Replay message generation method and device, electronic equipment and storage medium

Abstract

The invention discloses a reply information generation method and device, electronic equipment and a storage medium, and relates to the technical field of human-computer interaction. The specific implementation scheme is as follows: acquiring user interaction data; determining a reply information structure according to the user interaction data; and generating reply information according to the user interaction data and the reply information structure. According to the embodiment of the invention, the reply information structure of the reply information can be flexibly adjusted, and the man-machine interaction experience is effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of human-computer interaction, and in particular to a reply information generation method and device, an electronic device and a storage medium. BACKGROUND

[0002] With the development of vehicle technology, in the field of vehicle technology, users on the vehicle interact with the vehicle in a voice interaction manner. By recognizing the user's voice question, understanding the user's intention, determining the reply content according to the user's intention, and the vehicle outputting the reply content.

[0003] At present, the reply content determined according to the user's intention is usually output according to the same information structure, resulting in poor human-computer interaction experience. SUMMARY

[0004] The present application provides a reply information generation method and device, an electronic device and a storage medium, which can flexibly adjust the reply information structure of the reply information and effectively improve the human-computer interaction experience.

[0005] In a first aspect, the present application provides a reply information generation method, comprising:

[0006] obtaining user interaction data;

[0007] determining a reply information structure according to the user interaction data;

[0008] generating reply information according to the user interaction data and the reply information structure.

[0009] In a second aspect, the present application further provides a reply information generation device, comprising:

[0010] an interaction data acquisition module for obtaining user interaction data;

[0011] a reply information structure determination module for determining a reply information structure according to the user interaction data;

[0012] a reply information generation module for generating reply information according to the user interaction data and the reply information structure.

[0013] In a third aspect, the present application further provides an electronic device, comprising:

[0014] at least one processor; and

[0015] a memory in communication with the at least one processor; wherein

[0016] The memory stores instructions executable 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 reply information generation method provided by any of the embodiments of the present application.

[0017] In a fourth aspect, the embodiments of the present application further provide a computer readable storage medium, which stores computer instructions, and the computer instructions are used to enable a processor to perform the reply information generation method of any of the embodiments of the present application when the processor executes the computer instructions.

[0018] The embodiments of the present application can flexibly adjust the reply information structure of the reply information by determining the reply information structure matched with the user interaction data and generating the reply information corresponding to the user interaction data according to the reply information structure, and effectively improve the human-computer interaction experience.

[0019] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0021] Figure 1 is a flowchart of a reply information generation method according to an embodiment of the present application;

[0022] Figure 2 is a flowchart of another reply information generation method according to an embodiment of the present application;

[0023] Figure 3 is a flowchart of still another reply information generation method according to an embodiment of the present application;

[0024] Figure 4 is a flowchart of still another reply information generation method according to an embodiment of the present application;

[0025] Figure 5 is a flowchart of still another reply information generation method according to an embodiment of the present application;

[0026] Figure 6 is a schematic diagram of a reply information generation device according to an embodiment of the present application;

[0027] Figure 7Fig. 1 is a structural schematic diagram of an electronic device for implementing a reply information generation method according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to make the technical personnel in the art better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor should belong to the scope of protection of the present application.

[0029] It should be noted that the terms "first" and "second" and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0030] In the technical solutions of the embodiments of the present application, the acquisition, storage and application of user information and the like are in line with the relevant legal regulations and do not violate public order and good customs.

[0031] Figure 1 Fig. 1 is a structural schematic diagram of an electronic device for implementing a reply information generation method according to an embodiment of the present application. The embodiments of the present application can be applied to the case of replying to the user's question in the human-computer interaction process between the vehicle and the user. The method can be executed by a reply information generation device, which can be realized in the form of hardware and / or software and specifically configured in an electronic device. The electronic device can be a server or a terminal device. The terminal device can include a mobile phone, a computer, a vehicle-mounted terminal or a wearable device, etc.

[0032] Referring to Figure 1 The reply information generation method shown in Fig. 1 includes:

[0033] S101, acquiring user interaction data.

[0034] In an optional embodiment, the user interaction data can be data related to the interaction in the process of the human-computer interaction device interacting with the user. For example, the human-computer interaction device can include a mobile phone, a computer, a vehicle terminal, a wearable device, or the like. In an example, the human-computer interaction device is a vehicle terminal. The vehicle interacts with the user, and the vehicle terminal obtains the question content input by the user, which can be used as the user interaction data. Meanwhile, the vehicle terminal can also obtain the user-related data, which can also be used as the user interaction data.

[0035] It should be noted that the reply information generation method of the embodiments of the present application can be executed by a server or a human-computer interaction device. When the server executes the reply information generation method of the embodiments of the present application, the user interaction data obtained by the server can be data obtained by directly collecting and processing the question data input by the user. For example, the human-computer interaction device processes the question data to obtain the question content and the user-related data, and determines the user interaction data according to the question content and the user-related data. When the human-computer interaction device executes the reply information generation method of the embodiments of the present application, the human-computer interaction device directly collects the question data input by the user, processes the collected data, and uses the processing result as the user interaction data. The human-computer interaction device can obtain the question data input by the user by at least one of the following methods: obtaining text information input by the user, collecting the voice of the user, capturing the image of the user, or recording the video of the user.

[0036] In an optional embodiment, the human-computer interaction device that interacts with the user is a vehicle. In the process of the vehicle interacting with the user, the vehicle terminal generates interaction data related to the human-computer interaction operation, and determines the user interaction data according to the generated interaction data.

[0037] S102, determining a reply information structure according to the user interaction data.

[0038] In an optional embodiment, the reply information structure is used to represent the inclusion of different types of reply content. The different types of reply content can include: first reply content and second reply content.

[0039] In an optional embodiment, the reply information structure includes the first reply content. In an optional embodiment, the reply information structure can also include the first reply content and the second reply content. Whether the second reply content is included in the reply information structure is determined by the user interaction data.

[0040] In an optional embodiment, the reply information structure includes: the inclusion of the first reply content and / or the second reply content.

[0041] In an optional embodiment, the first response content corresponds to the user interaction data.

[0042] In an optional embodiment, the second response content is a follow-up question corresponding to the user interaction data.

[0043] In an optional embodiment, user interaction data is used to determine the structure of the response information. For example, user interaction data is used to determine whether the response information contains a first response content and a second response content; the first response content is the substantive content of the response information, and the first response content can be understood as the answer to the user interaction data.

[0044] The response information structure is used to determine whether the generated response information contains a second response. For example, the response information structure may include either the first response or both the first and second response. Whether the response information includes a second response corresponds to the response information structure; it can be understood as follows: if the response information structure includes both the first and second response, the corresponding response information includes the second response; if the response information structure includes the first response, the response information does not include the second response.

[0045] S103. Generate reply information based on the user interaction data and the reply information structure.

[0046] In an optional embodiment, the user interaction data and response information structure are used to determine the substantive content of the response information; for example, the user interaction data and response information structure are used to determine the answer to the user interaction data.

[0047] In an optional embodiment, generating response information based on the user interaction data and the response information structure can be achieved by: inputting the user interaction data and the response information structure into a pre-trained deep learning model to obtain the response information output by the deep learning model. In one example, the user interaction data and the response information structure can be filled into a prompt template corresponding to the response information structure to obtain input data, which is then input into a large language model to obtain the response information output by the large language model.

[0048] For example, generating response information based on the user interaction data and the response information structure can be achieved by: generating response instruction information based on the user interaction data and the response information structure, and generating response information based on the response instruction information and the user interaction data. Specifically, the user interaction data is input into a first language model to obtain the response information structure output by the first language model, and response instruction information is queried or generated based on the response information structure. Input data is determined based on the user interaction data and the response instruction information. The input data is then input into a second language model to obtain the response information output by the second language model. The first and second language models can be the same or different. The first language model can also be replaced by a classification model.

[0049] In an optional embodiment, generating response information based on the user interaction data, target information content, and response information structure can be achieved by inputting the user interaction data, target information content, and response information structure into a pre-trained deep learning model to obtain the response information output by the deep learning model. In one example, the user interaction data and response information structure can be filled into a prompt template corresponding to the target information content and response information structure to obtain input data, which is then input into a large language model to obtain the response information output by the large language model.

[0050] For example, generating response information based on the user interaction data, target information content, and response information structure can be achieved by: generating response indication information based on the user interaction data, target information content, and response information structure; and generating response information based on the response indication information and user interaction data. Specifically, the user interaction data is input into a first language model to obtain the target information content and response information structure output by the first language model, which serve as the response indication information. Input data is determined based on the user interaction data and the response indication information. The input data is then input into a second language model to obtain the response information output by the second language model. The first and second language models can be the same or different. The first language model can also be replaced by a classification model.

[0051] In one optional embodiment, the generated response information is typically text data, which can be sent to a human-computer interaction device (HCI device) to display the response information to the user in text form; alternatively, the generated response information can be an image displayed to the user; the generated response information can be corresponding audio played to the user; or the generated response information can be corresponding video played to the user, etc. Alternatively, based on the response information, at least one of the following data can be generated: a corresponding image, corresponding audio, or corresponding video, and at least one of these can be sent to the HCI device.

[0052] In one example, the user's question was: Why did the dinosaurs disappear?

[0053] For example, the reply message structure includes the content of the first reply, which could be: "Wow, that's a really good question!" The dinosaurs disappeared because a huge event happened on Earth a long, long time ago. Imagine a giant rock flying from the sky and crashing into the Earth with a bang—that's a meteorite. This giant rock created a lot of dust and smoke that blocked the sky, preventing sunlight from reaching the ground. The Earth became very cold, and many plants died. The dinosaurs had no food to eat, so they slowly disappeared.

[0054] In one example, the response message structure includes a first response and a second response. The corresponding response messages are: The main reason for the extinction of dinosaurs was an asteroid impact, which caused drastic environmental changes (first response). Would you like to know the story of this massive meteorite impact on Earth (second response)?

[0055] For example, generating response information based on the user interaction data can be achieved by inputting the user interaction data into a pre-trained deep learning model to obtain the response information output by the deep learning model. In one example, the user interaction data can be filled into a prompt template to obtain input data, which is then input into a large language model to obtain the response information output by the large language model.

[0056] For example, generating response information based on the user interaction data can be done by: generating response instruction information and response information structure based on the user interaction data, and generating response information based on the response instruction information, response information structure, and user interaction data.

[0057] In an optional embodiment, the response indication information is used to determine the amount of information in the response information. The response indication information, the response information structure, and the user interaction data are used together to generate response information that conforms to the amount of information corresponding to the response indication information.

[0058] For example, the response instruction information can be a type or a tag. Specifically, the response instruction information can include types such as little information and no follow-up questions, little information and follow-up questions, a lot of information and no follow-up questions, and a lot of information and follow-up questions.

[0059] For example, response instructions can be the model's input data. Specifically, a response instruction could be: "Please provide an answer that the child can understand for a child's question: XX, and add guiding content that matches the child's interests." Another example is: "Please provide a complete answer for an adult's question: XX." Furthermore, response instructions can be described in other ways, without specific limitations.

[0060] Specifically, generating response instruction information based on the user interaction data, and generating response information corresponding to the user interaction data based on the response instruction information, may include: inputting the user interaction data into a first language model to obtain the response instruction information output by the first language model, wherein the response instruction information may be a type or a tag; querying the prompt template corresponding to the response instruction information, filling the corresponding prompt template with the user interaction data to obtain input data, and inputting the input data into a second language model to obtain the response information output by the second language model. The first language model and the second language model may be the same or different. The first language model may also be replaced by a classification model.

[0061] For example, user interaction data is input into the first language model to obtain the response instruction information output by the first language model; this response instruction information is the prompt template. User interaction data is then filled into the response instruction information to obtain the input data. This input data is then input into the second language model to obtain the response information output by the second language model. The first and second language models can be the same or different.

[0062] For example, user interaction data is input into a first language model to obtain response indication information output by the first language model. This response indication information can be a type or a tag. The response indication information and user interaction data are then input into a second language model to obtain the response information output by the second language model. The first and second language models can be the same or different. The first language model can also be replaced by a classification model.

[0063] By processing user interaction data to obtain response instruction information, and generating response information based on the response instruction information and user interaction data, effective data that influences the amount of information in the user interaction data can be further extracted and response instruction information generated. Thus, generating response information based on response instruction information and user interaction data can improve the accuracy and flexibility of response information, while reducing the end-to-end load pressure caused by response information generated solely based on user interaction data.

[0064] This invention, by determining a response information structure that matches user interaction data and generating a response message corresponding to the user interaction data according to the response information structure, can flexibly adjust the response information structure and effectively improve the human-computer interaction experience.

[0065] Figure 2 This is a flowchart of another method for generating response information according to an embodiment of the present invention.

[0066] In an optional embodiment, "determine the response information structure based on the user interaction data" is further refined to: determine the response information structure based on the current user interaction data and / or historical user interaction data.

[0067] It should be noted that for any parts not described in detail in the embodiments of the present invention, please refer to the description in the foregoing embodiments.

[0068] See Figure 2 The method for generating response information shown includes:

[0069] S201. Obtain user interaction data.

[0070] S202. Determine the structure of the response information based on the current user interaction data and / or historical user interaction data.

[0071] In an optional embodiment, the response information structure is determined based on current user interaction data and / or historical user interaction data, and includes at least one of the following:

[0072] The structure of the response information is determined based on the user attribute information contained in the current user interaction data;

[0073] The structure of the response information is determined based on the difficulty level of the user questions contained in the current user interaction data.

[0074] The structure of the response information is determined based on the user's historical dialogues contained in the historical user interaction data;

[0075] The structure of the response information is determined based on the user behavior characteristics contained in the current user interaction data;

[0076] The structure of the response information is determined based on the vehicle data contained in the current user interaction data;

[0077] The structure of the response information is determined based on the user attribute information contained in the historical user interaction data;

[0078] The structure of the response information is determined based on the difficulty level of the user questions contained in the historical user interaction data.

[0079] The structure of the response information is determined based on the user behavior characteristics contained in the historical user interaction data;

[0080] The structure of the response information is determined based on the vehicle data contained in the historical user interaction data.

[0081] In an optional embodiment, user interaction data may include: current user interaction data and historical user interaction data; wherein, current user interaction data includes user attribute information; and historical user interaction data includes user's historical dialogues.

[0082] In an optional embodiment, the current user interaction data may include: user attribute information and user behavior characteristics.

[0083] In an optional embodiment, user interaction data may include: current user interaction data and historical user interaction data; wherein, current user interaction data includes user attribute information and user behavior characteristics; and historical user interaction data includes user history dialogues.

[0084] In an optional embodiment, the current user interaction data may include user attribute information and vehicle data.

[0085] In an optional embodiment, user interaction data may include: current user interaction data and historical user interaction data; wherein, current user interaction data may include user attribute information and vehicle data; and historical user interaction data may include user history conversations.

[0086] In an optional embodiment, the current user interaction data may include: user attribute information, user behavior characteristics, and vehicle data.

[0087] In an optional embodiment, user interaction data may include: current user interaction data and historical user interaction data; wherein, current user interaction data may include: user attribute information, user behavior characteristics and vehicle data; and historical user interaction data may include user history dialogues.

[0088] In an optional embodiment, user interaction data may include: current user interaction data and historical user interaction data; wherein, current user interaction data may include: user question content; and historical user interaction data may include user's historical dialogues.

[0089] In an optional embodiment, the current user interaction data may include: user-asked questions and user behavior characteristics.

[0090] In an optional embodiment, user interaction data may include: current user interaction data and historical user interaction data; wherein, current user interaction data may include: user question content and user behavior characteristics; historical user interaction data may include user's historical dialogues.

[0091] In an optional embodiment, the current user interaction data may include: user-asked questions and vehicle data.

[0092] In an optional embodiment, the current user interaction data may include: user questions, user history conversations, and vehicle data.

[0093] In an optional embodiment, the current user interaction data may include: user question content, user behavior characteristics, and vehicle data.

[0094] In an optional embodiment, user interaction data may include: current user interaction data and historical user interaction data; wherein, current user interaction data may include: user question content, user history dialogue, user behavior characteristics and vehicle data; historical user interaction data includes: user history dialogue.

[0095] In an optional embodiment, user interaction data may include: current user interaction data and historical user interaction data; wherein, historical user interaction data may include: user's historical dialogues; and current user interaction data may include: user behavior characteristics.

[0096] In an optional embodiment, user interaction data may include: current user interaction data and historical user interaction data; wherein, historical user interaction data may include: user history dialogues; and current user interaction data may include: vehicle data.

[0097] In an optional embodiment, user interaction data may include: current user interaction data and historical user interaction data; wherein, historical user interaction data may include: user history dialogues; and current user interaction data may include: user behavior characteristics and vehicle data.

[0098] In an optional embodiment, the current user interaction data may include user behavior characteristics and vehicle data.

[0099] Furthermore, for the same user question, different combinations of user interaction data result in different response information structures.

[0100] S203. Generate reply information based on the user interaction data and the reply information structure.

[0101] This embodiment increases the flexibility of the response information structure by flexibly combining and configuring the content of user interaction data, so as to meet diverse interaction needs and adapt to diverse interaction scenarios.

[0102] Figure 3 This is a flowchart of another method for generating response information according to an embodiment of the present invention.

[0103] In an optional embodiment, "determining the response information structure based on the user interaction data" is further refined as follows: when the user interaction data is first user interaction data, the response information structure corresponding to the first user interaction data is determined to be a first response information structure based on the first user interaction data; when the user interaction data is second user interaction data, the response information structure corresponding to the second user interaction data is determined to be a second response information structure based on the second user interaction data.

[0104] It should be noted that for any parts not described in detail in the embodiments of the present invention, please refer to the description in the foregoing embodiments.

[0105] See Figure 3 The method for generating response information shown includes:

[0106] S301. Obtain user interaction data.

[0107] S302. When the user interaction data is the first user interaction data, the reply information structure corresponding to the first user interaction data is determined to be the first reply information structure based on the first user interaction data.

[0108] The first response information structure refers to the structure that includes the content of the first response.

[0109] In one example, the first user interaction data may include, but is not limited to, at least one of the following: first user attribute information; user question content with a difficulty level of a first difficulty level; user history dialogue with known information as first known information; user behavior characteristics with preferred content as first preferred content; and vehicle data with an operation complexity level of a first operation complexity level.

[0110] In an optional embodiment, based on the first user interaction data, the response information structure corresponding to the first user interaction data is determined to be a first response information structure, including at least one of the following:

[0111] When the first user interaction data is the first user attribute information, the structure of the reply information corresponding to the first user attribute information is determined to include the first reply content based on the first user attribute information.

[0112] When the difficulty level of the user's question is the first difficulty level, the structure of the reply information corresponding to the user's question is determined to include the first reply content based on the user's question content.

[0113] When the known information corresponding to the user's historical dialogue contained in the user's historical interaction data is the first known information, the structure of the reply information corresponding to the user interaction data is determined to contain the first reply content based on the user's historical dialogue.

[0114] When the preference content corresponding to the user behavior feature is the first preference content, the structure of the reply information corresponding to the user interaction data is determined to include the first reply content based on the user behavior feature.

[0115] When the operation complexity corresponding to the vehicle data is the first operation complexity, the structure of the reply information corresponding to the user interaction data is determined to include the first reply content based on the vehicle data.

[0116] S303. When the user interaction data is the second user interaction data, determine the response information structure corresponding to the second user interaction data as the second response information structure based on the second user interaction data.

[0117] In one example, the second user interaction data may include, but is not limited to, at least one of the following: second user attribute information; user questions of a difficulty level of a second difficulty level; user history dialogues of known information of a second known information; user behavior characteristics of preference content of a second preference content; and vehicle data of operation complexity of a second operation complexity level.

[0118] In an optional embodiment, based on the second user interaction data, the response information structure corresponding to the second user interaction data is determined to be a second response information structure, including at least one of the following:

[0119] When the second user interaction data is the second user attribute information, the structure of the reply information corresponding to the first user attribute information is determined to include the first reply content and the second reply content based on the second user attribute information.

[0120] When the difficulty level of the user's question is the second difficulty level, the structure of the response information corresponding to the user's question is determined to include a first response and a second response, based on the user's question.

[0121] When the known information corresponding to the user's historical dialogue contained in the user's historical interaction data is the second known information, the structure of the reply information corresponding to the user interaction data is determined to include the first reply content and the second reply content based on the user's historical dialogue.

[0122] When the preference content corresponding to the user behavior feature is the second preference content, the structure of the reply information corresponding to the user interaction data is determined to include the first reply content and the second reply content based on the user behavior feature.

[0123] When the operation complexity corresponding to the vehicle data is the second operation complexity, the structure of the reply information corresponding to the user interaction data is determined to include the first reply content and the second reply content based on the vehicle data.

[0124] In an optional embodiment, user attribute information is further used to indicate whether to generate second response content. The user's comprehension level can be determined based on the user attribute information, and based on the user's comprehension level, it can be determined whether to generate second response content adapted to the user's comprehension level. Furthermore, the user's content interests can be determined based on the user attribute information, and based on the user's content interests, it can be determined whether to generate second response content adapted to the user's content interests. The response information structure differs for users with different user attribute information. For example, a mapping relationship between user attribute information and response information structure can be preset, and the corresponding response information structure can be determined based on the user attribute information.

[0125] For example, user attribute information includes the occupation of athlete, and the user's question is about the symbolic meaning of portrait XX. If it is determined that the user's interest in the artwork is low, a second response may not be generated; that is, the response structure includes the first response.

[0126] For example, user attribute information includes their profession as an e-sports player, and the user's question is an introduction to game XX. If it's determined that the user has a high level of interest in the game, a second response can be generated; that is, the response structure includes both the first and second responses.

[0127] For example, user attribute information includes the user's age as a child's age. If the user's comprehension ability is determined to be weak, a second response can be generated; that is, the response information structure includes both the first and second response content.

[0128] By determining whether to generate a second response based on user attribute information in user interaction data, the user's comprehension ability and preferred content can be taken into account. This allows for a better understanding of the user's comprehension ability and preferred content, helping them to access more interesting and understandable content.

[0129] In an optional embodiment, the decision to generate a second response can be based on the difficulty level of the user's question. If the user's question is at the first difficulty level, it indicates that the question is not difficult to understand, and the first response, which serves as the answer, can be used to provide a complete reply without adding a second response.

[0130] In an optional embodiment, the generation of a second response can be determined based on the difficulty level of the user's question. If the user's question is at the second level of difficulty, indicating that the question is difficult to understand, the first response can be partially provided as an answer, and a second response can be added to help the user quickly understand the answer from the first response.

[0131] In an optional embodiment, questions from difficult users can have a second response added, allowing for more rounds of feedback with the first response. This facilitates the user's understanding of the detailed and complete response, thus determining the response information structure to include both the first and second responses. Questions from easy users can be answered without a second response, using fewer rounds of feedback with the first response. This allows the user to quickly understand the first response, again determining the response information structure to include the first response. Different guidance types are used for user questions of varying difficulty. For example, a mapping relationship between difficulty level and guidance type can be preset, determining the corresponding response information structure based on the difficulty level.

[0132] By specifically limiting user interaction data to user questions, we can determine whether to add a second response based on the difficulty level of the questions. We can also add appropriate guidance content to match the difficulty level of the questions, helping users obtain more detailed and complete responses.

[0133] In an optional embodiment, the user can choose whether to repeatedly obtain known information. Correspondingly, a second response can be added to the known information, thus determining the response information structure to include both the first and second response, guiding the user to obtain previously received but still desired response content. Alternatively, the known information can be used to determine whether the user has explored content in the same domain as their question, thereby judging their interest in that domain. If the user is determined to be interested, a second response can be added, again determining the response information structure to include both the first and second response. The response information structure differs depending on the historical dialogue with different known information. For example, a mapping relationship between the similarity and guidance type between known information and the user's question can be preset, the similarity between the known information and the user's question can be calculated, and the corresponding response information structure can be determined based on the similarity.

[0134] By specifically limiting user interaction data to users' historical conversations, known information can be considered to determine whether to add a second response, which can help users retrieve previously accessed content or content of interest.

[0135] In an optional embodiment, the user desires more content of interest. The structure of the response information can be determined based on the user's preferred content, deciding whether to add a second response. For example, when the preferred content is similar to the user's question, the response information structure is determined to include both the first and second response content; when the preferred content is dissimilar to the user's question, the response information structure includes only the first response content. The response information structure differs for different preferred content. For instance, a mapping relationship between the similarity between the preferred content and the user's question and the guidance type can be preset, the similarity between the preferred content and the user's question can be calculated, and the corresponding response information structure can be determined based on the similarity.

[0136] By limiting user interaction data to user behavior characteristics, and determining the structure of response information based on the preferences identified by these characteristics, more response content can be provided for questions that users are interested in.

[0137] Generally, the more vehicle data required for a user's question, the more complex the process of obtaining the first response, and the more content is provided accordingly. This can be addressed through multiple rounds of dialogue to provide complete content, i.e., adding a second response, ensuring the response structure includes both the first and second responses. Conversely, the less vehicle data required for a user's question, the simpler the process of obtaining the first response, eliminating the need for multiple rounds of dialogue to provide more content. In this case, adding a second response is unnecessary, and the response structure still includes the first response. Generally, higher operational complexity corresponds to a response structure containing both the first and second responses, while lower operational complexity corresponds to a response structure containing only the first response.

[0138] S304. Generate reply information based on the user interaction data and the reply information structure.

[0139] In an optional embodiment, Figure 4 This is a flowchart of another method for generating response information according to an embodiment of the present invention. This embodiment, based on the above embodiment, adds the determination of initial response content based on user interaction data and explains the breakdown of the initial response content.

[0140] It should be noted that for any parts not described in detail in the embodiments of the present invention, please refer to the description in the foregoing embodiments.

[0141] See Figure 4 The method for generating response information shown includes:

[0142] S401. Obtain user interaction data.

[0143] S402. Determine the structure of the response information based on the user interaction data.

[0144] S403. Generate reply information based on the user interaction data and the reply information structure.

[0145] S404. Determine the initial response content based on the user interaction data.

[0146] In one example, the initial response content refers to the response content of the complete answer determined based on user interaction data. In this embodiment, the process of determining the initial response content based on user interaction data is similar to the process described above for determining the response content containing only the first response based on user interaction data, and will not be repeated here.

[0147] S405. Based on the user interaction data, determine the breakdown of the initial response content.

[0148] In one example, user interaction data can be user attribute information, such as, but not limited to, at least one of the following: user identifier, gender, age, education level, and occupation. The user identifier is used to uniquely identify the user; the education level is used to represent the user's level of knowledge, for example, education level can be categorized by academic level, including but not limited to, the following: preschool education, primary school, secondary school, junior college, undergraduate, postgraduate, etc.; education level can be categorized by professional skills education background, including but not limited to, the following: STEM majors, humanities majors, art and design majors, etc. Occupation is used to represent the type of work the user performs, and is related to factors such as the individual's skills, knowledge, work environment, and responsibilities; occupational categories can include but not limited to, at least one of the following: student, engineer (e.g., software engineer, mechanical engineer, electrical engineer, etc.), doctor, teacher, etc. In an optional embodiment, based on user interaction data, it can be determined whether the initial response content needs to be split to obtain multiple first response contents and the response order of each first response content; wherein, the amount of information contained in each first response content is less than the amount of information contained in the initial response content; and corresponding guiding follow-up questions are added to the first response content that appears first in the response order to obtain the final response information each time.

[0149] In an optional embodiment, when splitting the initial response content, the initial response content can be split into multiple first response contents according to a total-to-part logical relationship; or, the initial response content can be split into multiple first response contents according to a progressive relationship; or, the initial response content can be split into multiple first response contents according to a reasoning relationship.

[0150] In an optional embodiment, the initial response content may include two types of first response content: one is the last first response in the response order, and the other is a first response content that is not the last in the response order. There is one last first response in the response order, while there can be one or more first responses that are not the last in the response order. In an optional embodiment, follow-up questions can be added after the first responses that are not the last in the response order, i.e., a second response can be added to guide the user to further inquire about the later first responses.

[0151] In an optional embodiment, the content of the second response can be determined based on the specific content of the first response, or it can be determined using a uniform script, or it can be determined based on the keywords or a summary of the first sentence of the next first response in the order of the sub-response information. This can be set as needed and is not specifically limited. Since there is no first response after the last response in the response order, it is not necessary to add guiding follow-up questions to the last first response in the response order.

[0152] For example, if a user's education level is at least a bachelor's degree and the user's interaction data is a question related to their own career, then there is no need to break down the initial response content; the user's question can be answered completely at once, reducing the tedious questioning process for the user and greatly improving the user experience. If a user's education level is preschool education and the user's interaction data is a more complex question, then the user's comprehension ability is weaker. In this case, the initial response content can be broken down so that the user receives less information each time, making it easier for the user to understand.

[0153] In an optional embodiment, "determining the segmentation of the initial response content based on the user interaction data" is further refined to: determining the segmentation of the initial response content based on current user interaction data and / or historical user interaction data.

[0154] In an optional embodiment, the user interaction data includes: current user interaction data and historical user interaction data; determining the segmentation of the initial response content based on the user interaction data includes one of the following:

[0155] Based on the user attribute information and user identity information contained in the current user interaction data, determine the breakdown of the initial response content;

[0156] Based on the difficulty level of the user's questions and the user's identity information contained in the current user interaction data, determine the breakdown of the initial response content;

[0157] Based on the historical user interaction data and user identity information, determine the breakdown of the initial response content;

[0158] Based on the user behavior characteristics and user identity information contained in the current user interaction data, determine the breakdown of the initial response content;

[0159] Based on the vehicle data and user identity information contained in the current user interaction data, the breakdown of the initial response content is determined.

[0160] In an optional embodiment, the initial response content can be segmented based on user attribute information and user identity information contained in the current user interaction data. User attribute information and user identity information can also be used to indicate whether the initial response content should be segmented. The user's comprehension and expertise can be determined based on the user attribute information and user identity information, and a decision can be made based on these comprehension and expertise levels to determine whether the initial response content needs to be segmented.

[0161] For example, assuming a user's attributes include an age of 4 and their question is "Why is the Earth round?", it can be determined that the user's comprehension and expertise are relatively low. In this case, the initial response can be broken down into multiple first responses to facilitate user understanding. A second response can then be added to each of these first responses (not necessarily the last in the response order) to guide the user to ask further questions, thus satisfying their curiosity while facilitating comprehension.

[0162] In an optional embodiment, the segmentation of the first response content can be determined based on historical user interaction data and user identity information. Historical user interaction data may include: user history conversations, used to determine whether the user has accessed content in the same domain as the current user's question, thereby determining the user's interest in and understanding of that domain, and thus whether to segment the initial response content. If, based on the user history conversations, it is determined that the user has limited knowledge of content in the same domain as the current question, then the initial response content is segmented, allowing for multiple rounds of follow-up questions, with each sub-response containing less information for easier user comprehension. If, based on the user history conversations, it is determined that the user has extensive knowledge of content in the same domain as the current question, then the initial response content is not segmented, allowing for a direct and detailed response to the user, reducing multiple rounds of follow-up questions and enabling the user to quickly understand the initial response content.

[0163] In an optional embodiment, "determining the splitting status of the initial response content based on the user interaction data" is further refined as follows: when the user interaction data is first user interaction data, the splitting status of the initial response content is determined to be split based on the first user interaction data; when the user interaction data is second user interaction data, the splitting status of the initial response content is determined to be not split based on the second user interaction data.

[0164] In an optional embodiment, the segmentation of the initial response content is determined based on the user interaction data, including at least one of the following:

[0165] When the first user interaction data is the first user attribute information, the initial response content is split according to the first user attribute information.

[0166] When the difficulty level of the user's question is the first difficulty level, the initial response content is split according to the user's question.

[0167] When the known information corresponding to the user's historical dialogue contained in the user's historical interaction data is the first known information, the initial response content is determined to be split according to the user's historical dialogue.

[0168] When the preference content corresponding to the user behavior feature is the first preference content, the initial response content is determined to be split according to the user behavior feature.

[0169] When the operation complexity corresponding to the vehicle data is the first operation complexity, the initial response content is determined to be split based on the vehicle data.

[0170] In an optional embodiment, based on the second user interaction data, the response information structure corresponding to the second user interaction data is determined to be a second response information structure, including at least one of the following:

[0171] When the second user interaction data is the second user attribute information, the initial response content is determined to be split without splitting based on the second user attribute information.

[0172] When the difficulty level of the user's question is the second difficulty level, the initial response content is determined to be split without splitting, based on the user's question.

[0173] When the known information corresponding to the user's historical dialogue contained in the user's historical interaction data is the second known information, the initial response content is determined to be split without splitting based on the user's historical dialogue.

[0174] When the preference content corresponding to the user behavior feature is the second preference content, the initial response content is determined to be split without splitting based on the user behavior feature.

[0175] When the operation complexity corresponding to the vehicle data is the second operation complexity, the initial response content is determined to be split without splitting based on the vehicle data.

[0176] In an optional embodiment, the initial response content can be split based on the first user interaction data. If the user's question is of a first level of difficulty, and the user's comprehension and expertise are deemed relatively strong based on their identity information, the initial response content can be left unsplited, and a detailed and complete response can be provided directly to the user. This reduces the need for multiple follow-up questions and allows the user to quickly understand the initial response. If the user's question is of a second level of difficulty, or if the user's comprehension and expertise are deemed relatively weak based on their identity information, the initial response content can be split. This allows for multiple rounds of follow-up questions, with each sub-response containing less information to facilitate user comprehension.

[0177] In an optional embodiment, the initial response content can be segmented based on user behavior characteristics and user identity information contained in the current user interaction data. If the user behavior characteristics correspond to a first preferred content, and the user's comprehension and professional abilities are determined to be superior based on the user identity information, the initial response content can be left unsegmented, and a detailed and complete response can be directly provided to the user to reduce multiple rounds of follow-up questions and facilitate the user's faster understanding of the initial response content. If the user behavior characteristics correspond to a second preferred content, or if the user's comprehension and professional abilities are determined to be poor based on the user identity information, the initial response content can be segmented based on the user behavior characteristics and user identity information, allowing for multiple rounds of follow-up questions, with each initial response containing less information to facilitate user comprehension. In one example, the first preferred content is used to represent content in the same domain as the user's question; the second preferred content is used to represent content in a different domain than the user's question.

[0178] In an optional embodiment, the initial response content can be segmented based on vehicle data and user identity information included in the user's current interaction data. If the operation complexity corresponding to the vehicle data is at a first level of complexity, and the user's comprehension and professional skills are determined to be superior based on their identity information, then the initial response content is not segmented. A detailed and complete response can be directly provided to the user to reduce multiple rounds of follow-up questions and allow the user to understand the initial response content more quickly. Conversely, if the operation complexity corresponding to the vehicle data is at a second level of complexity, or if the user's comprehension and professional skills are determined to be poor based on their identity information, then the initial response content is segmented. This allows for multiple rounds of follow-up questions, with each initial response containing less information to facilitate user comprehension.

[0179] Generally, the more vehicle data a user's question requires, the more complex the process of obtaining the initial response becomes, resulting in a larger volume of response content. In this case, a multi-turn dialogue can be used to provide complete content, meaning the initial response is broken down. Conversely, the less vehicle data a user's question requires, the simpler the process of obtaining the initial response becomes, eliminating the need for multi-turn dialogues to provide more content, and thus, the initial response does not need to be broken down. Typically, high operational complexity necessitates breaking down the initial response; low operational complexity necessitates not breaking down the initial response.

[0180] In an optional embodiment, when the initial response content is split, the initial response content includes at least two first response contents. In another optional embodiment, splitting the initial response content can be done by dividing it into at least two first response contents with a general-to-specific logical relationship, or by dividing it into at least two first response contents with a progressive or inferential relationship. The information content of the initial response content is greater than the information content of the first response contents.

[0181] For example, the initial response might be: The dinosaurs disappeared because a major event happened on Earth a long, long time ago. Imagine a huge rock flying down from the sky and crashing into the Earth with a bang—that's a large meteorite. This rock created a lot of dust and smoke that blocked out the sky, preventing sunlight from reaching the ground. The Earth became very cold, and many plants died. The dinosaurs had no food to eat, so they slowly disappeared.

[0182] In one optional embodiment, the split first response is: The extinction of dinosaurs was due to an asteroid impact on Earth, causing drastic environmental changes that made survival impossible. Another first response is: A huge rock flew from the sky and crashed into Earth with a bang—this is a large meteorite. This large rock created a lot of dust and smoke that blocked the sky, making the Earth very cold, leading to the extinction of the dinosaurs. Yet another first response is: The Earth became very cold, and many plants died. The dinosaurs had no food to eat, so they gradually disappeared.

[0183] In an optional embodiment, "generating reply information based on the user interaction data and the reply information structure" is further refined as follows: determining the target information amount corresponding to the user interaction data based on the user interaction data; generating reply information based on the user interaction data, the target information amount, and the reply information structure; wherein the reply information includes at least first reply content; and the information amount of the first reply content corresponds to the target information amount.

[0184] In an optional embodiment, Figure 5 This is a flowchart of another method for generating response information according to an embodiment of the present invention. This embodiment, based on the above embodiments, provides a more detailed explanation of how to generate response information according to the user interaction data and the response information structure.

[0185] It should be noted that for any parts not described in detail in the embodiments of the present invention, please refer to the description in the foregoing embodiments.

[0186] See Figure 5 The method for generating response information shown includes:

[0187] S501, Obtain user interaction data.

[0188] S502. Determine the structure of the response information based on user interaction data.

[0189] S503. Determine the target information amount corresponding to the user interaction data based on the user interaction data.

[0190] In an optional embodiment, the target information content is used to indicate the information content of the generated first response. The target information content can be a specific numerical value, such as the number of words. Alternatively, the target information content can be a level or type. For example, the target information content can be either "more information content" or "less information content".

[0191] In one optional embodiment, the target information content can be determined as follows: For example, a mapping relationship between user interaction data and target information content can be preset, and the target information content corresponding to the user interaction data can be queried according to the mapping relationship. Alternatively, the user interaction data can be input into a pre-trained deep learning model to obtain the target information content. Specifically, feature extraction can be performed on the user interaction data to obtain feature vectors, and the feature vectors can be decoded and classified to obtain the target information content. Another example is quantizing the user interaction data to obtain the target information content.

[0192] S504. Generate reply information based on the user interaction data, the target information amount, and the reply information structure; wherein the reply information includes at least a first reply content; the information amount of the first reply content corresponds to the target information amount.

[0193] In an optional embodiment, the response information can refer to the content of a response to user interaction data. The information content can refer to a numerical value obtained by quantifying the response information. For example, it can be represented by the amount of data included in the response information, such as the number of bytes, characters, or words. Alternatively, the information content of the response information can be represented by the amount of valid data in the response information; for example, the number of entities included in the response information can be used as the information content of the response information, or the amount of data of keywords included in the response information can be used as the information content of the response information.

[0194] In an optional embodiment, the user interaction data is also used to determine the information content of the first response content, that is, the information content of the first response content is determined by the user interaction data.

[0195] In an optional embodiment, the target information content corresponding to the information content of the first response content can mean that the target information content and the information content of the first response content are the same, or that the type of the target information content and the type of the information content of the first response content are the same. Specifically, the target information content and the first response content being the same type can mean, for example, that both the target information content and the first response content are of the type with more information; or that both the target information content and the first response content are of the type with less information.

[0196] In an optional embodiment, generating response information based on the user interaction data, the target information content, and the response information structure can be achieved by inputting the user interaction data, the target information content, and the response information structure into a pre-trained deep learning model to obtain the response information output by the deep learning model. In one example, the user interaction data, the target information content, and the response information structure can be filled into a prompt template corresponding to the target information content to obtain input data. This input data can then be input into a large language model to obtain the response information output by the large language model.

[0197] For example, generating response information based on the user interaction data, the target information content, and the response information structure can be achieved by: generating response instruction information based on the user interaction data and the target information content; and generating response information based on the response instruction information, the user interaction data, and the response information structure. Specifically, the user interaction data is input into a first language model to obtain the target information content output by the first language model, and response instruction information is queried or generated based on the target information content. Input data is determined based on the user interaction data, the response instruction information, and the response information structure. The input data is then input into a second language model to obtain the response information output by the second language model. The first and second language models can be the same or different. The first language model can also be replaced by a classification model.

[0198] This invention obtains the target information amount by processing user interaction data, and generates reply information including a first reply content corresponding to the target information amount based on the target information amount, user interaction data, and reply information structure. The target information amount can be directly extracted from the user interaction data, and reply information can be generated based on the target information amount, reply information structure, and user interaction data to determine whether the reply information includes a second reply content in addition to the first reply content, thereby achieving precise control over the information amount of the reply information.

[0199] Optionally, user interaction data includes current user interaction data and historical user interaction data; current user interaction data includes user attribute information.

[0200] In an optional embodiment, user attribute information can refer to stable and unchanging user information. User attribute information may include user identifier (identity), gender, age, height, and cognitive level, etc. For example, a user's voiceprint can be identified through voice recognition, and the voiceprint can uniquely identify the user; similarly, a user's facial features can be identified through an image, and the facial features can uniquely identify the user. Typically, users interact with the vehicle via voice, and the user identifier can be represented by the voiceprint features identified through voice recognition. Furthermore, a user's voice, image, and video can all be used to identify the user's gender, age, and cognitive level. Users can also directly input their user identifier, gender, age, height, and cognitive level via text.

[0201] In an optional embodiment, user attribute information can be collected in multiple ways, such as by receiving text information directly input by the user, by collecting the user's voice, by collecting the user's image, and by collecting the user's video. Accordingly, the media type of the user information obtained by collecting the user's voice is audio; the media type of the user information obtained by receiving text data input by the user is text; the media type of the user information obtained by collecting the user's image is image; and the media type of the user information obtained by collecting the user's video is video.

[0202] In an optional embodiment, the user's comprehension ability and / or preferred content can be determined based on user attribute information, thereby determining the target information content based on the user's comprehension ability and / or preferred content.

[0203] For example, user attribute information includes the occupation of athlete, and the user's question is about the symbolic meaning of portrait XX. It is determined that the user has a low level of interest in the artwork, and the target information content is determined to be low.

[0204] For example, user attribute information includes the profession of e-sports personnel, and the user's question is an introduction to game XX. This indicates that the user has a high level of interest in the game, and the target information volume is determined to be large.

[0205] For example, user attribute information includes the user's age being a child's age. This indicates that the user has weak comprehension abilities and that the target information amount is relatively small.

[0206] Specifically, user attribute information may include the user's age. For example, if the user's age is less than or equal to a preset age threshold, the target information content is determined to be low, and a first reply with low information content is generated; if the user's age is greater than the preset age threshold, the target information content is determined to be high, and a first reply with high information content is generated.

[0207] Specifically, user attribute information may include user identifiers. For example, when the user identifier is outside the preset identifier range, the target information amount is determined to be low, and a first reply with low information amount is generated; when the user identifier is within the preset identifier range, the target information amount is determined to be high, and a first reply with high information amount is generated.

[0208] Specifically, user attribute information may include gender. For example, when the user's gender is primary, the target information content is determined to be low, and a first reply with low information content is generated; when the user's gender is secondary, the target information content is determined to be high, and a first reply with high information content is generated.

[0209] Specifically, user attribute information may include height. For example, when a user's height is less than or equal to a preset height threshold, the target information content is determined to be low, and a first reply with low information content is generated; when a user's height is greater than the preset height threshold, the target information content is determined to be high, and a first reply with high information content is generated.

[0210] Specifically, user attribute information may include cognitive level. For example, when a user's cognitive level is lower than a preset cognitive level, the target information amount is determined to be low, and a first response with low information amount is generated; when a user's cognitive level is higher than the preset cognitive level, the target information amount is determined to be high, and a first response with high information amount is generated.

[0211] Optionally, user attribute information may also include other content such as occupation, without specific limitations.

[0212] Furthermore, when user attribute information includes multiple items such as user ID, gender, age, height, and cognitive level, the system can select whether each item meets the corresponding condition to generate a first response with the appropriate amount of information. Alternatively, a weighted score can be calculated for each item, and a first response with the appropriate amount of information can be generated based on the weighted score.

[0213] In one example, user attribute information includes the user's age. The default age threshold is 12 years old.

[0214] When the user is under 12 years old, the target information content is determined to be low, and the first reply content with low information content is generated as follows: This is a very good question. The extinction of dinosaurs was due to an asteroid impact on Earth, which caused drastic changes in the environment, making it impossible to survive. However, this process is also very complex and interesting.

[0215] When the user is 12 years of age or older, the target information content is determined to be high. The first response with high information content is: Wow, that's a very good question! Dinosaurs disappeared because a huge event happened on Earth a long, long time ago. Imagine a giant rock flying from the sky and crashing into the Earth with a bang—that's a giant meteorite. This giant rock caused a lot of dust and smoke to block out the sky, preventing sunlight from reaching the ground. The Earth became very cold, and many plants died. Dinosaurs had no food to eat, so they slowly disappeared.

[0216] In an optional embodiment, determining the target information content corresponding to the user interaction data based on the user interaction data includes: determining the target information content corresponding to the user interaction data based on current user interaction data and / or historical user interaction data. Both current user interaction data and historical user interaction data may include at least one of the following: user attribute information; user question content; user historical dialogue; user behavior characteristics; vehicle data. Current user interaction data refers to data collected during the current user interaction process; historical user interaction data refers to data collected during historical user interactions.

[0217] In an optional embodiment, the current user attribute information includes a first age; the historical user attribute information includes a second age; the first age and the second age are different, and the first information content corresponding to the first age and the second information content corresponding to the second age are different.

[0218] In one example, younger users have weaker comprehension abilities, while older users have stronger comprehension abilities. Therefore, younger users can be given content with less information to reduce the difficulty of understanding the response. Since the first age group is younger than the second age group, the first group should contain less information than the second group.

[0219] Optionally, when the difficulty level of the current user's question is the first level, a third information quantity corresponding to the current user interaction data is determined based on the current user's question; when the difficulty level of the historical user's question is the second level, a fourth information quantity corresponding to the historical user interaction data is determined based on the historical user's question; when the first level and the second level are different, the third information quantity and the fourth information quantity are different.

[0220] In one optional embodiment, the difficulty level of the user's question is used to determine the complexity of the answer, thereby determining the target information content. The user's question can be categorized by difficulty level, such as hardness or ease. For example, a mapping relationship between difficulty level and target information content can be preset, and the corresponding target information content can be determined based on the difficulty level. Alternatively, the user's question can be input into a deep learning model to obtain the target information content.

[0221] Optionally, when the preference content corresponding to the current user behavior feature is the first preference content, the seventh information content corresponding to the current user interaction data is determined based on the current user behavior feature; when the preference content corresponding to the historical user behavior feature is the second preference content, the eighth information content corresponding to the historical user interaction data is determined based on the historical user behavior feature; when the first preference content and the second preference content are different, the seventh information content and the eighth information content are different.

[0222] In one optional embodiment, user behavior characteristics are used to determine preferred content. The target information content is then determined based on this preferred content. The target information content varies depending on the specific preferred content. In reality, users tend to want more content that interests them. Therefore, the target information content can be determined based on their preferred content.

[0223] Optionally, when the operation complexity corresponding to the current vehicle data is the first operation complexity, the ninth information quantity corresponding to the current user interaction data is determined based on the current vehicle data; when the operation complexity corresponding to the historical vehicle data is the second operation complexity, the tenth information quantity corresponding to the historical user interaction data is determined based on the historical vehicle data; when the first operation complexity and the second operation complexity are different, the ninth information quantity and the tenth information quantity are different.

[0224] Operational complexity describes the complexity of processing the information to obtain the first response. It involves analyzing user queries and vehicle data, identifying vehicle data relevant to the user query, categorizing this relevant vehicle data, and determining the operational complexity.

[0225] The target information content varies depending on the complexity of the operation. Generally, the more vehicle data required for a user's question, the more complex the process to obtain the first response, and consequently, the more first response content is provided, resulting in a higher target information content. Conversely, the less vehicle data required for a user's question, the simpler the process to obtain the first response, and consequently, the less first response content is provided, resulting in a lower target information content. Different user questions can correspond to different levels of operational complexity. Generally, higher operational complexity results in a higher target information content, while lower operational complexity results in a lower target information content. For example, a mapping relationship between operational complexity and target information content can be pre-defined, and the corresponding target information content can be determined based on the operational complexity. Alternatively, the operational complexity can be input into a deep learning model to obtain the target information content.

[0226] In an optional embodiment, determining the target information content corresponding to the user interaction data based on the user interaction data includes:

[0227] When the user interaction data is the first user interaction data, the target information quantity corresponding to the first user interaction data is determined as the first information quantity based on the first user interaction data.

[0228] When the user interaction data is the second user interaction data, the target information quantity corresponding to the second user interaction data is determined as the second information quantity based on the second user interaction data.

[0229] Optionally, when the user attribute information is first user attribute information, a first information quantity corresponding to the first user attribute information is determined based on the first user attribute information; when the user attribute information is second user attribute information, a second information quantity corresponding to the second user attribute information is determined based on the second user attribute information; the first user attribute information and the second user attribute information are different, and the first information quantity and the second information quantity are different.

[0230] In an optional embodiment, the target information quantity differs for users with different user attribute information. Specifically, the user attribute information of the first user is designated as first user attribute information, and the target information quantity is determined as the first information quantity based on the first user attribute information; the user attribute information of the second user is designated as second user attribute information, and the target information quantity is determined as the second information quantity based on the second user attribute information. When the first user attribute information and the second user attribute information are different, the first information quantity and the second information quantity are different. For example, a mapping relationship between user attribute information and target information quantity can be preset, and the corresponding target information quantity can be determined according to the level of difficulty. Alternatively, user attribute information can be input into a deep learning model to obtain the target information quantity.

[0231] By specifically limiting user interaction data to user attribute information, we can take into account the user's comprehension ability and preferred content, determine the amount of information that matches the user's comprehension ability and preferred content, generate first response content that is easy for the user to understand, and generate first response content that the user is interested in.

[0232] In an optional embodiment, the first user attribute information includes a first age; the second user attribute information includes a second age; the first age and the second age are different, and the first information content corresponding to the first age and the second information content corresponding to the second age are different.

[0233] In one example, younger users have weaker comprehension abilities, while older users have stronger comprehension abilities. Therefore, younger users can be given content with less information to reduce the difficulty of understanding the response. Since the first age group is younger than the second age group, the first group should contain less information than the second group.

[0234] In one example, the first user is younger than the second user, and correspondingly, the first user receives more information than the second. Younger users have weaker comprehension abilities, while older users have stronger comprehension abilities. Therefore, providing younger users with more informative and engaging content can help them understand the responses.

[0235] By specifically limiting user attribute information to user age, the user's comprehension ability can be determined based on age, thereby determining the amount of information appropriate to the user's age-appropriate comprehension ability. This allows for the generation of response information corresponding to the target amount of information, providing response information that is easy for the user's age to understand.

[0236] Optional, user interaction data includes: user questions.

[0237] In an optional embodiment, the user's question content may refer to the content that the user inputs to ask the human-computer interaction device during the interaction process.

[0238] Optionally, when the difficulty level corresponding to the user's question is a first difficulty level, a third information quantity corresponding to the user interaction data is determined based on the user's question; when the difficulty level corresponding to the user's question is a second difficulty level, a fourth information quantity corresponding to the user interaction data is determined based on the user's question; when the first difficulty level and the second difficulty level are different, the third information quantity and the fourth information quantity are different.

[0239] In one optional embodiment, the difficulty level of the user's question is used to determine the complexity of the answer, thereby determining the target information content. The user's question can be categorized by difficulty level, such as hardness or ease. For example, a mapping relationship between difficulty level and target information content can be preset, and the corresponding target information content can be determined based on the difficulty level. Alternatively, the user's question can be input into a deep learning model to obtain the target information content.

[0240] In an optional embodiment, the target information content varies depending on the difficulty level of the user's question. This can involve the same user inputting questions of varying difficulty. For difficult user questions, the target information content can be increased, allowing for a more informative first response to facilitate user comprehension. Conversely, for simple user questions, the target information content can be reduced, allowing for a less informative first response to facilitate quick understanding.

[0241] By specifically limiting user interaction data to user questions, the amount of information can be determined based on the difficulty of the questions, and the amount of information can be adapted to generate a first response that is easy for users to understand.

[0242] Optional, user interaction data includes: user's historical conversations.

[0243] In an optional embodiment, user history dialogue can refer to the user's historical dialogue information related to user interaction data. In practice, embodiments of the present invention can be applied to multi-turn dialogue scenarios, storing the user's historical dialogue rounds as user history dialogue. Furthermore, all dialogues sent by the user by the human-computer interaction device before the current moment can also be recorded as historical dialogue.

[0244] In an optional embodiment, the electronic device implementing the response information generation method can record user data and can locally query user-related data. For example, multiple sessions of the same user can be recorded, and a session can include at least one round of dialogue. User identifiers can be stored in correspondence with user sessions, and a corresponding relationship can be established. User historical sessions can be queried based on the user identifier in the user attribute information, thereby obtaining the user's historical dialogues.

[0245] Optionally, when the known information corresponding to the user's historical dialogue is the first known information, a fifth information quantity corresponding to the user interaction data is determined based on the user's historical dialogue; when the known information corresponding to the user's historical dialogue is the second known information, a sixth information quantity corresponding to the user interaction data is determined based on the user's historical dialogue; when the first known information and the second known information are different, the fifth information quantity and the sixth information quantity are different.

[0246] In an optional embodiment, user history dialogues are used to determine known information, thereby determining the target information content based on the known information. Key information can be extracted from user history dialogues as known information. The target information content differs for different user history dialogues. In fact, the first response does not need to repeat content already provided in the user history dialogue. Therefore, the known information corresponding to the first response content can be determined based on the user history dialogues. The target information content is then determined based on the known information. For example, a mapping relationship between known information and target information content can be preset, such as an inverse relationship between the information content of known information and the target information content. The corresponding target information content is then determined based on the known information. Alternatively, the known information can be input into a deep learning model to obtain the target information content.

[0247] The amount of target information varies depending on the amount of known information. The same user's question can correspond to different user history conversations with different known information, and different users can correspond to different user history conversations with different known information.

[0248] In an optional embodiment, the user's preferences and comprehension level can be determined based on the user's historical dialogue, and the amount of information can be determined based on the user's preferences and comprehension level.

[0249] By specifically limiting user interaction data to users' historical conversations, known information can be considered, thereby reducing the duplication of known content, providing effective new responses, and reducing redundant interactions.

[0250] Optional, user interaction data includes: user behavior characteristics.

[0251] In an optional embodiment, user behavior features are used to determine a user's preferred content, etc. User behavior features can be determined based on user history conversations and user attribute information, etc. For example, user behavior features indicate the user's historical preferred content. User behavior features are retrieved based on the user identifier in the user attribute information. User behavior features can be user type or tags.

[0252] Optionally, when the preference content corresponding to the user behavior feature is the first preference content, a seventh information quantity corresponding to the user interaction data is determined based on the user behavior feature; when the preference content corresponding to the user behavior feature is the second preference content, an eighth information quantity corresponding to the user interaction data is determined based on the user behavior feature; when the first preference content and the second preference content are different, the seventh information quantity and the eighth information quantity are different.

[0253] In one optional embodiment, user behavior characteristics are used to determine preferred content. The target information content is then determined based on this preferred content. The target information content varies depending on the specific preferred content. In reality, users tend to want more content that interests them. Therefore, the target information content can be determined based on their preferred content.

[0254] For example, a mapping relationship between the similarity between preferred content and user questions and the target information content can be pre-defined. The similarity between preferred content and user questions can then be detected, and the target information content can be queried based on the similarity and correspondence. Alternatively, preferred content and user questions can be input into a deep learning model to obtain the target information content.

[0255] The amount of target information varies depending on different user preferences. The same user's question can correspond to different user preferences, and different users can have different user preferences.

[0256] In one optional embodiment, the more similar the preferred content and the user's question, the more target information is generated; conversely, the more the preferred content and the user's question deviate from each other, the less target information is generated. For example, user behavioral characteristics such as strong curiosity or multiple multi-turn conversations in the user's historical dialogues indicate a high amount of information. Alternatively, if the user's question is related to games, and the user's behavioral characteristics include a preference for astronomy or multiple simple one-turn conversations in the user's historical dialogues, the information is low and does not include second responses.

[0257] In an optional embodiment, the user's historical dialogues and / or user behavior characteristics can be queried based on the user identifier in the user attribute information to obtain query results; user interaction data can be generated based on the query results, user attribute information, and user question content.

[0258] By limiting user interaction data to user behavior characteristics, and determining the target information content based on the preferences identified by these user behavior characteristics, more response content can be provided for questions that users are interested in.

[0259] Optionally, the user interaction data may also include vehicle data.

[0260] In an optional embodiment, the human-machine interface device may be a vehicle, and the vehicle data may be vehicle-related data. Vehicle data may include vehicle status, vehicle infotainment system status, and external environment status. Vehicle status includes, but is not limited to, at least one of vehicle speed, tire pressure, and interior temperature; vehicle infotainment system status may include, but is not limited to, at least one of foreground application usage status and background application status; external environment status includes, but is not limited to, at least one of weather, GPS positioning data, and road conditions.

[0261] In an optional embodiment, vehicle data is used as the basis for responding to user questions related to the vehicle. For example, the user question might be: "How long until the vehicle arrives?" The system can obtain vehicle speed, route, current location, real-time traffic conditions along the route, and current time, and use this data as vehicle data. Based on this data, the system calculates the vehicle's arrival time and duration. The vehicle data, combined with the user question, is used to determine the substantive content of the first response, which may be the answer.

[0262] In an optional embodiment, vehicle information may include vehicle attribute information obtained locally and real-time vehicle status data, such as vehicle model, brand, size, or structure. Vehicle status can be further subdivided into vehicle status, vehicle infotainment system status, and external environment status. More specifically, vehicle status may include, but is not limited to, at least one of vehicle speed, tire pressure, and interior temperature; vehicle infotainment system status may include, but is not limited to, at least one of foreground application usage status and background application status; and external environment status may include, but is not limited to, at least one of weather, GPS (Global Positioning System) positioning data, and road conditions.

[0263] It should be noted that the acquisition, storage, and application of user information involved in the embodiments of the present invention all comply with the provisions of relevant laws and regulations, do not violate public order and good morals, and the vehicle will only acquire user information with the user's authorization. Furthermore, the user information transmitted by the vehicle is processed data, and the vehicle will not transmit the source data of the user information to the electronic device implementing the response information generation method of the embodiments of the present invention.

[0264] In one example, a brief initial response could be: "The vehicle will arrive in XX minutes, estimated arrival time is 10:32."

[0265] For example, a first reply with a lot of information could be: The vehicle has now reached location XX, and is XX meters away from the destination. The road ahead is clear, and the vehicle will arrive in XX minutes. It is expected to arrive at 10:32.

[0266] By limiting user interaction data to vehicle data and interaction scenarios to human-vehicle interaction scenarios, the input data can be enriched based on the user's questions, making the input data more diverse. Based on the richer and more complete input data, response information can be generated, which can add or remove response information in human-vehicle interaction in vehicle scenarios, helping users to understand the real-time status of the vehicle more quickly.

[0267] Optionally, when the operation complexity corresponding to the vehicle data is a first operation complexity, a ninth information quantity corresponding to the user interaction data is determined based on the vehicle data; when the operation complexity corresponding to the vehicle data is a second operation complexity, a tenth information quantity corresponding to the user interaction data is determined based on the vehicle data; when the first operation complexity and the second operation complexity are different, the ninth information quantity and the tenth information quantity are different.

[0268] Operational complexity describes the complexity of processing the information to obtain the first response. It involves analyzing user queries and vehicle data, identifying vehicle data relevant to the user query, categorizing this relevant vehicle data, and determining the operational complexity.

[0269] The target information content varies depending on the complexity of the operation. Generally, the more vehicle data required for a user's question, the more complex the process to obtain the first response, and consequently, the more first response content is provided, resulting in a higher target information content. Conversely, the less vehicle data required for a user's question, the simpler the process to obtain the first response, and consequently, the less first response content is provided, resulting in a lower target information content. Different user questions can correspond to different levels of operational complexity. Generally, higher operational complexity results in a higher target information content, while lower operational complexity results in a lower target information content. For example, a mapping relationship between operational complexity and target information content can be pre-defined, and the corresponding target information content can be determined based on the operational complexity. Alternatively, the operational complexity can be input into a deep learning model to obtain the target information content.

[0270] By specifically limiting user interaction data to vehicle data, the amount of information can be determined by considering the complexity of vehicle data processing in human-vehicle interaction scenarios. This allows for the generation of first-response content that is easy for users to understand, tailored to the complexity of vehicle data processing.

[0271] In an optional embodiment, user interaction data may include user-asked questions and user attribute information.

[0272] In one example, the user's question was: Why did the dinosaurs disappear? The user's attributes included their age, which was 10 years old. Accordingly, the first response, generating minimal information, was: This is an excellent question. The dinosaurs disappeared because an asteroid impact caused drastic environmental changes, making survival impossible. However, this process was also complex and interesting.

[0273] In one example, a user asked: "Why did the dinosaurs disappear?" The user's attributes included their age, which was 20 years old. Accordingly, the first response, generating the most information, stated: "The dinosaurs disappeared because a long time ago, a huge rock flew from the sky and crashed into the earth, creating a lot of dust and smoke that blocked out the sky. Sunlight couldn't reach the ground, the earth became very cold, and many plants died. The dinosaurs had no food to eat, so they gradually disappeared."

[0274] In an optional embodiment, user interaction data may include user questions, user attribute information, and user dialogue history.

[0275] In one example, the user's question is: Why is A's speed greater than B's speed? The user's dialogue history shows the user's first question: A travels S1 kilometers at T1, and B travels S2 kilometers at T2. Who is faster, A or B? The first response was: A's speed is greater than B's speed. User attributes include age, which is 12 years old. Accordingly, the first response, which contains less information, is: S1 / T1 = V1, therefore, V1 > V2.

[0276] In one example, the user's question is: Why is A's speed greater than B's speed? The user's dialogue history shows the user's first question: A travels S1 kilometers at T1, and B travels S2 kilometers at T2. Who is faster, A or B? The first response was: A's speed is greater than B's speed. User attributes include age, which is 12 years old. Accordingly, the first response with the most information is: S1 / T1 = V1, S2 / T2 = V2, V1 > V2.

[0277] In an optional embodiment, user interaction data may include user questions, user attribute information, and user behavior characteristics.

[0278] In one example, the user's question was: Why did dinosaurs disappear? User attributes included age (10 years old) and a preference for cute and adorable things. Accordingly, the first response, with minimal information, was: This is an excellent question. The dinosaurs disappeared because an asteroid impact caused drastic environmental changes, making survival impossible. However, this process was also complex and interesting.

[0279] In one example, a user asked: "Why did the dinosaurs disappear?" User attributes included age (10 years old) and a liking for dinosaurs. The resulting first response, rich in information, stated: "The dinosaurs disappeared because a long time ago, a huge rock flew from the sky and crashed into the earth, creating dust and smoke that blocked out the sun. The earth became very cold, and many plants died. The dinosaurs had no food, so they gradually disappeared."

[0280] In an optional embodiment, user interaction data may include user questions, user attribute information, user history conversations, and user behavior characteristics.

[0281] In one example, the user's question is: Why did the dinosaurs disappear? User attributes include age (10 years old), and past conversations include questions about dolls, indicating a preference for cute and adorable items. Accordingly, the first, less informative response is generated: This is a very good question. The dinosaurs disappeared because an asteroid impact caused drastic environmental changes, making survival impossible. However, this process was also complex and interesting.

[0282] In one example, a user's question was: "Why did the dinosaurs disappear?" User attributes included age (10 years old), a history of conversations with questions related to Tyrannosaurus Rex, and a dinosaur-related behavioral characteristic. Accordingly, the first, most informative reply was generated: "The dinosaurs disappeared because a long time ago, a huge rock flew from the sky and crashed into the earth, creating a lot of dust and smoke that blocked out the sun. The earth became very cold, and many plants died. The dinosaurs had no food, so they gradually disappeared."

[0283] In an optional embodiment, user interaction data may include user questions, user attribute information, and vehicle data.

[0284] In one example, the user's question is: "How far is the service area?" User attributes include age, which is 10 years old. Based on the vehicle's location in the vehicle data, the nearest service area is determined, the distance between the vehicle and the service area is obtained, and the travel time is calculated based on the vehicle's current speed and the distance to the service area. Accordingly, the first response, with minimal information, is generated: "Arrival in 20 minutes."

[0285] In one example, the user's question is: "How far is the service area?" User attributes include age, which is 20 years old. Based on the vehicle's location in the vehicle data, the nearest service area is determined, the distance between the vehicle and the service area is obtained, and the travel time is calculated based on the vehicle's current speed and the distance to the service area. Accordingly, the first, more informative response is generated: "The nearest service area is 20km from the current location. At the current constant speed, it is estimated to arrive in 30 minutes."

[0286] In an optional embodiment, user interaction data may include user questions, user attribute information, user history conversations, and vehicle data.

[0287] In one example, the user's question is: "How far is the restaurant?" User attributes include age, which is 10 years old. The user's history shows their first question: "Are there any noodle shops nearby?" and their first reply: "Yes, there are noodle shops." Based on the vehicle's location in the vehicle data, the nearest noodle shop is determined, the distance between the vehicle and the noodle shop is obtained, and the travel time is calculated based on the vehicle's current speed and the distance to the noodle shop. Accordingly, the first reply, with less information, is generated: "Arriving in 20 minutes."

[0288] In one example, the user's question is: "How far is the restaurant?" User attributes include age, which is 20 years old. The user's history shows their first question: "Are there any noodle shops nearby?" and their first reply: "Yes, there are noodle shops." Based on the vehicle's location in the vehicle data, the nearest noodle shop is determined, the distance between the vehicle and the shop is obtained, and the travel time is calculated based on the vehicle's current speed and the distance to the shop. Accordingly, the first reply, which contains more information, is generated as: "The nearest noodle shop is 20km from the current location. At the current constant speed, it is estimated to arrive in 30 minutes."

[0289] In an optional embodiment, user interaction data may include user questions, user attribute information, user behavior characteristics, and vehicle data.

[0290] In one example, the user's question is: "What are some fun things to do nearby?" User attributes include age (10 years old) and a preference for swimming. Based on the vehicle's location in the vehicle data, the nearest swimming pool is determined, the distance between the vehicle and the pool is calculated, and the travel time is calculated using the vehicle's current speed and the distance to the pool. Accordingly, the first, less informative response is: "There's a swimming pool about 1km away."

[0291] In one example, the user's question is: "What are some fun things to do nearby?" User attributes include age (20 years old) and a preference for swimming. Based on the vehicle's location in the vehicle data, the nearest swimming pool is determined, the distance between the vehicle and the pool is calculated, and the travel time is calculated using the vehicle's current speed and the distance to the pool. The resulting first, more informative response is: "There's a swimming pool about 1km away; it should take about 10 minutes to get there."

[0292] In an optional embodiment, user interaction data may include user questions, user attribute information, user history conversations, user behavior characteristics, and vehicle data.

[0293] In one example, the user's question is: "How long will it take to get to a fun place?" User attributes include age, which is 10 years old. The user's history shows their first question: "What fun places are nearby?" and their first response: "There are swimming pools, basketball courts, and badminton courts." The user's behavioral characteristic is: "I like swimming." Based on the vehicle's location in the vehicle data, the nearest swimming pool is determined. The distance between the vehicle and the swimming pool is obtained, and the travel time is calculated based on the vehicle's current speed and the distance to the swimming pool. Accordingly, the first response, which contains less information, is generated: "There is a swimming pool more than 1 km away."

[0294] In one example, the user's question is: "What are some fun things to do nearby?" User attributes include age, which is 20 years old. The user's history shows their first question: "What are some fun places to do nearby?" The first response was: "There are swimming pools, basketball courts, and badminton courts." The user's behavioral characteristic is: "I like playing basketball." Based on the vehicle's location in the vehicle data, the nearest swimming pool is determined. The distance between the vehicle and the swimming pool is obtained, and the travel time is calculated based on the vehicle's current speed and the distance to the swimming pool. Accordingly, the first response, which generates more information, is: "There is a basketball court 2km away, estimated to take 20 minutes to reach. There is a swimming pool 1km away, estimated to take 10 minutes to reach. There are no badminton courts nearby."

[0295] Figure 6 This is a schematic diagram of a response information generation device according to an embodiment of the present invention. The embodiments of the present invention are applicable to situations where a user's questions are answered during human-computer interaction between a vehicle and a user. The device can execute a response information generation method and can be implemented in hardware and / or software. The device can be configured in an electronic device.

[0296] See Figure 6The response information generation device shown includes:

[0297] The interaction data acquisition module 601 is used to acquire user interaction data;

[0298] The reply information structure determination module 602 is used to determine the reply information structure based on the user interaction data;

[0299] The reply information generation module 603 is used to generate reply information based on the user interaction data and the reply information structure.

[0300] This invention, by determining a response information structure that matches user interaction data and generating a response message corresponding to the user interaction data according to the response information structure, can flexibly adjust the response information structure and effectively improve the human-computer interaction experience.

[0301] Optionally, the response information structure includes: the inclusion of the first response content and / or the second response content;

[0302] Optionally, the first response content corresponds to the user interaction data.

[0303] Optionally, the second response content is a follow-up question corresponding to the user interaction data.

[0304] Optionally, the response information structure determination module 602 is specifically used to determine the response information structure based on current user interaction data and / or historical user interaction data.

[0305] The response information structure determination module 602 is specifically used to determine the response information structure corresponding to the first user interaction data as the first response information structure when the user interaction data is the first user interaction data.

[0306] When the user interaction data is the second user interaction data, the response information structure corresponding to the second user interaction data is determined to be the second response information structure based on the second user interaction data.

[0307] Optionally, the response information generation device further includes:

[0308] The initial response content determination module is used to determine the initial response content based on the user interaction data.

[0309] The splitting determination module is used to determine the splitting status of the initial response content based on the user interaction data.

[0310] Optionally, the module for determining the split situation is specifically used for:

[0311] The initial response content is split based on current user interaction data and / or historical user interaction data.

[0312] Optionally, the module for determining the split situation is specifically used for:

[0313] When the user interaction data is the first user interaction data, the initial response content is determined to be split based on the first user interaction data.

[0314] When the user interaction data is the second user interaction data, the initial response content is determined to be split without splitting based on the second user interaction data.

[0315] Optionally, if the initial response content is split into two parts, the initial response content may contain at least two first response parts.

[0316] Optionally, the response information generation module 603 includes:

[0317] The target information content determination unit is used to determine the target information content corresponding to the user interaction data based on the user interaction data.

[0318] The first response content generation unit is used to generate response information based on the user interaction data, the target information volume, and the response information structure; wherein the response information includes at least a first response content; and the information volume of the first response content corresponds to the target information volume.

[0319] Optionally, the target information content determination unit is specifically used to: determine the target information content corresponding to the user interaction data based on the current user interaction data and / or historical user interaction data.

[0320] Optional,

[0321] The target information content determination unit is specifically used for:

[0322] When the user attribute information is the first user interaction data, the target information quantity corresponding to the first user interaction data is determined as the first information quantity based on the first user interaction data.

[0323] When the user interaction data is the second user interaction data, the target information quantity corresponding to the second user interaction data is determined as the second information quantity based on the second user interaction data.

[0324] Optionally, the user interaction data includes at least one of the following: user attribute information; user questions; user history conversations; user behavior characteristics; vehicle data.

[0325] The response information generation device provided in this embodiment of the invention can execute the response information generation method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the response information generation method.

[0326] Figure 7 A schematic diagram of an electronic device 700 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, 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 (e.g., helmets, glasses, watches, etc.), 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 invention described and / or claimed herein.

[0327] like Figure 7 As shown, the electronic device 700 includes at least one processor 701 and a memory, such as a read-only memory (ROM) 702 and a random access memory (RAM) 703, communicatively connected to the at least one processor 701. The memory stores computer programs executable by the at least one processor. The processor 701 can perform various appropriate actions and processes based on the computer program stored in the ROM 702 or loaded into the RAM 703 from storage unit 708. The RAM 703 can also store various programs and data required for the operation of the electronic device 700. The processor 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0328] Multiple components in electronic device 700 are connected to I / O interface 705, including: input unit 706, such as keyboard, mouse, etc.; output unit 707, such as various types of displays, speakers, etc.; storage unit 708, such as disk, optical disk, etc.; and communication unit 709, such as network card, modem, wireless transceiver, etc. Communication unit 709 allows electronic device 700 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0329] Processor 701 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 701 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 processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 701 performs the various methods and processes described above, such as response information generation methods.

[0330] In some embodiments, the response information generation method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by processor 701, one or more steps of the response information generation method described above may be performed. Alternatively, in other embodiments, processor 701 may be configured to perform the response information generation method by any other suitable means (e.g., by means of firmware).

[0331] 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.

[0332] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable response information generation device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can 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.

[0333] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. 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 fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0334] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device 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 provides input to the electronic device. 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).

[0335] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include 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), blockchain networks, and the Internet.

[0336] A computing system can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system. It addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability.

[0337] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0338] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for generating response information, characterized in that, The method includes: Obtain user interaction data; The structure of the response information is determined based on the user interaction data; Reply information is generated based on the user interaction data and the reply information structure.

2. The method according to claim 1, characterized in that, The response information structure includes: the inclusion of the first response content and / or the second response content.

3. The method according to claim 2, characterized in that, The first response corresponds to the user interaction data.

4. The method according to claim 2, characterized in that, The second response is a follow-up question corresponding to the user interaction data.

5. The method according to any one of claims 1-4, characterized in that, Determining the response information structure based on the user interaction data includes: Determine the structure of the response information based on current user interaction data and / or historical user interaction data.

6. The method according to any one of claims 1-4, characterized in that, Determining the response information structure based on the user interaction data includes: When the user interaction data is the first user interaction data, the response information structure corresponding to the first user interaction data is determined to be the first response information structure based on the first user interaction data. When the user interaction data is the second user interaction data, the response information structure corresponding to the second user interaction data is determined to be the second response information structure based on the second user interaction data.

7. The method according to any one of claims 1-4, characterized in that, The method further includes: The initial response content is determined based on the user interaction data; Based on the user interaction data, determine the breakdown of the initial response content.

8. The method according to claim 7, characterized in that, Determining the segmentation of the initial response content based on the user interaction data includes: The initial response content is split based on current user interaction data and / or historical user interaction data.

9. The method according to claim 7, characterized in that, Determining the segmentation of the initial response content based on the user interaction data includes: When the user interaction data is the first user interaction data, the initial response content is determined to be split based on the first user interaction data. When the user interaction data is the second user interaction data, the initial response content is determined to be split without splitting based on the second user interaction data.

10. The method according to claim 7, characterized in that, If the initial response content is split, the initial response content contains at least two first response contents.

11. The method according to any one of claims 1-4, characterized in that, The step of generating reply information based on the user interaction data and the reply information structure includes: Based on the user interaction data, determine the target information content corresponding to the user interaction data; A response is generated based on the user interaction data, the target information amount, and the response information structure; wherein the response information includes at least a first response content; the information amount of the first response content corresponds to the target information amount.

12. The method according to claim 11, characterized in that, The step of determining the target information content corresponding to the user interaction data based on the user interaction data includes: Based on current user interaction data and / or historical user interaction data, determine the target information content corresponding to the user interaction data.

13. The method according to claim 11, characterized in that, The step of determining the target information content corresponding to the user interaction data based on the user interaction data includes: When the user interaction data is the first user interaction data, the target information quantity corresponding to the first user interaction data is determined as the first information quantity based on the first user interaction data. When the user interaction data is the second user interaction data, the target information quantity corresponding to the second user interaction data is determined as the second information quantity based on the second user interaction data.

14. The method according to any one of claims 1-13, characterized in that, The user interaction data includes at least one of the following: user attribute information; user questions; user history conversations; user behavior characteristics; vehicle data.

15. A response information generation device, characterized in that, include: The interaction data acquisition module is used to acquire user interaction data; The response information structure determination module is used to determine the response information structure based on the user interaction data. The reply information generation module is used to generate reply information based on the user interaction data and the reply information structure.

16. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the response information generation method according to any one of claims 1-14.

17. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the response information generation method according to any one of claims 1-14.

18. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the response information generation method according to any one of claims 1-14.

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