related applications, communication applications (such as email applications), short message service (SMS) applications, and may use various communication protocols.

[0030] The network 110 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including but not limited to TCP / IP, SNA, IPX, etc. By way of example only, the one or more networks 110 may be a local area network (LAN), an Ethernet-based network, a token ring, a wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a blockchain network, a public switched telephone network (PSTN), an infrared network, a wireless network (e.g., Bluetooth, WIFI), and / or any combination of these and / or other networks.

[0031] Server 120 may include one or more general purpose computers, dedicated server computers (e.g., PC (personal computer) servers, UNIX servers, mid-range servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running virtual operating systems, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain a server's virtual storage device). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0032] The computing units in the server 120 may run one or more operating systems including any of the above operating systems and any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and / or middle-tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0033] In some implementations, server 120 may include one or more applications to analyze and consolidate data feeds and / or event updates received from users of client devices 101, 102, 103, 104, 105, and / or 106. Server 120 may also include one or more applications to display data feeds and / or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105, and / or 106.

[0034] In some embodiments, the server 120 may be a server of a distributed system, or a server combined with a blockchain. The server 120 may also be a cloud server, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology. A cloud server is a host product in a cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and virtual private servers (VPS) services.

[0035] The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 130 may be used to store information such as audio files and video files. The databases 130 may reside in various locations. For example, the database used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The databases 130 may be of different types. In some embodiments, the databases used by the server 120 may be, for example, relational databases. One or more of these databases may store, update, and retrieve data to and from the databases in response to commands.

[0036] In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by the applications may be different types of databases, such as a key-value store, an object store, or a conventional store backed by a file system.

[0037] Figure 1The system 100 may be configured and operated in various ways to enable the application of various methods and apparatuses described in the present disclosure.

[0038] In modern enterprise information exchange, instant messaging products are tools that users rely heavily on in work scenarios. With the development of corporate business, the amount of instant messaging messages within the enterprise has shown explosive growth. Statistics show that the average daily total number of messages received by enterprise users exceeds 35 million, and each person receives more than 700 messages per day on average. Faced with such a huge amount of messages, the efficiency of users obtaining and processing important information in multiple sessions has become a major challenge in office work.

[0039] The instant messaging methods provided by related technologies require users to manually flip through the conversation list, search, extract and summarize information one by one, which is not only time-consuming and laborious, but also easy to miss important content, significantly reducing work efficiency. Traditional solutions have limitations in solving these problems, making it difficult to further improve the office efficiency of corporate users. Therefore, instant messaging products urgently need more intelligent and efficient solutions to help users quickly process messages.

[0040] In order to solve the above problems, the present disclosure provides a new method for message management.

[0041] Figure 2 An exemplary flow chart of a data processing method according to an embodiment of the present disclosure is shown.

[0042] In step S202, conversation message data is acquired, wherein the conversation message data includes a plurality of conversation messages.

[0043] In step S204, the conversation content involving at least one subject of a person or an event in the conversation message data is determined.

[0044] In step S206, a content summary of the conversation content is obtained using the large language model.

[0045] By utilizing the data processing method of the embodiments of the present disclosure, conversation messages can be summarized based on characters, events, or a combination of characters and events as themes, thereby improving the accuracy and efficiency of content summarization. At the same time, it enables users to quickly obtain key content involved in conversation messages, reduces the complexity of user operations, and thus improves user experience.

[0046] The principles of the present disclosure will be described in detail below.

[0047] In step S202, conversation message data may be acquired, wherein the conversation message data includes a plurality of conversation messages.

[0048] Here, the principle of the present disclosure is described by taking the conversation message data as a message in an instant messaging as an example. However, it is to be understood that the scope of the present disclosure is not limited thereto. Without departing from the principle of the present disclosure, messages in any product capable of implementing a communication function, such as emails, text messages, etc., can be used as the conversation message data in the present disclosure.

[0049] In some embodiments, the conversation message data is unread messages in instant messaging messages. In the example, the unread messages may be from at least two different conversations. For example, the unread messages may be from a one-to-one conversation between two people, or from a group chat conversation including multiple people.

[0050] In other embodiments, the conversation message data in step S202 may also be other types of messages, such as read messages, messages marked as important, etc. The above other types of messages may come from any form of dialogue in the communication system.

[0051] In step S204, the conversation content involving at least one of characters and events in the conversation message data is determined, wherein the topic may include characters, events, or a combination of characters and events.

[0052] Step S204 may include: determining the character information and event keyword information included in the conversation message data, filtering the conversation message data according to the character information and event keyword information to obtain a filtering result, and determining the conversation content containing at least one of the character information and event keyword information in the conversation message data based on the filtering result.

[0053] By filtering based on character information, conversation content related to the relevant character topic can be obtained. By filtering based on event keyword information, conversation content related to the relevant event topic can be obtained. By performing two filterings, conversation content related to both "character" and "event" can be obtained.

[0054] In some examples, filtering the conversation message data according to the character information and event keyword information to obtain the filtering result may include: determining at least one of the following items included in the conversation message data: conversation content including the character information, conversation content sent by the character corresponding to the character information. For example, taking the character information of "Zhang San" as an example, the conversation content in which "Zhang San" or its alias (such as Manager Zhang) is mentioned can be filtered as the conversation content involving Zhang San. The conversation content sent by Zhang San can also be filtered as the conversation content involving Zhang San.

[0055] In some examples, filtering the conversation message data according to the character information and the event keyword information to obtain the filtering result may include: determining at least one of the following items included in the conversation message data: conversation content including the event keyword information, and conversation content having a semantic similarity with the event keyword information higher than a predetermined threshold. The conversation content including the event keyword information may be filtered by directly filtering keywords. The conversation content having similar semantics to the event keyword information may be filtered by determining the semantic similarity between the conversation content and the event keyword information, and further determining whether the semantic similarity is higher than a predetermined threshold.

[0056] One or more operations in step S204 may be implemented using natural language processing technology and / or machine learning algorithms. For example, the conversation message data obtained in step S202 may be analyzed using a large language model to obtain at least one of the character information and event keyword information to be screened. The characters or keywords appearing in the conversation message data may be determined as the character information or event keyword information to be screened by natural language analysis. The conversation message data may also be further screened using a large language model.

[0057] The large model can be guided to analyze the conversation message data by designing appropriate prompt information. For example, the large model can be instructed by prompt information to determine one or more characters or one or more keywords with the highest frequency of appearance as character information or event keyword information for screening. For another example, the character information or event keyword information for screening can also be specified by prompt information. In addition, other information for screening can also be specified by prompt information, such as the sending time range of the message, the receiving time range of the message, the length of the message content, the range of the message sender, the range of the message receiver, etc.

[0058] In step S206, the large language model is used to obtain a content summary of the conversation content. For example, the large language model can be used to analyze the conversation content of each topic, and a corresponding summary can be generated based on the analysis results, so that the user can efficiently understand the main information in the conversation message data. When the unread messages in the instant messaging product are processed using the present disclosure, the user can quickly understand the main information in the unread messages, reducing the complexity of user operations, thereby improving the speed and convenience of the user's processing of information and work tasks related to the information.

[0059] In some embodiments, step S206 may include: prioritizing each topic in at least one topic, aggregating the conversation content corresponding to each topic in at least one topic, and performing a structured display of the conversation content corresponding to each aggregated topic according to the priority sorting result.

[0060] In the example, the large language model can be used to prioritize the conversation content of each topic (including character topics, event topics, or topics of both characters and events). For example, the conversation content of each topic can be prioritized according to information such as the time the conversation content is sent, the amount of conversation content, and the importance of the conversation content.

[0061] In other examples, the priority ranking of each topic can also be achieved by a user-specified method, or by a user adjusting the ranking result of the large model. For example, the user can specify the priority of a person or event, such as specifying it as the highest priority or the lowest priority. The result of the priority ranking will indicate the importance of the corresponding topic to the user.

[0062] In the example, the large language model can be used to aggregate the conversation content corresponding to each topic in at least one topic to generate a content summary as a summary text of the conversation content corresponding to each topic. The natural language processing function of the large language model can be used to achieve the above functions, reducing the complexity of user operations and improving user experience.

[0063] The aggregated conversation contents of each topic can be structured and displayed according to the priority sorting results of different topics. The structured display results can include each topic and summary text related to each topic. By displaying the text summary results to the user in the above manner, the user can quickly understand the people, events and their combinations involved in the conversation message data. By displaying the summary text of each topic according to the priority sorting results, the user can read the information content with higher priority with higher efficiency.

[0064] The technical solution provided by the embodiments of the present disclosure aims to automatically process and summarize messages in multiple conversations by using artificial intelligence technology through intelligent summarization and information classification technology, thereby reducing the complexity of user operations and improving user experience. The embodiments of the present disclosure can solve the difficulties in reading and summarizing messages caused by excessive messages in enterprise office scenarios, thereby significantly improving user experience.

[0065] Figure 3 An exemplary data processing process according to an embodiment of the present disclosure is shown.

[0066] In step 301, the intelligent summary process of unread messages can be triggered. In the example, the user can trigger the intelligent summary by actively asking questions. For example, the user can trigger the intelligent summary by inputting a question message, such as triggering a dialogue with the big model in the interactive interface of the instant messaging software, and triggering the intelligent summary by inputting a message such as "Please summarize the unread messages". In another example, the big model can read the user's unread messages, and automatically trigger the intelligent summary when it detects that the user's unread messages reach a certain number or there are unread messages marked as important. The system can send a prompt message to the user when the intelligent summary is automatically triggered, so that the user can confirm to start or confirm to abandon the intelligent summary. In another example, the big model can be set to start the intelligent summary periodically. For example, the big model can be set to summarize the important information of the day at the end of each work day.

[0067] In step 302, the subject content involved in the intelligent summary can be obtained, including at least one of character information, event information, or a combination thereof. In some examples, the conversation message data to be summarized can be subjected to word slot extraction, and at least one of the following parameters in the conversation message can be extracted: conversation start time (start_query_time), conversation end time (end_query_time), character (person), event (topics), and filter type (filter_type). The conversation start time indicates the time when the conversation process involved in the conversation message starts. The conversation end time indicates the time when the conversation process involved in the conversation message ends. Characters and events can be extracted from the conversation content or specified by the user. The filter type indicates the filtering range, including the time range of the conversation message, filtering only characters, filtering only events, or filtering both characters and events. In the case where the filtering range includes a filtering task, the filtering type can further indicate whether to filter messages containing characters or to filter information related to characters in the conversation content.

[0068] In step 303, the character information can be screened, and according to the screening type, it can be determined that step 303 includes executing step 304 to screen messages sent by the character, or includes executing step 305 to screen information related to the character in the conversation content. The conversation content screened in step 303 is the conversation message screened by the time range. The conversation content and the screened character information can be matched in a rule-based manner to determine whether the conversation content is sent by a specified character, or to determine whether the conversation content involves a specified character. Then, the conversation content screened by the character information can be subjected to natural language processing using a large model to obtain a summary text of the conversation content.

[0069] In step 306, the event information may be screened. The screened conversation content in step 306 is the conversation message screened by time range. In the example, event keyword screening and / or semantic similarity comparison may be used to determine whether the conversation content in the conversation message involves a specified event. Then, the conversation content screened by the event information may be subjected to natural language processing using a large model to obtain a summary text of the conversation content.

[0070] In step 307, both person and event information may be filtered. The conversation content filtered in step 306 is the conversation message filtered by time range. In step 307, the filtering operations involved in step 306 and step 303 may be performed simultaneously to obtain conversation content related to both the specified person and the specified event. Then, the conversation content filtered in step 307 may be processed by natural language using the large model to obtain a summary text of the conversation content.

[0071] Figure 4 An exemplary block diagram of a data processing device according to an embodiment of the present disclosure is shown. Figure 4 The data processing apparatus shown performs the combination Figure 2 A data processing method 200 is described.

[0072] As shown in the figure, the data processing device 400 may include a conversation message obtaining unit 410 , a topic determining unit 420 , and a content summarizing unit 430 .

[0073] The conversation message acquiring unit 410 may be configured to acquire conversation message data, wherein the conversation message data includes a plurality of conversation messages.

[0074] The topic determination unit 420 may be configured to determine the conversation content involving at least one topic of a person or an event in the conversation message data.

[0075] The content summarizing unit 430 may be configured to obtain a content summary of the conversation content using a large language model.

[0076] In some embodiments, the conversation message data is unread messages in instant messaging messages.

[0077] In some embodiments, the unread messages are from at least two different conversations.

[0078] In some embodiments, determining the conversation content in conversation message data involving at least one topic of characters and events includes: determining the character information and event keyword information included in the conversation message data; filtering the conversation message data according to the character information and event keyword information to obtain a filtering result; and determining the conversation content in the conversation message data that contains at least one item of the character information and event keyword information based on the filtering result.

[0079] In some embodiments, filtering conversation message data according to character information and event keyword information to obtain filtering results includes: determining at least one of the following items included in the conversation message data: conversation content including character information, conversation content sent by a character corresponding to the character information.

[0080] In some embodiments, filtering conversation message data according to character information and event keyword information to obtain filtering results includes: determining at least one of the following items included in the conversation message data: conversation content including event keyword information, conversation content whose semantic similarity with the event keyword information is higher than a predetermined threshold.

[0081] In some embodiments, using a large language model to obtain content summary information of conversation content includes: prioritizing each topic in at least one topic to obtain a priority sorting result; aggregating the content of the conversation corresponding to each topic in at least one topic to obtain aggregate information; and performing a structured display of the conversation content corresponding to each topic based on the priority sorting result and the aggregate information.

[0082] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0083] According to an embodiment of the present disclosure, an electronic device is also provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to an embodiment of the present disclosure.

[0084] According to an embodiment of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is further provided, wherein the computer instructions are used to enable the computer to execute the method according to the embodiment of the present disclosure.

[0085] According to an embodiment of the present disclosure, a computer program product is further provided, including a computer program, wherein the computer program implements the method according to the embodiment of the present disclosure when executed by a processor.

[0086] According to an embodiment of the present disclosure, an instant messaging product is also provided, including a computer program product according to an embodiment of the present disclosure.

[0087] refer to Figure 5, a block diagram of an electronic device 500 that can be used as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0088] like Figure 5 As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0089] Multiple components in the electronic device 500 are connected to the I / O interface 505, including: an input unit 506, an output unit 507, a storage unit 508, and a communication unit 509. The input unit 506 can be any type of device that can input information to the electronic device 500. The input unit 506 can receive input digital or character information and generate key signal input related to user settings and / or function control of the electronic device, and can include but is not limited to a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and / or a remote controller. The output unit 507 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 508 can include but is not limited to a disk, an optical disk. The communication unit 509 allows the electronic device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0090] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 501 performs the various methods and processes described above, such as method 200. For example, in some embodiments, the method 200 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the method 200 described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the method 200 in any other appropriate manner (e.g., by means of firmware).

[0091] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0092] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0093] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0094] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0095] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0096] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0097] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0098] Although the embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above-mentioned methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but only by the claims after authorization and their equivalent scope. Various elements in the embodiments or examples can be omitted or replaced by their equivalent elements. In addition, each step can be performed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples can be combined in various ways. It is important that with the evolution of technology, many elements described herein can be replaced by equivalent elements that appear after the present disclosure.

Claims

1. A data processing method for message management, comprising: Acquire conversation message data, wherein the conversation message data includes a plurality of conversation messages; Determining the conversation content involving at least one of characters and events in the conversation message data; and The content summary information of the conversation content is obtained by using a large language model.

2. The data processing method according to claim 1, wherein: The conversation message data includes unread messages in instant messaging messages.

3. The data processing method according to claim 2, wherein: The unread messages are from at least two different conversations.

4. The data processing method according to claim 1, wherein: The determining of the conversation content involving at least one of the topics of characters and events in the conversation message data includes: Determining character information and event keyword information included in the conversation message data; Filtering the conversation message data according to the character information and the event keyword information to obtain a filtering result; The conversation content including at least one of the character information and the event keyword information in the conversation message data is determined based on the screening result.

5. The data processing method according to claim 4, wherein: The filtering of the conversation message data according to the character information and the event keyword information to obtain the filtering result includes: At least one of the following items included in the conversation message data is determined: conversation content including the character information, and conversation content sent by the character corresponding to the character information.

6. The data processing method according to claim 4, wherein: The filtering of the conversation message data according to the character information and the event keyword information to obtain the filtering result includes: At least one of the following items included in the conversation message data is determined: conversation content including the event keyword information, and conversation content having a semantic similarity with the event keyword information higher than a predetermined threshold.

7. The data processing method according to claim 1, wherein: The obtaining of content summary information of the conversation content by using a large language model includes: Prioritizing each topic in the at least one topic to obtain a priority ranking result; Aggregating the conversation content corresponding to each topic in the at least one topic to obtain aggregated information; The conversation content corresponding to each topic is structuredly displayed according to the priority sorting result and the aggregate information.

8. A data processing device for message management, comprising: A session message acquisition unit, configured to acquire session message data, wherein the session message data includes a plurality of session messages; a topic determination unit configured to determine the conversation content involving at least one topic of a person or an event in the conversation message data; and The content summarizing unit is configured to obtain a content summary of the conversation content using a large language model.

9. The data processing apparatus according to claim 8, wherein: The conversation message data includes unread messages in instant messaging messages.

10. The data processing apparatus according to claim 9, wherein: The unread messages are from at least two different conversations.

11. The data processing apparatus according to claim 8, wherein: The determining of the conversation content involving at least one of the topics of characters and events in the conversation message data includes: Determining character information and event keyword information included in the conversation message data; Filtering the conversation message data according to the character information and the event keyword information to obtain a filtering result; The conversation content including at least one of the character information and the event keyword information in the conversation message data is determined based on the screening result.

12. The data processing apparatus according to claim 11, wherein: The filtering of the conversation message data according to the character information and the event keyword information to obtain the filtering result includes: Determine at least one of the following items included in the conversation message data: conversation content including the character information and conversation content sent by the character corresponding to the character information.

13. The data processing apparatus according to claim 11, wherein: The filtering of the conversation message data according to the character information and the event keyword information to obtain the filtering result includes: At least one of the following items included in the conversation message data is determined: conversation content including the event keyword information, and conversation content having a semantic similarity with the event keyword information higher than a predetermined threshold.

14. The data processing apparatus according to claim 8, wherein: The obtaining of content summary information of the conversation content by using a large language model includes: Prioritizing each topic in the at least one topic to obtain a priority ranking result; Aggregating the conversation content corresponding to each topic in the at least one topic to obtain aggregated information; The conversation content corresponding to each topic is structuredly displayed according to the priority sorting result and the aggregate information.

15. An electronic device, comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; in The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

16. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

17. A computer program product comprising a computer program, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

18. An instant messaging product, comprising the computer program product according to claim 15.

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