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 guide type corresponding to the user interaction data according to the user interaction data; according to the user interaction data and the guide type, reply information is generated, the reply information at least comprises first reply content, and according to the user interaction data and the guide type, reply information is generated. According to the embodiment of the invention, the user can be flexibly guided to ask questions, 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 vehicles interact with vehicles 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 outputting the reply content by the vehicle.

[0003] At present, the reply content determined according to the user's intention is directly outputted, which cannot guide the user and reduces the 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 guide the user to ask questions 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 guidance type corresponding to the user interaction data according to the user interaction data;

[0008] generating reply information according to the user interaction data and the guidance type, the reply information at least including first reply content; whether the reply information includes second reply content corresponds to the guidance type.

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

[0010] an interaction data obtaining module configured to obtain user interaction data;

[0011] a guidance type determining module configured to determine a guidance type corresponding to the user interaction data according to the user interaction data;

[0012] a reply information generating module configured to generate reply information according to the user interaction data and the guidance type, the reply information at least including first reply content; whether the reply information includes second reply content corresponds to the guidance type.

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

[0014] at least one processor; and

[0015] The memory is in communication connection 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 executed.

[0018] The embodiments of the present application can flexibly guide the user by determining the guidance type adapted to the user interaction data and generating the reply information adapted to the user interaction data and the guidance type, 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 as follows. 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 on the basis of these drawings.

[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 another reply information generation method according to an embodiment of the present application;

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

[0025] Figure 5 is a flowchart of 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 7 is a structural schematic diagram of an electronic device implementing the reply information generation method of the embodiment of the present application. DETAILED DESCRIPTION

[0028] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within 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 have to 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 those 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 that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units that are 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 A flowchart of a reply information generation method provided by the embodiment of the present application, the present embodiment 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 the figure comprises:

[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 in which the human-computer interaction device interacts 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 processing the question data input by the user directly collected, 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 to obtain data, directly uses the collected data as the user interaction data, or processes the collected data to use 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 in which the vehicle interacts 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 guidance type corresponding to the user interaction data according to the user interaction data.

[0038] The guidance type is used to determine whether to generate the second reply content. For example, the guidance type includes a guided type and an unguided type. Whether the reply information includes the second reply content corresponds to the guidance type, which means that the guidance type is the guided type, the reply information includes the second reply content, and the guidance type is the unguided type, the reply information does not include the second reply content.

[0039] S103, generating reply information according to the user interaction data and the guidance type; the reply information at least includes the first reply content; and whether the reply information includes the second reply content corresponds to the guidance type.

[0040] In an optional embodiment, according to the user interaction data and the guidance type, generating the reply information can be: inputting the user interaction data and the guidance type into a pre-trained deep learning model to obtain the reply information output by the deep learning model. In an example, the user interaction data and the guidance type can be filled into a prompt template corresponding to the guidance type to obtain input data, and the input data can be input into a large language model to obtain the reply information output by the large language model.

[0041] For another example, according to the user interaction data and the guidance type, generating the reply information can be: generating reply indication information according to the user interaction data and the guidance type, and generating the reply information according to the reply indication information and the user interaction data. Specifically, the user interaction data is input into a first large language model to obtain the guidance type output by the first large language model, and the reply indication information is queried or generated according to the guidance type. Input data is determined according to the user interaction data and the reply indication information. The input data is input into a second large language model to obtain the reply information output by the second large language model. The first large language model and the second large language model can be the same or different. The first large language model can also be replaced by a classification model.

[0042] In an optional embodiment, according to the user interaction data, generating the reply information includes: determining a target information amount corresponding to the user interaction data according to the user interaction data; determining a guidance type corresponding to the user interaction data according to the user interaction data; and generating the reply information according to the user interaction data, the target information amount, and the guidance type.

[0043] In an optional embodiment, according to the user interaction data, the target information amount, and the guidance type, generating the reply information can be: inputting the user interaction data, the target information amount, and the guidance type into a pre-trained deep learning model to obtain the reply information output by the deep learning model. In an example, the user interaction data and the guidance type can be filled into a prompt template corresponding to the target information amount and the guidance type to obtain input data, and the input data can be input into a large language model to obtain the reply information output by the large language model.

[0044] For another example, according to the user interaction data, the target information quantity, and the guidance type, generating the reply information can be: according to the user interaction data, the target information quantity, and the guidance type, generating reply indication information, and according to the reply indication information and the user interaction data, generating the reply information. Specifically, inputting the user interaction data into a first large language model to obtain the target information quantity and the guidance type output by the first large language model, and taking the target information quantity and the guidance type as the reply indication information. According to the user interaction data and the reply indication information, determining input data. Inputting the input data into a second large language model to obtain the reply information output by the second large language model. The first large language model and the second large language model can be the same or different. The first large language model can also be replaced by a classification model.

[0045] In an optional embodiment, the reply information can be content that replies to the user interaction data. The information quantity can be a numerical value obtained by quantifying the reply information. For example, the data quantity included in the reply information can be used to represent, for example, the data quantity can be the number of bytes, the number of characters, or the number of words (characters), etc. Alternatively, the data quantity of the valid data in the reply information can also be used to represent the information quantity of the reply information, for example, the number of entities included in the reply information is taken as the information quantity in the reply information, and for another example, the data quantity of the keywords included in the reply information is taken as the information quantity in the reply information.

[0046] In an optional embodiment, the user interaction data is used to determine the substantial content of the reply information, for example, the user interaction data is used to determine the answer of the user interaction data. The user interaction data is also used to determine the information quantity of the first reply content. The first reply content is the substantial content of the reply information, and the first reply content can be understood as the answer of the user interaction data, and the information quantity of the first reply content is determined by the user interaction data.

[0047] In an optional embodiment, the generated reply information is usually text data, which can be sent to a human-computer interaction device, and the human-computer interaction device displays the reply information in text form to the user; or the generated reply information is an image displayed to the user; the generated reply information is corresponding speech played to the user; or the generated reply information is a corresponding video played to the user, etc. Alternatively, according to the reply information, at least one of the corresponding image, the corresponding speech, or the corresponding video is generated, and at least one of the image, the speech, and the video is sent to the human-computer interaction device.

[0048] In an example, the user's question content is: why did the dinosaurs disappear?

[0049] For example, the reply information only includes a first reply content with a large amount of information, and the first reply content is: Wow, this is a very good question! Dinosaurs disappeared because a long time ago, a big event happened on Earth. Imagine a huge stone flying from the sky and hitting the earth with a loud bang, which is a large meteorite. This large stone caused a lot of dust and smoke to block the sky, and the sun's light could not reach the ground, and the earth became very cold, and many plants died. Dinosaurs had no food to eat, so they also slowly disappeared.

[0050] For another example, the reply content includes a first reply content with a small amount of information. The first reply content is: This is a very good question, and the disappearance of dinosaurs is because a small asteroid hit the Earth, causing a dramatic change in the environment, leading to an inability to survive, but the process is also complex and interesting.

[0051] For example, according to the user interaction data, generating the reply information can be: inputting the user interaction data into a pre-trained deep learning model to obtain the reply 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, and the input data can be input into a large language model to obtain the reply information output by the large language model.

[0052] For another example, according to the user interaction data, generating the reply information can be: generating reply instruction information according to the user interaction data, and generating the reply information according to the reply instruction information and the user interaction data.

[0053] In an optional embodiment, the reply instruction information is used to determine the amount of information of the reply information, and the reply instruction information and the user interaction data are used to cooperate to generate the reply information that meets the amount of information corresponding to the reply instruction information.

[0054] For example, the reply instruction information can be a type or a label. Specifically, the reply instruction information can include a type of a small amount of information and no guiding follow-up questions, a small amount of information and guiding follow-up questions, a large amount of information and no guiding follow-up questions, and a large amount of information and guiding follow-up questions.

[0055] For another example, the reply instruction information can be input data of a model. Specifically, the reply instruction information can be: please provide an answer that a child can understand and add guiding content that meets the child's interests for a child's question: XX. For another example, the reply instruction information can be: please provide a complete answer for an adult's question: XX. In addition, the reply instruction information can have other description methods, which are not specifically limited.

[0056] Specifically, according to the user interaction data, the reply indication information is generated, and according to the reply indication information, the reply information corresponding to the user interaction data is generated, which can include: inputting the user interaction data into a first large language model to obtain the reply indication information output by the first large language model, wherein the reply indication information can be a type or a label; querying the prompt template corresponding to the reply indication information according to the reply indication information, filling the user interaction data into the corresponding prompt template to obtain input data, and inputting the input data into a second large language model to obtain the reply information output by the second large language model. The first large language model and the second large language model can be the same or different. Wherein, the first large language model can also be replaced by a classification model.

[0057] For example, the user interaction data is input into the first large language model to obtain the reply indication information output by the first large language model, which is a prompt template. The user interaction data is filled into the reply indication information to obtain input data, and the input data is input into the second large language model to obtain the reply information output by the second large language model. The first large language model and the second large language model can be the same or different.

[0058] For example, the user interaction data is input into the first large language model to obtain the reply indication information output by the first large language model, wherein the reply indication information can be a type or a label; the reply indication information and the user interaction data are input into the second large language model to obtain the reply information output by the second large language model. The first large language model and the second large language model can be the same or different. Wherein, the first large language model can also be replaced by a classification model.

[0059] By processing the user interaction data to obtain the reply indication information, and generating the reply information according to the reply indication information and the user interaction data, the effective data affecting the information amount in the user interaction data can be further extracted, and the reply indication information can be generated, so that the reply information can be generated based on the reply indication information and the user interaction data, which can improve the accuracy of the reply information, increase the flexibility of the reply information, and reduce the load pressure caused by the end-to-end generation of the reply information based on the user interaction data.

[0060] According to the user interaction data, the reply indication information is generated, and according to the reply indication information, the reply information corresponding to the user interaction data is generated, which can directly extract the guide type from the user interaction data, and generate the reply information based on the guide type and the user interaction data, which can realize accurate control of whether to generate the second reply content, and can flexibly guide the user to ask questions, effectively improving the human-computer interaction experience.

[0061] In an optional embodiment, the first reply content corresponds to the user interaction data in information amount; and the second reply content is guided follow-up query content corresponding to the user interaction data.

[0062] The second reply content can or can not be included in the reply information. Whether the second reply content is included in the reply information is determined by the user interaction data. As in the previous example, the reply indication information determined according to the user interaction data can also determine whether the second reply content is included in the reply information.

[0063] In fact, when the reply information only includes the first reply content with a small amount of information, when the user's curiosity is relatively strong, or when the user is relatively interested, the user can be prompted to continue to ask for more and richer content in addition to the reply information. The specific prompting manner can be to supplement guided follow-up query content in the reply information to prompt the user to continue to ask for more and richer content in addition to the reply information of the current query content. The guided follow-up query content is used to guide the user to continue to ask the query content, and the guided follow-up query content is content for guiding the user to continue to ask based on the first reply content. The guided follow-up query content can include suggestion information, counter-question information, and recommendation information, etc.

[0064] In an example, as in the previous example, the reply information without the second reply content is: the main reason for the disappearance of dinosaurs is that a small planet hits the earth, causing environmental changes (first reply content).

[0065] For example, the reply information including the second reply content is: the main reason for the disappearance of dinosaurs is that a small planet hits the earth, causing environmental changes (first reply content). Do you want to know the story of this big meteorite hitting the earth (second reply content)?

[0066] Determining whether the second reply content is included in the reply information through the user interaction data can facilitate the user to select whether to obtain the reply information with complete information amount, and through the guided follow-up query second reply content, in combination with the first reply content with flexible increase and decrease of information amount, the user can be prompted and guided to continue to ask for complete reply content while ensuring the brevity of the reply content, so as to gradually ask the user, conform to the real dialogue scene, thereby improving the authenticity of the reply information and improving the user experience.

[0067] Figure 2 A flowchart of another reply information generation method provided by an embodiment of the present application.

[0068] In an optional embodiment, the "determining the guided type corresponding to the user interaction data according to the user interaction data" is refined as: determining the guided type corresponding to the user interaction data according to the current user interaction data and the historical user interaction data.

[0069] 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.

[0070] See Figure 2 The method for generating response information shown includes:

[0071] S201. Obtain user interaction data.

[0072] S202. Determine the guidance type corresponding to the user interaction data based on the current user interaction data and / or the historical user interaction data.

[0073] In an optional embodiment, the user interaction data includes: current user interaction data and historical user interaction data; determining the guidance type corresponding to the user interaction data based on the user interaction data includes: determining the guidance type corresponding to the user interaction data based on the current user interaction data and the historical user interaction data.

[0074] In an optional embodiment, current user interaction data may include: user attribute information; historical user interaction data may include: user history dialogues and user attribute information. The guidance type corresponding to the user interaction data can be determined as either a first guidance type or a second guidance type based on the user attribute information and user history dialogues.

[0075] In an optional embodiment, both current user interaction data and historical user interaction data can include user attribute information and user behavior characteristics. The guidance type corresponding to the user interaction data can be determined as either a first guidance type or a second guidance type based on the user attribute information and user behavior characteristics.

[0076] In an optional embodiment, current user interaction data may include: user attribute information and user behavior characteristics; historical user interaction data may include: user attribute information, user history dialogues, and user behavior characteristics.

[0077] In an optional embodiment, both current user interaction data and historical user interaction data may include user attribute information and vehicle data. The guidance type corresponding to the user interaction data can be determined as either a first guidance type or a second guidance type based on at least one of user attribute information, user historical dialogues, and user behavior characteristics.

[0078] In an optional embodiment, current user interaction data may include user attribute information and vehicle data; historical user interaction data may include user attribute information, user history dialogues, and vehicle data. The guidance type corresponding to the user interaction data can be determined as either a first guidance type or a second guidance type based on at least one of the user attribute information, user history dialogues, and vehicle data.

[0079] In an optional embodiment, both current user interaction data and historical user interaction data may include: user attribute information, user behavior characteristics, and vehicle data. The guidance type corresponding to the user interaction data can be determined as either a first guidance type or a second guidance type based on at least one of the user attribute information, user behavior characteristics, and vehicle data.

[0080] In an optional embodiment, current user interaction data may include: user attribute information, user behavior characteristics, and vehicle data; historical user interaction data may include: user attribute information, user history dialogues, user behavior characteristics, and vehicle data. The guidance type corresponding to the user interaction data can be determined as a first guidance type or a second guidance type based on at least one of user attribute information, user history dialogues, user behavior characteristics, and vehicle data.

[0081] In an optional embodiment, current user interaction data may include user questions; historical user interaction data may include both user questions and historical user conversations. The guidance type corresponding to the user interaction data can be determined as either a first guidance type or a second guidance type based on the user questions and historical user conversations.

[0082] In an optional embodiment, both current user interaction data and historical user interaction data can include: user question content and user behavior characteristics. The guidance type corresponding to the user interaction data can be determined as either a first guidance type or a second guidance type based on the user question content and user behavior characteristics.

[0083] In an optional embodiment, current user interaction data may include: user question content and user behavior characteristics; historical user interaction data may include: user question content, user history dialogues, and user behavior characteristics. The guidance type corresponding to the user interaction data can be determined as either a first guidance type or a second guidance type based on the user question content, user history dialogues, and user behavior characteristics.

[0084] In an optional embodiment, both current user interaction data and historical user interaction data can include: user question content and vehicle data. The guidance type corresponding to the user interaction data can be determined as either a first guidance type or a second guidance type based on the user question content and vehicle data.

[0085] In an optional embodiment, current user interaction data may include: user question content and vehicle data; historical user interaction data may include: user question content, user history dialogues, and vehicle data. The guidance type corresponding to the user interaction data can be determined as either a first guidance type or a second guidance type based on at least one of the user question content, user history dialogues, and vehicle data.

[0086] In an optional embodiment, both current user interaction data and historical user interaction data can include: user question content, user behavior characteristics, and vehicle data. The guidance type corresponding to the user interaction data can be determined as either a first guidance type or a second guidance type based on at least one of the user question content, user behavior characteristics, and vehicle data.

[0087] In an optional embodiment, current user interaction data may include: user question content, user behavior characteristics, and vehicle data; historical user interaction data may include: user question content, user history dialogues, user behavior characteristics, and vehicle data. The guidance type corresponding to the user interaction data can be determined as either a first guidance type or a second guidance type based on at least one of the user question content, user history dialogues, user behavior characteristics, and vehicle data.

[0088] In an optional embodiment, current user interaction data may include: user behavior characteristics; historical user interaction data may include: user history dialogues and user behavior characteristics. The guidance type corresponding to the user interaction data can be determined as either a first guidance type or a second guidance type based on the user history dialogues and user behavior characteristics.

[0089] In an optional embodiment, current user interaction data may include vehicle data; historical user interaction data may include user history conversations and vehicle data. The guidance type corresponding to the user interaction data can be determined as either a first guidance type or a second guidance type based on the user history conversations and vehicle data.

[0090] In an optional embodiment, current user interaction data may include user behavior characteristics and vehicle data; historical user interaction data may include user history dialogues, user behavior characteristics, and vehicle data. The guidance type corresponding to the user interaction data can be determined as a first guidance type or a second guidance type based on at least one of user history dialogues, user behavior characteristics, and vehicle data.

[0091] In an optional embodiment, both current user interaction data and historical user interaction data can include user behavior characteristics and vehicle data. The guidance type corresponding to the user interaction data can be determined as either a first guidance type or a second guidance type based on the user behavior characteristics and vehicle data.

[0092] S203. Generate reply information based on the user interaction data and the guidance type; the reply information includes at least a first reply content; whether the reply information includes a second reply content corresponds to the guidance type.

[0093] In this embodiment, the corresponding guidance type can be determined by different types of user interaction data, which effectively improves the matching degree between the guidance type and the user's interaction intent, thereby ensuring the closeness between the output response information and the user interaction data and improving the user's human-computer interaction experience.

[0094] In an optional embodiment, Figure 3 This is a flowchart of another method for generating response information provided in an embodiment of the present invention.

[0095] In an optional embodiment, "determining the guidance type corresponding to the user interaction data based on the user interaction data" is refined to: when the user interaction data is first user interaction data, determining the first guidance type corresponding to the first user interaction data based on the first user interaction data;

[0096] When the user interaction data is the second user interaction data, the second guidance type corresponding to the second user interaction data is determined based on the second user interaction data.

[0097] 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.

[0098] See Figure 3 The method for generating response information shown includes:

[0099] S301. Obtain user interaction data.

[0100] S302. When the user interaction data is the first user interaction data, determine the first guidance type corresponding to the first user interaction data based on the first user interaction data.

[0101] S303. When the user interaction data is the second user interaction data, determine the second guidance type corresponding to the second user interaction data based on the second user interaction data.

[0102] The user interaction data includes: user attribute information; determining the guidance type corresponding to the user interaction data based on the user interaction data includes: when the user attribute information is first user attribute information, determining a first guidance type corresponding to the first user attribute information based on the first user attribute information; when the user attribute information is second user attribute information, determining a second guidance type corresponding to the second user attribute information based on the second user attribute information; the first user attribute information and the second user attribute information are different, and the first guidance type and the second guidance type are different.

[0103] 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. Different guidance types are used for users with different user attribute information. For example, a mapping relationship between user attribute information and guidance types can be preset, and the corresponding guidance type can be determined based on the user attribute information.

[0104] 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 has a low level of interest in the artwork, a second response may not be generated.

[0105] For example, if a user's attribute information includes their profession as an e-sports player and their question is about an introduction to a certain game, and it's determined that the user has a high level of interest in the game, a second response can be generated.

[0106] For example, if user attribute information includes the user's age as a child, it indicates that the user has weak comprehension skills, and a second response can be generated.

[0107] 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.

[0108] Optionally, the user interaction data includes: user question content; determining the guidance type corresponding to the user interaction data based on the user interaction data includes: when the difficulty level corresponding to the user question content is a first difficulty level, determining a first guidance type corresponding to the user interaction data based on the user question content; when the difficulty level corresponding to the user question content is a second difficulty level, determining a second guidance type corresponding to the user interaction data based on the user question content; when the first difficulty level and the second difficulty level are different, the first guidance type and the second guidance type are different.

[0109] 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 guidance type as guided. Questions from easy users can be answered without a second response, allowing for fewer rounds of feedback with the first response. This facilitates the user's quick understanding of the first response, thus determining the guidance type as unguided. The guidance type varies depending on the difficulty level of the user's question. For example, a mapping relationship between difficulty level and guidance type can be preset, and the corresponding guidance type can be determined based on the difficulty level.

[0110] By specifically limiting user interaction data to user questions, we can determine whether to add a second response based on the difficulty of the questions. We can also add appropriate guidance content to match the difficulty of the questions, helping users obtain more detailed and complete responses.

[0111] Optionally, the user interaction data includes: user history dialogue; determining the guidance type corresponding to the user interaction data based on the user interaction data includes: when the known information corresponding to the user history dialogue is first known information, determining a third guidance type corresponding to the user interaction data based on the user history dialogue; when the known information corresponding to the user history dialogue is second known information, determining a fourth guidance type corresponding to the user interaction data based on the user history dialogue; when the first known information and the second known information are different, the third guidance type and the fourth guidance type are different.

[0112] In one 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 guidance type as "guided," guiding the user to obtain previously replied content that they still want. 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 to that domain, thus determining the guidance type as "guided." The guidance type differs for different known information and historical dialogues. For example, a mapping relationship between the similarity between known information and the user's question and the guidance type can be preset, the similarity between the known information and the user's question can be calculated, and the corresponding guidance type can be determined based on the similarity.

[0113] 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.

[0114] Optionally, the user interaction data includes: user behavior characteristics; determining the guidance type corresponding to the user interaction data based on the user interaction data includes: when the preference content corresponding to the user behavior characteristics is a first preference content, determining the first guidance type corresponding to the user interaction data based on the user behavior characteristics; when the preference content corresponding to the user behavior characteristics is a second preference content, determining the second guidance type corresponding to the user interaction data based on the user behavior characteristics; when the first preference content and the second preference content are different, the first guidance type and the second guidance type are different.

[0115] In an optional embodiment, the user desires more content of interest. The type of guidance 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 guidance type is "guided"; when the preferred content is dissimilar, the guidance type is "no guidance." Different guidance types apply to different preferred content. For instance, a mapping relationship between the similarity between 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 guidance type can be determined based on the similarity.

[0116] By limiting user interaction data to user behavior characteristics, and determining the guidance type based on the preferred content identified by these characteristics, more response content can be provided for questions that users are interested in.

[0117] Optionally, the user interaction data includes: vehicle data; determining the guidance type corresponding to the user interaction data based on the user interaction data includes: when the operation complexity corresponding to the vehicle data is a first operation complexity, determining a first guidance type corresponding to the user interaction data based on the vehicle data; when the operation complexity corresponding to the vehicle data is a second operation complexity, determining a second guidance type corresponding to the user interaction data based on the vehicle data; when the first operation complexity and the second operation complexity are different, the first guidance type and the second guidance type are different.

[0118] Generally, the more vehicle data a user's question requires, the more complex the process of obtaining the first response becomes, and the more detailed the response content provided. This necessitates providing complete content through multiple rounds of dialogue, i.e., adding a second response, resulting in a guided response type. Conversely, the less vehicle data a user's question requires, the simpler the process of obtaining the first response becomes, eliminating the need for multiple rounds of dialogue to provide more content. This necessitates not adding a second response, resulting in an unguided response type. Generally, higher operational complexity corresponds to a guided response type, while lower operational complexity corresponds to an unguided response type.

[0119] Different guidance types are used for different vehicle data. For example, a mapping relationship between operation complexity and guidance type can be preset, and the corresponding guidance type can be determined based on the operation complexity.

[0120] By specifically limiting user interaction data to vehicle data, we can consider whether to add a second response based on the complexity of vehicle data processing in human-vehicle interaction scenarios. This allows for guidance types that adapt to the complexity of vehicle data processing, helping users to gain a more comprehensive understanding of the vehicle's status.

[0121] When user interaction data includes at least two items, determining the guidance type corresponding to the user interaction data can be done by: determining the guidance type for each item; weighting the guidance types determined for each item to obtain a weighted result; and using the weighted result as the guidance type corresponding to the user interaction data.

[0122] In one example, the user interaction data of the first user includes at least one of the following: first user attribute information, user questions of a first level of difficulty, user history dialogues of a first level of known information, user behavior characteristics of a first level of preferred content, and vehicle data of a first level of operational complexity. The guidance type corresponding to the user interaction data can be obtained by weighting at least one of the first guidance type, third guidance type, fifth guidance type, seventh guidance type, and ninth guidance type.

[0123] Furthermore, for the same user's question, different combinations of user interaction data result in different second responses.

[0124] S304. Generate reply information based on the user interaction data and the guidance type; the reply information includes at least a first reply content; whether the reply information includes a second reply content corresponds to the guidance type.

[0125] In an optional embodiment, generating response information based on the user interaction data includes: determining the target information volume and guidance type based on the user interaction data, and determining the response type; querying the response indication information corresponding to the response type from a set of preset response indication information; and generating response information corresponding to the user interaction data based on the response indication information and the user interaction data.

[0126] In an optional embodiment, the response type is used to query response instruction information. The response instruction information may include types such as low information content with no follow-up questions, low information content with follow-up questions, high information content with no follow-up questions, and high information content with follow-up questions. The amount of information can be determined based on the target information content, and the presence or absence of guidance can be determined based on the guidance type, thereby combining the target information content and the guidance type to obtain the response type.

[0127] Multiple response indication messages can be pre-configured, and a correspondence between response indication messages and response types can be established. Based on the response type, the corresponding response indication message can be queried in the correspondence.

[0128] For example, the response instruction for a response type with little information and no guiding follow-up questions could be: Please provide a simple answer to the question: XX.

[0129] For example, the response instructions for a response type with limited information and guiding follow-up questions 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.

[0130] For example, the response instruction for a response type that contains a lot of information but does not provide any follow-up questions could be: Please provide a complete answer to an adult's question: XX.

[0131] For example, the reply instruction information for a reply type that contains a lot of information and guides follow-up questions could be: Please provide a detailed answer to the question: XX, and add more guiding content for the answer.

[0132] By configuring response types and corresponding response instructions for each type, different response needs can be preset. Based on these needs, corresponding response instructions can be provided, and the most suitable response content can be generated. This adapts to response requirements, provides accurate response content, and increases the flexibility of response content to meet diverse interaction needs and adapt to various interaction scenarios. Furthermore, by presetting response instructions and establishing a correspondence between response types and response instructions, the response instructions can be quickly determined, thereby improving interaction efficiency.

[0133] Optionally, the response types include: little information and no follow-up questions, little information and some follow-up questions, a lot of information and no follow-up questions, and a lot of information and some follow-up questions.

[0134] In one optional embodiment, the response information corresponding to the type with little information and no guided follow-up questions only includes the first response content and does not include the second response content, and the first response content has relatively little information; the response information corresponding to the type with little information and guided follow-up questions includes both the first response content and the second response content, and the first response content has relatively little information; the response information corresponding to the type with a lot of information and no guided follow-up questions only includes the first response content and does not include the second response content, and the first response content has relatively much information; the response information corresponding to the type with a lot of information and guided follow-up questions includes both the first response content and the second response content, and the first response content has relatively much information.

[0135] As in the previous example, responses with limited information and no follow-up questions include: The main reason for the extinction of dinosaurs was an asteroid impact that caused drastic environmental changes (first response content).

[0136] For example, responses with limited information but prompting follow-up questions might include: The main reason for the extinction of dinosaurs was an asteroid impact that caused drastic environmental changes (first response). Would you like to know the story of this massive meteorite impact (second response)?

[0137] For example, responses to questions that are information-rich but lack follow-up questions might include: "Wow, that's a really good question! Dinosaurs disappeared because a huge event happened on Earth a long, long time ago. Imagine a giant rock flying down 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 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 (first response content)."

[0138] For example, responses to questions that are informative and encourage follow-up questions might include: "This is an excellent question. The extinction of dinosaurs was caused by an asteroid impact, which drastically altered the environment, making survival impossible. This process is also complex and interesting (first response). Would you like to hear about the impact process?" (second response)

[0139] By configuring multiple response types and corresponding response instructions for each type, the diversity of response types is increased, thereby enriching the response instructions. This allows for adaptation to various interaction scenarios and flexible adjustment of the information content in the response.

[0140] Optionally, generating reply instruction information based on the user interaction data includes: inputting the user interaction data into a classification model to obtain the reply type output by the classification model; and querying the reply instruction information corresponding to the reply type from a preset reply prompt template based on the reply type.

[0141] In an optional embodiment, a classification model is used to classify user interaction data to obtain response types. For example, the classification model can be a large language model. The specific processing steps of the classification model are: encoding the user interaction data to obtain a corresponding vector representation, and decoding the vector to obtain the response type.

[0142] By inputting user interaction data into a classification model to obtain response types, complex user interaction data can be processed, and response information can be generated quickly.

[0143] Optionally, the reply instruction information includes a reply prompt template; generating reply information corresponding to the user interaction data based on the reply instruction information and the user interaction data includes: filling the user interaction data into the reply prompt template to obtain target input content; and inputting the target input content into the generation model to obtain reply information corresponding to the user interaction data.

[0144] In an optional embodiment, the response prompt template includes slots for user interaction data. Filling the response prompt template with user interaction data involves adding the data to the corresponding slots. The filled response prompt template serves as the target input content. This target input content is used as input data for the generative model. The generative model processes the target input content to generate response information. For example, the specific processing steps of the generative model are: encoding the target input content to obtain a corresponding vector representation, and decoding the vector representation to obtain the response information. The generative model can be a large language model, used to input natural language content and generate response information.

[0145] By limiting the response instruction information to a response prompt template and inputting user interaction data into the response prompt template to obtain the target input content, and then inputting the target input content into the generative model to obtain the response information, complex question content can be processed, accurate and coherent response information can be generated, the realism of the interaction can be improved, and response information can be generated quickly, thus improving the efficiency of the interaction.

[0146] It should be noted that the aforementioned classification model and generative model can be the same model or two different, independent models. Optionally, user interaction data is input into the classification model to obtain the response type output by the classification model; the response indication information corresponding to the response type is queried, and the user interaction data is filled into the response indication information to obtain the target input content; the target input content is input into the generative model to obtain the response information output by the generative model. The size of the classification model is smaller than that of the generative model. Specifically, the number of levels in the classification model is less than the number of levels in the generative model, and / or the number of parameters in the classification model is less than the number of parameters in the generative model. This two-level connected model structure processes user interaction data. Simple operations, i.e., generating response types, can be performed by the small-scale, fast classification model at the front end, while complex operations, i.e., generating response information, can be performed by the large-scale, powerful inference and production model at the back end, which has better instruction compliance capabilities. This allows for reasonable task allocation, improving the speed of response information generation while ensuring accuracy, thus achieving a balance between cost and effectiveness.

[0147] Optionally, the generative model is obtained by: acquiring standard question content and corresponding standard response information; splitting the standard response information to obtain multiple sub-response information and the response order of each sub-response information; the information content of the standard response information is greater than the information content of each sub-response information; adding corresponding guiding follow-up questions to the sub-response information that responds first to obtain target response information; combining at least one target response information and the last sub-response information in the response order with the standard question content to generate multiple training samples; and fine-tuning the pre-trained initial model using each training sample to obtain the generative model.

[0148] In an optional embodiment, the initial model can be a pre-trained general-domain adapted large language model. The generating model can be a fine-tuned general-domain large language model to obtain a model adapted to the response content generation method provided in this embodiment of the invention. Standard question content and standard response information are a pair of question-response data, and standard response information is the correct response information to the standard question content. It should be noted that there can be more than one correct response information to the standard question content. One correct response information can be selected as the standard response information, or multiple correct response information can be selected and fused to obtain the standard response information. The methods for obtaining standard question content and standard response information can include at least one of the following: manual generation, obtaining high-scoring question-answer pairs and topic content and corresponding comments from question-and-answer websites, etc.

[0149] In an optional embodiment, the standard response information is split into sub-response information with a general-to-specific logical relationship, or into sub-response information with a progressive or inferential relationship. The information content of the standard response information is greater than that of the sub-response information.

[0150] A standard, example response: The dinosaurs disappeared because a huge event happened on Earth a long, long time ago. Imagine a giant rock flying down from the sky and crashing into the Earth with a bang—that's a 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.

[0151] In an optional embodiment, the total sub-response information in the split sub-response information is: The extinction of dinosaurs was due to an asteroid impact on Earth, causing drastic environmental changes that made survival impossible. A molecular response is: A huge rock flew from the sky and crashed into Earth with a bang—this is a large meteorite. This large rock caused a lot of dust and smoke to obscure the sky, making the Earth very cold, leading to the extinction of the dinosaurs. A molecular response is: The Earth became very cold, and many plants died. The dinosaurs had no food to eat, so they gradually disappeared.

[0152] For example, the standard response information is: S1 / T1=V1, S2 / T2=V2, V1>V2.

[0153] One of the sub-response messages in the split sub-response messages is: S1 / T1 = V1; another of the sub-response messages in the split sub-response messages is: S2 / T2 = V2; and yet another of the sub-response messages in the split sub-response messages is: V1 > V2.

[0154] In the example above, each sub-response message only includes a portion of the content in the standard response message. Therefore, the information content of the standard response message is greater than the information content of each sub-response message.

[0155] In addition, there are other splitting methods and relationships between sub-response information, which can be set as needed, without specific limitations.

[0156] The response order indicates the output sequence of the sub-response messages derived from the breakdown of the same standard response message. The response order of each sub-response message can be determined based on the logical relationships between them.

[0157] For example, when dealing with sub-response information in a total-to-sub logical relationship, the total sub-response information is usually responded to first, followed by the sub-response information. Furthermore, the order of sub-response information can be determined based on importance, causal relationship, or other logical relationships. If the sub-response information is in a parallel relationship, the order of response can be randomly determined.

[0158] For example, the order of responses to sub-response messages with progressive or inferential relationships can be determined according to the relationship. Specifically, in inferential relationships, sub-response messages without dependencies are responded to first, while those that depend on other sub-response messages are responded to after those dependent sub-response messages.

[0159] In an optional embodiment, each sub-response message is divided into two categories: the last sub-response message in the response order and the sub-response messages that are all present except the last one, i.e., the sub-response messages that are present in the response order first. There is at least one sub-response message that is present in the response order first, and this includes all sub-response messages except the last one. Follow-up questions can be added to the first-response messages to obtain the target response. Specifically, after adding follow-up questions to the first-response messages, the user can be guided to further inquire about the subsequent sub-response messages. The follow-up questions can be determined based on standard question content, uniform wording, or a summary of the keywords or the first sentence of the next adjacent sub-response message. These can be set as needed and are not specifically limited. Since there are no sub-response messages that are present in the last-response message, it is not necessary to add follow-up questions to them.

[0160] In an optional embodiment, for the same standard question content, each target response is combined with the standard question content to obtain corresponding training samples. The last sub-response information in the response order is combined with the standard question content to obtain corresponding training samples. The number of sub-response information obtained by splitting the standard response information is the same as the number of generated training samples.

[0161] Furthermore, a large number of standard question contents and corresponding standard question content pairs can be set, and multiple training samples can be generated for each pair. The initial model can then be fine-tuned using a large number of training samples. For example, one could...

[0162] SFT (Self-optimization and Self-adjusting) is a method for fine-tuning the initial model.

[0163] Besides splitting standard response information into sub-response information, standard response information can also be combined with standard question content to generate training samples. Furthermore, standard response information can be simplified using at least one simplification method to obtain at least one sub-response information, each of which is then combined with standard question content to generate corresponding training samples. Additionally, at least one related piece of information from the standard response information can be obtained and expanded to obtain at least one response with more information. Each expanded response is then combined with standard question content to generate corresponding training samples. These generated training samples can be input into the initial model for fine-tuning. This significantly expands the coverage of the training samples, improves their representativeness, and using these training samples to fine-tune the initial model improves the generalization ability of the fine-tuned generative model, increases the accuracy of the generated model's responses, and enhances the diversity of the responses.

[0164] By fine-tuning the initial model, the generated model can be made capable of differentiating user interaction data, adjusting the amount of information in the response, and guiding content production. This allows for gradual guidance of user interaction, step by step, and improved dialogue authenticity.

[0165] In an optional embodiment, Figure 4 This is a flowchart of another method for generating response information provided in an embodiment of the present invention.

[0166] In an optional embodiment, the response information generation method further includes: dynamically adjusting the associated guidance frequency based on user interaction data.

[0167] 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.

[0168] SeeFigure 4 The method for generating response information shown includes:

[0169] S401. Obtain user interaction data.

[0170] S402. Determine the guidance type corresponding to the user interaction data based on the user interaction data.

[0171] S403. Dynamically adjust the associated guidance frequency based on user interaction data.

[0172] S404. Generate response information according to the guidance frequency and based on the user interaction data and the guidance type; the response information includes at least a first response content; whether the response information includes a second response content corresponds to the guidance type.

[0173] The guidance frequency characterizes how frequently user interaction data is guided. In one example, the guidance frequency can be the ratio of the number of guidance rounds to the total number of interactions; that is, given a fixed total number of interactions, the number of guidance rounds is directly proportional to the guidance frequency. For instance, assuming a user interacts 10 times in total and has 4 guidance rounds, the guidance frequency is 0.4. In this embodiment, the user's comprehension and expertise can be determined based on the user interaction data. This allows for dynamic adjustments to the guidance frequency based on the user's comprehension and expertise, thereby improving the user's interactive experience.

[0174] In an optional embodiment, the user interaction data includes: current user interaction data and historical user interaction data; the step of dynamically adjusting the associated guidance frequency based on the user interaction data includes:

[0175] The associated guidance frequency is dynamically adjusted based on the current user interaction data and the historical user interaction data.

[0176] Both current user interaction data and historical user interaction data can include at least one of the following: user attribute information; user questions; user history dialogues; user behavior characteristics; and 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 past user interactions.

[0177] 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 guidance frequency corresponding to the first age and the second guidance frequency corresponding to the second age are different.

[0178] In one example, younger users have weaker comprehension abilities, while older users have stronger comprehension abilities. Therefore, younger users can be prompted with more frequent responses to reduce the difficulty of understanding the content. Since the first age group is younger than the second age group, the first prompting frequency should be less than the second.

[0179] Optionally, when the difficulty level of the current user's question is the first level, a third guidance frequency 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 guidance frequency is determined based on the historical user's question; when the first and second levels of difficulty are different, the third and fourth guidance frequencies are different.

[0180] In one optional embodiment, the difficulty level of the user's question is used to determine the complexity of the answer, thereby determining the guidance frequency. The user's questions can be categorized by difficulty level, such as hardness or ease. For example, a mapping relationship between difficulty level and guidance frequency can be preset, and the corresponding guidance frequency can be determined based on the difficulty level. Alternatively, the user's question can be input into a deep learning model to obtain the guidance frequency.

[0181] Optionally, when the preference content corresponding to the current user behavior feature is the first preference content, the seventh guidance frequency 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 guidance frequency 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 guidance frequency and the eighth guidance frequency are different.

[0182] In one optional embodiment, user behavior characteristics are used to determine preferred content. The onboarding frequency is then determined based on the preferred content. The onboarding frequency varies depending on the preferred content. In reality, users tend to want more content that interests them. Therefore, the onboarding frequency can be determined based on preferred content.

[0183] Optionally, when the operation complexity corresponding to the current vehicle data is the first operation complexity, the ninth guidance frequency 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 guidance frequency 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 guidance frequency and the tenth guidance frequency are different.

[0184] 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.

[0185] The guidance frequency varies depending on the complexity of the operation. Generally, the more vehicle data required for a user's question, the more complex the process of obtaining the first response, and the more first responses are provided, resulting in a higher guidance frequency. Conversely, the less vehicle data required for a user's question, the simpler the process of obtaining the first response, and the fewer first responses are provided, resulting in a lower guidance frequency. Different user questions can correspond to different levels of operational complexity. Generally, higher operational complexity results in a higher guidance frequency, and lower operational complexity results in a lower guidance frequency. For example, a mapping relationship between operational complexity and guidance frequency can be preset, and the corresponding guidance frequency can be determined based on the operational complexity. Alternatively, the operational complexity can be input into a deep learning model to obtain the guidance frequency.

[0186] In an optional embodiment, dynamically adjusting the associated guidance frequency based on user interaction data includes:

[0187] When the user attribute information is the first user interaction data, the associated guidance frequency is dynamically increased based on the first user interaction data;

[0188] When the user attribute information is the second user interaction data, the associated guidance frequency is dynamically reduced based on the second user interaction data.

[0189] In one example, if the first user interaction data is the first user attribute information, indicating that the user's comprehension and professional abilities are poor, the associated guidance frequency can be dynamically increased; if the user interaction data is the second user attribute information, indicating that the user's comprehension and professional abilities are superior, the associated guidance frequency can be dynamically decreased, thus realizing the dynamic adjustment of the guidance frequency.

[0190] 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 guidance frequency corresponding to the first age and the guidance frequency corresponding to the second age are different. In one example, the second age is greater than the first age. Exemplarily, the first age and the second age can be represented by age ranges, for example, the range of the first age is under 18 years old; the range of the second age is over 18 years old. In one example, when the user's age falls within the range of the first age, the corresponding guidance frequency can be increased; when the user's age falls within the range of the second age, the corresponding guidance frequency can be decreased, thereby achieving the effect of dynamically adjusting the guidance frequency based on user attribute information, thus enabling dynamic adjustment of the information content contained in the response information.

[0191] In an optional embodiment, the response information generation method further includes: dynamically switching the associated guidance type in response to a received guidance stop operation. Guidance types include guided types and unguided types. Whether the response information includes second response content corresponds to the guidance type can mean: if the guidance type is guided, the response information includes second response content; if the guidance type is unguided, the response information does not include second response content. In actual operation, the dynamic switching between guided and unguided types can be determined based on the user's interaction. In one example, if the guidance type of the user's interaction data is guided, but a guidance stop operation is received during the interaction, the guidance type is switched from guided to unguided to dynamically adjust the amount of information contained in the response information.

[0192] In an optional embodiment, the guidance stop operation includes at least one of the following: guidance rejection operation; no guidance response content received within a preset valid time period; receiving information unrelated to the user's question. In one example, a guidance type switch icon can be configured and displayed on the user interface, allowing for a clear visual representation of the guidance type configuration options. If the user can directly turn off the guidance type switch icon on the user interface, indicating a guidance rejection operation, the guidance type is set to "no guidance," and the corresponding response information does not include a second response. In one example, when generating response information, if no guidance response content provided by the user is received within a preset valid time period, it can be considered that no guidance is needed. The guidance type is then switched from "guidance type" to "no guidance type," and the next generated response information only includes the first response content, thereby reducing the amount of information and computation in the response information. The preset effective duration represents the waiting time for guiding content after receiving a response. For example, if the preset effective duration is 3 seconds, then if no guiding content is received from the user within 3 seconds after generating and outputting the response information, the guiding type will be switched from guided to unguided. In one example, information unrelated to the user's question refers to information in the user's response that is irrelevant to the question. For instance, if the user's question is "Why is the Earth round?", the response would be "Earth's gravity is a major reason for the Earth's spherical shape. Do you want to know the specific process by which the Earth became a sphere?" If the user then replies "Why is the moon sometimes crescent-shaped?", it can be determined that the user is unlikely to ask further questions, and the guiding type can be switched from guided to unguided to improve the user's questioning experience.

[0193] In an optional embodiment, the response information generation method further includes: determining the associated actual guidance rounds and the guidance phrases associated with each actual guidance round based on user interaction data and user attribute information. In one example, the actual guidance rounds represent the total number of times the guidance type is "guidance type" during an interaction; the guidance phrases represent the format associated with the guidance content, and can also be used to guide the user on whether to terminate the guidance process. For example, the guidance phrases may include: guiding the user to continue asking questions; or guiding the user to terminate asking questions.

[0194] In one example, user interaction data may include: user questions, and the actual number of guidance rounds and associated guidance phrases for each round can be determined based on the difficulty level of the questions and user attribute information. If the user's question is at the highest difficulty level, and the user's comprehension and expertise are determined to be relatively poor based on attribute information, then the number of associated guidance rounds will be higher, and the associated guidance phrase for each round will guide the user to continue asking questions. If the user's question is at the second highest difficulty level, or if the user's comprehension and expertise are determined to be relatively good based on attribute information, then the number of associated guidance rounds will be lower, and the associated guidance phrase for each round will guide the user to stop asking questions. This allows for providing users with detailed and complete answers directly, reducing the tedious process of repeated questioning.

[0195] In one example, user interaction data may include: user's historical dialogues. Based on these dialogues and user attribute information, the actual number of guidance rounds and the associated guidance phrases for each round can be determined. If the user's historical dialogues are the first known information, and the user's comprehension and expertise are determined to be poor based on attribute information, then the number of associated guidance rounds is determined to be larger, and the associated guidance phrase for each round is one that guides the user to continue asking questions. Conversely, if the user's historical dialogues are the first known information, or if the user's comprehension and expertise are determined to be superior based on attribute information, then the number of associated guidance rounds is determined to be smaller, and the associated guidance phrase for each round is one that guides the user to terminate their questions. This facilitates providing the user with a detailed and complete answer directly, reducing the tedious process of repeated questioning. In one example, the first and second known information are different.

[0196] In one example, user interaction data may include: user behavior characteristics. Based on these characteristics and user attribute information, the actual number of guidance rounds and the associated guidance phrases for each round can be determined. If the user behavior characteristics correspond to a first preferred content, and the user's comprehension and expertise are determined to be relatively poor based on attribute information, then the associated actual guidance rounds are determined to be longer, and the associated guidance phrases for each round are phrases that guide the user to continue asking questions. If the user behavior characteristics correspond to a second preferred content, or if the user's comprehension and expertise are determined to be relatively good based on attribute information, then the associated actual guidance rounds are determined to be shorter, and the associated guidance phrases for each round are phrases that guide the user to stop asking questions. This facilitates providing users with detailed and complete answers directly, reducing the tedious process of repeated questioning. In one example, the first and second preferred contents are different.

[0197] In one example, user interaction data may include vehicle data. The actual number of guidance rounds and the associated guidance phrases for each round can be determined based on the operational complexity corresponding to the vehicle data and user attribute information. If the operational complexity corresponding to the vehicle data is at the first operational complexity level, and the user's comprehension and professional abilities are determined to be relatively poor based on user attribute information, then the associated actual guidance rounds will be higher, and the associated guidance phrases for each round will guide the user to continue asking questions. Conversely, if the operational complexity corresponding to the vehicle data is at the second operational complexity level, or if the user's comprehension and professional abilities are determined to be relatively good based on user attribute information, then the associated actual guidance rounds will be lower, and the associated guidance phrases for each round will guide the user to stop asking questions. This facilitates providing users with detailed and complete answers directly, reducing the tedious process of repeated questioning. In one example, the first operational complexity level and the second operational complexity level are different.

[0198] In an optional embodiment, the user interaction data includes: current user interaction data and historical user interaction data; the step of determining the associated actual guidance rounds and the guidance phrases associated with each actual guidance round based on the user interaction data includes: determining the associated actual guidance rounds and the guidance phrases associated with each actual guidance round based on the current user interaction data and the historical user interaction data.

[0199] If the user's questions in the current user interaction data are understood by the user to some extent, and the user's age is within the first age range, then there is no need to guide the user multiple times. In this case, the actual number of guidance rounds is set to a value less than the preset guidance round threshold, and the guidance sentence is set to the first guidance sentence. This guides the user to stop asking questions, making it easier to provide the user with a detailed and complete answer directly, reducing the tedious process of the user asking questions repeatedly.

[0200] In an optional embodiment, determining the associated actual guidance rounds and the guidance phrases associated with each actual guidance round based on user interaction data includes:

[0201] When the user interaction data is the first user interaction data, it is determined that the associated actual guidance round is less than the preset guidance round threshold, and the guidance sentence associated with each actual guidance round is the first guidance sentence;

[0202] When the user interaction data is the second user interaction data, it is determined that the associated actual guidance round is greater than a preset guidance round threshold, and the guidance sentence associated with each actual guidance round is the second guidance sentence. In an optional embodiment, when the user question content contained in the user interaction data matches the third user attribute information, and the user attribute information includes the second user attribute information, it is determined that the associated actual guidance round is less than a preset guidance round threshold, and the guidance sentence associated with each actual guidance round is the first guidance sentence;

[0203] When the user question content contained in the user interaction data does not match the third user attribute information, and the user attribute information includes the first user attribute information, it is determined that the associated actual guidance round is greater than the preset guidance round threshold, and the guidance sentence associated with each actual guidance round is the second guidance sentence.

[0204] In one example, the first prompting phrase and the second prompting phrase are different. For instance, the first prompting phrase can guide the user to stop asking questions, while the second prompting phrase can guide the user to continue asking questions.

[0205] In one example, the third attribute information is used to represent relevant information that matches the user's profession and areas of interest. In another example, if the user interaction data contains a user question that matches the third user attribute information, it indicates that the user has some understanding of the question. If the user attribute information includes the second user attribute information, i.e., the user's age falls within the second age range, then there's no need for multiple guidance rounds. The actual number of guidance rounds is set to a value less than a preset guidance round threshold, and the guidance phrase is set to the first guidance phrase. This guides the user to stop asking questions, facilitating a direct and detailed answer and reducing the tedious process of repeated questioning.

[0206] In one example, if the user interaction data contains user questions that do not match the third user attribute information, it indicates that the user does not have much basic understanding of the questions. Alternatively, if the user attribute information includes the first user attribute information, i.e., the user's age is within the first age range, it means that the user's comprehension and professional abilities are somewhat weaker. In this case, multiple guidance rounds are needed. The actual number of guidance rounds is set to a value greater than the preset guidance round threshold, and the guidance sentence is set to the second guidance sentence to guide the user to continue asking questions and satisfy the user's curiosity.

[0207] Figure 5 This is a flowchart of another method for generating response information provided in an embodiment of the present invention.

[0208] In an optional embodiment, "generating reply information based on the user interaction data" is further refined as follows: determining the target information quantity corresponding to the user interaction data based on the user interaction data; generating reply information based on the user interaction data and the target information quantity, wherein the information quantity of the first reply content corresponds to the target information quantity.

[0209] 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.

[0210] See Figure 5 The method for generating response information shown includes:

[0211] S501, Obtain user interaction data.

[0212] S502. Determine the target information amount corresponding to the user interaction data based on the user interaction data.

[0213] 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".

[0214] 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.

[0215] S503. Generate reply information based on the user interaction data, the target information amount, and the guidance type, wherein the information amount of the first reply content corresponds to the target information amount.

[0216] 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.

[0217] In an optional embodiment, generating response information based on the user interaction data and the target information content can be achieved by: inputting the user interaction data and the target information content 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 target information content can be filled into a prompt template corresponding to the target information content to obtain input data, which is then input into a large language model to obtain the response information output by the large language model.

[0218] For example, generating response information based on the user interaction data and the target information quantity can be achieved by: generating response instruction information based on the user interaction data and the target information quantity, and generating response information based on the response instruction information and the user interaction data. Specifically, the user interaction data is input into the first language model to obtain the target information quantity output by the first language model, and the response instruction information is queried or generated based on the target information quantity. Input data is determined based on the user interaction data and the response instruction information. The 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. The first language model can also be replaced by a classification model.

[0219] This invention obtains the target information amount by processing user interaction data, and generates reply information including the first reply content corresponding to the target information amount based on the target information amount and user interaction data. The target information amount can be directly extracted from user interaction data, and reply information can be generated based on the target information amount and user interaction data, which can achieve precise control of the information amount of the first reply content.

[0220] Optionally, user interaction data includes user attribute information.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] Optionally, user attribute information may also include other content such as occupation, without specific limitations.

[0233] 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.

[0234] In one example, user attribute information includes the user's age. The default age threshold is 12 years old.

[0235] 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.

[0236] 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.

[0237] Optionally, the user interaction data includes: user attribute information; determining the target information quantity corresponding to the user interaction data based on the user interaction data includes: when the user attribute information is first user attribute information, determining a first information quantity corresponding to the first user attribute information based on the first user attribute information; when the user attribute information is second user attribute information, determining a second information quantity corresponding to the second user attribute information 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] In an optional embodiment, determining the target information amount corresponding to the user interaction data based on the user interaction data includes:

[0249] When the user interaction data is the first user interaction data, the first information content corresponding to the first user interaction data is determined based on the first user interaction data.

[0250] When the user interaction data is the second user interaction data, the second information content corresponding to the second user interaction data is determined based on the second user interaction data.

[0251] 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.

[0252] 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.

[0253] 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.

[0254] 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.

[0255] 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.

[0256] 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.

[0257] Optional, user interaction data includes: user-asked questions.

[0258] 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.

[0259] Optionally, the user interaction data includes: user question content; determining the target information content corresponding to the user interaction data based on the user interaction data includes: when the difficulty level corresponding to the user question content is a first difficulty level, determining a third information content corresponding to the user interaction data based on the user question content; when the difficulty level corresponding to the user question content is a second difficulty level, determining a fourth information content corresponding to the user interaction data based on the user question content; when the first difficulty level and the second difficulty level are different, the third information content and the fourth information content are different.

[0260] 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.

[0261] 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.

[0262] 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.

[0263] Optional, user interaction data includes: user's historical conversations.

[0264] 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.

[0265] 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.

[0266] Optionally, the user interaction data includes: user historical dialogues; determining the target information quantity corresponding to the user interaction data based on the user interaction data includes: when the known information corresponding to the user historical dialogue is first known information, determining a fifth information quantity corresponding to the user interaction data based on the user historical dialogue; when the known information corresponding to the user historical dialogue is second known information, determining a sixth information quantity corresponding to the user interaction data based on the user 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.

[0267] 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.

[0268] 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.

[0269] 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.

[0270] 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.

[0271] Optional, user interaction data includes: user behavior characteristics.

[0272] 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.

[0273] Optionally, the user interaction data includes: user behavior features; determining the target information content corresponding to the user interaction data based on the user interaction data includes: when the preference content corresponding to the user behavior features is a first preference content, determining a seventh information content corresponding to the user interaction data based on the user behavior features; when the preference content corresponding to the user behavior features is a second preference content, determining an eighth information content corresponding to the user interaction data based on the user behavior features; when the first preference content and the second preference content are different, the seventh information content and the eighth information content are different.

[0274] 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.

[0275] 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.

[0276] 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.

[0277] 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.

[0278] 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.

[0279] 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.

[0280] Optionally, the user interaction data may also include vehicle data.

[0281] 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.

[0282] 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.

[0283] 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.

[0284] 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.

[0285] In one example, a brief initial response could be: "The vehicle will arrive in XX minutes, estimated arrival time is 10:32."

[0286] 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.

[0287] 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.

[0288] Optionally, the user interaction data includes: vehicle data; determining the target information quantity corresponding to the user interaction data based on the user interaction data includes: when the operation complexity corresponding to the vehicle data is a first operation complexity, determining a ninth information quantity corresponding to the user interaction data based on the vehicle data; when the operation complexity corresponding to the vehicle data is a second operation complexity, determining a tenth information quantity corresponding to the user interaction data 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.

[0289] 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.

[0290] 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.

[0291] 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.

[0292] In an optional embodiment, user interaction data may include user-asked questions and user attribute information.

[0293] 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.

[0294] 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."

[0295] In an optional embodiment, user interaction data may include user questions, user attribute information, and user dialogue history.

[0296] 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.

[0297] 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.

[0298] In an optional embodiment, user interaction data may include user questions, user attribute information, and user behavior characteristics.

[0299] 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.

[0300] 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."

[0301] In an optional embodiment, user interaction data may include user questions, user attribute information, user history conversations, and user behavior characteristics.

[0302] 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.

[0303] 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."

[0304] In an optional embodiment, user interaction data may include user questions, user attribute information, and vehicle data.

[0305] 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."

[0306] 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."

[0307] In an optional embodiment, user interaction data may include user questions, user attribute information, user history conversations, and vehicle data.

[0308] 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."

[0309] 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."

[0310] In an optional embodiment, user interaction data may include user questions, user attribute information, user behavior characteristics, and vehicle data.

[0311] 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."

[0312] 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."

[0313] In an optional embodiment, user interaction data may include user questions, user attribute information, user history conversations, user behavior characteristics, and vehicle data.

[0314] 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."

[0315] 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."

[0316] In an optional embodiment, user interaction data may include user attribute information and user history conversations.

[0317] In an optional embodiment, user interaction data may include user attribute information and user behavior characteristics.

[0318] In an optional embodiment, user interaction data may include: user attribute information, user history conversations, and user behavior characteristics.

[0319] In an optional embodiment, user interaction data may include user attribute information and vehicle data.

[0320] In an optional embodiment, user interaction data may include: user attribute information, user history conversations, and vehicle data.

[0321] In an optional embodiment, user interaction data may include: user attribute information, user behavior characteristics, and vehicle data.

[0322] In an optional embodiment, user interaction data may include: user attribute information, user history conversations, user behavior characteristics, and vehicle data.

[0323] In an optional embodiment, user interaction data may include: user questions and user conversation history.

[0324] In an optional embodiment, user interaction data may include: user-asked questions and user behavior characteristics.

[0325] In an optional embodiment, user interaction data may include: user questions, user dialogue history, and user behavior characteristics.

[0326] In an optional embodiment, user interaction data may include: user-generated questions and vehicle data.

[0327] In an optional embodiment, user interaction data may include: user questions, user history conversations, and vehicle data.

[0328] In an optional embodiment, user interaction data may include: user questions, user behavior characteristics, and vehicle data.

[0329] In an optional embodiment, user interaction data may include: user questions, user dialogue history, user behavior characteristics, and vehicle data.

[0330] In an optional embodiment, user interaction data may include: user history conversations and user behavior characteristics.

[0331] In an optional embodiment, user interaction data may include: user history conversations and vehicle data.

[0332] In an optional embodiment, user interaction data may include: user history conversations, user behavior characteristics, and vehicle data.

[0333] In an optional embodiment, user interaction data may include user behavior characteristics and vehicle data.

[0334] When user interaction data includes at least two items, the target information content corresponding to the user interaction data can be determined by: determining the information content corresponding to each item; weighting the determined information content of each item to obtain a weighted result; and using the weighted result as the target information content corresponding to the user interaction data.

[0335] In one example, the user interaction data of the first user includes at least one of the following: first user attribute information, user questions of a first level of difficulty, user history dialogues of a first level of known information, user behavior characteristics of a first level of preferred content, and vehicle data of a first level of operational complexity. The target information content corresponding to the user interaction data can be obtained by weighting at least one of the first, third, fifth, seventh, and ninth information contents.

[0336] Furthermore, for the same user question, different combinations of user interaction data result in different first responses.

[0337] By flexibly combining and configuring user interaction data, the flexibility of response content is increased to meet diverse interaction needs and adapt to various interaction scenarios.

[0338] Figure 6 This is a schematic diagram of a response information generation device provided in an embodiment of the present invention. This embodiment of the present invention is 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.

[0339] See Figure 6 The response information generation device shown includes:

[0340] Interaction data acquisition module 601 is used to acquire user interaction data;

[0341] The guidance type determination module 602 is used to determine the guidance type corresponding to the user interaction data based on the user interaction data.

[0342] The reply information generation module 603 is used to generate reply information based on the user interaction data and the guidance type, wherein the reply information includes at least a first reply content; whether the reply information includes a second reply content corresponds to the guidance type.

[0343] The embodiments of the present invention generate response information with an amount of information adapted to user interaction data. The amount of information in the response information can be flexibly adjusted, specifically increasing or decreasing the amount of information in the response information, thereby providing feedback that is easy for users to understand.

[0344] Optionally, the information content of the first reply corresponds to the user interaction data; the second reply is a follow-up question corresponding to the user interaction data.

[0345] Optionally, the boot type determination module 602 is specifically used for:

[0346] When the user interaction data is the first user interaction data, the first information content corresponding to the first user interaction data is determined based on the first user interaction data.

[0347] When the user interaction data is the second user interaction data, the second information content corresponding to the second user interaction data is determined based on the second user interaction data.

[0348] Optionally, the user interaction data includes: user attribute information;

[0349] The boot type determination module 602 is specifically used for:

[0350] When the user interaction data is the first user interaction data information, the first guidance type corresponding to the first user interaction data is determined based on the first user interaction data;

[0351] When the user interaction data is the second user interaction data, the second guidance type corresponding to the second user interaction data is determined based on the second user interaction data.

[0352] In an optional embodiment, the response information generation apparatus further includes:

[0353] The boot frequency adjustment module is used to dynamically adjust the associated boot frequency based on user attribute information.

[0354] In an optional embodiment, the user interaction data includes: current user interaction data and historical user interaction data; the guidance frequency adjustment module is specifically used for:

[0355] The associated guidance frequency is dynamically adjusted based on the current user interaction data and the historical user interaction data.

[0356] In an optional embodiment, the user interaction data includes: first user interaction data and second user interaction data; the guidance frequency adjustment module is specifically used for:

[0357] When the user interaction data is the first user interaction data information, the associated guidance frequency is dynamically increased according to the first user interaction data;

[0358] When the user interaction data is the second user interaction data, the associated guidance frequency is dynamically reduced based on the second user interaction data.

[0359] In an optional embodiment, the response information generation apparatus further includes:

[0360] The boot type switching module is used to dynamically switch the associated boot type in response to a received boot stop operation.

[0361] In an optional embodiment, the guidance stop operation includes at least one of the following: guidance rejection operation; no guidance response content is received within a preset valid time period; information unrelated to the user's question is received.

[0362] In an optional embodiment, the response information generation apparatus further includes:

[0363] The module for determining the guidance rounds and phrases is used to determine the associated actual guidance rounds and the associated guidance phrases for each actual guidance round based on user interaction data and user attribute information.

[0364] In an optional embodiment, the user interaction data includes: current user interaction data and historical user interaction data; the guidance round and sentence structure determination module is specifically used for:

[0365] Based on the current user interaction data and the historical user interaction data, determine the associated actual guidance rounds and the guidance phrases associated with each actual guidance round.

[0366] In an optional embodiment, the guiding round and sentence structure determination module is specifically used for:

[0367] When the user interaction data is the first user interaction data, it is determined that the associated actual guidance round is less than the preset guidance round threshold, and the guidance sentence associated with each actual guidance round is the first guidance sentence;

[0368] When the user interaction data is the second user interaction data, it is determined that the associated actual guidance round is greater than the preset guidance round threshold, and the guidance sentence associated with each actual guidance round is the second guidance sentence.

[0369] Optionally, the response information generation module 603 includes:

[0370] 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.

[0371] The first response content generation unit is used to generate response information based on the user interaction data and the target information amount, wherein the information amount of the first response content corresponds to the target information amount.

[0372] Optionally, the user interaction data includes: current user interaction data and historical user interaction data; 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.

[0373] Optionally, the user interaction data includes: first user interaction data and second user interaction data; the target information content determination unit is specifically used for:

[0374] When the user interaction data is the first user interaction data, the first information content corresponding to the first user interaction data is determined based on the first user interaction data.

[0375] When the user interaction data is the second user interaction data, the second information content corresponding to the second user interaction data is determined based on the second user interaction data.

[0376] In an optional embodiment, the user interaction data includes at least one of the following: user attribute information; user questions; user history conversations; user behavior characteristics; and vehicle data.

[0377] 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.

[0378] 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.

[0379] like Figure 5As 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.

[0380] 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.

[0381] 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.

[0382] 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).

[0383] 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.

[0384] 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.

[0385] 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.

[0386] 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).

[0387] 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.

[0388] 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.

[0389] 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.

[0390] 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; Based on the user interaction data, determine the guidance type corresponding to the user interaction data; Based on the user interaction data and the guidance type, a response message is generated; the response message includes at least a first response content; whether the response message includes a second response content corresponds to the guidance type.

2. The method according to claim 1, characterized in that, The information content of the first reply corresponds to the user interaction data; the second reply is a follow-up question that corresponds to the user interaction data.

3. The method according to claim 1 or 2, characterized in that, The user interaction data includes: current user interaction data and historical user interaction data; determining the guidance type corresponding to the user interaction data based on the user interaction data includes: Based on the current user interaction data and / or the historical user interaction data, determine the guidance type corresponding to the user interaction data.

4. The method according to claim 1 or 2, characterized in that, The user interaction data includes: first user interaction data and second user interaction data; The step of determining the guidance type 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 first guidance type corresponding to the first user interaction data is determined based on the first user interaction data; When the user interaction data is the second user interaction data, the second guidance type corresponding to the second user interaction data is determined based on the second user interaction data.

5. The method according to claim 1 or 2, characterized in that, The method further includes: The frequency of related guidance is dynamically adjusted based on user interaction data.

6. The method according to claim 5, characterized in that, The user interaction data includes: current user interaction data and historical user interaction data; the step of dynamically adjusting the associated guidance frequency based on the user interaction data includes: The associated guidance frequency is dynamically adjusted based on the current user interaction data and the historical user interaction data.

7. The method according to claim 5, characterized in that, The user interaction data includes: first user interaction data and second user interaction data; the step of dynamically adjusting the associated guidance frequency based on the user interaction data includes: When the user interaction data is the first user interaction data, the associated guidance frequency is dynamically increased based on the first user interaction data; When the user interaction data is the second user interaction data, the associated guidance frequency is dynamically reduced based on the second user interaction data.

8. The method according to any one of claims 1-7, characterized in that, The method further includes: In response to a received boot stop operation, the associated boot type is dynamically switched.

9. The method according to claim 8, characterized in that, The guidance stop operation includes at least one of the following: guidance rejection operation; no guidance response content is received within a preset valid time period; information unrelated to the user's question is received.

10. The method according to claim 1 or 2, characterized in that, The method further includes: Based on user interaction data, determine the relevant actual guidance rounds and the guidance phrases associated with each actual guidance round.

11. The method according to claim 10, characterized in that, The user interaction data includes: current user interaction data and historical user interaction data; the step of determining the associated actual guidance rounds and the associated guidance phrases for each actual guidance round based on the user interaction data includes: Based on the current user interaction data and the historical user interaction data, determine the associated actual guidance rounds and the guidance phrases associated with each actual guidance round.

12. The method according to claim 10, characterized in that, The process of determining the associated actual guidance rounds and the associated guidance phrases for each actual guidance round based on user interaction data includes: When the user interaction data is the first user interaction data, it is determined that the associated actual guidance round is less than the preset guidance round threshold, and the guidance sentence associated with each actual guidance round is the first guidance sentence; When the user interaction data is the second user interaction data, it is determined that the associated actual guidance round is greater than the preset guidance round threshold, and the guidance sentence associated with each actual guidance round is the second guidance sentence.

13. The method according to claim 1 or 2, characterized in that, The step of generating response information based on the user interaction data and the guidance type includes: Based on the user interaction data, determine the target information content corresponding to the user interaction data; Based on the user interaction data, the target information volume, and the guidance type, a response message is generated.

14. The method according to claim 13, characterized in that, The user interaction data includes: current user interaction data and historical user interaction data; 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.

15. The method according to claim 13, characterized in that, The user interaction data includes: first user interaction data and second user interaction data; 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 first information content corresponding to the first user interaction data is determined based on the first user interaction data. When the user interaction data is the second user interaction data, the second information content corresponding to the second user interaction data is determined based on the second user interaction data.

16. The method according to any one of claims 1-15, 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.

17. A response information generation device, characterized in that, include: The interaction data acquisition module is used to acquire user interaction data; The guidance type determination module is used to determine the guidance type corresponding to the user interaction data 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 guidance type. The reply information includes at least a first reply content. Whether the reply information includes a second reply content corresponds to the guidance type.

18. 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 1-16.

19. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the response information generation method according to any one of claims 1-16.

20. 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 1-16.

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