System

Abstract

The system according to the embodiment aims to allow a user to efficiently proceed with a process until purchase of a new vehicle.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, a guidance unit, and a provision unit. The reception unit receives a type of a vehicle that a user is considering purchasing. The generation unit analyzes the information received by the reception unit, and generates a road map up to the purchase of a new vehicle based on the needs of the user. The guide unit guides each step on the basis of the road map generated by the generation unit. The providing unit provides details of each step guided by the guiding unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem that it is difficult for users to understand the process leading up to purchasing a new car, making it difficult to proceed efficiently.

[0005] The system according to the embodiment aims to enable a user to efficiently proceed through the process of purchasing a new car. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a guidance unit, and a provision unit. The reception unit inputs the type of car a user is considering purchasing. The generation unit analyzes the information received by the reception unit and generates a roadmap for purchasing a new car based on the user's needs. The guidance unit provides guidance for each step based on the roadmap generated by the generation unit. The provision unit provides details of each step guided by the guidance unit. [Effects of the Invention]

[0007] The system according to the embodiment allows the user to efficiently proceed through the process of purchasing a new car. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A web service according to an embodiment of the present invention is a system that provides a roadmap for purchasing a new car when a user inputs the type of car they are considering purchasing. This system allows users to input the type of car they are considering purchasing, and AI analyzes the information to generate a roadmap for purchasing a new car that is optimal for the user. For example, the roadmap includes each step from selecting a car, booking a test drive, completing the purchase process, selecting insurance, and car delivery. This allows users to smoothly proceed through the new car purchasing process, significantly reducing time and effort. This web service allows users to smoothly proceed through the new car purchasing process, significantly reducing time and effort. For example, when a user inputs the type of car they are considering purchasing, AI analyzes the information to generate a roadmap for purchasing a new car that is optimal for the user. This roadmap includes each step from selecting a car, booking a test drive, completing the purchase process, selecting insurance, and car delivery. This allows users to smoothly proceed through the new car purchasing process.

[0029] A web service according to an embodiment includes a reception unit, a generation unit, a guidance unit, and a provision unit. The reception unit inputs the type of car a user is considering purchasing. Examples of car types that the user is considering purchasing include, but are not limited to, SUVs, sedans, and hatchbacks. The reception unit, for example, accepts the type of car input by the user. Furthermore, if the user inputs the type of car by voice, the reception unit can also support the input using voice recognition technology. The generation unit uses AI to analyze the information received by the reception unit and generate an optimal roadmap for purchasing a new car based on the user's needs. For example, the generation unit generates a roadmap including each step from car selection, test drive reservation, purchase procedure, insurance selection, to car delivery based on the user's needs. The generation unit can also analyze the user's past car purchase history and select the optimal input method. The guidance unit guides the user through each step based on the roadmap generated by the generation unit. For example, the guidance unit guides the user through the test drive reservation procedure and the purchase procedure. The guidance unit can also estimate the user's emotions and adjust the way the guidance is presented based on the estimated user emotions. The provision unit provides details of each step guided by the guidance unit. For example, the provision unit provides the user with information on selecting insurance and a schedule leading up to car delivery. The provision unit can also estimate the user's emotions and adjust the way the information is presented based on the estimated user emotions. This allows the web service according to the embodiment to smoothly guide the user through the new car purchasing process. For example, when the user inputs the type of car they are considering purchasing, the AI ​​analyzes the information and generates a roadmap for purchasing a new car that is optimal for the user. This includes each step from selecting a car, booking a test drive, completing the purchase procedure, selecting insurance, and car delivery. This allows the user to smoothly proceed through the new car purchasing process.

[0030] The reception unit can analyze the user's past car purchase history and select an input method. For example, the reception unit can display similar car models as candidates based on the car models the user has purchased in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also preferentially display specific brands and models based on the user's past purchase history. This improves user convenience by providing the optimal input method based on the past purchase history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past purchase history data into the generation AI and have the generation AI select the optimal input method.

[0031] When the type of car is input, the reception unit can perform filtering based on the user's current living situation and areas of interest. For example, if the user has a family, the reception unit can prioritize displaying family-friendly car models. Furthermore, if the user is environmentally conscious, the reception unit can also prioritize displaying eco-cars and electric cars. Furthermore, if the user is interested in sports cars, the reception unit can also prioritize displaying high-performance sports cars. In this way, by filtering according to the user's living situation and areas of interest, a more appropriate car model can be suggested. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's living situation data into the generation AI and cause the generation AI to perform filtering.

[0032] When inputting the type of car, the reception unit can select an input means according to the user's input method. For example, when the user inputs the type of car by voice, the reception unit supports the input using voice recognition technology. Furthermore, when the user inputs the type of car by text, the reception unit can provide an autocomplete function to simplify input. Furthermore, when the user inputs the type of car by image, the reception unit can identify the car model using image recognition technology. This improves input efficiency by providing the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal input means.

[0033] When inputting the type of car, the reception unit can prioritize inputting highly relevant car models based on the user's geographical location information. For example, if the user lives in an urban area, the reception unit can prioritize displaying compact cars and hybrid cars. Furthermore, if the user lives in a suburban area, the reception unit can prioritize displaying SUVs and minivans. Furthermore, if the user lives in a cold region, the reception unit can prioritize displaying four-wheel drive vehicles and winter-compatible vehicles. In this way, by taking the geographical location information into consideration, a car model suitable for the user is suggested. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant car models.

[0034] When the type of car is input, the reception unit can analyze the user's social media activity and input related car models. For example, the reception unit can prioritize displaying car models of brands the user follows on social media. The reception unit can also identify car models that the user is interested in from the content of the user's posts and display them preferentially. The reception unit can also display related car models by referring to the car models owned by the user's friends. In this way, by analyzing social media activity, car models that match the user's interests are suggested. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to select related car models.

[0035] The reception unit can customize the input method based on the user's past feedback when inputting the type of car. For example, the reception unit can preferentially provide input methods that the user has previously preferred. The reception unit can also improve the input interface based on the user's past feedback. The reception unit can also avoid input methods that the user has previously dissatisfied with and suggest the optimal input method. In this way, the optimal input method is provided to the user by reflecting the past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the input method.

[0036] When generating a roadmap, the generation unit can determine the level of detail of the roadmap based on the importance of the car. For example, in the case of a luxury car, the generation unit generates a roadmap that includes detailed purchase procedures and option information. In addition, in the case of an eco-car, the generation unit can also generate a roadmap that emphasizes information about environmental performance and fuel efficiency. In addition, in the case of a family car, the generation unit can also generate a roadmap that includes information about family-friendly features and safety. In this way, by providing a detailed roadmap according to the importance of the car, information that meets the user's needs is provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input car importance data into the generation AI and cause the generation AI to determine the level of detail of the roadmap.

[0037] When generating a roadmap, the generation unit can use different generation algorithms depending on the vehicle category. For example, in the case of a sports car, the generation unit generates a roadmap that emphasizes information about high performance and design. In addition, in the case of a commercial vehicle, the generation unit can generate a roadmap that includes information about functions suitable for business use and cost performance. In addition, in the case of an electric vehicle, the generation unit can generate a roadmap that emphasizes information about charging infrastructure and battery performance. In this way, by providing an optimal roadmap according to the vehicle category, information that meets the user's needs is provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input vehicle category data into the generation AI and cause the generation AI to select a generation algorithm.

[0038] When generating a roadmap, the generation unit can improve the accuracy of the generation based on the user's past roadmap results. The generation unit improves the generation algorithm, for example, based on feedback from roadmaps the user has used in the past. The generation unit can also learn the user's preferences and needs from the user's past roadmap results and generate an optimal roadmap. The generation unit can also generate a customized roadmap by referring to options and procedures selected by the user in the past. This provides a more accurate roadmap by referring to past results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past roadmap data into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0039] When generating the roadmap, the generation unit can set priorities for the roadmap based on the timing of the car purchase. For example, if a user is considering an immediate purchase, the generation unit generates a roadmap that prioritizes guides for test drive reservations and purchase procedures. Furthermore, if a user is considering a purchase in the next few months, the generation unit can also generate a roadmap that prioritizes guides for information gathering and comparison. Furthermore, if a user is considering a purchase over the long term, the generation unit can generate a roadmap that includes information on the latest models and future trends. This provides information tailored to the user's needs by providing priorities according to the timing of purchase. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input data on the timing of the car purchase into the generation AI and cause the generation AI to set the priorities for the roadmap.

[0040] When generating a roadmap, the generation unit can determine the order of the roadmap based on the relevance of the cars. For example, if a user is interested in a particular brand, the generation unit generates a roadmap that prioritizes car models of that brand. Furthermore, if a user is interested in a particular feature, the generation unit can prioritize car models with that feature. Furthermore, if a user is interested in a particular price range, the generation unit can prioritize car models in that price range. In this way, by providing an order based on relevance, information that meets the user's needs is provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input car relevance data into the generation AI and cause the generation AI to determine the order of the roadmap.

[0041] When generating a roadmap, the generation unit can determine the use of technical terminology in the roadmap according to the user's level of expertise. For example, if the user is knowledgeable about automobiles, the generation unit can generate a detailed roadmap that uses a lot of technical terminology. Furthermore, if the user is not knowledgeable about automobiles, the generation unit can generate an easy-to-understand roadmap that avoids technical terminology. Furthermore, the generation unit can generate a roadmap that uses technical terminology in an appropriate balance according to the user's level of expertise. This provides information appropriate to the user by providing information according to the level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0042] When providing guidance, the guidance unit can determine the level of detail of the guidance based on the importance of each step. For example, the guidance unit provides detailed explanations for important steps (e.g., purchasing procedures). The guidance unit can also provide brief explanations for relatively easy steps (e.g., test drive reservations). The guidance unit can also customize the level of detail of each step according to the user's needs. This allows detailed guidance to be provided according to the importance of each step, thereby providing information that meets the user's needs. Some or all of the above-mentioned processing in the guidance unit may be performed using, or without, AI, for example. For example, the guidance unit can input importance data for each step into the generation AI and have the generation AI determine the level of detail of the guidance.

[0043] When providing guidance, the guidance unit can improve the accuracy of the guidance based on the user's past guidance results. The guidance unit, for example, improves the guidance algorithm based on feedback from guidance used by the user in the past. The guidance unit can also learn the user's preferences and needs from the user's past guidance results and provide optimal guidance. The guidance unit can also provide customized guidance by referring to options and procedures selected by the user in the past. In this way, by referring to the past guidance results, more accurate guidance can be provided. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input the user's past guidance data into the generation AI and have the generation AI improve the accuracy of the guidance.

[0044] When providing guidance, the guidance unit can set a priority of guidance based on the implementation time of each step. For example, the guidance unit prioritizes guidance for steps that need to be performed immediately (e.g., booking a test drive). The guidance unit can also postpone steps that will be performed over a long period of time (e.g., selecting insurance). The guidance unit can also customize the priority of each step according to the user's schedule. This allows the provision of information that meets the user's needs by providing priorities according to the implementation time. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input implementation time data for each step into the generation AI and have the generation AI set the priority of guidance.

[0045] When providing guidance, the guidance unit can determine the order of guidance based on the relevance of each step. For example, if the user is interested in a particular brand, the guidance unit can prioritize guidance of car models of that brand. Also, if the user is interested in a particular feature, the guidance unit can prioritize guidance of car models with that feature. Also, if the user is interested in a particular price range, the guidance unit can prioritize guidance of car models in that price range. In this way, by providing an order based on relevance, information that meets the user's needs is provided. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input relevance data of each step into a generation AI and have the generation AI determine the order of guidance.

[0046] When providing guidance, the guidance unit can determine the use of technical terminology in the guidance depending on the user's level of expertise. For example, if the user is knowledgeable about automobiles, the guidance unit can provide detailed guidance using a lot of technical terminology. Furthermore, if the user is not knowledgeable about automobiles, the guidance unit can also provide easy-to-understand guidance that avoids technical terminology. Furthermore, the guidance unit can provide guidance that uses technical terminology in an appropriate balance depending on the user's level of expertise. This provides information appropriate to the user by providing information according to the level of expertise. Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input the user's level of expertise data into a generation AI and have the generation AI use technical terminology.

[0047] The providing unit can determine the level of detail of the information to be provided based on the importance of each step when providing the information. For example, the providing unit provides detailed explanations for important steps (e.g., purchase procedures). The providing unit can also provide brief explanations for relatively simple steps (e.g., test drive reservations). The providing unit can also customize the level of detail of each step according to the user's needs. This allows providing detailed information according to the importance of each step, thereby providing information that meets the user's needs. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input importance data of each step into the generating AI and have the generating AI determine the level of detail of the information.

[0048] The providing unit can apply different providing algorithms depending on the category of each step when providing the information. For example, in the purchase procedure step, the providing unit provides information about legal procedures and required documents. Furthermore, in the test drive reservation step, the providing unit can also provide information about the date, time, and location of test drives. Furthermore, in the insurance selection step, the providing unit can also provide comparison information for each insurance plan. In this way, optimal information is provided according to the category, thereby providing information that meets the user's needs. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input category data for each step into the generation AI and have the generation AI select a providing algorithm.

[0049] The providing unit can improve the accuracy of the information provided based on the user's past provision results when providing the information. For example, the providing unit improves the provision algorithm based on feedback on the provided information the user has used in the past. The providing unit can also learn the user's preferences and needs from the user's past provision results and provide optimal information. The providing unit can also provide customized information by referring to options and procedures selected by the user in the past. In this way, by referring to the past provision results, more accurate information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past provision data into the generation AI and cause the generation AI to improve the accuracy of the information provided.

[0050] When providing information, the providing unit can set a priority order for the information to be provided based on the timing of each step. For example, the providing unit can prioritize steps that need to be performed immediately (e.g., test drive reservation). The providing unit can also postpone steps that will be performed over a long period of time (e.g., insurance selection). The providing unit can also customize the priority order for each step according to the user's schedule. This allows information that meets the user's needs to be provided by providing priorities according to the timing of execution. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the timing of execution of each step into the generating AI and cause the generating AI to set the priority order of the information.

[0051] The providing unit can determine the order of information to be provided based on the relevance of each step when providing the information. For example, if a user is interested in a particular brand, the providing unit can prioritize information about models of that brand. Furthermore, if a user is interested in a particular function, the providing unit can prioritize information about models with that function. Furthermore, if a user is interested in a particular price range, the providing unit can prioritize information about models in that price range. In this way, by providing the information in an order based on the relevance, information that meets the user's needs is provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input relevance data of each step into a generating AI and have the generating AI determine the order of the information.

[0052] The providing unit can determine the use of technical terms in the information to be provided according to the user's level of expertise when providing the information. For example, if the user is knowledgeable about automobiles, the providing unit can provide detailed information using a lot of technical terms. Furthermore, if the user is not knowledgeable about automobiles, the providing unit can provide easy-to-understand information that avoids technical terms. Furthermore, the providing unit can provide information that uses technical terms in an appropriate balance according to the user's level of expertise. In this way, by providing information according to the level of expertise, information suitable for the user is provided. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into the generating AI and cause the generating AI to use technical terms.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit can also estimate the user's health condition and determine the input method for the type of car based on the estimated health condition. For example, if the user is visually impaired, voice input can be given priority. Also, if the user has a disability in their hands, input support can be provided using voice recognition technology. Furthermore, if the user is elderly, large characters and a simple interface can be provided. In this way, a more appropriate input experience can be provided by providing an input method that suits the user's health condition.

[0055] The reception unit can also analyze the user's past car purchase history and select an input method. For example, it can display similar car models as candidates based on the car models the user has purchased in the past. It can also prioritize suggestions based on input methods (voice, text, etc.) that the user has used in the past. It can also prioritize displaying specific brands and models based on the user's past purchase history. This improves user convenience by providing the optimal input method based on the user's past purchase history.

[0056] When the type of car is input, the reception unit can also perform filtering based on the user's current living situation and areas of interest. For example, if the user has a family, family-oriented car models can be displayed preferentially. Also, if the user is environmentally conscious, eco-cars and electric cars can be displayed preferentially. Furthermore, if the user is interested in sports cars, high-performance sports cars can be displayed preferentially. In this way, by filtering according to the user's living situation and areas of interest, more suitable car models can be suggested.

[0057] When inputting the car type, the reception unit can also select an input means according to the user's input method. For example, if the user inputs the car type by voice, the input can be supported using voice recognition technology. Also, if the user inputs the car type by text, an auto-complete function can be provided to simplify the input. Furthermore, if the user inputs the car type by image, the model can be identified using image recognition technology. This improves input efficiency by providing the optimal input means according to the user's input method.

[0058] When inputting the type of car, the reception unit can also prioritize inputting car models that are highly relevant based on the user's geographical location information. For example, if the user lives in an urban area, compact cars and hybrid cars can be displayed with priority. Also, if the user lives in a suburban area, SUVs and minivans can be displayed with priority. Furthermore, if the user lives in a cold region, four-wheel drive vehicles and winter-compatible vehicles can be displayed with priority. In this way, car models that are suitable for the user can be suggested by taking geographical location information into consideration.

[0059] When the type of car is input, the reception unit can analyze the user's social media activity and input related car models. For example, it can prioritize displaying car models of brands that the user follows on social media. It can also identify car models that the user is interested in from the content of their posts and display them preferentially. It can also display related car models by referring to the car models owned by the user's friends. In this way, by analyzing social media activity, it can suggest car models that match the user's interests.

[0060] The reception unit can also customize the input method based on the user's past feedback when inputting the type of car. For example, it can preferentially provide the input method that the user has previously preferred. It can also improve the input interface based on the user's past feedback. Furthermore, it can avoid input methods that the user has previously been dissatisfied with and suggest the most appropriate input method. In this way, the most appropriate input method is provided to the user by reflecting past feedback.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit inputs the type of car the user is considering purchasing. Examples of the type of car the user is considering purchasing include, but are not limited to, an SUV, a sedan, and a hatchback. The reception unit, for example, accepts the type of car input by the user. Furthermore, if the user inputs the type of car by voice, the reception unit can also support the input using voice recognition technology. Step 2: The generation unit uses AI to analyze the information received by the reception unit and generate an optimal roadmap for purchasing a new car based on the user's needs. For example, the generation unit generates a roadmap including each step from car selection, test drive reservation, purchase procedure, insurance selection, to car delivery based on the user's needs. The generation unit can also analyze the user's past car purchase history and select the optimal input method. Step 3: The guidance unit guides the user through each step based on the roadmap generated by the generation unit. For example, the guidance unit guides the user through the procedures for booking a test drive or purchasing a vehicle. The guidance unit can also estimate the user's emotions and adjust the way the guidance is presented based on the estimated user's emotions. Step 4: The providing unit provides details of each step guided by the guiding unit. For example, the providing unit provides the user with information on selecting insurance and a schedule leading up to delivery. The providing unit can also estimate the user's emotions and adjust the way the information is presented based on the estimated user emotions.

[0063] (Example 2) A web service according to an embodiment of the present invention is a system that provides a roadmap for purchasing a new car when a user inputs the type of car they are considering purchasing. This system allows users to input the type of car they are considering purchasing, and AI analyzes the information to generate a roadmap for purchasing a new car that is optimal for the user. For example, the roadmap includes each step from selecting a car, booking a test drive, completing the purchase process, selecting insurance, and car delivery. This allows users to smoothly proceed through the new car purchasing process, significantly reducing time and effort. This web service allows users to smoothly proceed through the new car purchasing process, significantly reducing time and effort. For example, when a user inputs the type of car they are considering purchasing, AI analyzes the information to generate a roadmap for purchasing a new car that is optimal for the user. This roadmap includes each step from selecting a car, booking a test drive, completing the purchase process, selecting insurance, and car delivery. This allows users to smoothly proceed through the new car purchasing process.

[0064] A web service according to an embodiment includes a reception unit, a generation unit, a guidance unit, and a provision unit. The reception unit inputs the type of car a user is considering purchasing. Examples of car types that the user is considering purchasing include, but are not limited to, SUVs, sedans, and hatchbacks. The reception unit, for example, accepts the type of car input by the user. Furthermore, if the user inputs the type of car by voice, the reception unit can also support the input using voice recognition technology. The generation unit uses AI to analyze the information received by the reception unit and generate an optimal roadmap for purchasing a new car based on the user's needs. For example, the generation unit generates a roadmap including each step from car selection, test drive reservation, purchase procedure, insurance selection, to car delivery based on the user's needs. The generation unit can also analyze the user's past car purchase history and select the optimal input method. The guidance unit guides the user through each step based on the roadmap generated by the generation unit. For example, the guidance unit guides the user through the test drive reservation procedure and the purchase procedure. The guidance unit can also estimate the user's emotions and adjust the way the guidance is presented based on the estimated user emotions. The provision unit provides details of each step guided by the guidance unit. For example, the provision unit provides the user with information on selecting insurance and a schedule leading up to car delivery. The provision unit can also estimate the user's emotions and adjust the way the information is presented based on the estimated user emotions. This allows the web service according to the embodiment to smoothly guide the user through the new car purchasing process. For example, when the user inputs the type of car they are considering purchasing, the AI ​​analyzes the information and generates a roadmap for purchasing a new car that is optimal for the user. This includes each step from selecting a car, booking a test drive, completing the purchase procedure, selecting insurance, and car delivery. This allows the user to smoothly proceed through the new car purchasing process.

[0065] The reception unit can estimate the user's emotions and determine the input method for the car type based on the estimated user emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input the car type. This provides a more appropriate input experience by adjusting the input method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0066] The reception unit can analyze the user's past car purchase history and select an input method. For example, the reception unit can display similar car models as candidates based on the car models the user has purchased in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also preferentially display specific brands and models based on the user's past purchase history. This improves user convenience by providing the optimal input method based on the past purchase history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past purchase history data into the generation AI and have the generation AI select the optimal input method.

[0067] When the type of car is input, the reception unit can perform filtering based on the user's current living situation and areas of interest. For example, if the user has a family, the reception unit can prioritize displaying family-friendly car models. Furthermore, if the user is environmentally conscious, the reception unit can also prioritize displaying eco-cars and electric cars. Furthermore, if the user is interested in sports cars, the reception unit can also prioritize displaying high-performance sports cars. In this way, by filtering according to the user's living situation and areas of interest, a more appropriate car model can be suggested. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's living situation data into the generation AI and cause the generation AI to perform filtering.

[0068] When inputting the type of car, the reception unit can select an input means according to the user's input method. For example, when the user inputs the type of car by voice, the reception unit supports the input using voice recognition technology. Furthermore, when the user inputs the type of car by text, the reception unit can provide an autocomplete function to simplify input. Furthermore, when the user inputs the type of car by image, the reception unit can identify the car model using image recognition technology. This improves input efficiency by providing the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal input means.

[0069] The reception unit can estimate the user's emotions and prioritize the types of cars to be input based on the estimated user emotions. For example, when the user is excited, the reception unit can prioritize the latest models and high-performance cars. Furthermore, when the user is relaxed, the reception unit can also prioritize a balanced display of car models that match the user's preferences. Furthermore, when the user is stressed, the reception unit can prioritize simple and easy-to-use car models. Thus, by prioritizing car types according to the user's emotions, a more appropriate car model is suggested. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0070] When inputting the type of car, the reception unit can prioritize inputting highly relevant car models based on the user's geographical location information. For example, if the user lives in an urban area, the reception unit can prioritize displaying compact cars and hybrid cars. Furthermore, if the user lives in a suburban area, the reception unit can prioritize displaying SUVs and minivans. Furthermore, if the user lives in a cold region, the reception unit can prioritize displaying four-wheel drive vehicles and winter-compatible vehicles. In this way, by taking the geographical location information into consideration, a car model suitable for the user is suggested. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant car models.

[0071] When the type of car is input, the reception unit can analyze the user's social media activity and input related car models. For example, the reception unit can prioritize displaying car models of brands the user follows on social media. The reception unit can also identify car models that the user is interested in from the content of the user's posts and display them preferentially. The reception unit can also display related car models by referring to the car models owned by the user's friends. In this way, by analyzing social media activity, car models that match the user's interests are suggested. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to select related car models.

[0072] The reception unit can customize the input method based on the user's past feedback when inputting the type of car. For example, the reception unit can preferentially provide input methods that the user has previously preferred. The reception unit can also improve the input interface based on the user's past feedback. The reception unit can also avoid input methods that the user has previously dissatisfied with and suggest the optimal input method. In this way, the optimal input method is provided to the user by reflecting the past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the input method.

[0073] The generation unit can estimate the user's emotions and determine how to display the roadmap based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a roadmap that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate a roadmap that emphasizes the shortest route. If the user is excited, the generation unit can also generate a roadmap that adds visually stimulating effects. This allows for the generation of a more appropriate roadmap by providing an expression method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI determine how to display the roadmap.

[0074] When generating a roadmap, the generation unit can determine the level of detail of the roadmap based on the importance of the car. For example, in the case of a luxury car, the generation unit generates a roadmap that includes detailed purchase procedures and option information. In addition, in the case of an eco-car, the generation unit can also generate a roadmap that emphasizes information about environmental performance and fuel efficiency. In addition, in the case of a family car, the generation unit can also generate a roadmap that includes information about family-friendly features and safety. In this way, by providing a detailed roadmap according to the importance of the car, information that meets the user's needs is provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input car importance data into the generation AI and cause the generation AI to determine the level of detail of the roadmap.

[0075] When generating a roadmap, the generation unit can use different generation algorithms depending on the vehicle category. For example, in the case of a sports car, the generation unit generates a roadmap that emphasizes information about high performance and design. In addition, in the case of a commercial vehicle, the generation unit can generate a roadmap that includes information about functions suitable for business use and cost performance. In addition, in the case of an electric vehicle, the generation unit can generate a roadmap that emphasizes information about charging infrastructure and battery performance. In this way, by providing an optimal roadmap according to the vehicle category, information that meets the user's needs is provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input vehicle category data into the generation AI and cause the generation AI to select a generation algorithm.

[0076] When generating a roadmap, the generation unit can improve the accuracy of the generation based on the user's past roadmap results. The generation unit improves the generation algorithm, for example, based on feedback from roadmaps the user has used in the past. The generation unit can also learn the user's preferences and needs from the user's past roadmap results and generate an optimal roadmap. The generation unit can also generate a customized roadmap by referring to options and procedures selected by the user in the past. This provides a more accurate roadmap by referring to past results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past roadmap data into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0077] The generation unit can estimate the user's emotions and determine the length of the roadmap based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise roadmap. If the user is relaxed, the generation unit can generate a longer roadmap with detailed explanations. If the user is excited, the generation unit can generate a roadmap with visually stimulating effects. This provides more appropriate information by providing a roadmap length that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI determine the length of the roadmap.

[0078] When generating the roadmap, the generation unit can set priorities for the roadmap based on the timing of the car purchase. For example, if a user is considering an immediate purchase, the generation unit generates a roadmap that prioritizes guides for test drive reservations and purchase procedures. Furthermore, if a user is considering a purchase in the next few months, the generation unit can also generate a roadmap that prioritizes guides for information gathering and comparison. Furthermore, if a user is considering a purchase over the long term, the generation unit can generate a roadmap that includes information on the latest models and future trends. This provides information tailored to the user's needs by providing priorities according to the timing of purchase. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input data on the timing of the car purchase into the generation AI and cause the generation AI to set the priorities for the roadmap.

[0079] When generating a roadmap, the generation unit can determine the order of the roadmap based on the relevance of the cars. For example, if a user is interested in a particular brand, the generation unit generates a roadmap that prioritizes car models of that brand. Furthermore, if a user is interested in a particular feature, the generation unit can prioritize car models with that feature. Furthermore, if a user is interested in a particular price range, the generation unit can prioritize car models in that price range. In this way, by providing an order based on relevance, information that meets the user's needs is provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input car relevance data into the generation AI and cause the generation AI to determine the order of the roadmap.

[0080] When generating a roadmap, the generation unit can determine the use of technical terminology in the roadmap according to the user's level of expertise. For example, if the user is knowledgeable about automobiles, the generation unit can generate a detailed roadmap that uses a lot of technical terminology. Furthermore, if the user is not knowledgeable about automobiles, the generation unit can generate an easy-to-understand roadmap that avoids technical terminology. Furthermore, the generation unit can generate a roadmap that uses technical terminology in an appropriate balance according to the user's level of expertise. This provides information appropriate to the user by providing information according to the level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0081] The guidance unit can estimate the user's emotions and determine how to present the guidance based on the estimated user's emotions. For example, if the user is nervous, the guidance unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the guidance unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the guidance unit can provide a display method that focuses on the main points. This provides more appropriate guidance by providing a display method that matches the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the guidance unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the guidance unit can input the user's facial expression data into the generation AI and have the generation AI determine how to present the guidance.

[0082] When providing guidance, the guidance unit can determine the level of detail of the guidance based on the importance of each step. For example, the guidance unit provides detailed explanations for important steps (e.g., purchasing procedures). The guidance unit can also provide brief explanations for relatively easy steps (e.g., test drive reservations). The guidance unit can also customize the level of detail of each step according to the user's needs. This allows detailed guidance to be provided according to the importance of each step, thereby providing information that meets the user's needs. Some or all of the above-mentioned processing in the guidance unit may be performed using, or without, AI, for example. For example, the guidance unit can input importance data for each step into the generation AI and have the generation AI determine the level of detail of the guidance.

[0083] When providing guidance, the guidance unit can improve the accuracy of the guidance based on the user's past guidance results. The guidance unit, for example, improves the guidance algorithm based on feedback from guidance used by the user in the past. The guidance unit can also learn the user's preferences and needs from the user's past guidance results and provide optimal guidance. The guidance unit can also provide customized guidance by referring to options and procedures selected by the user in the past. In this way, by referring to the past guidance results, more accurate guidance can be provided. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input the user's past guidance data into the generation AI and have the generation AI improve the accuracy of the guidance.

[0084] The guidance unit can estimate the user's emotions and determine the length of the guidance based on the estimated user's emotions. For example, if the user is in a hurry, the guidance unit can provide short, to-the-point guidance. Furthermore, if the user is relaxed, the guidance unit can provide longer guidance with detailed explanations. Furthermore, if the user is excited, the guidance unit can provide guidance with visually stimulating effects. This allows for providing more appropriate information by adjusting the length of the guidance according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the guidance unit can be performed using, for example, AI, or without AI. For example, the guidance unit can input the user's facial expression data into the generation AI and have the generation AI determine the length of the guidance.

[0085] When providing guidance, the guidance unit can set a priority of guidance based on the implementation time of each step. For example, the guidance unit prioritizes guidance for steps that need to be performed immediately (e.g., booking a test drive). The guidance unit can also postpone steps that will be performed over a long period of time (e.g., selecting insurance). The guidance unit can also customize the priority of each step according to the user's schedule. This allows the provision of information that meets the user's needs by providing priorities according to the implementation time. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input implementation time data for each step into the generation AI and have the generation AI set the priority of guidance.

[0086] When providing guidance, the guidance unit can determine the order of guidance based on the relevance of each step. For example, if the user is interested in a particular brand, the guidance unit can prioritize guidance of car models of that brand. Also, if the user is interested in a particular feature, the guidance unit can prioritize guidance of car models with that feature. Also, if the user is interested in a particular price range, the guidance unit can prioritize guidance of car models in that price range. In this way, by providing an order based on relevance, information that meets the user's needs is provided. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input relevance data of each step into a generation AI and have the generation AI determine the order of guidance.

[0087] When providing guidance, the guidance unit can determine the use of technical terminology in the guidance depending on the user's level of expertise. For example, if the user is knowledgeable about automobiles, the guidance unit can provide detailed guidance using a lot of technical terminology. Furthermore, if the user is not knowledgeable about automobiles, the guidance unit can also provide easy-to-understand guidance that avoids technical terminology. Furthermore, the guidance unit can provide guidance that uses technical terminology in an appropriate balance depending on the user's level of expertise. This provides information appropriate to the user by providing information according to the level of expertise. Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input the user's level of expertise data into a generation AI and have the generation AI use technical terminology.

[0088] The providing unit can estimate the user's emotions and determine how to present the information to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This provides more appropriate information by providing an expression method that matches the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and have the generation AI determine how to present the information.

[0089] The providing unit can determine the level of detail of the information to be provided based on the importance of each step when providing the information. For example, the providing unit provides detailed explanations for important steps (e.g., purchase procedures). The providing unit can also provide brief explanations for relatively simple steps (e.g., test drive reservations). The providing unit can also customize the level of detail of each step according to the user's needs. This allows providing detailed information according to the importance of each step, thereby providing information that meets the user's needs. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input importance data of each step into the generating AI and have the generating AI determine the level of detail of the information.

[0090] The providing unit can apply different providing algorithms depending on the category of each step when providing the information. For example, in the purchase procedure step, the providing unit provides information about legal procedures and required documents. Furthermore, in the test drive reservation step, the providing unit can also provide information about the date, time, and location of test drives. Furthermore, in the insurance selection step, the providing unit can also provide comparison information for each insurance plan. In this way, optimal information is provided according to the category, thereby providing information that meets the user's needs. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input category data for each step into the generation AI and have the generation AI select a providing algorithm.

[0091] The providing unit can improve the accuracy of the information provided based on the user's past provision results when providing the information. For example, the providing unit improves the provision algorithm based on feedback on the provided information the user has used in the past. The providing unit can also learn the user's preferences and needs from the user's past provision results and provide optimal information. The providing unit can also provide customized information by referring to options and procedures selected by the user in the past. In this way, by referring to the past provision results, more accurate information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past provision data into the generation AI and cause the generation AI to improve the accuracy of the information provided.

[0092] The providing unit can estimate the user's emotions and determine the length of information to be provided based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can provide short, to-the-point information. Furthermore, if the user is relaxed, the providing unit can provide longer information with detailed explanations. Furthermore, if the user is excited, the providing unit can provide information with visually stimulating effects. This allows for providing information of a length appropriate to the user's emotions, thereby providing more appropriate information. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and have the generation AI determine the length of the information.

[0093] When providing information, the providing unit can set a priority order for the information to be provided based on the timing of each step. For example, the providing unit can prioritize steps that need to be performed immediately (e.g., test drive reservation). The providing unit can also postpone steps that will be performed over a long period of time (e.g., insurance selection). The providing unit can also customize the priority order for each step according to the user's schedule. This allows information that meets the user's needs to be provided by providing priorities according to the timing of execution. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the timing of execution of each step into the generating AI and cause the generating AI to set the priority order of the information.

[0094] The providing unit can determine the order of information to be provided based on the relevance of each step when providing the information. For example, if a user is interested in a particular brand, the providing unit can prioritize information about models of that brand. Furthermore, if a user is interested in a particular function, the providing unit can prioritize information about models with that function. Furthermore, if a user is interested in a particular price range, the providing unit can prioritize information about models in that price range. In this way, by providing the information in an order based on the relevance, information that meets the user's needs is provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input relevance data of each step into a generating AI and have the generating AI determine the order of the information.

[0095] The providing unit can determine the use of technical terms in the information to be provided according to the user's level of expertise when providing the information. For example, if the user is knowledgeable about automobiles, the providing unit can provide detailed information using a lot of technical terms. Furthermore, if the user is not knowledgeable about automobiles, the providing unit can provide easy-to-understand information that avoids technical terms. Furthermore, the providing unit can provide information that uses technical terms in an appropriate balance according to the user's level of expertise. In this way, by providing information according to the level of expertise, information suitable for the user is provided. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into the generating AI and cause the generating AI to use technical terms. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, guidance unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives user input using a touch panel 38A or microphone 38B of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the user's input information using AI, and generates an optimal roadmap for purchasing a new car. The guidance unit is realized by the control unit 46A of the smart device 14, and provides guidance for each step based on the generated roadmap. The provision unit provides details of each step using the display 40A or speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, generation unit, guidance unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a user's voice input using the microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the user's input information using AI, and generates an optimal roadmap for purchasing a new car. The guidance unit is realized by the control unit 46A of the smart glasses 214, and provides guidance for each step based on the generated roadmap. The provision unit provides details of each step using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, guidance unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives voice input from the user using the microphone 238 of the headset type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's input information using AI to generate an optimal roadmap for purchasing a new car. The guidance unit is realized by the control unit 46A of the headset type terminal 314, and provides guidance for each step based on the generated roadmap. The provision unit provides details of each step using the display 343 and speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, guidance unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives voice input from the user using the microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the user's input information using AI, and generates an optimal roadmap for purchasing a new car. The guidance unit is realized by the control unit 46A of the robot 414, and provides guidance for each step based on the generated roadmap. The provision unit provides details of each step using the speaker 240 or display device of the robot 414.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The reception unit can also estimate the user's health condition and determine the input method for the type of car based on the estimated health condition. For example, if the user is visually impaired, voice input can be given priority. Also, if the user has a disability in their hands, input support can be provided using voice recognition technology. Furthermore, if the user is elderly, large characters and a simple interface can be provided. In this way, a more appropriate input experience can be provided by providing an input method that suits the user's health condition.

[0098] The reception unit can also estimate the user's emotions and determine the input method for the car type based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize the input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable the user to quickly input the car type. In this way, the input method can be adjusted according to the user's emotions to provide a more appropriate input experience.

[0099] The reception unit can also analyze the user's past car purchase history and select an input method. For example, it can display similar car models as candidates based on the car models the user has purchased in the past. It can also prioritize suggestions based on input methods (voice, text, etc.) that the user has used in the past. It can also prioritize displaying specific brands and models based on the user's past purchase history. This improves user convenience by providing the optimal input method based on the user's past purchase history.

[0100] When the type of car is input, the reception unit can also perform filtering based on the user's current living situation and areas of interest. For example, if the user has a family, family-oriented car models can be displayed preferentially. Also, if the user is environmentally conscious, eco-cars and electric cars can be displayed preferentially. Furthermore, if the user is interested in sports cars, high-performance sports cars can be displayed preferentially. In this way, by filtering according to the user's living situation and areas of interest, more suitable car models can be suggested.

[0101] When inputting the car type, the reception unit can also select an input means according to the user's input method. For example, if the user inputs the car type by voice, the input can be supported using voice recognition technology. Also, if the user inputs the car type by text, an auto-complete function can be provided to simplify the input. Furthermore, if the user inputs the car type by image, the model can be identified using image recognition technology. This improves input efficiency by providing the optimal input means according to the user's input method.

[0102] The reception unit can also estimate the user's emotions and prioritize the types of cars to be input based on the estimated user's emotions. For example, if the user is excited, the latest models and high-performance cars can be displayed preferentially. Also, if the user is relaxed, a balanced display of car models that match the user's preferences can be made. Furthermore, if the user is stressed, simple and easy-to-use car models can be displayed preferentially. In this way, by prioritizing car types according to the user's emotions, more appropriate car models can be suggested.

[0103] When inputting the type of car, the reception unit can also prioritize inputting car models that are highly relevant based on the user's geographical location information. For example, if the user lives in an urban area, compact cars and hybrid cars can be displayed with priority. Also, if the user lives in a suburban area, SUVs and minivans can be displayed with priority. Furthermore, if the user lives in a cold region, four-wheel drive vehicles and winter-compatible vehicles can be displayed with priority. In this way, car models that are suitable for the user can be suggested by taking geographical location information into consideration.

[0104] When the type of car is input, the reception unit can analyze the user's social media activity and input related car models. For example, it can prioritize displaying car models of brands that the user follows on social media. It can also identify car models that the user is interested in from the content of their posts and display them preferentially. It can also display related car models by referring to the car models owned by the user's friends. In this way, by analyzing social media activity, it can suggest car models that match the user's interests.

[0105] The reception unit can also customize the input method based on the user's past feedback when inputting the type of car. For example, it can preferentially provide the input method that the user has previously preferred. It can also improve the input interface based on the user's past feedback. Furthermore, it can avoid input methods that the user has previously been dissatisfied with and suggest the most appropriate input method. In this way, the most appropriate input method is provided to the user by reflecting past feedback.

[0106] The generation unit can also estimate the user's emotions and determine how to display the roadmap based on the estimated user's emotions. For example, if the user is relaxed, a roadmap that progresses at a leisurely pace can be generated. If the user is in a hurry, a roadmap that emphasizes the shortest route can be generated. Furthermore, if the user is excited, a roadmap that adds visually stimulating effects can be generated. In this way, a more appropriate roadmap can be generated by providing an expression method that corresponds to the user's emotions.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The reception unit inputs the type of car the user is considering purchasing. Examples of the type of car the user is considering purchasing include, but are not limited to, an SUV, a sedan, and a hatchback. The reception unit, for example, accepts the type of car input by the user. Furthermore, if the user inputs the type of car by voice, the reception unit can also support the input using voice recognition technology. Step 2: The generation unit uses AI to analyze the information received by the reception unit and generate an optimal roadmap for purchasing a new car based on the user's needs. For example, the generation unit generates a roadmap including each step from car selection, test drive reservation, purchase procedure, insurance selection, to car delivery based on the user's needs. The generation unit can also analyze the user's past car purchase history and select the optimal input method. Step 3: The guidance unit guides the user through each step based on the roadmap generated by the generation unit. For example, the guidance unit guides the user through the procedures for booking a test drive or purchasing a vehicle. The guidance unit can also estimate the user's emotions and adjust the way the guidance is presented based on the estimated user's emotions. Step 4: The providing unit provides details of each step guided by the guiding unit. For example, the providing unit provides the user with information on selecting insurance and a schedule leading up to delivery. The providing unit can also estimate the user's emotions and adjust the way the information is presented based on the estimated user emotions.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0153] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0154] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0173] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0174] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0180] [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting the type of car a user is considering purchasing; a generation unit that analyzes the information received by the reception unit and generates a roadmap for purchasing a new car based on the user's needs; a guidance unit that guides the user through each step based on the roadmap generated by the generation unit; a providing unit that provides details of each step guided by the guiding unit. A system characterized by:

2. The reception unit Estimate the user's emotion and determine the method for inputting the type of car based on the estimated user emotion.

2. The system of claim 1.

3. The reception unit Analyze the user's past car purchase history and select the input method 2. The system of claim 1.

4. The reception unit When entering a car type, it filters based on the user's current life situation and interests.

2. The system of claim 1.

5. The reception unit When entering the type of car, select the input method according to the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize the types of cars to input based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When entering a car type, the app prioritizes relevant car models based on the user's geographic location.

2. The system of claim 1.

8. The reception unit When you input your car type, it analyzes your social media activity to determine related car models.

2. The system of claim 1.

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