Dialogue method and apparatus, device and medium

Abstract

The present disclosure provides a virtual role-based dialogue method and device, equipment and medium, relates to the technical field of artificial intelligence, and particularly relates to the technical field of natural language processing. The implementation scheme is: obtaining historical dialogue data of a user and a virtual role; determining a plurality of user feature information of the user based on the historical dialogue data; determining a feature privacy density corresponding to each user feature information based on the plurality of user feature information, the feature privacy density being capable of indicating a public degree of the user feature information; and in response to receiving first dialogue information sent by the user to the virtual role, determining first reply information for sending to the user based on the plurality of user feature information, the feature privacy density and the first dialogue information.

Description

Technical Field

[0001] This disclosure relates to the field of artificial intelligence technology, and more particularly to the field of natural language processing technology, specifically to a dialogue method, apparatus, electronic device, computer-readable storage medium, and computer program product. Background Technology

[0002] Artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies mainly include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0003] With the development of computer technology, the application of human-computer dialogue is becoming more and more widespread, especially dialogue between virtual characters and users to simulate real interpersonal dialogue.

[0004] The methods described in this section are not necessarily methods that had been previously conceived or adopted. Unless otherwise specified, no method described in this section should be assumed to be prior art simply because it is included in this section. Similarly, unless otherwise specified, the issues mentioned in this section should not be considered to be accepted in any prior art. Summary of the Invention

[0005] This disclosure provides a dialogue method, apparatus, electronic device, computer-readable storage medium, and computer program product based on virtual characters.

[0006] According to one aspect of this disclosure, a dialogue method based on a virtual character is provided, comprising: acquiring historical dialogue data between a user and a virtual character; determining multiple user feature information of the user based on the historical dialogue data; determining a feature privacy density corresponding to each user feature information based on the multiple user feature information, wherein the feature privacy density can indicate the degree of public disclosure of the user feature information; and, in response to receiving first dialogue information sent by the user to the virtual character, determining a first reply information to be sent to the user based on the multiple user feature information, the feature privacy density, and the first dialogue information.

[0007] According to another aspect of this disclosure, a dialogue device based on a virtual character is provided, comprising: a first acquisition unit configured to acquire historical dialogue data between a user and a virtual character; a first determination unit configured to determine multiple user feature information of the user based on the historical dialogue data; a second determination unit configured to determine a feature privacy density corresponding to each user feature information based on the multiple user feature information, the feature privacy density being able to indicate the degree of public disclosure of the user feature information; and a third determination unit configured to, in response to receiving first dialogue information sent by the user to the virtual character, determine a first reply information to be sent to the user based on the multiple user feature information, the feature privacy density, and the first dialogue information.

[0008] According to another aspect of this 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, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described virtual character-based dialogue method.

[0009] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause the computer to perform the above-described virtual role-based dialogue method.

[0010] According to another aspect of this disclosure, a computer program product is provided, including a computer program, wherein the computer program, when executed by a processor, is capable of implementing the above-described virtual character-based dialogue method.

[0011] According to one or more embodiments of this disclosure, dialogue content to be sent to a user can be generated more accurately.

[0012] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0013] The accompanying drawings exemplify embodiments and form part of the specification, serving together with the textual description to explain exemplary implementations of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0014] Figure 1 A schematic diagram of an exemplary system in which various methods described herein may be implemented, according to exemplary embodiments of the present disclosure;

[0015] Figure 2 A flowchart of a dialogue method according to an exemplary embodiment of the present disclosure is shown;

[0016] Figure 3 A flowchart of a dialogue method according to an exemplary embodiment of the present disclosure is shown;

[0017] Figure 4 A schematic diagram of a dialogue process according to an exemplary embodiment of the present disclosure is shown;

[0018] Figure 5 A structural block diagram of a dialogue device according to an exemplary embodiment of the present disclosure is shown;

[0019] Figure 6 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure is shown. Detailed Implementation

[0020] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0021] In this disclosure, unless otherwise stated, the use of terms such as "first," "second," etc., to describe various elements is not intended to limit the positional, temporal, or importance relationships of these elements; such terms are merely used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of that element, while in other cases, based on the context, they may refer to different instances.

[0022] The terminology used in the description of the various examples described in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context explicitly indicates otherwise, an element may be one or more unless the number of elements is specifically limited. Furthermore, the term "and / or" as used in this disclosure covers any one of the listed items and all possible combinations thereof.

[0023] In related technologies, deep learning models are typically trained using large corpora to generate dialogue content based on user-sent conversations or to generate conversations that can be proactively sent to users. While semantic analysis of user-sent conversation information can be combined with features such as gender and age to generate responses tailored to user needs, this approach fails to consider deeper user preferences or personality traits, resulting in a poor user experience.

[0024] Based on this, the present invention provides a dialogue method based on virtual characters. By mining user feature information and corresponding feature privacy based on the historical dialogue data between the user and the virtual character, the method can determine the response information by combining the user features and privacy based on semantic analysis, so that the response information can be more adapted to the user's preferences and improve the user experience.

[0025] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0026] Figure 1 A schematic diagram of an exemplary system 100 in which the various methods and apparatus described herein can be implemented according to embodiments of this disclosure is shown. Reference 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 can be configured to execute one or more applications.

[0027] In embodiments of this disclosure, server 120 may run one or more services or software applications that enable the execution of dialogue methods.

[0028] In some embodiments, server 120 may also provide other services or software applications, which may include non-virtual and virtual environments. In some embodiments, these services may be provided as web-based services or cloud services, such as to users of client devices 101, 102, 103, 104, 105, and / or 106 under a Software as a Service (SaaS) model.

[0029] exist Figure 1In the configuration shown, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or combinations thereof that can be executed by one or more processors. Users operating client devices 101, 102, 103, 104, 105, and / or 106 can sequentially interact with server 120 using one or more client applications to utilize the services provided by these components. It should be understood that various different system configurations are possible and may differ from system 100. Therefore, Figure 1 This is an example of a system used to implement the various methods described herein, and is not intended to be limiting.

[0030] Users can use client devices 101, 102, 103, 104, 105, and / or 106 to send conversational messages. The client devices can provide an interface that allows users to interact with the client devices. The client devices can also output information to the user through this interface. Although... Figure 1 Only six client devices are described, but those skilled in the art will understand that this disclosure can support any number of client devices.

[0031] Client devices 101, 102, 103, 104, 105, and / or 106 may include various categories of computer devices, such as portable handheld devices, general-purpose computers (such as personal computers and laptops), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, gaming systems, thin clients, various messaging devices, sensors, or other sensing devices. These computer devices can run various categories 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, and Android. Portable handheld devices may include cellular phones, smartphones, tablets, personal digital assistants (PDAs), etc. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. Gaming systems may include various handheld gaming devices, internet-enabled gaming devices, etc. Client devices can run a variety of different applications, such as various Internet-related applications, communication applications (e.g., email applications), short message service (SMS) applications, and can use various communication protocols.

[0032] Network 110 can be any type of network well known to those skilled in the art, and can use any of a variety of available protocols (including but not limited to TCP / IP, SNA, IPX, etc.) to support data communication. By way of example only, one or more networks 110 can be a local area network (LAN), an Ethernet-based network, a token ring network, 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.

[0033] Server 120 may include one or more general-purpose computers, special-purpose 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 a virtual operating system, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that can be virtualized to maintain virtual storage devices for servers). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0034] The computing unit in server 120 can run one or more operating systems, including any of the aforementioned operating systems and any commercially available server operating system. Server 120 can also run any of a variety of additional server applications and / or middleware applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0035] In some implementations, server 120 may include one or more applications to analyze and merge data feeds and / or event updates received from users of client devices 101, 102, 103, 104, 105, and 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 106.

[0036] In some implementations, server 120 can be a server for a distributed system or a server integrated with blockchain. Server 120 can 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 the cloud computing service system, designed to address the shortcomings of traditional physical hosts and Virtual Private Server (VPS) services, such as high management difficulty and weak business scalability.

[0037] 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. Databases 130 may reside in various locations. For example, a database used by server 120 may be local to server 120, or it may be located away from server 120 and may communicate with server 120 via a network-based or dedicated connection. Databases 130 may be of different categories. In some embodiments, the database used by server 120 may be, for example, a relational database. One or more of these databases may store, update, and retrieve data from and from the databases in response to commands.

[0038] In some embodiments, one or more of the databases 130 may also be used by an application to store application data. The databases used by the application may be different categories of databases, such as key-value stores, object stores, or regular stores supported by a file system.

[0039] Figure 1 The system 100 can be configured and operated in various ways to enable the application of the various methods and apparatus described in this disclosure.

[0040] Figure 2 A flowchart of a dialogue method 200 according to an exemplary embodiment of this disclosure is shown. Figure 2 As shown, method 200 includes:

[0041] Step S210: Obtain historical dialogue data between the user and the virtual character;

[0042] Step S220: Based on the historical dialogue data, determine multiple user characteristic information of the user;

[0043] Step S230: Based on the multiple user feature information, determine the feature privacy density corresponding to each user feature information, wherein the feature privacy density can indicate the degree to which the user feature information can be disclosed; and

[0044] Step S240: In response to receiving the first dialogue information sent by the user to the virtual character, determine the first reply information to be sent to the user based on the multiple user feature information, the feature privacy and the first dialogue information.

[0045] By analyzing historical dialogue data between users and virtual characters, user feature information can be extracted, and the feature privacy level corresponding to each user feature information can be determined to indicate its public disclosure level. Thus, based on semantic analysis, user feature information can be combined with feature privacy level to determine the response information, so that the response information can better meet the user's needs and improve the user experience.

[0046] In some examples, the virtual character may be deployed within a physical chatbot, enabling the chatbot to interact with the user, and the responses may be sent to the user in text or voice format. However, this is not a limitation; for example, it may also be deployed on a specific application or website platform, and this disclosure does not impose any restrictions on this.

[0047] In some examples, the historical dialogue data between the user and the virtual character can be filtered to obtain multiple historical dialogue fragments that can characterize user features. Then, the corresponding user feature information for each historical dialogue fragment can be determined to obtain multiple sets of user feature information. Furthermore, the initially obtained multiple sets of user feature information can be filtered, merged, etc., and this disclosure does not limit this process.

[0048] In some examples, the feature privacy of the user characteristic information is represented in the form of a quantified privacy score. For example, a privacy score model can be used to analyze the user characteristic information to obtain a corresponding privacy score. The privacy score model can be trained using sample user characteristic information that includes manually labeled privacy score tags.

[0049] According to some embodiments, step S230, determining the feature privacy density corresponding to each user feature information based on the plurality of user feature information includes: determining the feature information category corresponding to each user feature information from a plurality of preset information categories; obtaining the privacy density corresponding to each feature information category from a feature information category library, wherein the feature information category library includes the mapping relationship between the plurality of preset information categories and their privacy densities; and determining the feature privacy density corresponding to each user feature information based on the privacy density corresponding to each feature information category. According to some embodiments, the plurality of preset information categories includes at least one of the following: user interests and hobbies, user consumption habits, and user personality traits. Therefore, the feature privacy density of user feature information can be determined more efficiently and accurately using the feature privacy density information stored in the feature information category library, making the process simpler and more accurate.

[0050] In some examples, the multiple preset information categories may also include other content, such as user preference information for specific things, user short-term goal information (e.g., fitness, travel, etc.) or long-term planning information (e.g., career planning, long-term learning plans, etc.).

[0051] In some examples, after determining the privacy density of the feature category corresponding to user feature information, further analysis of the user feature information or the corresponding historical dialogue content can be performed to more accurately determine the corresponding feature privacy density. In one example, semantic analysis of the historical dialogue content corresponding to the user feature information can be used to more accurately determine the feature privacy density based on the user's expression. For instance, when a user states their frequent participation in a sport as a fact, the feature privacy density of that user feature information can be determined to be relatively low, while when a user expresses their love for a sport as an opinion, the feature privacy density of that user feature information can be determined to be relatively high.

[0052] Generally, the dialogue between the user and the virtual character is user-driven; that is, the virtual character can only respond to the dialogue messages initiated by the user, and the conversation revolves solely around the topics initiated by the user. In some examples, the virtual character can also proactively initiate other topics to simulate real interpersonal conversations, further enhancing the user experience.

[0053] Based on this, according to some embodiments, method 200 further includes: determining a first topic corresponding to the first dialogue information sent by the user to the virtual character; determining a second topic based on the plurality of user feature information and feature privacy, wherein the second topic is different from the first topic; and determining second dialogue information to be sent to the user based on the second topic. Thus, it is possible to determine dialogue information to be sent to the user based on user feature information and its feature privacy, thereby initiating a dialogue with the user around a new topic and further improving the user experience.

[0054] In some examples, historical dialogue data can be analyzed, and the second topic can be determined based on the analysis results and the privacy level of user characteristic information. For example, multiple historical topics can be identified based on the historical dialogue content between the user and the virtual character. The second topic can then be determined based on the user characteristic information represented by the historical dialogue content corresponding to each historical topic. For instance, historical topics corresponding to user characteristic information with higher privacy level can be identified as historical topics. This allows the sent second dialogue information to more realistically simulate interpersonal dialogue and improve the user experience.

[0055] According to some embodiments, determining the second topic based on the plurality of user feature information and feature privacy includes: in response to the feature privacy corresponding to the plurality of user feature information satisfying a preset condition, determining a plurality of historical topics associated with the plurality of user feature information; and determining a second topic that is different from all of the plurality of historical topics. The method 200 further includes: obtaining second reply information sent by the user in response to the second dialogue information; and determining user feature information associated with the second topic based on the second reply information. Therefore, when the feature privacy corresponding to existing user feature information satisfies a preset condition, a new topic can be proactively initiated with the user, and new user feature information can be determined based on the user's reply information to further improve the user feature information.

[0056] Generally speaking, the privacy level of user characteristic information obtained by a virtual character can indicate the degree of relationship development between the user and the virtual character. For example, a higher privacy level in the user characteristic information obtained by the virtual character indicates a closer relationship. In some examples, other content can be used to more accurately indicate the degree of relationship development.

[0057] Based on this, according to some embodiments, method 200 further includes: determining the intimacy of the historical dialogue content between the user and the virtual character based on the historical dialogue data; determining the relationship level between the user and the virtual character based on the intimacy and the feature privacy, and wherein, in step S240, a first reply message to be sent to the user is determined based on the plurality of user feature information, the relationship level, and the first dialogue information. Thus, by determining the intimacy of the historical dialogue content between the user and the virtual character, and combining the intimacy and feature privacy, the relationship level between the user and the virtual character can be determined. The relationship level can then be used to indicate the degree of relationship development between the user and the virtual character, simulating real interpersonal relationship stages. This allows the reply message to more accurately simulate real interpersonal dialogue, improving the user experience.

[0058] In some examples, the relationship level between the user and the virtual character can be determined from multiple preset relationship levels, which may be pre-configured manually. In one example, the intimacy of the historical dialogue content is represented by a quantified intimacy score. Therefore, the relationship level between the user and the virtual character can be determined based on the relative magnitude of the intimacy score and a preset threshold. The intimacy score can be obtained using an intimacy scoring model, or it can be determined by analyzing keywords contained in the historical dialogue content, based on the frequency of occurrence of specific keywords that indicate higher intimacy; this disclosure does not limit this approach.

[0059] In some examples, the relationship level between the user and the virtual character can be determined more accurately by combining the duration and frequency of their historical conversations.

[0060] As described above, the privacy level of user characteristic information obtained by a virtual character can indicate the degree of relationship development between the user and the virtual character. At the same time, the comprehensiveness of user characteristic information obtained by the virtual character can also indicate the degree of relationship development between the two.

[0061] Based on this, according to some embodiments, method 200 further includes: determining the ratio of the number of multiple feature information categories corresponding to multiple user feature information to the number of multiple preset information categories, and wherein, in step S240, the relationship level between the user and the virtual character is determined based on the intimacy, the feature privacy, and the ratio. Thus, the comprehensiveness of existing user feature information can be determined by utilizing the ratio of the number of multiple feature information categories corresponding to multiple user feature information to the number of multiple preset information categories, thereby more accurately determining the relationship level between the two.

[0062] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0063] Figure 3 A flowchart of a dialogue method 300 according to an exemplary embodiment of this disclosure is shown. Figure 3 As shown, method 300 includes:

[0064] Step S301: Obtain historical dialogue data between the user and the virtual character;

[0065] Step S302: Based on the historical dialogue data, determine multiple user characteristic information of the user;

[0066] Step S303: Determine the feature information category corresponding to each user feature information from multiple preset information categories;

[0067] Step S304: Obtain the privacy density corresponding to each feature information category from the feature information category library, wherein the feature information category library includes the mapping relationship between the multiple preset information categories and their privacy densities;

[0068] Step S305: Determine the feature privacy density corresponding to each user feature information based on the privacy density corresponding to each feature information category;

[0069] Step S306: Based on the historical dialogue data, determine the intimacy of the historical dialogue content between the user and the virtual character;

[0070] Step S307: Determine the relationship level between the user and the virtual character based on the intimacy and the feature privacy.

[0071] Step S308: Based on the multiple user feature information, the relationship level, and the first dialogue information, determine the first reply information to be sent to the user.

[0072] By utilizing the aforementioned method 300, user feature information can be extracted from the historical dialogue data between the user and the virtual character. Then, based on the feature information category corresponding to the user feature information, the feature privacy density can be determined. Based on the feature privacy density of the user feature information obtained by the virtual character and the intimacy of the historical dialogue content, the relationship level between the two can be determined. Thus, based on semantic analysis and combined with the relationship level, the response information can be determined so that the response information can better adapt to the user's preferences, more accurately simulate real interpersonal dialogue, and improve the user experience.

[0073] Figure 4 A schematic diagram of a dialogue process according to an exemplary embodiment of this disclosure is shown. In this example, after analyzing historical dialogue data to obtain user feature information and its corresponding feature privacy, this information can be stored in a user feature memory, allowing for direct retrieval of the relevant information without repeating the data analysis and determination steps. In response to receiving first dialogue information sent by a user to a virtual character, a first response message sent by the virtual character to the user can be determined based on the user feature information, feature privacy, and the semantic content of the first dialogue information.

[0074] When the privacy of existing user feature information meets the preset conditions, user feature information can be extracted from the user feature memory, combined with historical dialogue content to determine new topics that can be initiated, and a second dialogue message can be generated based on the new topic and sent to the user to initiate a dialogue around the new topic.

[0075] According to one aspect of this disclosure, a dialogue device based on virtual characters is also provided. Figure 5 A structural block diagram of a dialogue device 500 according to an exemplary embodiment of the present disclosure is shown. Figure 5 As shown, the device 500 includes:

[0076] The first acquisition unit 510 is configured to acquire historical dialogue data between the user and the virtual character.

[0077] The first determining unit 520 is configured to determine multiple user characteristic information of the user based on the historical dialogue data;

[0078] The second determining unit 530 is configured to determine the feature privacy density corresponding to each user feature information based on the plurality of user feature information, wherein the feature privacy density can indicate the degree of public disclosure of the user feature information; and

[0079] The third determining unit 540 is configured to, in response to receiving first dialogue information sent by the user to the virtual character, determine first reply information to be sent to the user based on the plurality of user feature information, the feature privacy and the first dialogue information.

[0080] According to some embodiments, the second determining unit 530 includes: a first determining subunit configured to determine the feature information category corresponding to each user feature information from a plurality of preset information categories; an obtaining subunit configured to obtain the privacy density corresponding to each feature information category from a feature information category library, the feature information category library including the mapping relationship between the plurality of preset information categories and their privacy densities; and a second determining subunit configured to determine the feature privacy density corresponding to each user feature information based on the privacy density corresponding to each feature information category.

[0081] According to some embodiments, the plurality of preset information categories include at least one of the following: user's interests and hobbies, user's consumption habits, and user's personality traits.

[0082] According to some embodiments, the device 500 further includes: a fourth determining unit configured to determine a first topic corresponding to the first dialogue information sent by the user to the virtual character; a fifth determining unit configured to determine a second topic based on the plurality of user feature information and feature privacy, wherein the second topic is different from the first topic; and a sixth determining unit configured to determine second dialogue information to be sent to the user based on the second topic.

[0083] According to some embodiments, the fifth determining subunit is configured to: determine a plurality of historical topics associated with the plurality of user feature information in response to the feature privacy corresponding to the plurality of user feature information satisfying a preset condition; and determine a second topic that is different from all of the plurality of historical topics. The device 500 further includes: a second obtaining unit configured to obtain second reply information sent by the user in response to the second dialogue information; and a seventh determining unit configured to determine user feature information associated with the second topic based on the second reply information.

[0084] According to some embodiments, the device 500 further includes: an eighth determining unit configured to determine the intimacy of the historical dialogue content between the user and the virtual character based on the historical dialogue data; a ninth determining unit configured to determine the relationship level between the user and the virtual character based on the intimacy and the feature privacy, and wherein a third determining unit 540 is configured to determine a first reply message to be sent to the user based on the plurality of user feature information, the relationship level and the first dialogue information.

[0085] According to some embodiments, the device 500 further includes: a tenth determining unit, configured to determine the ratio of the number of multiple feature information categories corresponding to multiple user feature information to the number of multiple preset information categories, and wherein the ninth determining unit is configured to determine the relationship level between the user and the virtual character based on the intimacy, the feature privacy and the ratio.

[0086] The operation of units 510-540 of the dialogue device 500 is similar to the operation of steps S210-S240 described above, and will not be repeated here.

[0087] According to another aspect of this 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, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described virtual character-based dialogue method.

[0088] According to another aspect of this disclosure, a non-transitory computer-readable storage medium storing computer instructions is also provided, wherein the computer instructions are used to cause the computer to perform the above-described virtual role-based dialogue method.

[0089] According to another aspect of this disclosure, a computer program product is also provided, comprising a computer program, wherein the computer program, when executed by a processor, implements the above-described virtual role-based dialogue method.

[0090] refer to Figure 6The present invention describes a structural block diagram of an electronic device 600 that can serve as a server or client of the present disclosure, 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, workstations, 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 processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0091] like Figure 6 As shown, device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 602 or a computer program loaded from storage unit 608 into random access memory (RAM) 603. RAM 603 may also store various programs and data required for the operation of device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0092] Multiple components in device 600 are connected to I / O interface 605, including: input unit 606, output unit 607, storage unit 608, and communication unit 609. Input unit 606 can be any type of device capable of inputting information to device 600. Input unit 606 can receive input numerical or character information and generate key signal inputs related to user settings and / or function control of the electronic device, and may include, but is not limited to, a mouse, keyboard, touchscreen, trackpad, trackball, joystick, microphone, and / or remote control. Output unit 607 can be any type of device capable of presenting information, and may include, but is not limited to, a monitor, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 608 may include, but is not limited to, a hard disk and an optical disk. Communication unit 609 allows device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, 802.11 devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0093] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as a virtual role-based dialogue method. For example, in some embodiments, the virtual role-based dialogue method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed on device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the virtual role-based dialogue method described above can be performed. Alternatively, in other embodiments, the computing unit 601 can be configured to perform the virtual role-based dialogue method by any other suitable means (e.g., by means of firmware).

[0094] Various embodiments 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-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0095] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0096] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0097] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, 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 sound input, voice input, or tactile input).

[0098] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), the Internet, and blockchain networks.

[0099] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0100] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0101] While embodiments or examples of this disclosure have been described with reference to the accompanying drawings, it should be understood that the methods, systems, and devices described above are merely exemplary embodiments or examples, and the scope of the invention is not limited by these embodiments or examples, but only by the granted claims and their equivalents. Various elements in the embodiments or examples may be omitted or replaced by their equivalents. Furthermore, the steps may be performed in a different order than that described in this disclosure. Further, various elements in the embodiments or examples may be combined in various ways. Importantly, as the technology evolves, many elements described herein can be replaced by equivalents that appear after this disclosure.

Claims

1. A dialogue method based on virtual characters, comprising: Obtain historical dialogue data between users and virtual characters; Based on the historical dialogue data, multiple user feature information of the user is determined, including: determining multiple historical dialogue segments that can characterize user feature information from the historical dialogue data; and determining the corresponding user feature information for each of the multiple historical dialogue segments. Based on the multiple user feature information, a feature privacy density is determined for each user feature information, wherein the feature privacy density indicates the degree to which the user feature information can be disclosed; and In response to receiving the first dialogue information sent by the user to the virtual character, based on the multiple user feature information, the feature privacy, and the first dialogue information, a first reply information to be sent to the user is determined. The method further includes: In response to the fact that the feature privacy corresponding to the multiple user feature information meets a preset condition, multiple historical topics associated with the multiple user feature information and a first topic corresponding to the first dialogue information are determined. Identify a second topic that differs from the plurality of historical topics and the first topic; Based on the second topic, determine the second dialogue information to be sent to the user; and Based on the second reply information sent by the user in response to the second dialogue information, the user's user characteristic information associated with the second topic is determined.

2. The method as described in claim 1, wherein, The step of determining the feature privacy density corresponding to each user feature based on the multiple user feature information includes: The feature information category corresponding to each user feature information is determined from multiple preset information categories; The privacy density corresponding to each feature information category is obtained from a feature information category library, wherein the feature information category library includes the mapping relationship between the plurality of preset information categories and their privacy densities; and Based on the privacy density corresponding to each feature information category, the feature privacy density corresponding to each user feature information is determined.

3. The method as described in claim 2, wherein, The plurality of preset information categories include at least one of the following: Users' interests, consumption habits, and personality traits.

4. The method according to any one of claims 1-3, further comprising: Based on the historical dialogue data, determine the intimacy level of the historical dialogue content between the user and the virtual character; Based on the intimacy level and the privacy feature, the relationship level between the user and the virtual character is determined. Furthermore, based on the multiple user characteristic information, the relationship level, and the first dialogue information, a first reply information to be sent to the user is determined.

5. The method of claim 4, further comprising: Determine the ratio of the number of feature categories corresponding to multiple user feature information to the number of multiple preset information categories. Furthermore, the relationship level between the user and the virtual character is determined based on the intimacy, the privacy of the features, and the ratio.

6. A dialogue device based on virtual characters, comprising: The first acquisition unit is configured to acquire historical dialogue data between the user and the virtual character. A first determining unit is configured to determine multiple user feature information of the user based on the historical dialogue data. The first determining unit is further configured to: determine multiple historical dialogue segments that can characterize user feature information from the historical dialogue data; and determine the corresponding user feature information for each of the multiple historical dialogue segments. The second determining unit is configured to determine the feature privacy density corresponding to each user feature information based on the plurality of user feature information, wherein the feature privacy density can indicate the degree to which the user feature information can be disclosed; as well as The third determining unit is configured to, in response to receiving first dialogue information sent by the user to the virtual character, determine first reply information to be sent to the user based on the plurality of user feature information, the feature privacy, and the first dialogue information. The device further includes: The fourth determining unit is configured to determine, in response to the fact that the feature privacy corresponding to the multiple user feature information meets a preset condition, multiple historical topics associated with the multiple user feature information and a first topic corresponding to the first dialogue information. The fifth determining unit is configured to determine a second topic that is different from the plurality of historical topics and the first topic; The sixth determining unit is configured to determine, based on the second topic, second dialogue information to be sent to the user; and The seventh determining unit is configured to determine the user's user characteristic information associated with the second topic based on the second reply information sent by the user in response to the second dialogue information.

7. The apparatus of claim 6, wherein, The second determining unit includes: The first determining subunit is configured to determine the feature information category corresponding to each user feature information from multiple preset information categories; The acquisition subunit is configured to acquire the private density corresponding to each feature information category from a feature information category library, wherein the feature information category library includes the mapping relationship between the plurality of preset information categories and their private densities; and The second determining subunit is configured to determine the feature privacy density corresponding to each user feature information based on the privacy density corresponding to each feature information category.

8. The apparatus of claim 7, wherein, The plurality of preset information categories include at least one of the following: Users' interests, consumption habits, and personality traits.

9. The apparatus of any one of claims 6-8, further comprising: The eighth determining unit is configured to determine the intimacy of the user's historical dialogue content with the virtual character based on the historical dialogue data. The ninth determining unit is configured to determine the relationship level between the user and the virtual character based on the intimacy level and the feature privacy level. Furthermore, the third determining unit is configured to determine, based on the plurality of user feature information, the relationship level, and the first dialogue information, a first reply message to be sent to the user.

10. The apparatus of claim 9, further comprising: The tenth determining unit is configured to determine the ratio of the number of multiple feature information categories corresponding to multiple user feature information to the number of multiple preset information categories. Furthermore, the ninth determining unit is configured to determine the relationship level between the user and the virtual character based on the intimacy, the feature privacy, and the ratio.

11. An electronic device, comprising: At least one processor; as well as A memory that is communicatively connected to the at least one processor; in The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

12. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-5.

13. A computer program product comprising a computer program, wherein, The computer program, when executed by a processor, implements the method according to any one of claims 1-5.

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