system

Abstract

The system according to the embodiment aims to efficiently search for and suggest facilities that can be reached from the current location within a desired arrival time. [Solution] A system according to an embodiment includes a reception unit, a search unit, and a suggestion unit. The reception unit receives input from a user of a current location and a desired arrival time. The search unit searches for facilities that can be reached from the current location within the desired arrival time based on the information received by the reception unit. The suggestion unit suggests appropriate facilities from among the facilities found by the search unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to efficiently search for and suggest facilities that can be reached from the current location within a desired arrival time.

[0005] The system according to the embodiment aims to efficiently search for and suggest facilities that can be reached from the current location within a desired arrival time. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a search unit, and a suggestion unit. The reception unit receives input of a current location and a desired arrival time from a user. The search unit searches for facilities that can be reached from the current location within the desired arrival time based on the information received by the reception unit. The suggestion unit suggests appropriate facilities from among the facilities searched for by the search unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently search for and suggest facilities that can be reached from the current location within a desired arrival time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A search tool according to an embodiment of the present invention is a tool that allows users to optimally search for hotels, restaurants, hospitals, etc. that are reachable within a certain time from their current location when traveling or on a business trip. The search tool allows users to input their current location and desired arrival time, and a generation AI searches for facilities that can be reached within the desired arrival time from the current location and suggests the most suitable facility. This tool allows users to efficiently find facilities during travel or business trips. For example, a user may input that they want to find a restaurant that is reachable within 10 minutes from their current location. This information is input into the generation AI. The generation AI then analyzes the input information and searches for facilities that can be reached within the desired arrival time from the current location. The generation AI calculates the optimal route based on map data and traffic information and searches for facilities along that route. For example, if a user inputs a restaurant that is reachable within 10 minutes from their current location, the generation AI searches for restaurants that meet the criteria. The generation AI then suggests the most suitable facility based on the search results. For example, if a user inputs a restaurant that is reachable within 10 minutes from their current location, the generation AI suggests highly rated restaurants or restaurants that match the user's preferences from among multiple restaurants that meet the criteria. This allows the search tool to easily find facilities that are reachable from the current location within a short time, even when the user is in a hurry. Furthermore, by suggesting facilities that match the user's preferences, the search tool can provide more personalized suggestions based on the user's past search history and ratings. For example, by preferentially suggesting restaurants or hotels that the user has previously given high ratings, the user's satisfaction can be improved. This allows the search tool to optimally search for facilities that are reachable from the current location within a certain time frame when traveling or on a business trip, greatly improving user convenience.

[0029] A search tool according to an embodiment includes a reception unit, a search unit, and a suggestion unit. The reception unit accepts input of a current location and a desired arrival time from a user. For example, the reception unit provides an interface for the user to input the current location and the desired arrival time. The reception unit can acquire the current location using GPS data or Wi-Fi location information. The reception unit also allows the user to input the desired arrival time in minutes or hours. The search unit uses a generation AI to search for facilities that can be reached from the current location within the desired arrival time based on the information accepted by the reception unit. For example, the search unit calculates an optimal route based on map data and traffic information, and searches for facilities along that route. The generation AI receives map data and traffic information as input, calculates an optimal route, and searches for facilities along that route. For example, the generation AI analyzes map data to identify facilities that can be reached from the current location within the desired arrival time. The generation AI can also calculate an optimal route based on traffic information, taking into account factors such as traffic congestion and road construction. The suggestion unit suggests optimal facilities from among the facilities searched by the search unit. For example, the suggestion unit suggests highly rated facilities or facilities that match the user's preferences based on the search results. The suggestion unit can also make more personalized suggestions based on the user's past search history and ratings. For example, the suggestion unit preferentially suggests facilities that the user has previously given high ratings. This allows the search tool according to the embodiment to efficiently search for facilities that can be reached from the user's current location within the desired arrival time and suggest the most suitable facility.

[0030] The reception unit can analyze the user's past input history and suggest an appropriate input method. For example, the reception unit can automatically display as candidates the current location and desired arrival time that the user has frequently input in the past. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the current location and desired arrival time to be used during a specific time period based on the user's past input history. This improves user convenience by suggesting the optimal input method based on the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI suggest the optimal input method.

[0031] When inputting the current location and desired arrival time, the reception unit can automatically complete the input content based on the user's current activity status and schedule. The reception unit, for example, references the user's calendar information and automatically sets the current location and desired arrival time based on the schedule. The reception unit can also suggest optimal input content taking into account the user's current activity status (e.g., in a meeting, traveling, etc.). The reception unit can also analyze the user's past schedule patterns and automatically complete the optimal current location and desired arrival time. This reduces the effort required for input by automatically completing the input content based on the user's activity status and schedule. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's calendar information to the generation AI and cause the generation AI to automatically complete the input content.

[0032] The reception unit can provide an appropriate input interface according to the user's device information when the user inputs the current location and desired arrival time. For example, if the user is using a smartphone, the reception unit can provide an input interface that matches the screen size. Furthermore, if the user is using a tablet, the reception unit can provide an input interface that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the reception unit can provide a simple and highly visible input interface. This improves input convenience by providing an input interface that matches the user's device information. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's device information to the generation AI and cause the generation AI to provide an optimal input interface.

[0033] The reception unit can present highly relevant input candidates by taking into account the user's geographical location information when the user inputs the current location and desired arrival time. For example, when the user is in a specific area, the reception unit can preferentially display input candidates related to the area. Furthermore, when the user is near a specific facility, the reception unit can also present input candidates related to the facility. Furthermore, when the user is in a specific city, the reception unit can also preferentially display input candidates related to the city. In this way, highly relevant input candidates can be presented by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to present highly relevant input candidates.

[0034] The reception unit can analyze the user's social media activity when the user inputs the current location and desired arrival time, and present related input candidates. For example, the reception unit presents locations where the user has checked in on social media as input candidates. The reception unit can also analyze the content of the user's social media posts and present related input candidates. The reception unit can also present related input candidates by referring to the activities of the user's friends on social media. In this way, related input candidates can be presented by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to present related input candidates.

[0035] The reception unit can customize the input method by reflecting the user's past feedback when inputting the current location and desired arrival time. For example, the reception unit preferentially suggests input methods that the user has previously rated highly. The reception unit can also customize the optimal input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and improve the input method. In this way, the input method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the input method.

[0036] The search unit can incorporate real-time data during a search to improve the accuracy of map data and traffic information. The search unit can propose an optimal route based on, for example, real-time traffic congestion information. The search unit can also propose an optimal route taking into account the real-time operation status of public transportation. The search unit can also propose a detour route based on real-time road construction information. By incorporating real-time data, the accuracy of search results is improved. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can input real-time traffic information data into the generation AI and cause the generation AI to propose an optimal route.

[0037] When performing a search, the search unit can optimize the search algorithm by referring to the user's past search history. For example, the search unit may preferentially display facilities that the user has previously searched for. The search unit can also suggest optimal search results based on the user's past search history. The search unit can also analyze the user's past search history and improve the search algorithm. This allows the search algorithm to be optimized by referring to the past search history. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input the user's past search history data into a generation AI and cause the generation AI to optimize the search algorithm.

[0038] During a search, the search unit can calculate the optimal route according to the user's current means of transportation. For example, if the user is traveling on foot, the search unit can suggest the shortest route. Furthermore, if the user is traveling by bicycle, the search unit can also suggest a route that includes a bicycle-only road. Furthermore, if the user is traveling by car, the search unit can also suggest a route that avoids traffic congestion. This enables efficient travel by calculating the optimal route according to the user's means of transportation. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the user's means of transportation data into a generation AI and cause the generation AI to calculate the optimal route.

[0039] During a search, the search unit can prioritize searching for highly relevant facilities by taking into account the user's geographical movement history. For example, the search unit prioritizes searching for places the user has visited in the past. The search unit can also prioritize searching for highly relevant facilities based on the user's geographical movement history. The search unit can also analyze the user's geographical movement history and suggest optimal search results. This makes it possible to prioritize searching for highly relevant facilities by taking into account the user's geographical movement history. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the user's geographical movement history data into the generation AI and cause the generation AI to search for highly relevant facilities.

[0040] During a search, the search unit can analyze the user's social media activity and search for related facilities. For example, the search unit may prioritize searching for places where the user has checked in on social media. The search unit can also analyze the content of the user's social media posts and search for related facilities. The search unit can also search for related facilities by referring to the activities of the user's friends on social media. In this way, related facilities can be searched for by analyzing the user's social media activity. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input the user's social media data into the generation AI and cause the generation AI to search for related facilities.

[0041] The search unit can customize the search algorithm by reflecting the user's past feedback when searching. For example, the search unit prioritizes searching for facilities that the user has previously given high ratings. The search unit can also suggest optimal search results based on the user's past feedback. The search unit can also analyze the user's past feedback and improve the search algorithm. In this way, the search algorithm can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the user's past feedback data into the generation AI and have the generation AI customize the search algorithm.

[0042] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on facility ratings and user preferences. For example, the suggestion unit prioritizes suggesting facilities with high ratings. The suggestion unit can also prioritize suggesting facilities that match the user's preferences. The suggestion unit can also suggest optimal facilities based on the user's past ratings. This enables more appropriate suggestions by adjusting the level of detail of the proposal based on facility ratings and user preferences. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input user rating data into a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0043] When making a proposal, the suggestion unit can apply different proposal algorithms depending on the category of the facility. For example, in the case of a hotel, the suggestion unit makes a proposal based on ratings and price. In addition, in the case of a restaurant, the suggestion unit can also make a proposal based on the type of cuisine and ratings. In addition, in the case of a hospital, the suggestion unit can also make a proposal based on the medical department and reputation. In this way, by applying a proposal algorithm depending on the category of the facility, more appropriate proposals can be made. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input facility category data into the generation AI and cause the generation AI to apply different proposal algorithms.

[0044] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit prioritizes suggestions that the user has previously given high ratings. The suggestion unit can also make optimal suggestions based on the user's past suggestion results. The suggestion unit can also analyze the user's past suggestion results and improve the suggestion algorithm. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0045] When making a proposal, the proposal unit can determine the priority of the proposal based on the evaluation date of the facility. For example, the proposal unit can prioritize proposals based on the evaluation date of the facility. The proposal unit can also prioritize proposals based on the evaluation date of the facility over facilities with older evaluations. The proposal unit can also propose the most suitable facility by taking the evaluation date into consideration. This enables more appropriate proposals by determining the priority of proposals based on the evaluation date of the facility. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal unit can input evaluation date data of the facility into the generation AI and cause the generation AI to determine the priority of the proposals.

[0046] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the facilities. For example, the suggestion unit prioritizes suggesting facilities close to the user's current location. The suggestion unit can also prioritize suggesting facilities that match the user's preferences. The suggestion unit can also suggest optimal facilities based on the user's past ratings. This enables more appropriate suggestions by adjusting the order of suggestions based on the relevance of the facilities. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input facility relevance data into a generation AI and cause the generation AI to adjust the order of suggestions.

[0047] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can use detailed technical terminology to make the proposal. Furthermore, if the user does not have technical expertise, the suggestion unit can also use concise and easy-to-understand language to make the proposal. The suggestion unit can also make optimal suggestions taking into account the user's level of expertise. This allows for more appropriate suggestions by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The search tool can also suggest optimal facilities taking into account the user's current weather information. For example, if it is raining, indoor facilities will be suggested first. On hot days, air-conditioned facilities can be suggested. Furthermore, on snowy days, cafes and restaurants that serve hot drinks can be suggested. This allows the tool to suggest facilities based on the user's current weather conditions, improving user comfort.

[0050] The search tool can also suggest optimal facilities taking into account the user's current battery level. For example, if the battery level is low, it can suggest cafes and restaurants with charging facilities. If the battery level is sufficient, it can suggest regular facilities. Furthermore, if the battery level is moderate, it can suggest facilities that can be used in a short time. This makes it possible to suggest facilities based on the battery level of the user's device, improving user convenience.

[0051] The search tool can also suggest optimal facilities taking into account the user's current mode of transportation. For example, if the user is traveling on foot, it can suggest facilities within walking distance. If the user is traveling by bicycle, it can also suggest facilities that are easily accessible by bicycle. Furthermore, if the user is traveling by car, it can also suggest facilities with parking facilities. This makes it possible to suggest facilities according to the user's mode of transportation, improving the convenience of the user's travel.

[0052] The search tool can also suggest optimal facilities based on the user's current time of day. For example, in the morning, it can suggest cafes and restaurants that serve breakfast. In the afternoon, it can suggest facilities that serve lunch. In the evening, it can suggest facilities that serve dinner. This allows it to suggest facilities based on the user's time of day, satisfying the user's dietary needs.

[0053] The search tool can also suggest optimal facilities taking into account the user's current budget. For example, if the user has a limited budget, it can suggest reasonably priced facilities. If the user has a larger budget, it can suggest luxury facilities. Furthermore, if the user has a medium budget, it can suggest facilities with good value for money. This allows it to suggest facilities that fit the user's budget and meet the user's financial needs.

[0054] The search tool can also suggest optimal facilities taking into account the user's current language setting. For example, if the user speaks English, it can suggest facilities that support English. If the user speaks Japanese, it can also suggest facilities that support Japanese. Furthermore, if the user speaks multiple languages, it can also suggest facilities that support multiple languages. This makes it possible to suggest facilities that match the user's language setting, improving the convenience of communication for the user.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The reception unit receives input of the current location and desired arrival time from the user. For example, the reception unit provides an interface for the user to input the current location and desired arrival time. The reception unit can obtain the current location using GPS data or Wi-Fi location information. The reception unit also allows the user to input the desired arrival time in minutes or hours. Step 2: The search unit uses the generation AI to search for facilities that can be reached from the current location within the desired arrival time, based on the information received by the reception unit. For example, the search unit calculates the optimal route based on map data and traffic information, and searches for facilities along that route. The generation AI receives map data and traffic information as input, calculates the optimal route, and searches for facilities along that route. For example, the generation AI analyzes map data and identifies facilities that can be reached from the current location within the desired arrival time. The generation AI can also calculate the optimal route based on traffic information, taking into account the effects of congestion, road construction, etc. Step 3: The suggestion unit suggests the most suitable facility from among the facilities searched by the search unit. For example, the suggestion unit suggests highly rated facilities or facilities that match the user's preferences based on the search results. The suggestion unit can also make more personalized suggestions based on the user's past search history and ratings. For example, the suggestion unit preferentially suggests facilities that the user has given high ratings to in the past.

[0057] (Example 2) A search tool according to an embodiment of the present invention is a tool that allows users to optimally search for hotels, restaurants, hospitals, etc. that are reachable within a certain time from their current location when traveling or on a business trip. The search tool allows users to input their current location and desired arrival time, and a generation AI searches for facilities that can be reached within the desired arrival time from the current location and suggests the most suitable facility. This tool allows users to efficiently find facilities during travel or business trips. For example, a user may input that they want to find a restaurant that is reachable within 10 minutes from their current location. This information is input into the generation AI. The generation AI then analyzes the input information and searches for facilities that can be reached within the desired arrival time from the current location. The generation AI calculates the optimal route based on map data and traffic information and searches for facilities along that route. For example, if a user inputs a restaurant that is reachable within 10 minutes from their current location, the generation AI searches for restaurants that meet the criteria. The generation AI then suggests the most suitable facility based on the search results. For example, if a user inputs a restaurant that is reachable within 10 minutes from their current location, the generation AI suggests highly rated restaurants or restaurants that match the user's preferences from among multiple restaurants that meet the criteria. This allows the search tool to easily find facilities that are reachable from the current location within a short time, even when the user is in a hurry. Furthermore, by suggesting facilities that match the user's preferences, the search tool can provide more personalized suggestions based on the user's past search history and ratings. For example, by preferentially suggesting restaurants or hotels that the user has previously given high ratings, the user's satisfaction can be improved. This allows the search tool to optimally search for facilities that are reachable from the current location within a certain time frame when traveling or on a business trip, greatly improving user convenience.

[0058] A search tool according to an embodiment includes a reception unit, a search unit, and a suggestion unit. The reception unit accepts input of a current location and a desired arrival time from a user. For example, the reception unit provides an interface for the user to input the current location and the desired arrival time. The reception unit can acquire the current location using GPS data or Wi-Fi location information. The reception unit also allows the user to input the desired arrival time in minutes or hours. The search unit uses a generation AI to search for facilities that can be reached from the current location within the desired arrival time based on the information accepted by the reception unit. For example, the search unit calculates an optimal route based on map data and traffic information, and searches for facilities along that route. The generation AI receives map data and traffic information as input, calculates an optimal route, and searches for facilities along that route. For example, the generation AI analyzes map data to identify facilities that can be reached from the current location within the desired arrival time. The generation AI can also calculate an optimal route based on traffic information, taking into account factors such as traffic congestion and road construction. The suggestion unit suggests optimal facilities from among the facilities searched by the search unit. For example, the suggestion unit suggests highly rated facilities or facilities that match the user's preferences based on the search results. The suggestion unit can also make more personalized suggestions based on the user's past search history and ratings. For example, the suggestion unit preferentially suggests facilities that the user has previously given high ratings. This allows the search tool according to the embodiment to efficiently search for facilities that can be reached from the user's current location within the desired arrival time and suggest the most suitable facility.

[0059] The reception unit estimates the user's emotions and adjusts the input method for the current location and desired arrival time based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input the current location and desired arrival time. This allows for more appropriate input by adjusting the input method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit may input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0060] The reception unit can analyze the user's past input history and suggest an appropriate input method. For example, the reception unit can automatically display as candidates the current location and desired arrival time that the user has frequently input in the past. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the current location and desired arrival time to be used during a specific time period based on the user's past input history. This improves user convenience by suggesting the optimal input method based on the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI suggest the optimal input method.

[0061] When inputting the current location and desired arrival time, the reception unit can automatically complete the input content based on the user's current activity status and schedule. The reception unit, for example, references the user's calendar information and automatically sets the current location and desired arrival time based on the schedule. The reception unit can also suggest optimal input content taking into account the user's current activity status (e.g., in a meeting, traveling, etc.). The reception unit can also analyze the user's past schedule patterns and automatically complete the optimal current location and desired arrival time. This reduces the effort required for input by automatically completing the input content based on the user's activity status and schedule. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's calendar information to the generation AI and cause the generation AI to automatically complete the input content.

[0062] The reception unit can provide an appropriate input interface according to the user's device information when the user inputs the current location and desired arrival time. For example, if the user is using a smartphone, the reception unit can provide an input interface that matches the screen size. Furthermore, if the user is using a tablet, the reception unit can provide an input interface that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the reception unit can provide a simple and highly visible input interface. This improves input convenience by providing an input interface that matches the user's device information. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's device information to the generation AI and cause the generation AI to provide an optimal input interface.

[0063] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, when the user is stressed, the reception unit can prioritize displaying important input items and omit other items. Furthermore, when the user is relaxed, the reception unit can display detailed input items and suggest customizable input methods. Furthermore, when the user is in a hurry, the reception unit can prioritize displaying the most important input items to enable quick input. This allows important information to be input preferentially by prioritizing input content according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0064] The reception unit can present highly relevant input candidates by taking into account the user's geographical location information when the user inputs the current location and desired arrival time. For example, when the user is in a specific area, the reception unit can preferentially display input candidates related to the area. Furthermore, when the user is near a specific facility, the reception unit can also present input candidates related to the facility. Furthermore, when the user is in a specific city, the reception unit can also preferentially display input candidates related to the city. In this way, highly relevant input candidates can be presented by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to present highly relevant input candidates.

[0065] The reception unit can analyze the user's social media activity when the user inputs the current location and desired arrival time, and present related input candidates. For example, the reception unit presents locations where the user has checked in on social media as input candidates. The reception unit can also analyze the content of the user's social media posts and present related input candidates. The reception unit can also present related input candidates by referring to the activities of the user's friends on social media. In this way, related input candidates can be presented by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to present related input candidates.

[0066] The reception unit can customize the input method by reflecting the user's past feedback when inputting the current location and desired arrival time. For example, the reception unit preferentially suggests input methods that the user has previously rated highly. The reception unit can also customize the optimal input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and improve the input method. In this way, the input method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the input method.

[0067] The search unit estimates the user's emotions and adjusts the display method of search results based on the estimated user emotions. For example, when the user is relaxed, the search unit displays detailed search results. Furthermore, when the user is in a hurry, the search unit can display concise search results that focus on the main points. Furthermore, when the user is excited, the search unit can display search results with visually stimulating effects. This allows for more appropriate search results to be provided by adjusting the display method of search results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit may input the user's facial expression data into the generation AI and have the generation AI adjust the display method of the search results.

[0068] The search unit can incorporate real-time data during a search to improve the accuracy of map data and traffic information. The search unit can propose an optimal route based on, for example, real-time traffic congestion information. The search unit can also propose an optimal route taking into account the real-time operation status of public transportation. The search unit can also propose a detour route based on real-time road construction information. By incorporating real-time data, the accuracy of search results is improved. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can input real-time traffic information data into the generation AI and cause the generation AI to propose an optimal route.

[0069] When performing a search, the search unit can optimize the search algorithm by referring to the user's past search history. For example, the search unit may preferentially display facilities that the user has previously searched for. The search unit can also suggest optimal search results based on the user's past search history. The search unit can also analyze the user's past search history and improve the search algorithm. This allows the search algorithm to be optimized by referring to the past search history. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input the user's past search history data into a generation AI and cause the generation AI to optimize the search algorithm.

[0070] During a search, the search unit can calculate the optimal route according to the user's current means of transportation. For example, if the user is traveling on foot, the search unit can suggest the shortest route. Furthermore, if the user is traveling by bicycle, the search unit can also suggest a route that includes a bicycle-only road. Furthermore, if the user is traveling by car, the search unit can also suggest a route that avoids traffic congestion. This enables efficient travel by calculating the optimal route according to the user's means of transportation. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the user's means of transportation data into a generation AI and cause the generation AI to calculate the optimal route.

[0071] The search unit estimates the user's emotions and prioritizes search results based on the estimated user emotions. For example, when the user is relaxed, the search unit prioritizes detailed search results. Furthermore, when the user is in a hurry, the search unit can prioritize concise search results that focus on the main points. Furthermore, when the user is excited, the search unit can prioritize search results that add visually stimulating effects. This allows for more appropriate search results to be provided by prioritizing search results according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit may input the user's facial expression data into the generation AI and have the generation AI determine the priority of the search results.

[0072] During a search, the search unit can prioritize searching for highly relevant facilities by taking into account the user's geographical movement history. For example, the search unit prioritizes searching for places the user has visited in the past. The search unit can also prioritize searching for highly relevant facilities based on the user's geographical movement history. The search unit can also analyze the user's geographical movement history and suggest optimal search results. This makes it possible to prioritize searching for highly relevant facilities by taking into account the user's geographical movement history. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the user's geographical movement history data into the generation AI and cause the generation AI to search for highly relevant facilities.

[0073] During a search, the search unit can analyze the user's social media activity and search for related facilities. For example, the search unit may prioritize searching for places where the user has checked in on social media. The search unit can also analyze the content of the user's social media posts and search for related facilities. The search unit can also search for related facilities by referring to the activities of the user's friends on social media. In this way, related facilities can be searched for by analyzing the user's social media activity. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input the user's social media data into the generation AI and cause the generation AI to search for related facilities.

[0074] The search unit can customize the search algorithm by reflecting the user's past feedback when searching. For example, the search unit prioritizes searching for facilities that the user has previously given high ratings. The search unit can also suggest optimal search results based on the user's past feedback. The search unit can also analyze the user's past feedback and improve the search algorithm. In this way, the search algorithm can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the user's past feedback data into the generation AI and have the generation AI customize the search algorithm.

[0075] The suggestion unit estimates the user's emotions and adjusts the way the suggestions are expressed based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit provides detailed suggestions. If the user is in a hurry, the suggestion unit can also provide concise suggestions that focus on the main points. If the user is excited, the suggestion unit can also provide suggestions that add visually stimulating effects. This enables more appropriate suggestions by adjusting the way the suggestions are expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit may input the user's facial expression data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0076] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on facility ratings and user preferences. For example, the suggestion unit prioritizes suggesting facilities with high ratings. The suggestion unit can also prioritize suggesting facilities that match the user's preferences. The suggestion unit can also suggest optimal facilities based on the user's past ratings. This enables more appropriate suggestions by adjusting the level of detail of the proposal based on facility ratings and user preferences. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input user rating data into a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0077] When making a proposal, the suggestion unit can apply different proposal algorithms depending on the category of the facility. For example, in the case of a hotel, the suggestion unit makes a proposal based on ratings and price. In addition, in the case of a restaurant, the suggestion unit can also make a proposal based on the type of cuisine and ratings. In addition, in the case of a hospital, the suggestion unit can also make a proposal based on the medical department and reputation. In this way, by applying a proposal algorithm depending on the category of the facility, more appropriate proposals can be made. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input facility category data into the generation AI and cause the generation AI to apply different proposal algorithms.

[0078] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit prioritizes suggestions that the user has previously given high ratings. The suggestion unit can also make optimal suggestions based on the user's past suggestion results. The suggestion unit can also analyze the user's past suggestion results and improve the suggestion algorithm. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0079] The suggestion unit estimates the user's emotion and adjusts the length of the suggestion based on the estimated user's emotion. For example, if the user is relaxed, the suggestion unit provides detailed suggestions. If the user is in a hurry, the suggestion unit can also provide concise suggestions that focus on the main points. If the user is excited, the suggestion unit can also provide suggestions that add visually stimulating effects. This enables more appropriate suggestions by adjusting the length of the suggestion according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit may input the user's facial expression data into the generation AI and cause the generation AI to adjust the length of the suggestion.

[0080] When making a proposal, the proposal unit can determine the priority of the proposal based on the evaluation date of the facility. For example, the proposal unit can prioritize proposals based on the evaluation date of the facility. The proposal unit can also prioritize proposals based on the evaluation date of the facility over facilities with older evaluations. The proposal unit can also propose the most suitable facility by taking the evaluation date into consideration. This enables more appropriate proposals by determining the priority of proposals based on the evaluation date of the facility. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal unit can input evaluation date data of the facility into the generation AI and cause the generation AI to determine the priority of the proposals.

[0081] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the facilities. For example, the suggestion unit prioritizes suggesting facilities close to the user's current location. The suggestion unit can also prioritize suggesting facilities that match the user's preferences. The suggestion unit can also suggest optimal facilities based on the user's past ratings. This enables more appropriate suggestions by adjusting the order of suggestions based on the relevance of the facilities. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input facility relevance data into a generation AI and cause the generation AI to adjust the order of suggestions.

[0082] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can use detailed technical terminology to make the proposal. Furthermore, if the user does not have technical expertise, the suggestion unit can also use concise and easy-to-understand language to make the proposal. The suggestion unit can also make optimal suggestions taking into account the user's level of expertise. This allows for more appropriate suggestions by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, search unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and provides an interface for inputting the user's current location and desired arrival time. The search unit is realized by the identification processing unit 290 of the data processing device 12 and uses a generation AI to search for facilities that can be reached from the current location within the desired arrival time. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and suggests optimal facilities based on the search results. The reception unit has a function of estimating the user's emotions and adjusting the input method based on the estimated emotions. The search unit adjusts the display method and priority of the search results according to the user's emotions. The suggestion unit adjusts the presentation method and length of suggestions based on the user's emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, search unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for inputting the user's current location and desired arrival time. The search unit is realized by the identification processing unit 290 of the data processing device 12 and uses a generation AI to search for facilities that can be reached from the current location within the desired arrival time. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and suggests optimal facilities based on the search results. The reception unit has a function of estimating the user's emotions and adjusting the input method based on the estimated emotions. The search unit adjusts the display method and priority of the search results according to the user's emotions. The suggestion unit adjusts the presentation method and length of suggestions based on the user's emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, search unit, and suggestion unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and provides an interface for inputting the user's current location and desired arrival time. The search unit is realized by the identification processing unit 290 of the data processing device 12 and uses a generation AI to search for facilities that can be reached from the current location within the desired arrival time. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and suggests optimal facilities based on the search results. The reception unit has a function of estimating the user's emotions and adjusting the input method based on the estimated emotions. The search unit adjusts the display method and priority of search results according to the user's emotions. The suggestion unit adjusts the presentation method and length of suggestions based on the user's emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, search unit, and suggestion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and provides an interface for inputting the user's current location and desired arrival time. The search unit is realized by the specific processing unit 290 of the data processing device 12 and uses a generation AI to search for facilities that can be reached from the current location within the desired arrival time. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal facilities based on the search results. The reception unit has a function of estimating the user's emotions and adjusting the input method based on the estimated emotions. The search unit adjusts the display method and priority of search results according to the user's emotions. The suggestion unit adjusts the presentation method and length of suggestions based on the user's emotions.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The search tool can also monitor the user's health status and suggest optimal facilities based on that status. For example, if the user is tired, it can suggest a spa or cafe where they can relax. If the user feels they are lacking exercise, it can suggest nearby gyms or parks. Furthermore, if the user has a specific health problem, it can prioritize medical facilities that can address that problem. This makes it possible to suggest facilities based on the user's health status, thereby improving user satisfaction.

[0085] The search tool can also suggest optimal facilities taking into account the user's current weather information. For example, if it is raining, indoor facilities will be suggested first. On hot days, air-conditioned facilities can be suggested. Furthermore, on snowy days, cafes and restaurants that serve hot drinks can be suggested. This allows the tool to suggest facilities based on the user's current weather conditions, improving user comfort.

[0086] The search tool can also monitor the user's current activity level and suggest optimal facilities based on the user's activity level. For example, if the user is tired after exercise, it can suggest facilities where the user can relax. If the user has been sitting for a long time, it can suggest facilities where the user can do light exercise. Furthermore, if the user is active, it can suggest facilities where the user can enjoy activities. This makes it possible to suggest facilities according to the user's activity level, thereby improving user satisfaction.

[0087] The search tool can also monitor the user's current heart rate and suggest the most suitable facilities based on the heart rate. For example, if the user's heart rate is high, it can suggest facilities where they can relax. If the user's heart rate is low, it can suggest facilities where they can enjoy activities. Furthermore, if the user's heart rate is stable, it can suggest regular facilities. This makes it possible to suggest facilities based on the user's heart rate, thereby supporting the user's health.

[0088] The search tool can also monitor the user's current stress level and suggest optimal facilities based on the stress level. For example, if the user's stress level is high, it can suggest facilities where they can relax. If the user's stress level is low, it can suggest facilities where they can enjoy activities. Furthermore, if the user's stress level is medium, it can suggest facilities that offer a balanced lifestyle. This makes it possible to suggest facilities according to the user's stress level, thereby supporting the user's mental health.

[0089] The search tool can also suggest optimal facilities taking into account the user's current battery level. For example, if the battery level is low, it can suggest cafes and restaurants with charging facilities. If the battery level is sufficient, it can suggest regular facilities. Furthermore, if the battery level is moderate, it can suggest facilities that can be used in a short time. This makes it possible to suggest facilities based on the battery level of the user's device, improving user convenience.

[0090] The search tool can also suggest optimal facilities taking into account the user's current mode of transportation. For example, if the user is traveling on foot, it can suggest facilities within walking distance. If the user is traveling by bicycle, it can also suggest facilities that are easily accessible by bicycle. Furthermore, if the user is traveling by car, it can also suggest facilities with parking facilities. This makes it possible to suggest facilities according to the user's mode of transportation, improving the convenience of the user's travel.

[0091] The search tool can also suggest optimal facilities based on the user's current time of day. For example, in the morning, it can suggest cafes and restaurants that serve breakfast. In the afternoon, it can suggest facilities that serve lunch. In the evening, it can suggest facilities that serve dinner. This allows it to suggest facilities based on the user's time of day, satisfying the user's dietary needs.

[0092] The search tool can also suggest optimal facilities taking into account the user's current budget. For example, if the user has a limited budget, it can suggest reasonably priced facilities. If the user has a larger budget, it can suggest luxury facilities. Furthermore, if the user has a medium budget, it can suggest facilities with good value for money. This allows it to suggest facilities that fit the user's budget and meet the user's financial needs.

[0093] The search tool can also suggest optimal facilities taking into account the user's current language setting. For example, if the user speaks English, it can suggest facilities that support English. If the user speaks Japanese, it can also suggest facilities that support Japanese. Furthermore, if the user speaks multiple languages, it can also suggest facilities that support multiple languages. This makes it possible to suggest facilities that match the user's language setting, improving the convenience of communication for the user.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The reception unit receives input of the current location and desired arrival time from the user. For example, the reception unit provides an interface for the user to input the current location and desired arrival time. The reception unit can obtain the current location using GPS data or Wi-Fi location information. The reception unit also allows the user to input the desired arrival time in minutes or hours. Step 2: The search unit uses the generation AI to search for facilities that can be reached from the current location within the desired arrival time, based on the information received by the reception unit. For example, the search unit calculates the optimal route based on map data and traffic information, and searches for facilities along that route. The generation AI receives map data and traffic information as input, calculates the optimal route, and searches for facilities along that route. For example, the generation AI analyzes map data and identifies facilities that can be reached from the current location within the desired arrival time. The generation AI can also calculate the optimal route based on traffic information, taking into account the effects of congestion, road construction, etc. Step 3: The suggestion unit suggests the most suitable facility from among the facilities searched by the search unit. For example, the suggestion unit suggests highly rated facilities or facilities that match the user's preferences based on the search results. The suggestion unit can also make more personalized suggestions based on the user's past search history and ratings. For example, the suggestion unit preferentially suggests facilities that the user has given high ratings to in the past.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0099] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0140] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0157] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] 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.

[0159] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0160] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0167] [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of a current location and a desired arrival time from a user; a search unit that searches for facilities that can be reached from the current location within a desired arrival time based on the information received by the reception unit; a suggestion unit that suggests an appropriate facility from among the facilities searched by the search unit; A system characterized by:

2. The reception unit Inferring user emotions and adjusting the input method for current location and desired arrival time based on the estimated user emotions 2. The system of claim 1.

3. The reception unit Analyzes the user's past input history and suggests appropriate input methods 2. The system of claim 1.

4. The reception unit When entering your current location and desired arrival time, the app auto-completes the input based on your current activity and schedule.

2. The system of claim 1.

5. The reception unit When entering the current location and desired arrival time, provide an appropriate input interface according to the user's device information.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize input content based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When entering your current location and desired arrival time, the app takes your geographic location into account to provide relevant suggestions.

2. The system of claim 1.

8. The reception unit When you enter your current location and desired arrival time, the app analyzes your social media activity and offers relevant suggestions.

2. The system of claim 1.

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