system

Abstract

The system according to this embodiment aims to analyze the user's vague requests and suggest shops that match their tastes and preferences. [Solution] The system according to this embodiment comprises a reception unit, a suggestion unit, and a provision unit. The reception unit receives vague requests from the user. The suggestion unit analyzes the requests received by the reception unit and suggests shops that match the user's tastes and preferences. The provision unit provides detailed information about the shops suggested by the suggestion unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the user has to verbalize vague desires and it is difficult to find a suitable store.

[0005] The system according to the embodiment aims to analyze the user's vague desires and propose stores that match the user's hobbies and preferences.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a proposal unit, and a provision unit. The reception unit receives the user's vague desires. The proposal unit analyzes the desires received by the reception unit and proposes stores that match the user's hobbies and preferences. The provision unit provides detailed information on the stores proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's vague requests and suggest shops that match their hobbies and preferences. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The system according to an embodiment of the present invention is a groundbreaking solution for all generations who have difficulty reading maps or who can read maps but find them difficult to understand, by utilizing generative AI. In this system, the user inputs vague requests, and the generative AI analyzes those requests and suggests shops that match the user's tastes and preferences. For example, if the user inputs requests such as "I want to relax somewhere nice" or "A cafe would be good, but I also want to get my nails done," the generative AI analyzes those requests and narrows down the candidates to cafes, restaurants, bars, chiropractic clinics, beauty salons, etc., based on the user's past behavior data and evaluation data. For example, it considers the atmosphere and price range of nail salons the user has visited in the past and suggests nearby highly-rated cafes and nail salons. Furthermore, the generative AI provides detailed information about the suggested shops. For example, it displays the menu and business hours of the suggested cafes, and the reservation status of the nail salons. This allows the user to find a good place efficiently in a short amount of time. With this mechanism, the user does not need to verbalize their vague desires, and the generative AI provides support, thus solving problems such as information overload and time constraints. For example, when a user wants to relax amidst a busy daily routine, the AI ​​can suggest the perfect cafe or nail salon, allowing them to find a relaxing spot in a short amount of time. This means the system analyzes the user's vague requests, suggests establishments tailored to their hobbies and preferences, and provides detailed information, enabling users to find places efficiently.

[0029] The system according to this embodiment comprises a reception unit, a suggestion unit, and a service unit. The reception unit receives vague requests from the user. For example, the user can input requests such as "I want to relax somewhere nice" or "A cafe would be good, but I also want to get my nails done." When receiving user requests, the reception unit can provide multiple input methods, such as voice input and text input. For example, if the user selects voice input, the reception unit uses voice recognition technology to convert the request into text. The suggestion unit analyzes the requests received by the reception unit and suggests shops that match the user's tastes and preferences. The suggestion unit uses generative AI to analyze requests and makes suggestions based on the user's past behavior data and evaluation data. For example, the suggestion unit refers to data on shops the user has previously visited and suggests similar shops. The suggestion unit can use generative AI to select the shop that best suits the user's requests. The service unit provides detailed information about the shops suggested by the suggestion unit. The service unit displays detailed information such as the menu, business hours, and reservation status of the suggested shops. For example, the service unit displays the menu and business hours of a suggested cafe, allowing the user to check the reservation status. The service provider offers a visually intuitive interface that allows users to easily view detailed information about suggested shops. This enables the system according to the embodiment to efficiently find shops by analyzing the user's vague requests, suggesting shops that match their tastes and preferences, and providing detailed information.

[0030] The reception desk receives vague requests from users. For example, users can input requests such as "I want to relax somewhere nice" or "A cafe would be good, but I also want to get my nails done." The reception desk can offer multiple input methods, such as voice input and text input, when receiving user requests. For example, if a user chooses voice input, the reception desk uses speech recognition technology to convert the request into text. Speech recognition technology performs noise reduction and optimizes the speech model to accurately transcribe the user's speech into text. This ensures that the user's request is accurately conveyed to the system. Furthermore, the reception desk analyzes the text entered by the user using natural language processing technology to understand the intent of the request. For example, from the request "I want to relax," it extracts keywords for places and services that can help with relaxation. This allows the reception desk to transform the user's vague request into a concrete need and pass it on to the next processing step. In addition, the reception desk saves the user's input history and can understand the user's preferences and tendencies by referring to past requests. This allows the reception desk to understand user requests more accurately and build a foundation for making appropriate suggestions.

[0031] The suggestion department analyzes requests received by the reception department and proposes shops that match the user's tastes and preferences. The suggestion department uses generative AI to analyze requests and makes suggestions based on the user's past behavior data and evaluation data. For example, the suggestion department refers to data on shops the user has previously visited and suggests similar shops. Generative AI uses natural language processing technology to analyze the user's requests in detail and understand the intentions and desires behind them. For example, from the request "I want to relax," it identifies relaxing places such as spas, cafes, and parks. Furthermore, the suggestion department analyzes the user's past behavior data and understands the user's preferences based on information about shops the user has previously visited and rated. This allows the suggestion department to select the shop that best suits the user's tastes and preferences. The suggestion department can select the shop that best suits the user's requests using generative AI. Generative AI searches a vast database for shops that match the user's requests and presents multiple candidates. This allows the suggestion department to make quick and accurate suggestions to the user's requests. In addition, the suggestion department can collect user feedback and continuously improve the accuracy of its suggestions. For example, when a user rates a suggested restaurant, the suggestion team learns from that rating and incorporates it into future suggestions. This allows the suggestion team to provide suggestions that are better suited to the user's preferences.

[0032] The service provider provides detailed information about the establishments suggested by the suggestion provider. The service provider displays detailed information such as menus, business hours, and reservation status for the suggested establishments. For example, the service provider displays the menu and business hours of a suggested cafe and allows users to check the reservation status. The service provider provides a visually intuitive interface so that users can easily check the detailed information of the suggested establishments. Specifically, the service provider designs the user interface to be intuitive and organizes and displays information. For example, cafe menus are displayed by category so that users can easily find what they're looking for. Business hours and reservation status are updated in real time so that users can check the latest information. Furthermore, the service provider can provide a function for users to make reservations directly at the suggested establishments. For example, the service provider can integrate with a reservation system, allowing users to make reservations at their desired date and time. This allows users to check the detailed information of the suggested establishments and make reservations smoothly. The service provider can also collect user feedback and continuously improve the accuracy and content of the information it provides. For example, users can rate the information provided, and the service provider can use that rating to improve the accuracy of the information. This allows the service provider to offer users accurate and reliable information, thereby improving user satisfaction.

[0033] The suggestion unit can make suggestions based on the user's past behavior data and evaluation data. For example, the suggestion unit can refer to data on stores the user has previously visited and suggest similar stores. The suggestion unit can analyze the user's past behavior data and select stores that best suit the user's tastes and preferences. The suggestion unit can prioritize suggesting stores with high ratings based on the user's evaluation data. For example, the suggestion unit can refer to data on stores that the user has given high ratings to and suggest similar stores. In this way, the suggestion unit can suggest more appropriate stores based on the user's past behavior data and evaluation data. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit inputs the user's past behavior data and evaluation data into a generative AI, and the generative AI suggests the most suitable store.

[0034] The service provider can provide detailed information about the suggested restaurants, such as their menus, opening hours, and reservation status. For example, the service provider can display the menu and opening hours of a suggested cafe and allow the user to check the reservation status. The service provider can provide detailed information about the suggested restaurants in a visually easy-to-understand interface. For example, the service provider can display the menu of a suggested restaurant with photos so that the user can check the contents of the dishes. The service provider can display the opening hours of a suggested restaurant by day of the week so that the user can check the times when they can visit. The service provider can display the reservation status of a suggested restaurant in real time so that the user can check the availability of seats. In this way, the service provider can enable users to efficiently select a restaurant by providing detailed information about the suggested restaurants. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input detailed information about the suggested restaurants into a generative AI, and the generative AI can organize and provide the information.

[0035] The reception desk can analyze the user's past request history and select the most suitable reception method. For example, the reception desk can automatically display requests that the user has frequently entered in the past as suggestions. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest requests to be used during specific time periods based on the user's past request history. In this way, the reception desk can select the most suitable reception method by analyzing the user's past request history. Some or all of the above processing in the reception desk may be performed using a generation AI, or it may be performed without a generation AI. For example, the reception desk inputs the user's past request history into a generation AI, and the generation AI selects the most suitable reception method.

[0036] The reception unit can filter requests based on the user's current situation and areas of interest when receiving them. For example, when a user enters their current situation, the reception unit can suggest the most suitable candidates based on their areas of interest. If the user selects a specific area of ​​interest, the reception unit can prioritize receiving requests related to that area. The reception unit can filter and display requests that are highly relevant according to the user's current situation. In this way, the reception unit can receive highly relevant requests by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit inputs data on the user's current situation and areas of interest into a generative AI, and the generative AI filters the most suitable requests.

[0037] The reception unit can prioritize receiving requests that are highly relevant, taking into account the user's geographical location information. For example, when a user enters their current location, the reception unit can prioritize receiving nearby and relevant requests. When a user selects a specific region, the reception unit can prioritize receiving requests related to that region. The reception unit can filter and display the most relevant requests based on the user's geographical location information. This allows the reception unit to prioritize receiving requests that are highly relevant, taking into account the user's geographical location information. Some or all of the above processing in the reception unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reception unit inputs the user's geographical location information into a generation AI, and the generation AI filters the most relevant requests.

[0038] The reception department can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception department can analyze the content of the user's social media posts and prioritize accepting relevant requests. The reception department can suggest relevant requests based on the user's social media following information. The reception department can analyze the user's social media activity history and filter and display the most suitable requests. In this way, the reception department can accept relevant requests by analyzing the user's social media activity. Some or all of the above processing in the reception department may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception department inputs the user's social media activity data into a generative AI, and the generative AI filters the most suitable requests.

[0039] The suggestion unit can adjust the level of detail in its suggestions based on the user's past behavior data and evaluation data. For example, the suggestion unit might prioritize suggesting detailed information about stores that the user has previously given high ratings to. The suggestion unit can make detailed suggestions based on the user's past behavior data. The suggestion unit can make optimal suggestions by referring to the user's evaluation data. As a result, the suggestion unit can make more appropriate suggestions by adjusting the level of detail in its suggestions based on the user's past behavior data and evaluation data. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit inputs the user's past behavior data and evaluation data into a generation AI, and the generation AI adjusts the level of detail in its suggestions.

[0040] The suggestion unit can apply different suggestion algorithms depending on the user's current situation and areas of interest when making suggestions. For example, when the user inputs their current situation, the suggestion unit applies the most suitable suggestion algorithm based on their areas of interest. If the user selects a specific area of ​​interest, the suggestion unit can apply a suggestion algorithm related to that area. The suggestion unit can select the most suitable suggestion algorithm according to the user's current situation. As a result, the suggestion unit can make more appropriate suggestions by applying different suggestion algorithms depending on the user's current situation and areas of interest. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit inputs data on the user's current situation and areas of interest into a generative AI, and the generative AI applies the most suitable suggestion algorithm.

[0041] The suggestion unit can determine the priority of suggestions based on the user's past behavior data and evaluation data when making suggestions. For example, the suggestion unit may prioritize suggesting businesses that the user has previously given high ratings to. The suggestion unit can prioritize the most suitable suggestions based on the user's past behavior data. The suggestion unit can also determine the priority of suggestions by referring to the user's evaluation data. As a result, the suggestion unit can make more appropriate suggestions by determining the priority of suggestions based on the user's past behavior data and evaluation data. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit inputs the user's past behavior data and evaluation data into a generative AI, and the generative AI determines the priority of suggestions.

[0042] The suggestion unit can make optimal suggestions by considering the user's geographical location information. For example, when the user enters their current location, the suggestion unit can prioritize suggesting nearby relevant shops. When the user selects a specific region, the suggestion unit can prioritize suggesting shops related to that region. The suggestion unit can make optimal suggestions based on the user's geographical location information. In this way, the suggestion unit can make optimal suggestions by considering the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit inputs the user's geographical location information into a generative AI, and the generative AI makes optimal suggestions.

[0043] The service provider can customize the detailed information of suggested stores based on the user's past behavioral data and rating data when providing the information. For example, the service provider can prioritize displaying detailed information of stores that the user has previously given high ratings to. The service provider can customize and provide detailed information based on the user's past behavioral data. The service provider can customize and provide optimal information by referring to the user's rating data. In this way, the service provider can provide more appropriate information by customizing the information based on the user's past behavioral data and rating data. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider inputs the user's past behavioral data and rating data into a generation AI, and the generation AI customizes the information.

[0044] The information provider can adjust the level of detail of the information based on the user's current situation and areas of interest when providing it. For example, when the user inputs their current situation, the provider can provide the most relevant information based on their areas of interest. If the user selects a specific area of ​​interest, the provider can prioritize providing information related to that area. The provider can adjust the level of detail of the information provided according to the user's current situation. This allows the provider to provide more appropriate information by adjusting the level of detail based on the user's current situation and areas of interest. Some or all of the above processing in the information provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the provider inputs data on the user's current situation and areas of interest into a generative AI, and the generative AI adjusts the level of detail of the information.

[0045] The information provider can provide optimal information by considering the user's geographical location information at the time of provision. For example, when a user enters their current location, the information provider can prioritize providing relevant information nearby. When a user selects a specific region, the information provider can prioritize providing information related to that region. The information provider can provide optimal information based on the user's geographical location information. In this way, the information provider can provide optimal information by considering the user's geographical location information. Some or all of the above processing in the information provider may be performed using a generating AI, or it may be performed without a generating AI. For example, the information provider inputs the user's geographical location information into a generating AI, and the generating AI provides optimal information.

[0046] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider can analyze the content of the user's social media posts and prioritize providing relevant information. The service provider can suggest relevant information based on the user's social media following information. The service provider can analyze the user's social media activity history and filter and provide the most relevant information. In this way, the service provider can provide relevant information by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider inputs the user's social media activity data into a generative AI, and the generative AI filters the most relevant information.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The suggestion department can improve the accuracy of its suggestions by analyzing users' past behavioral data, evaluation data, and social media activity. For example, it can refer to data on places users have shared and businesses they have rated on social media to suggest similar businesses. It can also suggest businesses that users might be interested in based on the accounts they follow. Furthermore, the suggestion department can analyze the content of users' social media posts to make suggestions based on their current interests.

[0049] The reception desk can prioritize receiving requests that are highly relevant to the user, taking into account the user's geographical location. For example, when a user enters their current location, it can prioritize receiving nearby and relevant requests. Furthermore, if a user selects a specific region, it can prioritize receiving requests related to that region. In addition, the reception desk can filter and display the most relevant requests based on the user's geographical location. This allows the reception desk to prioritize receiving requests that are highly relevant to the user, taking their geographical location into consideration.

[0050] The reception desk can analyze a user's past request history and select the most suitable reception method. For example, it can automatically display requests that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, the reception desk can predict and suggest requests that will be used during specific time periods based on the user's past request history. In this way, the reception desk can select the most suitable reception method by analyzing the user's past request history.

[0051] The suggestion unit can apply different suggestion algorithms depending on the user's current situation and areas of interest. For example, when the user inputs their current situation, the unit can apply the most suitable suggestion algorithm based on their areas of interest. Furthermore, if the user selects a specific area of ​​interest, the unit can apply a suggestion algorithm related to that area. In addition, the suggestion unit can select the most suitable suggestion algorithm based on the user's current situation. This allows the suggestion unit to provide more appropriate suggestions by applying different suggestion algorithms depending on the user's current situation and areas of interest.

[0052] The reception desk can analyze users' social media activity and receive relevant requests. For example, it can analyze the content of users' social media posts and prioritize receiving relevant requests. It can also suggest relevant requests based on users' social media following information. Furthermore, the reception desk can analyze users' social media activity history and filter and display the most relevant requests. In this way, the reception desk can receive relevant requests by analyzing users' social media activity.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The reception desk receives the user's vague requests. For example, the user can input requests such as "I want to relax somewhere nice" or "A cafe would be nice, but I also want to get my nails done." When receiving user requests, the reception desk can offer multiple input methods, such as voice input and text input. For example, if the user chooses voice input, the reception desk uses speech recognition technology to convert the request into text. Step 2: The suggestion department analyzes the requests received by the reception department and proposes restaurants that match the user's tastes and preferences. The suggestion department uses generative AI to analyze requests and makes suggestions based on the user's past behavior data and evaluation data. For example, the suggestion department can refer to data on restaurants the user has visited in the past and suggest similar restaurants. The suggestion department can use generative AI to select the restaurant that best suits the user's requests. Step 3: The service provider provides detailed information about the establishments suggested by the suggestion provider. The service provider displays detailed information such as the menu, opening hours, and reservation status of the suggested establishments. For example, the service provider displays the menu and opening hours of a suggested cafe and allows the user to check the reservation status. The service provider provides a visually easy-to-understand interface so that the user can easily check the detailed information of the suggested establishments.

[0055] (Example of form 2) The system according to an embodiment of the present invention is a groundbreaking solution for all generations who have difficulty reading maps or who can read maps but find them difficult to understand, by utilizing generative AI. In this system, the user inputs vague requests, and the generative AI analyzes those requests and suggests shops that match the user's tastes and preferences. For example, if the user inputs requests such as "I want to relax somewhere nice" or "A cafe would be good, but I also want to get my nails done," the generative AI analyzes those requests and narrows down the candidates to cafes, restaurants, bars, chiropractic clinics, beauty salons, etc., based on the user's past behavior data and evaluation data. For example, it considers the atmosphere and price range of nail salons the user has visited in the past and suggests nearby highly-rated cafes and nail salons. Furthermore, the generative AI provides detailed information about the suggested shops. For example, it displays the menu and business hours of the suggested cafes, and the reservation status of the nail salons. This allows the user to find a good place efficiently in a short amount of time. With this mechanism, the user does not need to verbalize their vague desires, and the generative AI provides support, thus solving problems such as information overload and time constraints. For example, when a user wants to relax amidst a busy daily routine, the AI ​​can suggest the perfect cafe or nail salon, allowing them to find a relaxing spot in a short amount of time. This means the system analyzes the user's vague requests, suggests establishments tailored to their hobbies and preferences, and provides detailed information, enabling users to find places efficiently.

[0056] The system according to this embodiment comprises a reception unit, a suggestion unit, and a service unit. The reception unit receives vague requests from the user. For example, the user can input requests such as "I want to relax somewhere nice" or "A cafe would be good, but I also want to get my nails done." When receiving user requests, the reception unit can provide multiple input methods, such as voice input and text input. For example, if the user selects voice input, the reception unit uses voice recognition technology to convert the request into text. The suggestion unit analyzes the requests received by the reception unit and suggests shops that match the user's tastes and preferences. The suggestion unit uses generative AI to analyze requests and makes suggestions based on the user's past behavior data and evaluation data. For example, the suggestion unit refers to data on shops the user has previously visited and suggests similar shops. The suggestion unit can use generative AI to select the shop that best suits the user's requests. The service unit provides detailed information about the shops suggested by the suggestion unit. The service unit displays detailed information such as the menu, business hours, and reservation status of the suggested shops. For example, the service unit displays the menu and business hours of a suggested cafe, allowing the user to check the reservation status. The service provider offers a visually intuitive interface that allows users to easily view detailed information about suggested shops. This enables the system according to the embodiment to efficiently find shops by analyzing the user's vague requests, suggesting shops that match their tastes and preferences, and providing detailed information.

[0057] The reception desk receives vague requests from users. For example, users can input requests such as "I want to relax somewhere nice" or "A cafe would be good, but I also want to get my nails done." The reception desk can offer multiple input methods, such as voice input and text input, when receiving user requests. For example, if a user chooses voice input, the reception desk uses speech recognition technology to convert the request into text. Speech recognition technology performs noise reduction and optimizes the speech model to accurately transcribe the user's speech into text. This ensures that the user's request is accurately conveyed to the system. Furthermore, the reception desk analyzes the text entered by the user using natural language processing technology to understand the intent of the request. For example, from the request "I want to relax," it extracts keywords for places and services that can help with relaxation. This allows the reception desk to transform the user's vague request into a concrete need and pass it on to the next processing step. In addition, the reception desk saves the user's input history and can understand the user's preferences and tendencies by referring to past requests. This allows the reception desk to understand user requests more accurately and build a foundation for making appropriate suggestions.

[0058] The suggestion department analyzes requests received by the reception department and proposes shops that match the user's tastes and preferences. The suggestion department uses generative AI to analyze requests and makes suggestions based on the user's past behavior data and evaluation data. For example, the suggestion department refers to data on shops the user has previously visited and suggests similar shops. Generative AI uses natural language processing technology to analyze the user's requests in detail and understand the intentions and desires behind them. For example, from the request "I want to relax," it identifies relaxing places such as spas, cafes, and parks. Furthermore, the suggestion department analyzes the user's past behavior data and understands the user's preferences based on information about shops the user has previously visited and rated. This allows the suggestion department to select the shop that best suits the user's tastes and preferences. The suggestion department can select the shop that best suits the user's requests using generative AI. Generative AI searches a vast database for shops that match the user's requests and presents multiple candidates. This allows the suggestion department to make quick and accurate suggestions to the user's requests. In addition, the suggestion department can collect user feedback and continuously improve the accuracy of its suggestions. For example, when a user rates a suggested restaurant, the suggestion team learns from that rating and incorporates it into future suggestions. This allows the suggestion team to provide suggestions that are better suited to the user's preferences.

[0059] The service provider provides detailed information about the establishments suggested by the suggestion provider. The service provider displays detailed information such as menus, business hours, and reservation status for the suggested establishments. For example, the service provider displays the menu and business hours of a suggested cafe and allows users to check the reservation status. The service provider provides a visually intuitive interface so that users can easily check the detailed information of the suggested establishments. Specifically, the service provider designs the user interface to be intuitive and organizes and displays information. For example, cafe menus are displayed by category so that users can easily find what they're looking for. Business hours and reservation status are updated in real time so that users can check the latest information. Furthermore, the service provider can provide a function for users to make reservations directly at the suggested establishments. For example, the service provider can integrate with a reservation system, allowing users to make reservations at their desired date and time. This allows users to check the detailed information of the suggested establishments and make reservations smoothly. The service provider can also collect user feedback and continuously improve the accuracy and content of the information it provides. For example, users can rate the information provided, and the service provider can use that rating to improve the accuracy of the information. This allows the service provider to offer users accurate and reliable information, thereby improving user satisfaction.

[0060] The suggestion unit can make suggestions based on the user's past behavior data and evaluation data. For example, the suggestion unit can refer to data on stores the user has previously visited and suggest similar stores. The suggestion unit can analyze the user's past behavior data and select stores that best suit the user's tastes and preferences. The suggestion unit can prioritize suggesting stores with high ratings based on the user's evaluation data. For example, the suggestion unit can refer to data on stores that the user has given high ratings to and suggest similar stores. In this way, the suggestion unit can suggest more appropriate stores based on the user's past behavior data and evaluation data. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit inputs the user's past behavior data and evaluation data into a generative AI, and the generative AI suggests the most suitable store.

[0061] The service provider can provide detailed information about the suggested restaurants, such as their menus, opening hours, and reservation status. For example, the service provider can display the menu and opening hours of a suggested cafe and allow the user to check the reservation status. The service provider can provide detailed information about the suggested restaurants in a visually easy-to-understand interface. For example, the service provider can display the menu of a suggested restaurant with photos so that the user can check the contents of the dishes. The service provider can display the opening hours of a suggested restaurant by day of the week so that the user can check the times when they can visit. The service provider can display the reservation status of a suggested restaurant in real time so that the user can check the availability of seats. In this way, the service provider can enable users to efficiently select a restaurant by providing detailed information about the suggested restaurants. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input detailed information about the suggested restaurants into a generative AI, and the generative AI can organize and provide the information.

[0062] The reception desk can estimate the user's emotions and adjust the way requests are received based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick request entry. This allows the reception desk to receive more appropriate requests by adjusting the way requests are received according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using generative AI or not. For example, the reception desk inputs the user's facial expression data into the generative AI, which then estimates the emotions.

[0063] The reception desk can analyze the user's past request history and select the most suitable reception method. For example, the reception desk can automatically display requests that the user has frequently entered in the past as suggestions. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest requests to be used during specific time periods based on the user's past request history. In this way, the reception desk can select the most suitable reception method by analyzing the user's past request history. Some or all of the above processing in the reception desk may be performed using a generation AI, or it may be performed without a generation AI. For example, the reception desk inputs the user's past request history into a generation AI, and the generation AI selects the most suitable reception method.

[0064] The reception unit can filter requests based on the user's current situation and areas of interest when receiving them. For example, when a user enters their current situation, the reception unit can suggest the most suitable candidates based on their areas of interest. If the user selects a specific area of ​​interest, the reception unit can prioritize receiving requests related to that area. The reception unit can filter and display requests that are highly relevant according to the user's current situation. In this way, the reception unit can receive highly relevant requests by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit inputs data on the user's current situation and areas of interest into a generative AI, and the generative AI filters the most suitable requests.

[0065] The reception desk can estimate the user's emotions and determine the priority of requests to be received based on the estimated emotions. For example, if the user is stressed, the reception desk can prioritize requests that promote relaxation. If the user is relaxed, the reception desk can prioritize requests that pique their interest. If the user is in a hurry, the reception desk can prioritize requests that can be handled quickly. In this way, the reception desk can prioritize more appropriate requests by determining the priority of requests according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using generative AI or not. For example, the reception desk inputs the user's facial expression data into the generative AI, and the generative AI estimates the emotions.

[0066] The reception unit can prioritize receiving requests that are highly relevant, taking into account the user's geographical location information. For example, when a user enters their current location, the reception unit can prioritize receiving nearby and relevant requests. When a user selects a specific region, the reception unit can prioritize receiving requests related to that region. The reception unit can filter and display the most relevant requests based on the user's geographical location information. This allows the reception unit to prioritize receiving requests that are highly relevant, taking into account the user's geographical location information. Some or all of the above processing in the reception unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reception unit inputs the user's geographical location information into a generation AI, and the generation AI filters the most relevant requests.

[0067] The reception department can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception department can analyze the content of the user's social media posts and prioritize accepting relevant requests. The reception department can suggest relevant requests based on the user's social media following information. The reception department can analyze the user's social media activity history and filter and display the most suitable requests. In this way, the reception department can accept relevant requests by analyzing the user's social media activity. Some or all of the above processing in the reception department may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception department inputs the user's social media activity data into a generative AI, and the generative AI filters the most suitable requests.

[0068] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will present suggestions in a relaxed manner. If the user is in a hurry, the suggestion unit can present suggestions in a concise and quick manner. If the user is excited, the suggestion unit can present suggestions in a visually stimulating manner. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the way it presents its suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit inputs the user's facial expression data into a generative AI, and the generative AI estimates the emotion.

[0069] The suggestion unit can adjust the level of detail in its suggestions based on the user's past behavior data and evaluation data. For example, the suggestion unit might prioritize suggesting detailed information about stores that the user has previously given high ratings to. The suggestion unit can make detailed suggestions based on the user's past behavior data. The suggestion unit can make optimal suggestions by referring to the user's evaluation data. As a result, the suggestion unit can make more appropriate suggestions by adjusting the level of detail in its suggestions based on the user's past behavior data and evaluation data. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit inputs the user's past behavior data and evaluation data into a generation AI, and the generation AI adjusts the level of detail in its suggestions.

[0070] The suggestion unit can apply different suggestion algorithms depending on the user's current situation and areas of interest when making suggestions. For example, when the user inputs their current situation, the suggestion unit applies the most suitable suggestion algorithm based on their areas of interest. If the user selects a specific area of ​​interest, the suggestion unit can apply a suggestion algorithm related to that area. The suggestion unit can select the most suitable suggestion algorithm according to the user's current situation. As a result, the suggestion unit can make more appropriate suggestions by applying different suggestion algorithms depending on the user's current situation and areas of interest. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit inputs data on the user's current situation and areas of interest into a generative AI, and the generative AI applies the most suitable suggestion algorithm.

[0071] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can make a short, concise suggestion. If the user is relaxed, the suggestion unit can make a longer suggestion with detailed explanations. If the user is excited, the suggestion unit can make a visually stimulating suggestion. In this way, the suggestion unit can make more appropriate suggestions by adjusting the length of the suggestion according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit inputs the user's facial expression data into a generative AI, and the generative AI estimates the emotions.

[0072] The suggestion unit can determine the priority of suggestions based on the user's past behavior data and evaluation data when making suggestions. For example, the suggestion unit may prioritize suggesting businesses that the user has previously given high ratings to. The suggestion unit can prioritize the most suitable suggestions based on the user's past behavior data. The suggestion unit can also determine the priority of suggestions by referring to the user's evaluation data. As a result, the suggestion unit can make more appropriate suggestions by determining the priority of suggestions based on the user's past behavior data and evaluation data. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit inputs the user's past behavior data and evaluation data into a generative AI, and the generative AI determines the priority of suggestions.

[0073] The suggestion unit can make optimal suggestions by considering the user's geographical location information. For example, when the user enters their current location, the suggestion unit can prioritize suggesting nearby relevant shops. When the user selects a specific region, the suggestion unit can prioritize suggesting shops related to that region. The suggestion unit can make optimal suggestions based on the user's geographical location information. In this way, the suggestion unit can make optimal suggestions by considering the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit inputs the user's geographical location information into a generative AI, and the generative AI makes optimal suggestions.

[0074] The service provider can estimate the user's emotions and adjust the way information is displayed based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can provide a display method that includes detailed information. If the user is in a hurry, the service provider can provide a display method that gets straight to the point. In this way, the service provider can provide more appropriate information by adjusting the way information is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using the generative AI or not. For example, the service provider inputs the user's facial expression data into the generative AI, and the generative AI estimates the emotions.

[0075] The service provider can customize the detailed information of suggested stores based on the user's past behavioral data and rating data when providing the information. For example, the service provider can prioritize displaying detailed information of stores that the user has previously given high ratings to. The service provider can customize and provide detailed information based on the user's past behavioral data. The service provider can customize and provide optimal information by referring to the user's rating data. In this way, the service provider can provide more appropriate information by customizing the information based on the user's past behavioral data and rating data. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider inputs the user's past behavioral data and rating data into a generation AI, and the generation AI customizes the information.

[0076] The information provider can adjust the level of detail of the information based on the user's current situation and areas of interest when providing it. For example, when the user inputs their current situation, the provider can provide the most relevant information based on their areas of interest. If the user selects a specific area of ​​interest, the provider can prioritize providing information related to that area. The provider can adjust the level of detail of the information provided according to the user's current situation. This allows the provider to provide more appropriate information by adjusting the level of detail based on the user's current situation and areas of interest. Some or all of the above processing in the information provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the provider inputs data on the user's current situation and areas of interest into a generative AI, and the generative AI adjusts the level of detail of the information.

[0077] The service provider can estimate the user's emotions and prioritize the information to be provided based on the estimated emotions. For example, if the user is stressed, the service provider can prioritize providing information that helps them relax. If the user is relaxed, the service provider can prioritize providing information that interests them. If the user is in a hurry, the service provider can prioritize providing information that allows for quick action. In this way, the service provider can provide more appropriate information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider inputs the user's facial expression data into a generative AI, and the generative AI estimates the emotions.

[0078] The information provider can provide optimal information by considering the user's geographical location information at the time of provision. For example, when a user enters their current location, the information provider can prioritize providing relevant information nearby. When a user selects a specific region, the information provider can prioritize providing information related to that region. The information provider can provide optimal information based on the user's geographical location information. In this way, the information provider can provide optimal information by considering the user's geographical location information. Some or all of the above processing in the information provider may be performed using a generating AI, or it may be performed without a generating AI. For example, the information provider inputs the user's geographical location information into a generating AI, and the generating AI provides optimal information.

[0079] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider can analyze the content of the user's social media posts and prioritize providing relevant information. The service provider can suggest relevant information based on the user's social media following information. The service provider can analyze the user's social media activity history and filter and provide the most relevant information. In this way, the service provider can provide relevant information by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider inputs the user's social media activity data into a generative AI, and the generative AI filters the most relevant information.

[0080] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0081] The reception system can analyze the tone and speed of the user's voice input to estimate their emotions. For example, if the user is in a hurry, the reception system can provide a concise interface to quickly receive their request. If the user is relaxed, it can provide detailed input options, allowing them to enter customizable requests. Furthermore, the reception system can provide appropriate feedback based on the user's emotions. For example, if the user is stressed, the reception system can display an encouraging message.

[0082] The suggestion department can improve the accuracy of its suggestions by analyzing users' past behavioral data, evaluation data, and social media activity. For example, it can refer to data on places users have shared and businesses they have rated on social media to suggest similar businesses. It can also suggest businesses that users might be interested in based on the accounts they follow. Furthermore, the suggestion department can analyze the content of users' social media posts to make suggestions based on their current interests.

[0083] The service provider can estimate the user's emotions and adjust the display method when providing detailed information about a suggested store. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, the service provider can provide more appropriate information by adjusting the way information is displayed according to the user's emotions.

[0084] The reception desk can prioritize receiving requests that are highly relevant to the user, taking into account the user's geographical location. For example, when a user enters their current location, it can prioritize receiving nearby and relevant requests. Furthermore, if a user selects a specific region, it can prioritize receiving requests related to that region. In addition, the reception desk can filter and display the most relevant requests based on the user's geographical location. This allows the reception desk to prioritize receiving requests that are highly relevant to the user, taking their geographical location into consideration.

[0085] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, it can present suggestions in a relaxed manner. If the user is in a hurry, it can present suggestions in a concise and quick manner. Furthermore, if the user is excited, it can present suggestions in a visually stimulating manner. In this way, the suggestion function can provide more appropriate suggestions by adjusting the presentation style according to the user's emotions.

[0086] The reception desk can analyze a user's past request history and select the most suitable reception method. For example, it can automatically display requests that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, the reception desk can predict and suggest requests that will be used during specific time periods based on the user's past request history. In this way, the reception desk can select the most suitable reception method by analyzing the user's past request history.

[0087] The suggestion unit can apply different suggestion algorithms depending on the user's current situation and areas of interest. For example, when the user inputs their current situation, the unit can apply the most suitable suggestion algorithm based on their areas of interest. Furthermore, if the user selects a specific area of ​​interest, the unit can apply a suggestion algorithm related to that area. In addition, the suggestion unit can select the most suitable suggestion algorithm based on the user's current situation. This allows the suggestion unit to provide more appropriate suggestions by applying different suggestion algorithms depending on the user's current situation and areas of interest.

[0088] The information delivery unit can estimate the user's emotions and prioritize the information to be delivered based on those estimated emotions. For example, if the user is stressed, it can prioritize providing information that helps them relax. If the user is relaxed, it can prioritize providing information that will pique their interest. Furthermore, if the user is in a hurry, it can prioritize providing information that allows for a quick response. In this way, the information delivery unit can provide more appropriate information by prioritizing information according to the user's emotions.

[0089] The reception desk can analyze users' social media activity and receive relevant requests. For example, it can analyze the content of users' social media posts and prioritize receiving relevant requests. It can also suggest relevant requests based on users' social media following information. Furthermore, the reception desk can analyze users' social media activity history and filter and display the most relevant requests. In this way, the reception desk can receive relevant requests by analyzing users' social media activity.

[0090] The suggestion function can estimate the user's emotions and adjust the length of the suggestion based on those emotions. For example, if the user is in a hurry, it can provide a short, to-the-point suggestion. If the user is relaxed, it can provide a longer suggestion with more detailed explanations. Furthermore, if the user is excited, it can provide a visually stimulating suggestion. In this way, the suggestion function can provide more appropriate suggestions by adjusting the length of the suggestion according to the user's emotions.

[0091] The following briefly describes the processing flow for example form 2.

[0092] Step 1: The reception desk receives the user's vague requests. For example, the user can input requests such as "I want to relax somewhere nice" or "A cafe would be nice, but I also want to get my nails done." When receiving user requests, the reception desk can offer multiple input methods, such as voice input and text input. For example, if the user chooses voice input, the reception desk uses speech recognition technology to convert the request into text. Step 2: The suggestion department analyzes the requests received by the reception department and proposes restaurants that match the user's tastes and preferences. The suggestion department uses generative AI to analyze requests and makes suggestions based on the user's past behavior data and evaluation data. For example, the suggestion department can refer to data on restaurants the user has visited in the past and suggest similar restaurants. The suggestion department can use generative AI to select the restaurant that best suits the user's requests. Step 3: The service provider provides detailed information about the establishments suggested by the suggestion provider. The service provider displays detailed information such as the menu, opening hours, and reservation status of the suggested establishments. For example, the service provider displays the menu and opening hours of a suggested cafe and allows the user to check the reservation status. The service provider provides a visually easy-to-understand interface so that the user can easily check the detailed information of the suggested establishments.

[0093] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0094] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0095] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0096] Each of the multiple elements described above, including the reception unit, proposal unit, and provision unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and accepts voice and text input from the user. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses generation AI to analyze the user's requests and propose shops that match their tastes and preferences. The provision unit is implemented by the control unit 46A of the smart device 14 and displays detailed information about the proposed shops. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0097] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0098] As shown in Figure 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.

[0099] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0103] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0104] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0105] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0106] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0107] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0109] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0111] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the reception unit, proposal unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and accepts voice and text input from the user. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses generating AI to analyze the user's requests and propose shops that match their tastes and preferences. The provision unit is implemented by the control unit 46A of the smart glasses 214 and displays detailed information about the proposed shops. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0114] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0116] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 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 a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the reception unit, proposal unit, and provision unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and accepts voice and text input from the user. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses a generation AI to analyze the user's requests and propose shops that match their hobbies and preferences. The provision unit is implemented by the control unit 46A of the headset terminal 314 and displays detailed information about the proposed shops. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0130] As shown in Figure 7, the 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.

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

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0137] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the reception unit, proposal unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and accepts voice and text input from the user. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses generating AI to analyze the user's requests and propose shops that match their hobbies and preferences. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and displays detailed information about the proposed shops. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0146] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0148] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0149] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0150] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0154] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0155] 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.

[0156] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0157] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0158] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0159] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0161] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0162] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0163] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0164] (Note 1) A reception desk that handles vague requests from users, The request received by the aforementioned reception department is analyzed, and the proposal department suggests shops that match the user's tastes and preferences. The system comprises a provisioning unit that provides detailed information about the store proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We make suggestions based on the user's past behavioral data and evaluation data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Provides detailed information about the suggested restaurant, such as its menu, opening hours, and reservation status. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is We estimate the user's emotions and adjust the way we receive requests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Analyze the user's past request history and select the most suitable method of receiving requests. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When receiving requests, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to be accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving requests, we prioritize requests that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving a request, the system analyzes the user's social media activity and accepts relevant requests. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the user's past behavioral data and evaluation data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making suggestions, we prioritize them based on the user's past behavioral data and evaluation data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, we take the user's geographical location into consideration to provide the most suitable suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing information, the detailed information of the suggested store is customized based on the user's past behavioral data and rating data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing information, the level of detail is adjusted based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing information, we will consider the user's geographical location to provide the most suitable information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that handles vague requests from users, The request received by the aforementioned reception department is analyzed, and the proposal department suggests shops that match the user's tastes and preferences. The system comprises a provisioning unit that provides detailed information about the store proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned proposal section is, We make suggestions based on the user's past behavioral data and evaluation data. The system according to feature 1.

3. The aforementioned supply unit is, Provides detailed information about the suggested restaurant, such as its menu, opening hours, and reservation status. The system according to feature 1.

4. The aforementioned reception unit is We estimate the user's emotions and adjust the way we receive requests based on those estimated emotions. The system according to feature 1.

5. The aforementioned reception unit is Analyze the user's past request history and select the most suitable method of receiving requests. The system according to feature 1.

6. The aforementioned reception unit is When receiving requests, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to be accepted based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is When receiving requests, we prioritize requests that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

9. The aforementioned reception unit is When receiving a request, the system analyzes the user's social media activity and accepts relevant requests. The system according to feature 1.

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