System

Abstract

An object of a system according to an embodiment is to make a proposal for effectively utilizing a waiting time.SOLUTION: A system according to an embodiment includes an input unit, an acquisition unit, a suggestion unit, and a collection unit. The input unit is configured to input a waiting time. The acquisition unit acquires the current location based on the waiting time input by the input unit. The suggestion unit makes a suggestion based on the current location information acquired by the acquisition unit. The collection unit collects feedback on the spot proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have not provided sufficient proposals for making effective use of waiting time, and there is room for improvement.

[0005] The system according to the embodiment aims to make suggestions for making effective use of waiting time. [Means for solving the problem]

[0006] A system according to an embodiment includes an input unit, an acquisition unit, a suggestion unit, and a collection unit. The input unit inputs a waiting time. The acquisition unit acquires a current location based on the waiting time input by the input unit. The suggestion unit makes suggestions based on the current location information acquired by the acquisition unit. The collection unit collects feedback on the spots suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can make suggestions for making effective use of waiting 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) An activity recommendation system according to an embodiment of the present invention is a system that makes suggestions for making effective use of waiting time and improves user satisfaction. The activity recommendation system allows a user to input a waiting time, checks whether the user can leave the location, and if so, suggests recommended spots such as nearby cafes and general stores. The suggestions take into account round-trip time, allowing the user to use their waiting time effectively. The system also makes suggestions based on the user's preferences. For example, the activity recommendation system allows a user to input a waiting time. The system then acquires the user's current location and checks whether the user can leave the location. If the user can leave, the system suggests recommended spots such as nearby cafes and general stores. The suggestions take into account round-trip time, allowing the user to use their waiting time effectively. The activity recommendation system also makes suggestions based on the user's preferences. For example, the activity recommendation system learns the user's behavioral history and makes suggestions based on the user's preferences. The activity recommendation system also provides information on how to spend waiting time. This allows the user to make the most of their waiting time. This allows the time-saving recommendation system to make effective use of waiting time at the hospital or waiting in line, improving user satisfaction. For example, by spending the time waiting at the hospital at a cafe, you can have a relaxing time. Also, by spending the time waiting in line at a general store, you can enjoy shopping. In this way, waiting time can be turned into fun time.

[0029] The activity recommendation system according to the embodiment includes a reception unit, an acquisition unit, a suggestion unit, and a collection unit. The reception unit receives a waiting time from a user. The waiting time may range from a few minutes to a few hours, for example, but is not limited to such examples. The acquisition unit acquires a current location based on the waiting time input by the reception unit. Methods for acquiring the current location include, for example, but are not limited to, GPS, Wi-Fi, and location information of cell towers. The suggestion unit makes suggestions based on the current location information acquired by the acquisition unit. The suggestions may include, for example, tourist spots, restaurants, and events, for example, but are not limited to such examples. The suggestion unit may also make suggestions based on a user's preferences. For example, the suggestion unit learns the user's behavior history and makes suggestions based on the user's preferences. The collection unit collects feedback on the spots suggested by the suggestion unit. Methods for collecting feedback include, for example, surveys, reviews, and ratings, for example, but are not limited to such examples. As a result, the activity recommendation system according to the embodiment can make suggestions for making effective use of waiting time and improve user satisfaction.

[0030] The suggestion unit includes a learning unit that learns the user's behavioral history. The learning unit learns the user's behavioral history. The behavioral history includes, for example, places visited, services used, and travel routes, but is not limited to these examples. The learning unit, for example, learns information about cafes and restaurants that the user has visited in the past and makes suggestions based on the user's preferences. The learning unit can also learn the user's travel routes and suggest information about areas that the user frequently visits. Furthermore, the learning unit can learn information about services used by the user and suggest services based on the user's preferences. In this way, the suggestion unit can make more appropriate suggestions by learning the user's behavioral history.

[0031] The suggestion unit includes an update unit that updates information in real time. The update unit updates information in real time. The real-time update frequency includes, for example, every second, every minute, when a specific event occurs, etc., but is not limited to these examples. The update unit, for example, updates the user's current location information in real time and makes suggestions based on the latest information. The update unit can also update information about suggested spots in real time and provide the latest information. Furthermore, the update unit can update the user's behavior history in real time and make suggestions based on the latest behavior history. As a result, the suggestion unit can make suggestions based on the latest information by updating the information in real time.

[0032] The suggestion unit can make suggestions according to the user's preferences. The suggestion unit makes suggestions according to the user's preferences. Methods for collecting the user's preferences include, but are not limited to, past selection history, survey results, and social media activity, for example. The suggestion unit makes suggestions according to the user's preferences, for example, based on information about cafes and restaurants selected by the user in the past. The suggestion unit can also make suggestions according to the user's preferences based on the results of a survey conducted by the user. Furthermore, the suggestion unit can analyze the user's social media activity and make suggestions according to the user's preferences. As a result, the suggestion unit makes suggestions according to the user's preferences, thereby improving user satisfaction.

[0033] The suggestion unit can provide information about the suggested spots. The suggestion unit provides information about the suggested spots. The information about the spots includes, for example, addresses, business hours, ratings, reviews, etc., but is not limited to these examples. For example, the suggestion unit provides the address and business hours of the suggested cafe. The suggestion unit can also provide ratings and reviews of the suggested restaurant. Furthermore, the suggestion unit can provide detailed information about the suggested tourist spot. In this way, the suggestion unit can provide information about the suggested spots, allowing the user to select a spot with peace of mind.

[0034] The collection unit can collect feedback on the proposed spots. The collection unit collects feedback on the proposed spots. Methods of collecting feedback include, but are not limited to, questionnaires, reviews, and ratings, for example. The collection unit, for example, collects questionnaires on cafes suggested by users. The collection unit can also collect reviews on restaurants suggested by users. Furthermore, the collection unit can collect ratings on tourist spots suggested by users. In this way, the collection unit can improve the accuracy of suggestions by collecting feedback.

[0035] The reception unit can improve the accuracy of input when inputting a waiting time by referring to the user's past waiting time data. The reception unit improves the accuracy of input when inputting a waiting time by referring to the user's past waiting time data. Past waiting time data includes, but is not limited to, a history database, a log file, etc. For example, the reception unit automatically displays waiting times previously input by the user as candidates. The reception unit can also analyze the user's past waiting time data to predict and suggest an optimal waiting time. Furthermore, the reception unit can predict waiting times for specific time periods based on the user's past waiting time data, thereby simplifying input. In this way, the reception unit improves the accuracy of input by referring to past data.

[0036] The reception unit can suggest a waiting time based on the user's current schedule when the user inputs the waiting time. The reception unit can suggest a waiting time based on the user's current schedule when the user inputs the waiting time. Methods for acquiring the current schedule include, but are not limited to, a calendar app, a schedule, and the like. The reception unit can, for example, refer to the user's calendar information and automatically set the waiting time. The reception unit can also suggest an optimal waiting time based on the user's schedule. Furthermore, the reception unit can adjust the waiting time to match the user's schedule. In this way, the reception unit can suggest an optimal waiting time by taking the current schedule into consideration.

[0037] The reception unit can select an appropriate input means according to the user's input method when inputting the waiting time. The reception unit selects an appropriate input means according to the user's input method when inputting the waiting time. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user inputs the waiting time by voice, the reception unit supports the input using voice recognition technology. Furthermore, when the user inputs the waiting time by text, the reception unit can also provide a text input interface. Furthermore, when the user inputs the waiting time by image, the reception unit can also support the input using image recognition technology. In this way, the reception unit can select the optimal means according to the user's input method, allowing for smooth input.

[0038] When inputting a waiting time, the reception unit can prioritize inputting a highly relevant waiting time by taking into consideration the user's geographical location information. When inputting a waiting time, the reception unit prioritizes inputting a highly relevant waiting time by taking into consideration the user's geographical location information. Methods for acquiring geographical location information include, but are not limited to, GPS, Wi-Fi, and cell tower location information. For example, the reception unit prioritizes inputting a waiting time for a location close to the user's current location. Furthermore, when the user is in a specific area, the reception unit can also prioritize inputting a waiting time for that area. Furthermore, when the user is moving, the reception unit can also input a waiting time based on the user's current location. In this way, the reception unit can input a highly relevant waiting time by taking into consideration the geographical location information.

[0039] The reception unit can analyze the user's social media activity when the waiting time is input and input the related waiting time. The reception unit can analyze the user's social media activity when the waiting time is input and input the related waiting time. Methods of collecting social media activity include, but are not limited to, for example, posted content, likes, comments, etc. The reception unit can input, for example, the waiting time of a location where the user checked in on social media. The reception unit can also analyze the user's social media posted content and input the related waiting time. Furthermore, the reception unit can input the waiting time with reference to the activity of the user's friends on social media. In this way, the reception unit can input the related waiting time by analyzing social media activity.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when the user inputs the waiting time. The reception unit customizes the input method by reflecting the user's past feedback when the user inputs the waiting time. Methods for collecting past feedback include, but are not limited to, survey results, reviews, and ratings, for example. The reception unit customizes the input interface based on feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and suggest an optimal input method. Furthermore, the reception unit can improve the input procedure based on the user's feedback. In this way, the reception unit can optimize the input method by reflecting the past feedback.

[0041] The acquisition unit can improve the accuracy of acquisition when acquiring the current location by referring to the user's past location information. The acquisition unit improves the accuracy of acquisition when acquiring the current location by referring to the user's past location information. Methods for collecting past location information include, but are not limited to, a history database, a log file, etc. The acquisition unit can improve the accuracy of acquisition of the current location, for example, based on location information of places the user has visited in the past. The acquisition unit can also analyze the user's past location information and acquire an optimal current location. Furthermore, the acquisition unit can optimize the method of acquiring the current location by referring to the user's past location information. As a result, the acquisition unit improves the accuracy of acquisition by referring to the past location information.

[0042] When acquiring the current location, the acquisition unit can select the optimal acquisition method taking into consideration the user's current activity status. When acquiring the current location, the acquisition unit selects the optimal acquisition method taking into consideration the user's current activity status. Methods for acquiring the current activity status include, but are not limited to, sensor information, app usage status, and the like. For example, when the user is walking, the acquisition unit acquires the current location using GPS. Furthermore, when the user is traveling by car, the acquisition unit can also acquire the current location using an in-car navigation system. Furthermore, when the user is indoors, the acquisition unit can also acquire the current location using Wi-Fi or Bluetooth (registered trademark). This allows the acquisition unit to select the optimal acquisition method by taking into consideration the current activity status.

[0043] The acquisition unit can select the optimal acquisition means depending on the user's input method when acquiring the current location. The acquisition unit selects the optimal acquisition means depending on the user's input method when acquiring the current location. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user inputs the current location by voice, the acquisition unit acquires the current location using voice recognition technology. Furthermore, when the user inputs the current location by text, the acquisition unit can also provide a text input interface. Furthermore, when the user inputs the current location by image, the acquisition unit can also acquire the current location using image recognition technology. This allows the acquisition unit to select the optimal means depending on the user's input method, thereby enabling smooth acquisition.

[0044] When acquiring the current location, the acquisition unit can prioritize acquiring highly relevant location information by taking into account the user's geographical location information. When acquiring the current location, the acquisition unit prioritizes acquiring highly relevant location information by taking into account the user's geographical location information. Methods for acquiring geographical location information include, but are not limited to, GPS, Wi-Fi, and cell tower location information. For example, the acquisition unit prioritizes acquiring location information of locations close to the user's current location. Furthermore, when the user is in a specific area, the acquisition unit can also prioritize acquiring location information of that area. Furthermore, when the user is moving, the acquisition unit can also acquire location information based on the user's current location. In this way, the acquisition unit can acquire highly relevant location information by taking into account the geographical location information.

[0045] The acquisition unit can analyze the user's social media activity when acquiring the current location and acquire related location information. The acquisition unit can analyze the user's social media activity when acquiring the current location and acquire related location information. Methods of collecting social media activity include, but are not limited to, posted content, likes, comments, etc. For example, the acquisition unit acquires location information of locations where the user has checked in on social media. The acquisition unit can also analyze the content of the user's social media posts and acquire related location information. Furthermore, the acquisition unit can acquire location information by referring to the activities of the user's friends on social media. In this way, the acquisition unit can acquire related location information by analyzing social media activity.

[0046] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring the current location. The acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring the current location. Methods for collecting past feedback include, but are not limited to, survey results, reviews, and ratings, for example. The acquisition unit customizes the current location acquisition method based on feedback provided by the user in the past. The acquisition unit can also analyze the user's past feedback and suggest an optimal acquisition method. Furthermore, the acquisition unit can improve the current location acquisition procedure based on the user's feedback. In this way, the acquisition unit can optimize the acquisition method by reflecting the past feedback.

[0047] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the spot when making a suggestion. The suggestion unit can adjust the level of detail of the suggestion based on the importance of the spot when making a suggestion. Criteria for evaluating the importance of a spot include, but are not limited to, for example, the number of visitors, review ratings, and popularity. For example, the suggestion unit can provide detailed information for an important spot. The suggestion unit can also provide concise information for a less important spot. Furthermore, the suggestion unit can adjust the level of detail of the spot according to the user's level of interest. In this way, the suggestion unit can provide appropriate information to the user by adjusting the level of detail of the suggestion based on the importance of the spot.

[0048] The suggestion unit can apply different suggestion algorithms depending on the category of the spot when making a suggestion. The suggestion unit applies different suggestion algorithms depending on the category of the spot when making a suggestion. Spot categories can be classified, for example, into tourist spots, restaurants, shopping malls, etc., but are not limited to these examples. For example, in the case of a cafe, the suggestion unit makes suggestions that emphasize information about the atmosphere and menu. In addition, in the case of a general store, the suggestion unit can make suggestions that emphasize information about the type of products and price range. Furthermore, in the case of a park, the suggestion unit can make suggestions that emphasize information about the facilities and scenery. In this way, the suggestion unit can make optimal suggestions to the user by applying a suggestion algorithm depending on the category of the spot.

[0049] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions. The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions. Methods for collecting past suggestion results include, but are not limited to, proposal history, user selection results, and the like. For example, the suggestion unit preferentially suggests spots that the user has previously given high ratings. The suggestion unit can also analyze the user's past suggestion results and suggest optimal spots. Furthermore, the suggestion unit can optimize the suggestion algorithm based on the user's past suggestion results. As a result, the suggestion unit can improve the accuracy of suggestions by referring to the past suggestion results.

[0050] The suggestion unit can determine the priority of suggestions based on the submission time of the spots when making suggestions. The suggestion unit can determine the priority of suggestions based on the submission time of the spots when making suggestions. Methods for acquiring the submission time include, but are not limited to, for example, the submission date and time, the event date and time, etc. The suggestion unit, for example, prioritizes suggestions of newly opened spots. The suggestion unit can also prioritize suggestions of seasonal spots. Furthermore, the suggestion unit can suggest optimal spots by referring to the user's past visit dates. In this way, the suggestion unit can provide the user with the latest information by determining the priority of suggestions based on the submission time.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of the spots when making suggestions. The suggestion unit adjusts the order of suggestions based on the relevance of the spots when making suggestions. Criteria for evaluating relevance include, but are not limited to, common themes, related keywords, and the like. For example, the suggestion unit prioritizes suggesting spots close to the user's current location. The suggestion unit can also prioritize suggesting spots that match the user's preferences. Furthermore, the suggestion unit can suggest highly relevant spots by referring to the user's past visit history. In this way, the suggestion unit can provide information to the user in an optimal order by adjusting the order of suggestions based on relevance.

[0052] The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. Evaluation criteria for the level of expertise include, but are not limited to, questionnaire results, past learning history, etc. For example, if the user has technical expertise, the suggestion unit can make a proposal using technical terms. Furthermore, if the user does not have technical expertise, the suggestion unit can make a proposal using simple language. Furthermore, the suggestion unit can make a proposal using optimal language by referring to the user's past suggestion results. In this way, the suggestion unit can provide appropriate information to the user by adjusting the use of technical terms according to the level of expertise.

[0053] The collection unit can improve the accuracy of feedback collection by referring to the user's past feedback data when collecting feedback. The collection unit improves the accuracy of feedback collection by referring to the user's past feedback data when collecting feedback. Methods for collecting past feedback data include, but are not limited to, survey results, reviews, and ratings, for example. The collection unit optimizes the collection method based on feedback previously provided by the user. The collection unit can also analyze the user's past feedback data and propose an optimal collection method. Furthermore, the collection unit can improve the collection procedure by referring to the user's past feedback data. As a result, the collection unit improves the accuracy of collection by referring to the past feedback data.

[0054] The collection unit can select an optimal collection method in consideration of the user's current activity status when collecting feedback. The collection unit selects an optimal collection method in consideration of the user's current activity status when collecting feedback. Methods for acquiring the current activity status include, but are not limited to, sensor information, app usage status, and the like. For example, the collection unit may request brief feedback when the user is walking. The collection unit may also request detailed feedback when the user is sitting. Furthermore, the collection unit may refrain from collecting feedback when the user is busy. This allows the collection unit to select an optimal collection method by considering the current activity status.

[0055] When collecting feedback, the collection unit can prioritize collecting highly relevant feedback by taking into account the user's geographical location information. When collecting feedback, the collection unit prioritizes collecting highly relevant feedback by taking into account the user's geographical location information. Methods for acquiring geographical location information include, but are not limited to, GPS, Wi-Fi, and cell tower location information. For example, the collection unit prioritizes collecting feedback from locations close to the user's current location. Furthermore, when the user is in a specific area, the collection unit can also prioritize collecting feedback from that area. Furthermore, when the user is moving, the collection unit can collect feedback based on the user's current location. In this way, the collection unit can collect highly relevant feedback by taking into account the geographical location information.

[0056] The collection unit can analyze the user's social media activity and collect relevant feedback when collecting feedback. The collection unit analyzes the user's social media activity and collects relevant feedback when collecting feedback. Methods of collecting social media activity include, but are not limited to, post content, likes, comments, etc. For example, the collection unit collects feedback on places where the user has checked in on social media. The collection unit can also analyze the user's social media post content and collect relevant feedback. Furthermore, the collection unit can collect feedback by referring to the activities of the user's friends on social media. In this way, the collection unit can collect relevant feedback by analyzing social media activity.

[0057] The learning unit can optimize the learning algorithm by referring to past learning data during learning. The learning unit optimizes the learning algorithm by referring to past learning data during learning. Methods for collecting past learning data include, but are not limited to, learning history, training datasets, etc. The learning unit selects an optimal learning algorithm, for example, based on the past learning data. The learning unit can also analyze the past learning data and improve the learning algorithm. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to the past learning data. As a result, the learning unit improves the accuracy of the learning algorithm by referring to the past learning data.

[0058] The learning unit can update the learning data during learning by reflecting user feedback. The learning unit updates the learning data during learning by reflecting user feedback. Methods of collecting feedback include, but are not limited to, questionnaires, reviews, and evaluations, for example. The learning unit updates the learning data based on user feedback, for example. The learning unit can also analyze user feedback and improve the learning data. Furthermore, the learning unit can improve the accuracy of the learning data by referring to user feedback. In this way, the learning unit reflects user feedback, thereby improving the accuracy of the learning data.

[0059] During learning, the learning unit can weight the learning data based on the time of submission of the spot. During learning, the learning unit weights the learning data based on the time of submission of the spot. Methods for obtaining the submission time include, but are not limited to, for example, the submission date and time, the event date and time, etc. The learning unit, for example, places emphasis on data of newly opened spots during learning. The learning unit can also place emphasis on data of seasonal spots during learning. Furthermore, the learning unit can weight the learning data with reference to the user's past visit dates. In this way, the learning unit weights the learning data based on the submission date, thereby improving the accuracy of learning.

[0060] The learning unit can integrate information from different data sources to enrich the learning data during learning. The learning unit integrates information from different data sources to enrich the learning data during learning. Types of different data sources include, but are not limited to, sensor data, user input data, and external API data. For example, the learning unit integrates data from social media to learn. The learning unit can also integrate user feedback data to learn. Furthermore, the learning unit can integrate information from public databases to learn. As a result, the learning unit improves the accuracy of the learning data by integrating information from different data sources.

[0061] The update unit can improve the accuracy of the update by referring to the user's past behavioral history when updating information. The update unit improves the accuracy of the update by referring to the user's past behavioral history when updating information. Methods for collecting past behavioral history include, but are not limited to, places visited, services used, and travel routes, for example. The update unit improves the accuracy of the information update based on the user's past behavioral history, for example. The update unit can also analyze the user's past behavioral history and improve the information update method. Furthermore, the update unit can optimize the information update procedure by referring to the user's past behavioral history. As a result, the update unit improves the accuracy of the update by referring to the past behavioral history.

[0062] The update unit can select the optimal update method in consideration of the user's current activity status when updating information. The update unit selects the optimal update method in consideration of the user's current activity status when updating information. Methods for acquiring the current activity status include, but are not limited to, sensor information, app usage status, and the like. For example, the update unit performs a brief information update when the user is walking. Furthermore, the update unit can also perform a detailed information update when the user is sitting. Furthermore, the update unit can refrain from updating information when the user is busy. This allows the update unit to select the optimal update method by considering the current activity status.

[0063] When updating information, the update unit can prioritize updating highly relevant information by taking into account the user's geographical location information. When updating information, the update unit prioritizes updating highly relevant information by taking into account the user's geographical location information. Methods for acquiring geographical location information include, but are not limited to, GPS, Wi-Fi, and cell tower location information. For example, the update unit prioritizes updating information about locations close to the user's current location. Furthermore, when the user is in a specific area, the update unit can also prioritize updating information about that area. Furthermore, when the user is moving, the update unit can also update information based on the user's current location. In this way, the update unit can update highly relevant information by taking into account the geographical location information.

[0064] The update unit can analyze the user's social media activity and update related information when updating information. The update unit can analyze the user's social media activity and update related information when updating information. Methods of collecting social media activity include, but are not limited to, posted content, likes, comments, etc. For example, the update unit can prioritize updating information about places where the user has checked in on social media. The update unit can also analyze the content of the user's social media posts and update related information. Furthermore, the update unit can update related information by referring to the activities of the user's friends on social media. In this way, the update unit can update related information by analyzing social media activity.

[0065] The update unit can customize the update method by reflecting the user's past feedback when updating information. The update unit customizes the update method by reflecting the user's past feedback when updating information. Methods for collecting past feedback include, but are not limited to, survey results, reviews, and ratings, for example. The update unit customizes the information update method based on feedback provided by the user in the past. The update unit can also analyze the user's past feedback and propose an optimal update method. Furthermore, the update unit can improve the information update procedure based on the user's feedback. In this way, the update unit can optimize the update method by reflecting the past feedback.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The suggestion unit can monitor the user's current health condition and make suggestions based on the health condition. For example, the suggestion unit can monitor the user's heart rate and stress level and suggest a quiet cafe or park if the user needs to relax. If the user is not getting enough exercise, the suggestion unit can suggest walking courses or fitness facilities. Furthermore, if the user is tired, the suggestion unit can suggest a spa or massage facility where the user can refresh themselves. In this way, the suggestion unit can improve the user's health and satisfaction by making suggestions according to the user's health condition.

[0068] The suggestion unit can analyze the user's past suggestion history to improve the accuracy of suggestions. For example, it can prioritize suggestions of spots that the user has given high ratings to in the past. It can also suggest similar spots based on information about spots the user has visited in the past. It can also analyze the user's past suggestion history to suggest new spots that match the user's preferences. In this way, the suggestion unit can improve the accuracy of suggestions by utilizing the past suggestion history.

[0069] The suggestion unit can make suggestions taking into account the user's current schedule. For example, by referring to the user's calendar information, if the user has some free time in their schedule, the suggestion unit can suggest spots where they can stay for a long time. Also, if the user's schedule is tight, the suggestion unit can suggest spots that can be enjoyed in a short time. Furthermore, the suggestion unit can make optimal suggestions based on the user's schedule. This allows the suggestion unit to make optimal suggestions by taking the user's schedule into account.

[0070] The suggestion unit can make suggestions taking into account the user's current activity status. For example, if the user is walking, nearby spots can be suggested. Also, if the user is traveling by car, spots that are easily accessible by car can be suggested. Furthermore, if the user is indoors, spots that can be enjoyed indoors can be suggested. In this way, the suggestion unit can make optimal suggestions by taking into account the user's activity status.

[0071] The suggestion unit can analyze the user's social media activity and suggest related spots. For example, it can suggest similar spots based on information about places where the user has checked in on social media. It can also analyze the content of the user's social media posts to suggest related spots. It can also suggest spots based on the activities of the user's friends on social media. In this way, the suggestion unit can suggest related spots by analyzing social media activity.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The user inputs a waiting time into the reception unit. The waiting time may range from a few minutes to a few hours, but is not limited to this example. Step 2: The acquisition unit acquires the current location based on the waiting time input by the reception unit. Methods for acquiring the current location include, but are not limited to, GPS, Wi-Fi, and cell tower location information. Step 3: The suggestion unit makes suggestions based on the current location information acquired by the acquisition unit. Suggestions include, but are not limited to, tourist spots, restaurants, and events. The suggestion unit can also make suggestions based on the user's preferences. For example, the suggestion unit learns the user's behavior history and makes suggestions based on the user's preferences. Step 4: The collection unit collects feedback on the spots proposed by the suggestion unit. Methods for collecting feedback include, but are not limited to, questionnaires, reviews, and ratings.

[0074] (Example 2) An activity recommendation system according to an embodiment of the present invention is a system that makes suggestions for making effective use of waiting time and improves user satisfaction. The activity recommendation system allows a user to input a waiting time, checks whether the user can leave the location, and if so, suggests recommended spots such as nearby cafes and general stores. The suggestions take into account round-trip time, allowing the user to use their waiting time effectively. The system also makes suggestions based on the user's preferences. For example, the activity recommendation system allows a user to input a waiting time. The system then acquires the user's current location and checks whether the user can leave the location. If the user can leave, the system suggests recommended spots such as nearby cafes and general stores. The suggestions take into account round-trip time, allowing the user to use their waiting time effectively. The activity recommendation system also makes suggestions based on the user's preferences. For example, the activity recommendation system learns the user's behavioral history and makes suggestions based on the user's preferences. The activity recommendation system also provides information on how to spend waiting time. This allows the user to make the most of their waiting time. This allows the time-saving recommendation system to make effective use of waiting time at the hospital or waiting in line, improving user satisfaction. For example, by spending the time waiting at the hospital at a cafe, you can have a relaxing time. Also, by spending the time waiting in line at a general store, you can enjoy shopping. In this way, waiting time can be turned into fun time.

[0075] The activity recommendation system according to the embodiment includes a reception unit, an acquisition unit, a suggestion unit, and a collection unit. The reception unit receives a waiting time from a user. The waiting time may range from a few minutes to a few hours, for example, but is not limited to such examples. The acquisition unit acquires a current location based on the waiting time input by the reception unit. Methods for acquiring the current location include, for example, but are not limited to, GPS, Wi-Fi, and location information of cell towers. The suggestion unit makes suggestions based on the current location information acquired by the acquisition unit. The suggestions may include, for example, tourist spots, restaurants, and events, for example, but are not limited to such examples. The suggestion unit may also make suggestions based on a user's preferences. For example, the suggestion unit learns the user's behavior history and makes suggestions based on the user's preferences. The collection unit collects feedback on the spots suggested by the suggestion unit. Methods for collecting feedback include, for example, surveys, reviews, and ratings, for example, but are not limited to such examples. As a result, the activity recommendation system according to the embodiment can make suggestions for making effective use of waiting time and improve user satisfaction.

[0076] The suggestion unit includes a learning unit that learns the user's behavioral history. The learning unit learns the user's behavioral history. The behavioral history includes, for example, places visited, services used, and travel routes, but is not limited to these examples. The learning unit, for example, learns information about cafes and restaurants that the user has visited in the past and makes suggestions based on the user's preferences. The learning unit can also learn the user's travel routes and suggest information about areas that the user frequently visits. Furthermore, the learning unit can learn information about services used by the user and suggest services based on the user's preferences. In this way, the suggestion unit can make more appropriate suggestions by learning the user's behavioral history.

[0077] The suggestion unit includes an update unit that updates information in real time. The update unit updates information in real time. The real-time update frequency includes, for example, every second, every minute, when a specific event occurs, etc., but is not limited to these examples. The update unit, for example, updates the user's current location information in real time and makes suggestions based on the latest information. The update unit can also update information about suggested spots in real time and provide the latest information. Furthermore, the update unit can update the user's behavior history in real time and make suggestions based on the latest behavior history. As a result, the suggestion unit can make suggestions based on the latest information by updating the information in real time.

[0078] The suggestion unit can make suggestions according to the user's preferences. The suggestion unit makes suggestions according to the user's preferences. Methods for collecting the user's preferences include, but are not limited to, past selection history, survey results, and social media activity, for example. The suggestion unit makes suggestions according to the user's preferences, for example, based on information about cafes and restaurants selected by the user in the past. The suggestion unit can also make suggestions according to the user's preferences based on the results of a survey conducted by the user. Furthermore, the suggestion unit can analyze the user's social media activity and make suggestions according to the user's preferences. As a result, the suggestion unit makes suggestions according to the user's preferences, thereby improving user satisfaction.

[0079] The suggestion unit can provide information about the suggested spots. The suggestion unit provides information about the suggested spots. The information about the spots includes, for example, addresses, business hours, ratings, reviews, etc., but is not limited to these examples. For example, the suggestion unit provides the address and business hours of the suggested cafe. The suggestion unit can also provide ratings and reviews of the suggested restaurant. Furthermore, the suggestion unit can provide detailed information about the suggested tourist spot. In this way, the suggestion unit can provide information about the suggested spots, allowing the user to select a spot with peace of mind.

[0080] The collection unit can collect feedback on the proposed spots. The collection unit collects feedback on the proposed spots. Methods of collecting feedback include, but are not limited to, questionnaires, reviews, and ratings, for example. The collection unit, for example, collects questionnaires on cafes suggested by users. The collection unit can also collect reviews on restaurants suggested by users. Furthermore, the collection unit can collect ratings on tourist spots suggested by users. In this way, the collection unit can improve the accuracy of suggestions by collecting feedback.

[0081] The reception unit can estimate the user's emotion and adjust the waiting time input method based on the estimated user emotion. The reception unit can estimate the user's emotion and adjust the waiting time input method based on the estimated user emotion. Emotion estimation methods include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input the waiting time. In this way, the reception unit can reduce the user's stress by adjusting the input method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] The reception unit can improve the accuracy of input when inputting a waiting time by referring to the user's past waiting time data. The reception unit improves the accuracy of input when inputting a waiting time by referring to the user's past waiting time data. Past waiting time data includes, but is not limited to, a history database, a log file, etc. For example, the reception unit automatically displays waiting times previously input by the user as candidates. The reception unit can also analyze the user's past waiting time data to predict and suggest an optimal waiting time. Furthermore, the reception unit can predict waiting times for specific time periods based on the user's past waiting time data, thereby simplifying input. In this way, the reception unit improves the accuracy of input by referring to past data.

[0083] The reception unit can suggest a waiting time based on the user's current schedule when the user inputs the waiting time. The reception unit can suggest a waiting time based on the user's current schedule when the user inputs the waiting time. Methods for acquiring the current schedule include, but are not limited to, a calendar app, a schedule, and the like. The reception unit can, for example, refer to the user's calendar information and automatically set the waiting time. The reception unit can also suggest an optimal waiting time based on the user's schedule. Furthermore, the reception unit can adjust the waiting time to match the user's schedule. In this way, the reception unit can suggest an optimal waiting time by taking the current schedule into consideration.

[0084] The reception unit can select an appropriate input means according to the user's input method when inputting the waiting time. The reception unit selects an appropriate input means according to the user's input method when inputting the waiting time. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user inputs the waiting time by voice, the reception unit supports the input using voice recognition technology. Furthermore, when the user inputs the waiting time by text, the reception unit can also provide a text input interface. Furthermore, when the user inputs the waiting time by image, the reception unit can also support the input using image recognition technology. In this way, the reception unit can select the optimal means according to the user's input method, allowing for smooth input.

[0085] The reception unit can estimate the user's emotion and determine the input priority of waiting time based on the estimated user emotion. The reception unit can estimate the user's emotion and determine the input priority of waiting time based on the estimated user emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the reception unit can prioritize input of waiting time. Furthermore, if the user is relaxed, the reception unit can prioritize other input tasks. Furthermore, if the user is in a hurry, the reception unit can quickly process input of waiting time. In this way, the reception unit can reduce the user's stress by determining input priority according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0086] When inputting a waiting time, the reception unit can prioritize inputting a highly relevant waiting time by taking into consideration the user's geographical location information. When inputting a waiting time, the reception unit prioritizes inputting a highly relevant waiting time by taking into consideration the user's geographical location information. Methods for acquiring geographical location information include, but are not limited to, GPS, Wi-Fi, and cell tower location information. For example, the reception unit prioritizes inputting a waiting time for a location close to the user's current location. Furthermore, when the user is in a specific area, the reception unit can also prioritize inputting a waiting time for that area. Furthermore, when the user is moving, the reception unit can also input a waiting time based on the user's current location. In this way, the reception unit can input a highly relevant waiting time by taking into consideration the geographical location information.

[0087] The reception unit can analyze the user's social media activity when the waiting time is input and input the related waiting time. The reception unit can analyze the user's social media activity when the waiting time is input and input the related waiting time. Methods of collecting social media activity include, but are not limited to, for example, posted content, likes, comments, etc. The reception unit can input, for example, the waiting time of a location where the user checked in on social media. The reception unit can also analyze the user's social media posted content and input the related waiting time. Furthermore, the reception unit can input the waiting time with reference to the activity of the user's friends on social media. In this way, the reception unit can input the related waiting time by analyzing social media activity.

[0088] The reception unit can customize the input method by reflecting the user's past feedback when the user inputs the waiting time. The reception unit customizes the input method by reflecting the user's past feedback when the user inputs the waiting time. Methods for collecting past feedback include, but are not limited to, survey results, reviews, and ratings, for example. The reception unit customizes the input interface based on feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and suggest an optimal input method. Furthermore, the reception unit can improve the input procedure based on the user's feedback. In this way, the reception unit can optimize the input method by reflecting the past feedback.

[0089] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring the current location based on the estimated user emotion. The acquisition unit can estimate the user's emotion and adjust the timing of acquiring the current location based on the estimated user emotion. Emotion estimation methods include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the acquisition unit can reduce the frequency of acquiring the current location when the user is relaxed. The acquisition unit can also increase the frequency of acquiring the current location when the user is in a hurry. Furthermore, the acquisition unit can adjust the timing of acquiring the current location when the user is stressed. In this way, the acquisition unit can acquire the current location at an appropriate time by adjusting the acquisition timing according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0090] The acquisition unit can improve the accuracy of acquisition when acquiring the current location by referring to the user's past location information. The acquisition unit improves the accuracy of acquisition when acquiring the current location by referring to the user's past location information. Methods for collecting past location information include, but are not limited to, a history database, a log file, etc. The acquisition unit can improve the accuracy of acquisition of the current location, for example, based on location information of places the user has visited in the past. The acquisition unit can also analyze the user's past location information and acquire an optimal current location. Furthermore, the acquisition unit can optimize the method of acquiring the current location by referring to the user's past location information. As a result, the acquisition unit improves the accuracy of acquisition by referring to the past location information.

[0091] When acquiring the current location, the acquisition unit can select the optimal acquisition method taking into consideration the user's current activity status. When acquiring the current location, the acquisition unit selects the optimal acquisition method taking into consideration the user's current activity status. Methods for acquiring the current activity status include, but are not limited to, sensor information, app usage status, and the like. For example, when the user is walking, the acquisition unit acquires the current location using GPS. Furthermore, when the user is traveling by car, the acquisition unit can also acquire the current location using an in-car navigation system. Furthermore, when the user is indoors, the acquisition unit can also acquire the current location using Wi-Fi or Bluetooth. This allows the acquisition unit to select the optimal acquisition method by taking into consideration the current activity status.

[0092] The acquisition unit can select the optimal acquisition means depending on the user's input method when acquiring the current location. The acquisition unit selects the optimal acquisition means depending on the user's input method when acquiring the current location. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user inputs the current location by voice, the acquisition unit acquires the current location using voice recognition technology. Furthermore, when the user inputs the current location by text, the acquisition unit can also provide a text input interface. Furthermore, when the user inputs the current location by image, the acquisition unit can also acquire the current location using image recognition technology. This allows the acquisition unit to select the optimal means depending on the user's input method, thereby enabling smooth acquisition.

[0093] The acquisition unit can estimate the user's emotion and determine the priority of the current location to be acquired based on the estimated user's emotion. The acquisition unit can estimate the user's emotion and determine the priority of the current location to be acquired based on the estimated user's emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the acquisition unit can prioritize acquiring the current location. Furthermore, if the user is relaxed, the acquisition unit can prioritize acquiring other information. Furthermore, if the user is in a hurry, the acquisition unit can quickly acquire the current location. In this way, the acquisition unit can acquire the current location in an appropriate order by determining the priority according to the user's emotion. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0094] When acquiring the current location, the acquisition unit can prioritize acquiring highly relevant location information by taking into account the user's geographical location information. When acquiring the current location, the acquisition unit prioritizes acquiring highly relevant location information by taking into account the user's geographical location information. Methods for acquiring geographical location information include, but are not limited to, GPS, Wi-Fi, and cell tower location information. For example, the acquisition unit prioritizes acquiring location information of locations close to the user's current location. Furthermore, when the user is in a specific area, the acquisition unit can also prioritize acquiring location information of that area. Furthermore, when the user is moving, the acquisition unit can also acquire location information based on the user's current location. In this way, the acquisition unit can acquire highly relevant location information by taking into account the geographical location information.

[0095] The acquisition unit can analyze the user's social media activity when acquiring the current location and acquire related location information. The acquisition unit can analyze the user's social media activity when acquiring the current location and acquire related location information. Methods of collecting social media activity include, but are not limited to, posted content, likes, comments, etc. For example, the acquisition unit acquires location information of locations where the user has checked in on social media. The acquisition unit can also analyze the content of the user's social media posts and acquire related location information. Furthermore, the acquisition unit can acquire location information by referring to the activities of the user's friends on social media. In this way, the acquisition unit can acquire related location information by analyzing social media activity.

[0096] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring the current location. The acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring the current location. Methods for collecting past feedback include, but are not limited to, survey results, reviews, and ratings, for example. The acquisition unit customizes the current location acquisition method based on feedback provided by the user in the past. The acquisition unit can also analyze the user's past feedback and suggest an optimal acquisition method. Furthermore, the acquisition unit can improve the current location acquisition procedure based on the user's feedback. In this way, the acquisition unit can optimize the acquisition method by reflecting the past feedback.

[0097] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the suggestion unit can make the suggestion in a relaxed manner. If the user is in a hurry, the suggestion unit can make the suggestion in a concise and quick manner. Furthermore, if the user is stressed, the suggestion unit can make the suggestion in a reassuring manner. This allows the suggestion unit to adjust the way the suggestion is expressed based on the user's emotion, thereby improving user satisfaction. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0098] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the spot when making a suggestion. The suggestion unit can adjust the level of detail of the suggestion based on the importance of the spot when making a suggestion. Criteria for evaluating the importance of a spot include, but are not limited to, for example, the number of visitors, review ratings, and popularity. For example, the suggestion unit can provide detailed information for an important spot. The suggestion unit can also provide concise information for a less important spot. Furthermore, the suggestion unit can adjust the level of detail of the spot according to the user's level of interest. In this way, the suggestion unit can provide appropriate information to the user by adjusting the level of detail of the suggestion based on the importance of the spot.

[0099] The suggestion unit can apply different suggestion algorithms depending on the category of the spot when making a suggestion. The suggestion unit applies different suggestion algorithms depending on the category of the spot when making a suggestion. Spot categories can be classified, for example, into tourist spots, restaurants, shopping malls, etc., but are not limited to these examples. For example, in the case of a cafe, the suggestion unit makes suggestions that emphasize information about the atmosphere and menu. In addition, in the case of a general store, the suggestion unit can make suggestions that emphasize information about the type of products and price range. Furthermore, in the case of a park, the suggestion unit can make suggestions that emphasize information about the facilities and scenery. In this way, the suggestion unit can make optimal suggestions to the user by applying a suggestion algorithm depending on the category of the spot.

[0100] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions. The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions. Methods for collecting past suggestion results include, but are not limited to, proposal history, user selection results, and the like. For example, the suggestion unit preferentially suggests spots that the user has previously given high ratings. The suggestion unit can also analyze the user's past suggestion results and suggest optimal spots. Furthermore, the suggestion unit can optimize the suggestion algorithm based on the user's past suggestion results. As a result, the suggestion unit can improve the accuracy of suggestions by referring to the past suggestion results.

[0101] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is in a hurry, the suggestion unit can make a short, to-the-point suggestion. If the user is relaxed, the suggestion unit can make a longer suggestion with detailed explanations. Furthermore, if the user is excited, the suggestion unit can make a suggestion with visually stimulating effects. This allows the suggestion unit to adjust the length of the suggestion based on the user's emotion, thereby improving user satisfaction. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] The suggestion unit can determine the priority of suggestions based on the submission time of the spots when making suggestions. The suggestion unit can determine the priority of suggestions based on the submission time of the spots when making suggestions. Methods for acquiring the submission time include, but are not limited to, for example, the submission date and time, the event date and time, etc. The suggestion unit, for example, prioritizes suggestions of newly opened spots. The suggestion unit can also prioritize suggestions of seasonal spots. Furthermore, the suggestion unit can suggest optimal spots by referring to the user's past visit dates. In this way, the suggestion unit can provide the user with the latest information by determining the priority of suggestions based on the submission time.

[0103] The suggestion unit can adjust the order of suggestions based on the relevance of the spots when making suggestions. The suggestion unit adjusts the order of suggestions based on the relevance of the spots when making suggestions. Criteria for evaluating relevance include, but are not limited to, common themes, related keywords, and the like. For example, the suggestion unit prioritizes suggesting spots close to the user's current location. The suggestion unit can also prioritize suggesting spots that match the user's preferences. Furthermore, the suggestion unit can suggest highly relevant spots by referring to the user's past visit history. In this way, the suggestion unit can provide information to the user in an optimal order by adjusting the order of suggestions based on relevance.

[0104] The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. Evaluation criteria for the level of expertise include, but are not limited to, questionnaire results, past learning history, etc. For example, if the user has technical expertise, the suggestion unit can make a proposal using technical terms. Furthermore, if the user does not have technical expertise, the suggestion unit can make a proposal using simple language. Furthermore, the suggestion unit can make a proposal using optimal language by referring to the user's past suggestion results. In this way, the suggestion unit can provide appropriate information to the user by adjusting the use of technical terms according to the level of expertise.

[0105] The collection unit can estimate the user's emotion and adjust the feedback collection method based on the estimated user's emotion. The collection unit can estimate the user's emotion and adjust the feedback collection method based on the estimated user's emotion. Emotion estimation methods include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the collection unit can request detailed feedback when the user is relaxed. The collection unit can also request brief feedback when the user is in a hurry. Furthermore, the collection unit can refrain from collecting feedback when the user is stressed. This allows the collection unit to collect appropriate feedback by adjusting the collection method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0106] The collection unit can improve the accuracy of feedback collection by referring to the user's past feedback data when collecting feedback. The collection unit improves the accuracy of feedback collection by referring to the user's past feedback data when collecting feedback. Methods for collecting past feedback data include, but are not limited to, survey results, reviews, and ratings, for example. The collection unit optimizes the collection method based on feedback previously provided by the user. The collection unit can also analyze the user's past feedback data and propose an optimal collection method. Furthermore, the collection unit can improve the collection procedure by referring to the user's past feedback data. As a result, the collection unit improves the accuracy of collection by referring to the past feedback data.

[0107] The collection unit can select an optimal collection method in consideration of the user's current activity status when collecting feedback. The collection unit selects an optimal collection method in consideration of the user's current activity status when collecting feedback. Methods for acquiring the current activity status include, but are not limited to, sensor information, app usage status, and the like. For example, the collection unit may request brief feedback when the user is walking. The collection unit may also request detailed feedback when the user is sitting. Furthermore, the collection unit may refrain from collecting feedback when the user is busy. This allows the collection unit to select an optimal collection method by considering the current activity status.

[0108] The collection unit can estimate the user's emotions and determine the priority of the feedback to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions and determine the priority of the feedback to be collected based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the collection unit can prioritize collecting feedback when the user is stressed. The collection unit can also prioritize collecting other information when the user is relaxed. Furthermore, the collection unit can quickly collect feedback when the user is in a hurry. In this way, the collection unit can collect feedback in an appropriate order by determining the priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0109] When collecting feedback, the collection unit can prioritize collecting highly relevant feedback by taking into account the user's geographical location information. When collecting feedback, the collection unit prioritizes collecting highly relevant feedback by taking into account the user's geographical location information. Methods for acquiring geographical location information include, but are not limited to, GPS, Wi-Fi, and cell tower location information. For example, the collection unit prioritizes collecting feedback from locations close to the user's current location. Furthermore, when the user is in a specific area, the collection unit can also prioritize collecting feedback from that area. Furthermore, when the user is moving, the collection unit can collect feedback based on the user's current location. In this way, the collection unit can collect highly relevant feedback by taking into account the geographical location information.

[0110] The collection unit can analyze the user's social media activity and collect relevant feedback when collecting feedback. The collection unit analyzes the user's social media activity and collects relevant feedback when collecting feedback. Methods of collecting social media activity include, but are not limited to, post content, likes, comments, etc. For example, the collection unit collects feedback on places where the user has checked in on social media. The collection unit can also analyze the user's social media post content and collect relevant feedback. Furthermore, the collection unit can collect feedback by referring to the activities of the user's friends on social media. In this way, the collection unit can collect relevant feedback by analyzing social media activity.

[0111] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the learning unit can select detailed training data. If the user is in a hurry, the learning unit can also select concise training data. Furthermore, if the user is stressed, the learning unit can select training data that gives a sense of security. This allows the learning unit to select training data according to the user's emotions, thereby improving learning accuracy. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0112] The learning unit can optimize the learning algorithm by referring to past learning data during learning. The learning unit optimizes the learning algorithm by referring to past learning data during learning. Methods for collecting past learning data include, but are not limited to, learning history, training datasets, etc. The learning unit selects an optimal learning algorithm, for example, based on the past learning data. The learning unit can also analyze the past learning data and improve the learning algorithm. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to the past learning data. As a result, the learning unit improves the accuracy of the learning algorithm by referring to the past learning data.

[0113] The learning unit can update the learning data during learning by reflecting user feedback. The learning unit updates the learning data during learning by reflecting user feedback. Methods of collecting feedback include, but are not limited to, questionnaires, reviews, and evaluations, for example. The learning unit updates the learning data based on user feedback, for example. The learning unit can also analyze user feedback and improve the learning data. Furthermore, the learning unit can improve the accuracy of the learning data by referring to user feedback. In this way, the learning unit reflects user feedback, thereby improving the accuracy of the learning data.

[0114] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the learning unit can reduce the frequency of learning when the user is relaxed. The learning unit can also increase the frequency of learning when the user is in a hurry. Furthermore, the learning unit can adjust the frequency of learning when the user is stressed. In this way, the learning unit can adjust the frequency of learning according to the user's emotions, thereby improving learning efficiency. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0115] During learning, the learning unit can weight the learning data based on the time of submission of the spot. During learning, the learning unit weights the learning data based on the time of submission of the spot. Methods for obtaining the submission time include, but are not limited to, for example, the submission date and time, the event date and time, etc. The learning unit, for example, places emphasis on data of newly opened spots during learning. The learning unit can also place emphasis on data of seasonal spots during learning. Furthermore, the learning unit can weight the learning data with reference to the user's past visit dates. In this way, the learning unit weights the learning data based on the submission date, thereby improving the accuracy of learning.

[0116] The learning unit can integrate information from different data sources to enrich the learning data during learning. The learning unit integrates information from different data sources to enrich the learning data during learning. Types of different data sources include, but are not limited to, sensor data, user input data, and external API data. For example, the learning unit integrates data from social media to learn. The learning unit can also integrate user feedback data to learn. Furthermore, the learning unit can integrate information from public databases to learn. As a result, the learning unit improves the accuracy of the learning data by integrating information from different data sources.

[0117] The update unit can estimate the user's emotion and adjust the information update frequency based on the estimated user's emotion. The update unit can estimate the user's emotion and adjust the information update frequency based on the estimated user's emotion. Emotion estimation methods include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the update unit can reduce the information update frequency when the user is relaxed. The update unit can also increase the information update frequency when the user is in a hurry. Furthermore, the update unit can adjust the information update frequency when the user is stressed. In this way, the update unit can update the information at an appropriate time by adjusting the update frequency according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0118] The update unit can improve the accuracy of the update by referring to the user's past behavioral history when updating information. The update unit improves the accuracy of the update by referring to the user's past behavioral history when updating information. Methods for collecting past behavioral history include, but are not limited to, places visited, services used, and travel routes, for example. The update unit improves the accuracy of the information update based on the user's past behavioral history, for example. The update unit can also analyze the user's past behavioral history and improve the information update method. Furthermore, the update unit can optimize the information update procedure by referring to the user's past behavioral history. As a result, the update unit improves the accuracy of the update by referring to the past behavioral history.

[0119] The update unit can select the optimal update method in consideration of the user's current activity status when updating information. The update unit selects the optimal update method in consideration of the user's current activity status when updating information. Methods for acquiring the current activity status include, but are not limited to, sensor information, app usage status, and the like. For example, the update unit performs a brief information update when the user is walking. Furthermore, the update unit can also perform a detailed information update when the user is sitting. Furthermore, the update unit can refrain from updating information when the user is busy. This allows the update unit to select the optimal update method by considering the current activity status.

[0120] The update unit can estimate the user's emotion and determine the priority of information to be updated based on the estimated user's emotion. The update unit can estimate the user's emotion and determine the priority of information to be updated based on the estimated user's emotion. Emotion estimation methods include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the update unit can prioritize updating information when the user is stressed. Furthermore, the update unit can prioritize updating other information when the user is relaxed. Furthermore, the update unit can quickly update information when the user is in a hurry. This allows the update unit to update information in an appropriate order by determining the priority according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0121] When updating information, the update unit can prioritize updating highly relevant information by taking into account the user's geographical location information. When updating information, the update unit prioritizes updating highly relevant information by taking into account the user's geographical location information. Methods for acquiring geographical location information include, but are not limited to, GPS, Wi-Fi, and cell tower location information. For example, the update unit prioritizes updating information about locations close to the user's current location. Furthermore, when the user is in a specific area, the update unit can also prioritize updating information about that area. Furthermore, when the user is moving, the update unit can also update information based on the user's current location. In this way, the update unit can update highly relevant information by taking into account the geographical location information.

[0122] The update unit can analyze the user's social media activity and update related information when updating information. The update unit can analyze the user's social media activity and update related information when updating information. Methods of collecting social media activity include, but are not limited to, posted content, likes, comments, etc. For example, the update unit can prioritize updating information about places where the user has checked in on social media. The update unit can also analyze the content of the user's social media posts and update related information. Furthermore, the update unit can update related information by referring to the activities of the user's friends on social media. In this way, the update unit can update related information by analyzing social media activity.

[0123] The update unit can customize the update method by reflecting the user's past feedback when updating information. The update unit customizes the update method by reflecting the user's past feedback when updating information. Methods for collecting past feedback include, but are not limited to, survey results, reviews, and ratings, for example. The update unit customizes the information update method based on feedback provided by the user in the past. The update unit can also analyze the user's past feedback and propose an optimal update method. Furthermore, the update unit can improve the information update procedure based on the user's feedback. In this way, the update unit can optimize the update method by reflecting the past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, suggestion unit, and collection unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit inputs the user's waiting time using the touch panel 38A or microphone 38B of the smart device 14. The acquisition unit acquires the current location using the GPS or Wi-Fi function of the smart device 14. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes suggestions based on the user's current location information. The collection unit collects feedback using the communication I / F 44 of the smart device 14. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the user's behavior history. The update unit updates information in real time by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, suggestion unit, and collection unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit inputs the user's waiting time using the microphone 238 of the smart glasses 214. The acquisition unit acquires the current location using the GPS or Wi-Fi function of the smart glasses 214. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes suggestions based on the user's current location information. The collection unit collects feedback using the communication I / F 44 of the smart glasses 214. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the user's behavior history. The update unit updates information in real time by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, suggestion unit, and collection unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit inputs the user's waiting time using the microphone 238 of the headset type terminal 314. The acquisition unit acquires the current location using the GPS or Wi-Fi function of the headset type terminal 314. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and makes suggestions based on the user's current location information. The collection unit collects feedback using the communication I / F 44 of the headset type terminal 314. The learning unit is realized by the identification processing unit 290 of the data processing device 12 and learns the user's behavior history. The update unit updates information in real time by the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, suggestion unit, and collection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit inputs the user's waiting time using the microphone 238 of the robot 414. The acquisition unit acquires the current location using the GPS or Wi-Fi function of the robot 414. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes suggestions based on the user's current location information. The collection unit collects feedback using the communication I / F 44 of the robot 414. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the user's behavior history. The update unit updates information in real time by the specific processing unit 290 of the data processing device 12.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] The suggestion unit can estimate the user's emotions and adjust the content of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can suggest relaxing places and activities. If the user is excited, the suggestion unit can suggest active spots and events. Furthermore, if the user is sad, the suggestion unit can suggest fun places and activities to lift the user's spirits. In this way, the suggestion unit can improve the user's satisfaction by making suggestions according to the user's emotions.

[0126] The suggestion unit can monitor the user's current health condition and make suggestions based on the health condition. For example, the suggestion unit can monitor the user's heart rate and stress level and suggest a quiet cafe or park if the user needs to relax. If the user is not getting enough exercise, the suggestion unit can suggest walking courses or fitness facilities. Furthermore, if the user is tired, the suggestion unit can suggest a spa or massage facility where the user can refresh themselves. In this way, the suggestion unit can improve the user's health and satisfaction by making suggestions according to the user's health condition.

[0127] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user's emotions. For example, if the user is relaxed, the timing of making suggestions can be delayed. Also, if the user is in a hurry, the suggestion unit can make suggestions quickly. Furthermore, if the user is feeling stressed, the suggestion unit can reduce the user's stress by making suggestions at an appropriate time. In this way, the suggestion unit can improve user satisfaction by adjusting the timing of suggestions according to the user's emotions.

[0128] The suggestion unit can analyze the user's past suggestion history to improve the accuracy of suggestions. For example, it can prioritize suggestions of spots that the user has given high ratings to in the past. It can also suggest similar spots based on information about spots the user has visited in the past. It can also analyze the user's past suggestion history to suggest new spots that match the user's preferences. In this way, the suggestion unit can improve the accuracy of suggestions by utilizing the past suggestion history.

[0129] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can make suggestions in a relaxed manner. If the user is in a hurry, the suggestion unit can make suggestions in a concise and quick manner. Furthermore, if the user is feeling stressed, the suggestion unit can make suggestions in a manner that gives a sense of security. In this way, the suggestion unit can improve user satisfaction by adjusting the way suggestions are expressed based on the user's emotions.

[0130] The suggestion unit can make suggestions taking into account the user's current schedule. For example, by referring to the user's calendar information, if the user has some free time in their schedule, the suggestion unit can suggest spots where they can stay for a long time. Also, if the user's schedule is tight, the suggestion unit can suggest spots that can be enjoyed in a short time. Furthermore, the suggestion unit can make optimal suggestions based on the user's schedule. This allows the suggestion unit to make optimal suggestions by taking the user's schedule into account.

[0131] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user's emotions. For example, if the user is in a hurry, the suggestion unit can provide a short, to-the-point suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide a suggestion with visually stimulating effects. In this way, the suggestion unit can improve user satisfaction by adjusting the length of the suggestion according to the user's emotions.

[0132] The suggestion unit can make suggestions taking into account the user's current activity status. For example, if the user is walking, nearby spots can be suggested. Also, if the user is traveling by car, spots that are easily accessible by car can be suggested. Furthermore, if the user is indoors, spots that can be enjoyed indoors can be suggested. In this way, the suggestion unit can make optimal suggestions by taking into account the user's activity status.

[0133] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize suggestions of relaxing spots. Also, if the user is feeling relaxed, it can prioritize suggestions of active spots. Furthermore, if the user is in a hurry, it can prioritize suggestions of spots that can be accessed quickly. In this way, the suggestion unit can improve user satisfaction by prioritizing suggestions according to the user's emotions.

[0134] The suggestion unit can analyze the user's social media activity and suggest related spots. For example, it can suggest similar spots based on information about places where the user has checked in on social media. It can also analyze the content of the user's social media posts to suggest related spots. It can also suggest spots based on the activities of the user's friends on social media. In this way, the suggestion unit can suggest related spots by analyzing social media activity.

[0135] The processing flow of the second embodiment will be briefly explained below.

[0136] Step 1: The user inputs a waiting time into the reception unit. The waiting time may range from a few minutes to a few hours, but is not limited to this example. Step 2: The acquisition unit acquires the current location based on the waiting time input by the reception unit. Methods for acquiring the current location include, but are not limited to, GPS, Wi-Fi, and cell tower location information. Step 3: The suggestion unit makes suggestions based on the current location information acquired by the acquisition unit. Suggestions include, but are not limited to, tourist spots, restaurants, and events. The suggestion unit can also make suggestions based on the user's preferences. For example, the suggestion unit learns the user's behavior history and makes suggestions based on the user's preferences. Step 4: The collection unit collects feedback on the spots proposed by the suggestion unit. Methods for collecting feedback include, but are not limited to, questionnaires, reviews, and ratings.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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).

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] [Explanation of symbols]

[0209] 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 section for inputting waiting times; an acquisition unit that acquires a current location based on the waiting time input by the unit; a suggestion unit that makes a suggestion based on the current location information acquired by the acquisition unit; a collection unit that collects feedback on the spots suggested by the suggestion unit; Equipped with A system characterized by:

2. The proposal unit Equipped with a learning unit that learns user behavior history 2. The system of claim 1.

3. The proposal unit Equipped with an update unit that updates information in real time 2. The system of claim 1.

4. The proposal unit Providing suggestions based on user preferences 2. The system of claim 1.

5. The proposal unit Providing information about the recommended spots 2. The system of claim 1.

6. The collecting unit Gather feedback on proposed spots 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the waiting time input method based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit When entering wait times, the accuracy of the input is improved by referencing the user's past wait time data.

2. The system of claim 1.

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