system

Abstract

The system according to the embodiment aims to collect local store information and reviews, and to facilitate searches that meet the needs of travelers. [Solution] A system according to an embodiment includes an information collection unit, an incentive unit, an attribute registration unit, a recommendation unit, and a plan generation unit. The information collection unit collects word-of-mouth reviews and store information. The incentive unit awards incentive points based on the information collected by the information collection unit. The attribute registration unit registers user attribute information. The recommendation unit makes recommendations based on the information registered by the attribute registration unit. The plan generation unit generates an appropriate travel plan based on the visit date and time, budget, and attributes.

Description

[Technical Field]

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

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

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

[0004] Conventional technology had the problem of lacking local store information and reviews, making it difficult to search for information that meets travelers' needs.

[0005] The system according to the embodiment aims to collect local store information and reviews, and to facilitate searches that meet the needs of travelers. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an incentive unit, an attribute registration unit, a recommendation unit, and a plan generation unit. The information collection unit collects word-of-mouth reviews and store information. The incentive unit awards incentive points based on the information collected by the information collection unit. The attribute registration unit registers user attribute information. The recommendation unit makes recommendations based on the information registered by the attribute registration unit. The plan generation unit generates an appropriate travel plan based on the visit date and time, budget, and attributes. [Effects of the Invention]

[0007] The system according to the embodiment collects local store information and reviews, making it easy for travelers to search for information that meets their needs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention solves the problem of travelers being unable to search for information that meets their needs due to a lack of information about local businesses. This system provides a mechanism for rewarding users with incentive points when they input reviews and store information. Next, users register their attributes (e.g., age, whether they have a spouse or children, whether they have pets, language, religion, etc.). The system then creates a map that automatically recommends places reviewed by users with similar attributes. Furthermore, when users input their visit date and time, budget, and attributes, AI automatically generates an optimal travel plan. For example, incentive points are awarded when users post reviews and photos of businesses they have visited. After users register their attributes, the system recommends businesses that allow pets and affordable, family-friendly businesses. Furthermore, for families traveling on a limited budget, AI automatically generates an optimal travel plan and suggests family-friendly tourist spots. This system enriches information about local businesses, allowing travelers to easily find businesses and tourist spots that meet their needs. Furthermore, users are encouraged to actively share information by receiving incentives for providing information.

[0029] An information provision system according to an embodiment includes an information collection unit, an incentive unit, an attribute registration unit, a recommendation unit, and a plan generation unit. The information collection unit collects user reviews and store information from users. For example, the information collection unit collects information by users posting reviews and photos of stores they have visited. The information collection unit can also collect detailed information entered by users, such as store location information and business hours. The information collection unit stores the information provided by users in a database so that other users can access it. The incentive unit awards incentive points based on the information collected by the information collection unit. For example, the incentive unit awards points each time a user posts a review. The incentive unit can also award additional points if a user provides detailed store information. The incentive unit can also award bonus points for information posted during a specific campaign period. The attribute registration unit registers user attribute information. For example, the attribute registration unit registers attribute information by users entering information such as their age, whether they have a spouse or children, whether they have pets, language, and religion. The attribute registration unit can also register places the user has visited in the past and categories of interest. Furthermore, the attribute registration unit stores the user's attribute information in a database so that other elements can access it. The recommendation unit recommends places that users with similar attributes have written reviews about, based on the information registered by the attribute registration unit. For example, the recommendation unit recommends pet-friendly restaurants or affordable restaurants for families. The recommendation unit can also recommend tourist spots based on the user's interests. The recommendation unit can also recommend new places based on the user's past behavior history. The plan generation unit generates an optimal travel plan based on the date and time of visit, budget, and attributes. For example, the plan generation unit suggests an optimal travel plan for a family with a limited budget. The plan generation unit can also suggest a plan that avoids crowds based on the date and time of the user's visit. The plan generation unit can also generate a travel plan based on a specific theme based on the user's attribute information.As a result, the information providing system according to the embodiment can collect user reviews and store information, award incentive points, make recommendations based on attribute information, and generate optimal travel plans.

[0030] The information collection unit can analyze the user's past posting history and select an appropriate information collection method. For example, if the user has posted using text in the past, the information collection unit can preferentially suggest text input. Furthermore, if the user has posted many images in the past, the information collection unit can also provide an interface that encourages image posting. Furthermore, if the user has posted using voice in the past, the information collection unit can preferentially suggest voice input. This allows information to be collected efficiently by selecting the optimal information collection method based on the user's past posting history. Some or all of the above-described processing in the information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the information collection unit can input the user's past posting data into the generation AI and cause the generation AI to select the optimal information collection method.

[0031] When collecting reviews and store information, the information collection unit can filter the information based on the user's current interests and concerns. For example, the information collection unit collects information based on categories in which the user is currently interested (e.g., restaurants, tourist attractions). The information collection unit can also preferentially collect related information based on keywords recently searched by the user. The information collection unit can also collect information based on topics in social media groups in which the user participates. This makes it possible to collect highly relevant information by filtering information based on the user's interests and concerns. Some or all of the above-described processing in the information collection unit may be performed using, or without, AI. For example, the information collection unit can input the user's search history data into the generation AI and have the generation AI perform filtering.

[0032] When collecting word-of-mouth information or store information, the information collection unit can select an appropriate collection means according to the user's input method. For example, if the user prefers voice input, the information collection unit can prioritize support for voice input. Also, if the user prefers text input, the information collection unit can prioritize support for text input. Also, if the user prefers image posting, the information collection unit can prioritize support for image posting. This allows information to be collected efficiently by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the information collection unit can input the user's input data into a generation AI and cause the generation AI to select the optimal collection means.

[0033] When collecting word-of-mouth reviews and store information, the information collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the information collection unit prioritizes collecting information about stores close to the user's current location. The information collection unit can also prioritize collecting information about areas the user plans to visit. The information collection unit can also prioritize collecting information near places the user has visited in the past. In this way, highly relevant information can be collected preferentially by taking the user's geographical location information into consideration. Some or all of the above-described processing in the information collection unit may be performed using AI, for example, or may be performed without using AI. For example, the information collection unit can input the user's location information data into the generation AI and cause the generation AI to collect highly relevant information.

[0034] The information collection unit can analyze the user's social media activities and collect related information when collecting word-of-mouth reviews and store information. For example, the information collection unit collects information on places where the user has checked in on social media. The information collection unit can also analyze the content of the user's social media posts to collect related store information. The information collection unit can also refer to the activities of the user's friends on social media to collect related information. In this way, highly relevant information can be collected by analyzing the user's social media activities. Some or all of the above-described processing in the information collection unit may be performed using AI, for example, or may be performed without using AI. For example, the information collection unit can input the user's social media data into the generation AI and cause the generation AI to collect related information.

[0035] When collecting word-of-mouth information and store information, the information collection unit can customize the collection method by reflecting the user's past feedback. For example, the information collection unit preferentially collects information from information sources that the user has previously rated highly. The information collection unit can also exclude information from information sources that the user has previously rated poorly. The information collection unit can also customize the collection method based on the user's past feedback. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the information collection unit may be performed using AI, for example, or may be performed without using AI. For example, the information collection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the collection method.

[0036] When awarding incentive points, the incentive unit can adjust the level of detail of the points based on the quality of the posted content. For example, the incentive unit awards more incentive points to users who post high-quality photos and detailed reviews. The incentive unit can also award fewer incentive points to users who post brief reviews and low-quality photos. The incentive unit can also evaluate the quality of the posted content using AI and adjust the level of detail of the points. This makes it possible to encourage high-quality posts by adjusting the level of detail of the points based on the quality of the posted content. Some or all of the above-described processing in the incentive unit may be performed using AI, for example, or may be performed without using AI. For example, the incentive unit can input the quality of the posted content to a generation AI and cause the generation AI to adjust the level of detail of the points.

[0037] When awarding incentive points, the incentive unit can apply different awarding algorithms depending on the posting category. For example, the incentive unit can award points to restaurant reviews based on the quality of the food and service. For tourist destination reviews, the incentive unit can also award points based on the scenery and accessibility. For shopping reviews, the incentive unit can also award points based on the quality and price of the product. In this way, by applying different awarding algorithms depending on the posting category, incentives appropriate for each category can be provided. Some or all of the above-mentioned processing in the incentive unit may be performed using, for example, AI, or may be performed without using AI. For example, the incentive unit can input posting category data into a generation AI and cause the generation AI to apply an awarding algorithm.

[0038] When awarding incentive points, the incentive unit can improve the accuracy of the awarding by referring to the user's past posting results. For example, the incentive unit awards more incentive points to posts that the user has previously received high ratings. The incentive unit can also award fewer incentive points to posts that the user has previously received low ratings. The incentive unit can also analyze the user's past posting results using AI to improve the accuracy of the awarding. In this way, by referring to the user's past posting results, the accuracy of the awarding of incentive points can be improved. Some or all of the above-described processing in the incentive unit may be performed using AI, for example, or may be performed without using AI. For example, the incentive unit can input the user's past posting data into the generation AI and cause the generation AI to improve the accuracy of the awarding.

[0039] When awarding incentive points, the incentive unit can determine the priority of awarding based on the time of submission of the post. For example, the incentive unit can award more incentive points to reviews posted early. The incentive unit can also preferentially award incentive points to reviews posted during a specific campaign period. The incentive unit can also analyze the time of submission of the post using AI and determine the priority of awarding. In this way, by determining the priority of awarding based on the time of submission of the post, early submission can be encouraged. Some or all of the above-mentioned processing in the incentive unit may be performed using AI, for example, or may be performed without using AI. For example, the incentive unit can input data on the time of submission of the post into a generation AI and have the generation AI determine the priority of awarding.

[0040] When awarding incentive points, the incentive unit can adjust the awarding order based on the relevance of the posts. For example, the incentive unit can prioritize awarding incentive points to reviews related to the user's attributes. The incentive unit can also prioritize awarding incentive points to reviews related to a specific category. The incentive unit can also analyze the relevance of posts using AI and adjust the awarding order. In this way, by adjusting the awarding order based on the relevance of posts, highly relevant posts can be prioritized for evaluation. Some or all of the above-described processing in the incentive unit may be performed using AI, for example, or may be performed without using AI. For example, the incentive unit can input post relevance data into a generation AI and have the generation AI adjust the awarding order.

[0041] When awarding incentive points, the incentive unit can adjust the use of technical terminology in the awarding depending on the user's level of expertise. For example, the incentive unit can provide an explanation of incentive points using technical terminology to a user with high level of expertise. The incentive unit can also provide an explanation of incentive points in simpler terms to a user with low level of expertise. The incentive unit can also analyze the user's level of expertise using AI and adjust the use of technical terminology in the awarding. This allows the user to be provided with an incentive that is appropriate for the user by adjusting the use of technical terminology depending on the user's level of expertise. Some or all of the above-described processing in the incentive unit can be performed using AI, for example, or without AI. For example, the incentive unit can input the user's technical knowledge data into a generation AI and have the generation AI adjust the use of technical terminology.

[0042] When registering attribute information, the attribute registration unit can select an appropriate registration method by referring to the user's past registration history. The attribute registration unit can, for example, suggest an optimal registration method based on attribute information previously registered by the user. The attribute registration unit can also preferentially suggest registration methods (voice, text, etc.) that the user has used in the past. The attribute registration unit can also analyze the user's past registration history using AI and select an optimal registration method. This allows attribute information to be registered efficiently by selecting an optimal registration method based on the user's past registration history. Some or all of the above-described processing in the attribute registration unit can be performed, for example, using AI or without AI. For example, the attribute registration unit can input the user's past registration data into a generation AI and have the generation AI select an optimal registration method.

[0043] When registering attribute information, the attribute registration unit can perform filtering based on the user's current living situation and interests. The attribute registration unit registers attribute information based on, for example, categories in which the user is currently interested (e.g., restaurants, tourist attractions). The attribute registration unit can also preferentially register related attribute information based on keywords recently searched by the user. The attribute registration unit can also register attribute information based on topics of social media groups in which the user participates. This allows highly relevant attribute information to be registered by filtering based on the user's living situation and interests. Some or all of the above-described processing in the attribute registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the attribute registration unit can input the user's living situation data into the generation AI and have the generation AI perform filtering.

[0044] When registering attribute information, the attribute registration unit can select an appropriate registration means according to the user's input method. For example, if the user prefers voice input, the attribute registration unit can preferentially support voice input. Furthermore, if the user prefers text input, the attribute registration unit can preferentially support text input. Furthermore, if the user prefers image posting, the attribute registration unit can preferentially support image posting. This allows attribute information to be registered efficiently by selecting the optimal registration means according to the user's input method. Some or all of the above-described processing in the attribute registration unit may be performed, for example, using AI or without AI. For example, the attribute registration unit can input the user's input data to a generation AI and cause the generation AI to select the optimal registration means.

[0045] When registering attribute information, the attribute registration unit can prioritize registering highly relevant information by taking into account the user's geographical location information. For example, the attribute registration unit prioritizes registering attribute information close to the user's current location. The attribute registration unit can also prioritize registering attribute information for areas the user plans to visit. The attribute registration unit can also prioritize registering attribute information close to places the user has visited in the past. In this way, highly relevant attribute information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the attribute registration unit may be performed using AI, for example, or may be performed without using AI. For example, the attribute registration unit can input the user's location information data to the generation AI and cause the generation AI to register highly relevant information.

[0046] The attribute registration unit can analyze the user's social media activity and register related information when registering attribute information. For example, the attribute registration unit registers attribute information of locations where the user has checked in on social media. The attribute registration unit can also analyze the content of the user's social media posts and register related attribute information. The attribute registration unit can also register related attribute information by referring to the activity of the user's friends on social media. In this way, highly relevant attribute information can be registered by analyzing the user's social media activity. Some or all of the above-described processing in the attribute registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the attribute registration unit can input the user's social media data into a generation AI and cause the generation AI to register related information.

[0047] The attribute registration unit can customize the registration method by reflecting the user's past feedback when registering attribute information. For example, the attribute registration unit preferentially suggests registration methods that the user has previously rated highly. The attribute registration unit can also exclude registration methods that the user has previously rated poorly. The attribute registration unit can also customize the registration method based on the user's past feedback. In this way, the registration method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the attribute registration unit may be performed using AI, for example, or may be performed without using AI. For example, the attribute registration unit can input the user's feedback data into the generation AI and cause the generation AI to customize the registration method.

[0048] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the review when making a recommendation. The recommendation unit can make detailed recommendations based on, for example, highly rated reviews. The recommendation unit can also make concise recommendations based on low-rated reviews. The recommendation unit can also evaluate the importance of the review using AI and adjust the level of detail of the recommendation. This allows for more appropriate recommendations to be made by adjusting the level of detail of the recommendation based on the importance of the review. Some or all of the above-mentioned processing in the recommendation unit can be performed using AI, for example, or without using AI. For example, the recommendation unit can input review importance data into a generation AI and cause the generation AI to adjust the level of detail of the recommendation.

[0049] When making recommendations, the recommendation unit can apply different recommendation algorithms depending on the category of the review. For example, for restaurant reviews, the recommendation unit can make recommendations based on the quality of the food and the service. For tourist destination reviews, the recommendation unit can also make recommendations based on the scenery and accessibility. For shopping reviews, the recommendation unit can also make recommendations based on the quality and price of the product. In this way, by applying different recommendation algorithms depending on the category of the review, recommendations appropriate for each category can be made. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input review category data into the generation AI and have the generation AI apply the recommendation algorithm.

[0050] When making a recommendation, the recommendation unit can improve the accuracy of the recommendation by referring to the user's past recommendation results. For example, the recommendation unit makes more accurate recommendations based on recommendations that the user has given high ratings to in the past. The recommendation unit can also eliminate less accurate recommendations based on recommendations that the user has given low ratings to in the past. The recommendation unit can also analyze the user's past recommendation results using AI to improve the accuracy of the recommendation. In this way, the accuracy of the recommendation can be improved by referring to the user's past recommendation results. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input the user's past recommendation data into the generation AI and cause the generation AI to improve the accuracy of the recommendations.

[0051] When making a recommendation, the recommendation unit can determine the priority of recommendations based on the time when the review was submitted. The recommendation unit, for example, prioritizes recommendations based on the most recent review. The recommendation unit can also prioritize recommendations based on reviews posted during a specific campaign period. The recommendation unit can also analyze the time when the review was submitted using AI to determine the priority of recommendations. In this way, by determining the priority of recommendations based on the time when the review was submitted, the most recent information can be prioritized for recommendation. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input review submission time data into the generation AI and have the generation AI determine the priority of recommendations.

[0052] The recommendation unit can adjust the order of recommendations based on the relevance of the reviews when making recommendations. For example, the recommendation unit prioritizes recommendations based on reviews related to the user's attributes. The recommendation unit can also prioritize recommendations based on reviews related to a specific category. The recommendation unit can also analyze the relevance of reviews using AI and adjust the order of recommendations. In this way, by adjusting the order of recommendations based on the relevance of reviews, highly relevant information can be prioritized for recommendation. Some or all of the above-described processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input review relevance data into a generation AI and have the generation AI adjust the order of recommendations.

[0053] The recommendation unit can adjust the use of technical terminology in the recommendation according to the user's level of expertise when making a recommendation. For example, the recommendation unit makes recommendations using technical terminology for a user with high level of expertise. The recommendation unit can also make recommendations in simpler terms for a user with low level of expertise. The recommendation unit can also analyze the user's level of expertise using AI and adjust the use of technical terminology in the recommendation. This allows recommendations that are suitable for the user to be made by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input the user's technical knowledge data into a generation AI and cause the generation AI to adjust the use of technical terminology.

[0054] When generating a plan, the plan generation unit can analyze the user's past travel history and select an appropriate plan generation method. The plan generation unit can, for example, propose an optimal plan based on places the user has visited in the past. The plan generation unit can also propose a plan that avoids crowds based on the user's past travel history. The plan generation unit can also analyze the user's past travel history using AI and propose the most efficient plan. This allows for efficient plan generation by selecting an optimal plan generation method based on the user's past travel history. Some or all of the above-mentioned processing in the plan generation unit can be performed using AI, for example, or without AI. For example, the plan generation unit can input the user's past travel data into the generation AI and have the generation AI select an optimal plan generation method.

[0055] When generating a plan, the plan generation unit can customize the means for plan generation based on the user's current living situation. For example, the plan generation unit generates a plan based on categories (e.g., restaurants, tourist attractions) that the user is currently interested in. The plan generation unit can also prioritize generating related plans based on keywords recently searched by the user. The plan generation unit can also generate a plan based on topics of social media groups in which the user participates. This allows for customizing the means for plan generation based on the user's living situation, thereby generating a more appropriate plan. Some or all of the above-described processing in the plan generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan generation unit can input the user's living situation data into the generation AI and cause the generation AI to customize the means for plan generation.

[0056] The plan generation unit can improve the plan generation method by reflecting user feedback when generating a plan. For example, the plan generation unit preferentially uses plan generation methods that users have previously rated highly. The plan generation unit can also exclude plan generation methods that users have previously rated poorly. The plan generation unit can also analyze users' past feedback using AI and improve the plan generation method. In this way, the plan generation method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the plan generation unit may be performed using AI, for example, or may be performed without using AI. For example, the plan generation unit can input user feedback data into the generation AI and cause the generation AI to improve the plan generation method.

[0057] When generating a plan, the plan generation unit can select an appropriate plan generation method by taking into account the user's geographical location information. The plan generation unit, for example, generates a plan that prioritizes locations close to the user's current location. The plan generation unit can also generate a plan that prioritizes locations that the user plans to visit. The plan generation unit can also generate a plan that prioritizes locations close to locations the user has visited in the past. This allows the optimal plan generation method to be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the plan generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan generation unit can input the user's location information data into the generation AI and cause the generation AI to select the optimal plan generation method.

[0058] When generating a plan, the plan generation unit can analyze the user's social media activity and suggest a means for generating the plan. For example, the plan generation unit generates a plan that includes places where the user has checked in on social media. The plan generation unit can also analyze the content of the user's social media posts and generate a related plan. The plan generation unit can also generate a related plan by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to suggest a highly relevant means for generating a plan. Some or all of the above-described processing in the plan generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan generation unit can input the user's social media data into the generation AI and cause the generation AI to suggest a means for generating a plan.

[0059] When generating a plan, the plan generation unit can customize the plan generation method by reflecting the user's past feedback. For example, the plan generation unit preferentially uses plan generation methods that the user has previously rated highly. The plan generation unit can also exclude plan generation methods that the user has previously rated poorly. The plan generation unit can also analyze the user's past feedback using AI and customize the plan generation method. In this way, the plan generation method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the plan generation unit may be performed using AI, for example, or may be performed without using AI. For example, the plan generation unit can input the user's feedback data into the generation AI and cause the generation AI to customize the plan generation method.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The information collection unit can analyze the user's past posting history and select an appropriate information collection method. For example, if the user has posted text in the past, it can preferentially suggest text input. Also, if the user has posted many images in the past, it can provide an interface that encourages image posting. Furthermore, if the user has posted by voice in the past, it can preferentially suggest voice input. This allows information to be collected efficiently by selecting the optimal information collection method based on the user's past posting history. Some or all of the above-mentioned processing in the information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the information collection unit can input the user's past posting data into the generation AI and have the generation AI select the optimal information collection method.

[0062] When awarding incentive points, the incentive unit can adjust the level of detail of the points based on the quality of the posted content. For example, more incentive points can be awarded to users who post high-quality photos and detailed reviews. Fewer incentive points can also be awarded to users who post brief reviews and low-quality photos. Furthermore, the incentive unit can evaluate the quality of the posted content using AI and adjust the level of detail of the points. This makes it possible to encourage high-quality posts by adjusting the level of detail of the points based on the quality of the posted content. Some or all of the above-described processing in the incentive unit may be performed using AI, for example, or may be performed without using AI. For example, the incentive unit can input the quality of the posted content into a generation AI and cause the generation AI to adjust the level of detail of the points.

[0063] When registering attribute information, the attribute registration unit can select an appropriate registration method by referring to the user's past registration history. For example, the attribute registration unit can suggest the optimal registration method based on the attribute information previously registered by the user. It can also preferentially suggest registration methods (voice, text, etc.) that the user has used in the past. Furthermore, the attribute registration unit can analyze the user's past registration history using AI and select the optimal registration method. This allows attribute information to be registered efficiently by selecting the optimal registration method based on the user's past registration history. Some or all of the above-mentioned processing in the attribute registration unit may be performed using AI, for example, or may be performed without using AI. For example, the attribute registration unit can input the user's past registration data into the generation AI and have the generation AI select the optimal registration method.

[0064] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the review when making a recommendation. For example, detailed recommendations can be made based on reviews with high ratings. Also, brief recommendations can be made based on reviews with low ratings. Furthermore, the recommendation unit can evaluate the importance of the review using AI and adjust the level of detail of the recommendation. In this way, by adjusting the level of detail of the recommendation based on the importance of the review, more appropriate recommendations can be made. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input review importance data into a generation AI and have the generation AI adjust the level of detail of the recommendation.

[0065] When generating a plan, the plan generation unit can analyze the user's past travel history and select an appropriate plan generation method. For example, the plan generation unit can propose an optimal plan based on places the user has visited in the past. It can also propose a plan that avoids crowds based on the user's past travel history. Furthermore, the plan generation unit can analyze the user's past travel history using AI to propose the most efficient plan. This allows for efficient plan generation by selecting the optimal plan generation method based on the user's past travel history. Some or all of the above-mentioned processing in the plan generation unit may be performed using AI, for example, or may be performed without using AI. For example, the plan generation unit can input the user's past travel data into the generation AI and have the generation AI select the optimal plan generation method.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The information collection unit collects word-of-mouth and store information from users. For example, information is collected when users post reviews and photos of stores they have visited. It can also collect detailed information entered by users, such as the store's location and business hours. Furthermore, the information collection unit stores the information provided by users in a database, making it accessible to other users. Step 2: The incentive unit awards incentive points based on the information collected by the information collection unit. For example, points can be awarded each time a user posts a review. Additional points can also be awarded if detailed store information is provided. Furthermore, bonus points can be awarded for information posted during a specific campaign period. Step 3: The attribute registration unit registers the user's attribute information. For example, the user can register attribute information by entering information such as their age, whether they have a spouse or children, whether they have pets, their language, and their religion. They can also register places they have visited in the past and categories of interest. The attribute registration unit then stores the user's attribute information in a database so that other elements can access it. Step 4: The recommendation unit recommends places that have been reviewed by users with similar attributes based on the information registered by the attribute registration unit. For example, it can recommend pet-friendly restaurants or reasonably priced restaurants for families. It can also recommend tourist spots based on the user's interests. It can also recommend new places based on the user's past behavioral history. Step 5: The plan generation unit generates an optimal travel plan based on the visit date and time, budget, and attributes. For example, it proposes an optimal travel plan for a family with a limited budget. It can also propose a plan that avoids crowds based on the user's visit date and time. It can also generate a travel plan based on a specific theme based on the user's attribute information.

[0068] (Example 2) A system according to an embodiment of the present invention solves the problem of travelers being unable to search for information that meets their needs due to a lack of information about local businesses. This system provides a mechanism for rewarding users with incentive points when they input reviews and store information. Next, users register their attributes (e.g., age, whether they have a spouse or children, whether they have pets, language, religion, etc.). The system then creates a map that automatically recommends places reviewed by users with similar attributes. Furthermore, when users input their visit date and time, budget, and attributes, AI automatically generates an optimal travel plan. For example, incentive points are awarded when users post reviews and photos of businesses they have visited. After users register their attributes, the system recommends businesses that allow pets and affordable, family-friendly businesses. Furthermore, for families traveling on a limited budget, AI automatically generates an optimal travel plan and suggests family-friendly tourist spots. This system enriches information about local businesses, allowing travelers to easily find businesses and tourist spots that meet their needs. Furthermore, users are encouraged to actively share information by receiving incentives for providing information.

[0069] An information provision system according to an embodiment includes an information collection unit, an incentive unit, an attribute registration unit, a recommendation unit, and a plan generation unit. The information collection unit collects user reviews and store information from users. For example, the information collection unit collects information by users posting reviews and photos of stores they have visited. The information collection unit can also collect detailed information entered by users, such as store location information and business hours. The information collection unit stores the information provided by users in a database so that other users can access it. The incentive unit awards incentive points based on the information collected by the information collection unit. For example, the incentive unit awards points each time a user posts a review. The incentive unit can also award additional points if a user provides detailed store information. The incentive unit can also award bonus points for information posted during a specific campaign period. The attribute registration unit registers user attribute information. For example, the attribute registration unit registers attribute information by users entering information such as their age, whether they have a spouse or children, whether they have pets, language, and religion. The attribute registration unit can also register places the user has visited in the past and categories of interest. Furthermore, the attribute registration unit stores the user's attribute information in a database so that other elements can access it. The recommendation unit recommends places that users with similar attributes have written reviews about, based on the information registered by the attribute registration unit. For example, the recommendation unit recommends pet-friendly restaurants or affordable restaurants for families. The recommendation unit can also recommend tourist spots based on the user's interests. The recommendation unit can also recommend new places based on the user's past behavior history. The plan generation unit generates an optimal travel plan based on the date and time of visit, budget, and attributes. For example, the plan generation unit suggests an optimal travel plan for a family with a limited budget. The plan generation unit can also suggest a plan that avoids crowds based on the date and time of the user's visit. The plan generation unit can also generate a travel plan based on a specific theme based on the user's attribute information.As a result, the information providing system according to the embodiment can collect user reviews and store information, award incentive points, make recommendations based on attribute information, and generate optimal travel plans.

[0070] The information collection unit can estimate the user's emotions and adjust the timing of collecting word-of-mouth and store information based on the estimated user emotions. For example, if the user is excited, the information collection unit can immediately send a notification encouraging the user to collect word-of-mouth and store information. Furthermore, if the user is relaxed, the information collection unit can delay the collection timing so that the user can receive information when they are calm. Furthermore, if the user is feeling stressed, the information collection unit can adjust the collection timing so that the information is provided after the user has relaxed. This allows information to be collected at a more appropriate time by adjusting the timing of information collection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information collection unit can be performed using, for example, an AI, or without an AI. For example, the information collection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0071] The information collection unit can analyze the user's past posting history and select an appropriate information collection method. For example, if the user has posted using text in the past, the information collection unit can preferentially suggest text input. Furthermore, if the user has posted many images in the past, the information collection unit can also provide an interface that encourages image posting. Furthermore, if the user has posted using voice in the past, the information collection unit can preferentially suggest voice input. This allows information to be collected efficiently by selecting the optimal information collection method based on the user's past posting history. Some or all of the above-described processing in the information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the information collection unit can input the user's past posting data into the generation AI and cause the generation AI to select the optimal information collection method.

[0072] When collecting reviews and store information, the information collection unit can filter the information based on the user's current interests and concerns. For example, the information collection unit collects information based on categories in which the user is currently interested (e.g., restaurants, tourist attractions). The information collection unit can also preferentially collect related information based on keywords recently searched by the user. The information collection unit can also collect information based on topics in social media groups in which the user participates. This makes it possible to collect highly relevant information by filtering information based on the user's interests and concerns. Some or all of the above-described processing in the information collection unit may be performed using, or without, AI. For example, the information collection unit can input the user's search history data into the generation AI and have the generation AI perform filtering.

[0073] When collecting word-of-mouth information or store information, the information collection unit can select an appropriate collection means according to the user's input method. For example, if the user prefers voice input, the information collection unit can prioritize support for voice input. Also, if the user prefers text input, the information collection unit can prioritize support for text input. Also, if the user prefers image posting, the information collection unit can prioritize support for image posting. This allows information to be collected efficiently by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the information collection unit can input the user's input data into a generation AI and cause the generation AI to select the optimal collection means.

[0074] The information collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, when the user is excited, the information collection unit can prioritize collecting the latest word-of-mouth information. Furthermore, when the user is relaxed, the information collection unit can prioritize collecting detailed store information. Furthermore, when the user is stressed, the information collection unit can prioritize collecting concise, to-the-point information. This allows for more appropriate information to be collected by determining the priority of information based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0075] When collecting word-of-mouth reviews and store information, the information collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the information collection unit prioritizes collecting information about stores close to the user's current location. The information collection unit can also prioritize collecting information about areas the user plans to visit. The information collection unit can also prioritize collecting information near places the user has visited in the past. In this way, highly relevant information can be collected preferentially by taking the user's geographical location information into consideration. Some or all of the above-described processing in the information collection unit may be performed using AI, for example, or may be performed without using AI. For example, the information collection unit can input the user's location information data into the generation AI and cause the generation AI to collect highly relevant information.

[0076] The information collection unit can analyze the user's social media activities and collect related information when collecting word-of-mouth reviews and store information. For example, the information collection unit collects information on places where the user has checked in on social media. The information collection unit can also analyze the content of the user's social media posts to collect related store information. The information collection unit can also refer to the activities of the user's friends on social media to collect related information. In this way, highly relevant information can be collected by analyzing the user's social media activities. Some or all of the above-described processing in the information collection unit may be performed using AI, for example, or may be performed without using AI. For example, the information collection unit can input the user's social media data into the generation AI and cause the generation AI to collect related information.

[0077] When collecting word-of-mouth information and store information, the information collection unit can customize the collection method by reflecting the user's past feedback. For example, the information collection unit preferentially collects information from information sources that the user has previously rated highly. The information collection unit can also exclude information from information sources that the user has previously rated poorly. The information collection unit can also customize the collection method based on the user's past feedback. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the information collection unit may be performed using AI, for example, or may be performed without using AI. For example, the information collection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the collection method.

[0078] The incentive unit can estimate the user's emotions and adjust the incentive point awarding method based on the estimated user emotions. For example, if the user is excited, the incentive unit can immediately award incentive points. If the user is relaxed, the incentive unit can also award incentive points for detailed feedback. If the user is stressed, the incentive unit can also award incentive points for a simple task. This allows for more effective incentive provision by adjusting the incentive point awarding method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the incentive unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the incentive unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the incentive point awarding method.

[0079] When awarding incentive points, the incentive unit can adjust the level of detail of the points based on the quality of the posted content. For example, the incentive unit awards more incentive points to users who post high-quality photos and detailed reviews. The incentive unit can also award fewer incentive points to users who post brief reviews and low-quality photos. The incentive unit can also evaluate the quality of the posted content using AI and adjust the level of detail of the points. This makes it possible to encourage high-quality posts by adjusting the level of detail of the points based on the quality of the posted content. Some or all of the above-described processing in the incentive unit may be performed using AI, for example, or may be performed without using AI. For example, the incentive unit can input the quality of the posted content to a generation AI and cause the generation AI to adjust the level of detail of the points.

[0080] When awarding incentive points, the incentive unit can apply different awarding algorithms depending on the posting category. For example, the incentive unit can award points to restaurant reviews based on the quality of the food and service. For tourist destination reviews, the incentive unit can also award points based on the scenery and accessibility. For shopping reviews, the incentive unit can also award points based on the quality and price of the product. In this way, by applying different awarding algorithms depending on the posting category, incentives appropriate for each category can be provided. Some or all of the above-mentioned processing in the incentive unit may be performed using, for example, AI, or may be performed without using AI. For example, the incentive unit can input posting category data into a generation AI and cause the generation AI to apply an awarding algorithm.

[0081] When awarding incentive points, the incentive unit can improve the accuracy of the awarding by referring to the user's past posting results. For example, the incentive unit awards more incentive points to posts that the user has previously received high ratings. The incentive unit can also award fewer incentive points to posts that the user has previously received low ratings. The incentive unit can also analyze the user's past posting results using AI to improve the accuracy of the awarding. In this way, by referring to the user's past posting results, the accuracy of the awarding of incentive points can be improved. Some or all of the above-described processing in the incentive unit may be performed using AI, for example, or may be performed without using AI. For example, the incentive unit can input the user's past posting data into the generation AI and cause the generation AI to improve the accuracy of the awarding.

[0082] The incentive unit can estimate the user's emotions and adjust the amount of incentive points to be awarded based on the estimated user emotions. For example, if the user is excited, the incentive unit can award more incentive points than usual. Furthermore, if the user is relaxed, the incentive unit can award normal incentive points. Furthermore, if the user is stressed, the incentive unit can award fewer incentive points. This allows for more effective incentive provision by adjusting the amount of incentive points to be awarded based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the incentive unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the incentive unit can input the user's emotion data into the generation AI and have the generation AI adjust the amount of incentive points to be awarded.

[0083] When awarding incentive points, the incentive unit can determine the priority of awarding based on the time of submission of the post. For example, the incentive unit can award more incentive points to reviews posted early. The incentive unit can also preferentially award incentive points to reviews posted during a specific campaign period. The incentive unit can also analyze the time of submission of the post using AI and determine the priority of awarding. In this way, by determining the priority of awarding based on the time of submission of the post, early submission can be encouraged. Some or all of the above-mentioned processing in the incentive unit may be performed using AI, for example, or may be performed without using AI. For example, the incentive unit can input data on the time of submission of the post into a generation AI and have the generation AI determine the priority of awarding.

[0084] When awarding incentive points, the incentive unit can adjust the awarding order based on the relevance of the posts. For example, the incentive unit can prioritize awarding incentive points to reviews related to the user's attributes. The incentive unit can also prioritize awarding incentive points to reviews related to a specific category. The incentive unit can also analyze the relevance of posts using AI and adjust the awarding order. In this way, by adjusting the awarding order based on the relevance of posts, highly relevant posts can be prioritized for evaluation. Some or all of the above-described processing in the incentive unit may be performed using AI, for example, or may be performed without using AI. For example, the incentive unit can input post relevance data into a generation AI and have the generation AI adjust the awarding order.

[0085] When awarding incentive points, the incentive unit can adjust the use of technical terminology in the awarding depending on the user's level of expertise. For example, the incentive unit can provide an explanation of incentive points using technical terminology to a user with high level of expertise. The incentive unit can also provide an explanation of incentive points in simpler terms to a user with low level of expertise. The incentive unit can also analyze the user's level of expertise using AI and adjust the use of technical terminology in the awarding. This allows the user to be provided with an incentive that is appropriate for the user by adjusting the use of technical terminology depending on the user's level of expertise. Some or all of the above-described processing in the incentive unit can be performed using AI, for example, or without AI. For example, the incentive unit can input the user's technical knowledge data into a generation AI and have the generation AI adjust the use of technical terminology.

[0086] The attribute registration unit can estimate the user's emotions and adjust the attribute information registration method based on the estimated user's emotions. For example, if the user is excited, the attribute registration unit can register the attribute information in the form of a simple question. Furthermore, if the user is relaxed, the attribute registration unit can register the attribute information in the form of a detailed question. Furthermore, if the user is stressed, the attribute registration unit can register the attribute information in the form of a minimal question. This allows the attribute information to be registered in a more appropriate manner by adjusting the attribute information registration method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the attribute registration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the attribute registration unit can input the user's emotion data into the generation AI and have the generation AI adjust the registration method.

[0087] When registering attribute information, the attribute registration unit can select an appropriate registration method by referring to the user's past registration history. The attribute registration unit can, for example, suggest an optimal registration method based on attribute information previously registered by the user. The attribute registration unit can also preferentially suggest registration methods (voice, text, etc.) that the user has used in the past. The attribute registration unit can also analyze the user's past registration history using AI and select an optimal registration method. This allows attribute information to be registered efficiently by selecting an optimal registration method based on the user's past registration history. Some or all of the above-described processing in the attribute registration unit can be performed, for example, using AI or without AI. For example, the attribute registration unit can input the user's past registration data into a generation AI and have the generation AI select an optimal registration method.

[0088] When registering attribute information, the attribute registration unit can perform filtering based on the user's current living situation and interests. The attribute registration unit registers attribute information based on, for example, categories in which the user is currently interested (e.g., restaurants, tourist attractions). The attribute registration unit can also preferentially register related attribute information based on keywords recently searched by the user. The attribute registration unit can also register attribute information based on topics of social media groups in which the user participates. This allows highly relevant attribute information to be registered by filtering based on the user's living situation and interests. Some or all of the above-described processing in the attribute registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the attribute registration unit can input the user's living situation data into the generation AI and have the generation AI perform filtering.

[0089] When registering attribute information, the attribute registration unit can select an appropriate registration means according to the user's input method. For example, if the user prefers voice input, the attribute registration unit can preferentially support voice input. Furthermore, if the user prefers text input, the attribute registration unit can preferentially support text input. Furthermore, if the user prefers image posting, the attribute registration unit can preferentially support image posting. This allows attribute information to be registered efficiently by selecting the optimal registration means according to the user's input method. Some or all of the above-described processing in the attribute registration unit may be performed, for example, using AI or without AI. For example, the attribute registration unit can input the user's input data to a generation AI and cause the generation AI to select the optimal registration means.

[0090] The attribute registration unit can estimate the user's emotions and determine the priority of attribute information to be registered based on the estimated user's emotions. For example, when the user is excited, the attribute registration unit can prioritize registering important attribute information. Furthermore, when the user is relaxed, the attribute registration unit can also prioritize registering detailed attribute information. Furthermore, when the user is stressed, the attribute registration unit can also prioritize registering concise attribute information. Thus, by determining the priority of attribute information based on the user's emotions, important attribute information can be prioritized and registered. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the attribute registration unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the attribute registration unit can input the user's emotion data to the generation AI and have the generation AI determine the priority of the attribute information.

[0091] When registering attribute information, the attribute registration unit can prioritize registering highly relevant information by taking into account the user's geographical location information. For example, the attribute registration unit prioritizes registering attribute information close to the user's current location. The attribute registration unit can also prioritize registering attribute information for areas the user plans to visit. The attribute registration unit can also prioritize registering attribute information close to places the user has visited in the past. In this way, highly relevant attribute information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the attribute registration unit may be performed using AI, for example, or may be performed without using AI. For example, the attribute registration unit can input the user's location information data to the generation AI and cause the generation AI to register highly relevant information.

[0092] The attribute registration unit can analyze the user's social media activity and register related information when registering attribute information. For example, the attribute registration unit registers attribute information of locations where the user has checked in on social media. The attribute registration unit can also analyze the content of the user's social media posts and register related attribute information. The attribute registration unit can also register related attribute information by referring to the activity of the user's friends on social media. In this way, highly relevant attribute information can be registered by analyzing the user's social media activity. Some or all of the above-described processing in the attribute registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the attribute registration unit can input the user's social media data into a generation AI and cause the generation AI to register related information.

[0093] The attribute registration unit can customize the registration method by reflecting the user's past feedback when registering attribute information. For example, the attribute registration unit preferentially suggests registration methods that the user has previously rated highly. The attribute registration unit can also exclude registration methods that the user has previously rated poorly. The attribute registration unit can also customize the registration method based on the user's past feedback. In this way, the registration method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the attribute registration unit may be performed using AI, for example, or may be performed without using AI. For example, the attribute registration unit can input the user's feedback data into the generation AI and cause the generation AI to customize the registration method.

[0094] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on the estimated user emotions. For example, if the user is excited, the recommendation unit can provide visually stimulating recommendations. If the user is relaxed, the recommendation unit can also provide detailed recommendations. If the user is stressed, the recommendation unit can also provide concise, to-the-point recommendations. This allows for more effective recommendations by adjusting the way recommendations are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the recommendation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the recommendation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way recommendations are presented.

[0095] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the review when making a recommendation. The recommendation unit can make detailed recommendations based on, for example, highly rated reviews. The recommendation unit can also make concise recommendations based on low-rated reviews. The recommendation unit can also evaluate the importance of the review using AI and adjust the level of detail of the recommendation. This allows for more appropriate recommendations to be made by adjusting the level of detail of the recommendation based on the importance of the review. Some or all of the above-mentioned processing in the recommendation unit can be performed using AI, for example, or without using AI. For example, the recommendation unit can input review importance data into a generation AI and cause the generation AI to adjust the level of detail of the recommendation.

[0096] When making recommendations, the recommendation unit can apply different recommendation algorithms depending on the category of the review. For example, for restaurant reviews, the recommendation unit can make recommendations based on the quality of the food and the service. For tourist destination reviews, the recommendation unit can also make recommendations based on the scenery and accessibility. For shopping reviews, the recommendation unit can also make recommendations based on the quality and price of the product. In this way, by applying different recommendation algorithms depending on the category of the review, recommendations appropriate for each category can be made. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input review category data into the generation AI and have the generation AI apply the recommendation algorithm.

[0097] When making a recommendation, the recommendation unit can improve the accuracy of the recommendation by referring to the user's past recommendation results. For example, the recommendation unit makes more accurate recommendations based on recommendations that the user has given high ratings to in the past. The recommendation unit can also eliminate less accurate recommendations based on recommendations that the user has given low ratings to in the past. The recommendation unit can also analyze the user's past recommendation results using AI to improve the accuracy of the recommendation. In this way, the accuracy of the recommendation can be improved by referring to the user's past recommendation results. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input the user's past recommendation data into the generation AI and cause the generation AI to improve the accuracy of the recommendations.

[0098] The recommendation unit can estimate the user's emotions and adjust the length of the recommendation based on the estimated user's emotions. For example, if the user is excited, the recommendation unit can provide short, to-the-point recommendations. Furthermore, if the user is relaxed, the recommendation unit can provide longer recommendations with detailed explanations. Furthermore, if the user is stressed, the recommendation unit can provide concise, visually easy-to-understand recommendations. By adjusting the length of the recommendation based on the user's emotions, more effective recommendations can be made. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit can be performed using, for example, an AI, or without an AI. For example, the recommendation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the recommendation.

[0099] When making a recommendation, the recommendation unit can determine the priority of recommendations based on the time when the review was submitted. The recommendation unit, for example, prioritizes recommendations based on the most recent review. The recommendation unit can also prioritize recommendations based on reviews posted during a specific campaign period. The recommendation unit can also analyze the time when the review was submitted using AI to determine the priority of recommendations. In this way, by determining the priority of recommendations based on the time when the review was submitted, the most recent information can be prioritized for recommendation. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input review submission time data into the generation AI and have the generation AI determine the priority of recommendations.

[0100] The recommendation unit can adjust the order of recommendations based on the relevance of the reviews when making recommendations. For example, the recommendation unit prioritizes recommendations based on reviews related to the user's attributes. The recommendation unit can also prioritize recommendations based on reviews related to a specific category. The recommendation unit can also analyze the relevance of reviews using AI and adjust the order of recommendations. In this way, by adjusting the order of recommendations based on the relevance of reviews, highly relevant information can be prioritized for recommendation. Some or all of the above-described processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input review relevance data into a generation AI and have the generation AI adjust the order of recommendations.

[0101] The recommendation unit can adjust the use of technical terminology in the recommendation according to the user's level of expertise when making a recommendation. For example, the recommendation unit makes recommendations using technical terminology for a user with high level of expertise. The recommendation unit can also make recommendations in simpler terms for a user with low level of expertise. The recommendation unit can also analyze the user's level of expertise using AI and adjust the use of technical terminology in the recommendation. This allows recommendations that are suitable for the user to be made by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input the user's technical knowledge data into a generation AI and cause the generation AI to adjust the use of technical terminology.

[0102] The plan generation unit can estimate the user's emotions and adjust the plan generation method based on the estimated user emotions. For example, if the user is excited, the plan generation unit can suggest an active plan. Furthermore, if the user is relaxed, the plan generation unit can suggest a relaxing plan. Furthermore, if the user is stressed, the plan generation unit can suggest a stress-reducing plan. By adjusting the plan generation method based on the user's emotions, a more appropriate plan can be generated. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the plan generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the plan generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the plan generation method.

[0103] When generating a plan, the plan generation unit can analyze the user's past travel history and select an appropriate plan generation method. The plan generation unit can, for example, propose an optimal plan based on places the user has visited in the past. The plan generation unit can also propose a plan that avoids crowds based on the user's past travel history. The plan generation unit can also analyze the user's past travel history using AI and propose the most efficient plan. This allows for efficient plan generation by selecting an optimal plan generation method based on the user's past travel history. Some or all of the above-mentioned processing in the plan generation unit can be performed using AI, for example, or without AI. For example, the plan generation unit can input the user's past travel data into the generation AI and have the generation AI select an optimal plan generation method.

[0104] When generating a plan, the plan generation unit can customize the means for plan generation based on the user's current living situation. For example, the plan generation unit generates a plan based on categories (e.g., restaurants, tourist attractions) that the user is currently interested in. The plan generation unit can also prioritize generating related plans based on keywords recently searched by the user. The plan generation unit can also generate a plan based on topics of social media groups in which the user participates. This allows for customizing the means for plan generation based on the user's living situation, thereby generating a more appropriate plan. Some or all of the above-described processing in the plan generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan generation unit can input the user's living situation data into the generation AI and cause the generation AI to customize the means for plan generation.

[0105] The plan generation unit can improve the plan generation method by reflecting user feedback when generating a plan. For example, the plan generation unit preferentially uses plan generation methods that users have previously rated highly. The plan generation unit can also exclude plan generation methods that users have previously rated poorly. The plan generation unit can also analyze users' past feedback using AI and improve the plan generation method. In this way, the plan generation method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the plan generation unit may be performed using AI, for example, or may be performed without using AI. For example, the plan generation unit can input user feedback data into the generation AI and cause the generation AI to improve the plan generation method.

[0106] The plan generation unit can estimate the user's emotions and determine the priority of plan generation based on the estimated user's emotions. For example, if the user is excited, the plan generation unit can prioritize generating active plans. Furthermore, if the user is relaxed, the plan generation unit can prioritize generating relaxing plans. Furthermore, if the user is stressed, the plan generation unit can prioritize generating stress-reducing plans. Thus, by determining the priority of plan generation based on the user's emotions, more appropriate plans can be generated preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the plan generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the plan generation unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of plan generation.

[0107] When generating a plan, the plan generation unit can select an appropriate plan generation method by taking into account the user's geographical location information. The plan generation unit, for example, generates a plan that prioritizes locations close to the user's current location. The plan generation unit can also generate a plan that prioritizes locations that the user plans to visit. The plan generation unit can also generate a plan that prioritizes locations close to locations the user has visited in the past. This allows the optimal plan generation method to be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the plan generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan generation unit can input the user's location information data into the generation AI and cause the generation AI to select the optimal plan generation method.

[0108] When generating a plan, the plan generation unit can analyze the user's social media activity and suggest a means for generating the plan. For example, the plan generation unit generates a plan that includes places where the user has checked in on social media. The plan generation unit can also analyze the content of the user's social media posts and generate a related plan. The plan generation unit can also generate a related plan by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to suggest a highly relevant means for generating a plan. Some or all of the above-described processing in the plan generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan generation unit can input the user's social media data into the generation AI and cause the generation AI to suggest a means for generating a plan.

[0109] When generating a plan, the plan generation unit can customize the plan generation method by reflecting the user's past feedback. For example, the plan generation unit preferentially uses plan generation methods that the user has previously rated highly. The plan generation unit can also exclude plan generation methods that the user has previously rated poorly. The plan generation unit can also analyze the user's past feedback using AI and customize the plan generation method. In this way, the plan generation method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the plan generation unit may be performed using AI, for example, or may be performed without using AI. For example, the plan generation unit can input the user's feedback data into the generation AI and cause the generation AI to customize the plan generation method. === Hard Collateral 1-1 === Each of the multiple elements, including the information collection unit, incentive unit, attribute registration unit, recommendation unit, and plan generation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the information collection unit collects word-of-mouth information and store information from users using the camera 42 and reception device 38 of the smart device 14 and transmits the information to the data processing device 12 via the control unit 46A. The incentive unit, realized, for example, by the specific processing unit 290 of the data processing device 12, awards incentive points based on the collected information. The attribute registration unit, for example, inputs user attribute information using the reception device 38 of the smart device 14 and stores the information in the data processing device 12 via the control unit 46A. The recommendation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, makes recommendations based on the registered attribute information. The plan generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates an optimal travel plan based on the visit date and time, budget, and attributes. === Hard Collateral 1-2 === Each of the multiple elements, including the information collection unit, incentive unit, attribute registration unit, recommendation unit, and plan generation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the information collection unit collects word-of-mouth information and store information from users using the camera 42 and microphone 238 of the smart glasses 214 and transmits the information to the data processing device 12 via the control unit 46A. The incentive unit, realized, for example, by the specific processing unit 290 of the data processing device 12, awards incentive points based on the collected information. The attribute registration unit, for example, inputs user attribute information using the microphone 238 of the smart glasses 214 and stores the information in the data processing device 12 via the control unit 46A. The recommendation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, makes recommendations based on the registered attribute information. The plan generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates an optimal travel plan based on the visit date and time, budget, and attributes. === Hard Collateral 1-3 === Each of the multiple elements, including the information collection unit, incentive unit, attribute registration unit, recommendation unit, and plan generation unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the information collection unit collects word-of-mouth information and store information from users using the camera 42 and microphone 238 of the headset terminal 314 and transmits the information to the data processing device 12 via the control unit 46A. The incentive unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and awards incentive points based on the collected information. The attribute registration unit inputs user attribute information using, for example, the microphone 238 of the headset terminal 314 and stores the information in the data processing device 12 via the control unit 46A. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes recommendations based on the registered attribute information. The plan generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal travel plan based on the visit date and time, budget, and attributes. === Hard Collateral 1-4 === Each of the multiple elements, including the information collection unit, incentive unit, attribute registration unit, recommendation unit, and plan generation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the information collection unit collects word-of-mouth information and store information from users using the camera 42 and microphone 238 of the robot 414 and transmits the information to the data processing device 12 via the control unit 46A. The incentive unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and awards incentive points based on the collected information. The attribute registration unit inputs user attribute information using, for example, the microphone 238 of the robot 414 and stores the information in the data processing device 12 via the control unit 46A. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes recommendations based on the registered attribute information. The plan generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal travel plan based on the visit date and time, budget, and attributes.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The information collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, if the user is excited, the latest word-of-mouth information can be collected first. Also, if the user is relaxed, detailed store information can be collected first. Furthermore, if the user is stressed, concise, to-the-point information can be collected first. By determining the priority of information based on the user's emotions, more appropriate information can be collected. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information collection unit can be performed using, for example, an AI, or without an AI. For example, the information collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0112] The incentive unit can estimate the user's emotions and adjust the incentive point awarding method based on the estimated user emotions. For example, if the user is excited, incentive points can be awarded immediately. If the user is relaxed, incentive points can be awarded for detailed feedback. Furthermore, if the user is stressed, incentive points can be awarded for a simple task. This allows for more effective incentive provision by adjusting the incentive point awarding method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the incentive unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the incentive unit can input the user's emotion data into the generation AI and have the generation AI adjust the incentive point awarding method.

[0113] The attribute registration unit can estimate the user's emotions and adjust the attribute information registration method based on the estimated user emotions. For example, if the user is excited, the attribute information can be registered in the form of a simple question. If the user is relaxed, the attribute information can be registered in the form of a detailed question. Furthermore, if the user is stressed, the attribute information can be registered in the form of a minimal question. This allows the attribute information to be registered in a more appropriate manner by adjusting the attribute information registration method based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the attribute registration unit can be performed using an AI, for example, or without an AI. For example, the attribute registration unit can input the user's emotion data into the generation AI and have the generation AI adjust the registration method.

[0114] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on the estimated user emotions. For example, if the user is excited, visually stimulating recommendations can be presented. If the user is relaxed, detailed recommendations can be presented. Furthermore, if the user is stressed, concise and to the point recommendations can be presented. This allows for more effective recommendations by adjusting the way recommendations are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the recommendation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the recommendation unit can input the user's emotion data into the generation AI and have the generation AI adjust the way recommendations are presented.

[0115] The plan generation unit can estimate the user's emotions and adjust the plan generation method based on the estimated user emotions. For example, if the user is excited, an active plan can be suggested. If the user is relaxed, a relaxing plan can be suggested. Furthermore, if the user is stressed, a stress-reducing plan can be suggested. By adjusting the plan generation method based on the user's emotions, a more appropriate plan can be generated. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the plan generation unit can be performed using AI, for example, or without AI. For example, the plan generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the plan generation method.

[0116] The information collection unit can analyze the user's past posting history and select an appropriate information collection method. For example, if the user has posted text in the past, it can preferentially suggest text input. Also, if the user has posted many images in the past, it can provide an interface that encourages image posting. Furthermore, if the user has posted by voice in the past, it can preferentially suggest voice input. This allows information to be collected efficiently by selecting the optimal information collection method based on the user's past posting history. Some or all of the above-mentioned processing in the information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the information collection unit can input the user's past posting data into the generation AI and have the generation AI select the optimal information collection method.

[0117] When awarding incentive points, the incentive unit can adjust the level of detail of the points based on the quality of the posted content. For example, more incentive points can be awarded to users who post high-quality photos and detailed reviews. Fewer incentive points can also be awarded to users who post brief reviews and low-quality photos. Furthermore, the incentive unit can evaluate the quality of the posted content using AI and adjust the level of detail of the points. This makes it possible to encourage high-quality posts by adjusting the level of detail of the points based on the quality of the posted content. Some or all of the above-described processing in the incentive unit may be performed using AI, for example, or may be performed without using AI. For example, the incentive unit can input the quality of the posted content into a generation AI and cause the generation AI to adjust the level of detail of the points.

[0118] When registering attribute information, the attribute registration unit can select an appropriate registration method by referring to the user's past registration history. For example, the attribute registration unit can suggest the optimal registration method based on the attribute information previously registered by the user. It can also preferentially suggest registration methods (voice, text, etc.) that the user has used in the past. Furthermore, the attribute registration unit can analyze the user's past registration history using AI and select the optimal registration method. This allows attribute information to be registered efficiently by selecting the optimal registration method based on the user's past registration history. Some or all of the above-mentioned processing in the attribute registration unit may be performed using AI, for example, or may be performed without using AI. For example, the attribute registration unit can input the user's past registration data into the generation AI and have the generation AI select the optimal registration method.

[0119] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the review when making a recommendation. For example, detailed recommendations can be made based on reviews with high ratings. Also, brief recommendations can be made based on reviews with low ratings. Furthermore, the recommendation unit can evaluate the importance of the review using AI and adjust the level of detail of the recommendation. In this way, by adjusting the level of detail of the recommendation based on the importance of the review, more appropriate recommendations can be made. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input review importance data into a generation AI and have the generation AI adjust the level of detail of the recommendation.

[0120] When generating a plan, the plan generation unit can analyze the user's past travel history and select an appropriate plan generation method. For example, the plan generation unit can propose an optimal plan based on places the user has visited in the past. It can also propose a plan that avoids crowds based on the user's past travel history. Furthermore, the plan generation unit can analyze the user's past travel history using AI to propose the most efficient plan. This allows for efficient plan generation by selecting the optimal plan generation method based on the user's past travel history. Some or all of the above-mentioned processing in the plan generation unit may be performed using AI, for example, or may be performed without using AI. For example, the plan generation unit can input the user's past travel data into the generation AI and have the generation AI select the optimal plan generation method.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The information collection unit collects word-of-mouth and store information from users. For example, information is collected when users post reviews and photos of stores they have visited. It can also collect detailed information entered by users, such as the store's location and business hours. Furthermore, the information collection unit stores the information provided by users in a database, making it accessible to other users. Step 2: The incentive unit awards incentive points based on the information collected by the information collection unit. For example, points can be awarded each time a user posts a review. Additional points can also be awarded if detailed store information is provided. Furthermore, bonus points can be awarded for information posted during a specific campaign period. Step 3: The attribute registration unit registers the user's attribute information. For example, the user can register attribute information by entering information such as their age, whether they have a spouse or children, whether they have pets, their language, and their religion. They can also register places they have visited in the past and categories of interest. The attribute registration unit then stores the user's attribute information in a database so that other elements can access it. Step 4: The recommendation unit recommends places that have been reviewed by users with similar attributes based on the information registered by the attribute registration unit. For example, it can recommend pet-friendly restaurants or reasonably priced restaurants for families. It can also recommend tourist spots based on the user's interests. It can also recommend new places based on the user's past behavioral history. Step 5: The plan generation unit generates an optimal travel plan based on the visit date and time, budget, and attributes. For example, it proposes an optimal travel plan for a family with a limited budget. It can also propose a plan that avoids crowds based on the user's visit date and time. It can also generate a travel plan based on a specific theme based on the user's attribute information.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 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. An information gathering department that collects reviews and store information, an incentive unit that awards incentive points based on the information collected by the information collection unit; an attribute registration unit that registers user attribute information; a recommendation unit that makes recommendations based on the information registered by the attribute registration unit; A plan generation unit that generates an appropriate travel plan based on the visit date and time, budget, and attributes. A system characterized by:

2. The information collecting unit Estimate user emotions and adjust the timing of collecting reviews and store information based on the estimated user emotions 2. The system of claim 1.

3. The information collecting unit Analyze users' past posting history and select the appropriate information collection method 2. The system of claim 1.

4. The information collecting unit When collecting reviews and store information, filter based on the user's current interests.

2. The system of claim 1.

5. The information collecting unit When collecting reviews and store information, select the appropriate collection method depending on the user's input method.

2. The system of claim 1.

6. The information collecting unit Estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions.

2. The system of claim 1.

7. The information collecting unit When collecting reviews and store information, the system prioritizes collecting relevant information based on the user's geographic location.

2. The system of claim 1.

8. The information collecting unit When collecting reviews and store information, analyze users' social media activity and collect related information.

2. The system of claim 1.

Images