system

Abstract

The system according to this embodiment aims to suggest local attractions and experiences based on taxpayers' interests and values. [Solution] The system according to the embodiment comprises an input unit, an analysis unit, a proposal unit, a guide unit, and an information provision unit. The input unit inputs the taxpayer's interests and values. The analysis unit analyzes the information input by the input unit. The proposal unit proposes areas or experiences based on the results of the analysis by the analysis unit. The guide unit introduces local tourist spots, recommended restaurants, and events to interact with local people based on the information proposed by the proposal unit. The information provision unit provides information on local specialties or events proposed by the proposal unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technologies, the hometown tax donation system has faced the challenge of being heavily focused on competition for return gifts, failing to fully utilize the charm and experiences of the region.

[0005] The system according to this embodiment aims to suggest local attractions and experiences based on taxpayers' interests and values. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a proposal unit, a guide unit, and an information provision unit. The input unit inputs the taxpayer's interests and values. The analysis unit analyzes the information input by the input unit. The proposal unit proposes areas or experiences based on the results of the analysis by the analysis unit. The guide unit introduces local tourist spots, recommended restaurants, and events to interact with local people based on the information proposed by the proposal unit. The information provision unit provides information on local specialties or events proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest local attractions and experiences based on the taxpayer's interests and values. [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 donation suggestion system according to an embodiment of the present invention helps taxpayers decide on donation destinations based on their "discovery" of their desired future, the people they want to meet, and the appeal of their local area. This donation suggestion system allows taxpayers to input their interests, concerns, and values, and analyzes this information to suggest areas, people, and experiences that are most suitable for each taxpayer. For example, it suggests areas rich in nature for nature lovers, and areas rich in historical sites for history buffs. It also provides information on local specialties and events, allowing taxpayers to gain a deeper understanding of the area's appeal. Furthermore, the donation suggestion system also functions as a guide for taxpayers when they actually visit the area. For example, it introduces local tourist spots, recommended restaurants, and events where people can interact with locals. This allows taxpayers to not only receive gifts in return, but also to develop a deeper connection with the local area. This system proposes a new form of hometown tax donation by allowing taxpayers to select donation destinations based on their own values ​​and interests. Taxpayers can enjoy connecting with the local area and making new discoveries, rather than simply receiving goods. The donation suggestion system allows taxpayers to select donation destinations based on their own values ​​and interests, allowing them to enjoy connecting with the local area and making new discoveries.

[0029] The donation recipient suggestion system according to this embodiment comprises an input unit, an analysis unit, a suggestion unit, a guide unit, and an information provision unit. The input unit inputs the taxpayer's interests or concerns and values. The taxpayer's interests or concerns include, but are not limited to, hobbies, specific themes, and activities. Values ​​include, but are not limited to, environmental protection, social contribution, and cultural values. The analysis unit analyzes the information input by the input unit. For analysis, methods such as data mining, statistical analysis, and machine learning are used, but are not limited to, such methods. The suggestion unit suggests regions or experiences based on the results analyzed by the analysis unit. For suggestions, algorithms for suggestions and evaluation criteria are used, but are not limited to, such methods. The guide unit introduces local tourist spots, recommended restaurants, and events for interacting with local people based on the information suggested by the suggestion unit. Tourist spots include, but are not limited to, historical buildings, natural landscapes, and theme parks. Recommended restaurants include, but are not limited to, local famous restaurants and specific cuisine genres. Examples of social events include, but are not limited to, local festivals, workshops, and social gatherings. The information providing unit provides information on local specialties or events proposed by the suggestion unit. Examples of specialties include, but are not limited to, local specialty products, handicrafts, and food. Examples of event information include, but are not limited to, the date and time of the event, the location, and how to participate. As a result, the donation suggestion system according to the embodiment allows taxpayers to select donation destinations based on their own values ​​and interests, allowing them to enjoy connecting with the community and making new discoveries.

[0030] The input section allows taxpayers to enter information about their hobbies or interests, past travel experiences, and favorite foods or cultures. Hobbies or interests include, but are not limited to, sports, music, and art. Past travel experiences include, but are not limited to, places visited, purposes of travel, and activities experienced. Favorite foods or cultures include, but are not limited to, specific cuisines, traditional culture, and modern culture. This allows for more accurate suggestions by providing detailed information about the taxpayer.

[0031] The analysis unit can analyze information input by the input unit using AI. AI includes, but is not limited to, technologies such as machine learning, deep learning, and natural language processing. For example, machine learning can be used to classify taxpayers' interests and concerns, and deep learning can be used to analyze taxpayers' values. Furthermore, natural language processing can be used to analyze taxpayers' input and provide optimal suggestions. This improves the accuracy of information analysis through the use of AI.

[0032] The proposal department can suggest the most suitable regions and experiences to taxpayers based on the results analyzed by the analysis department. These optimal regions and experiences may include, but are not limited to, evaluation criteria and selection algorithms. For example, evaluation criteria could include selecting regions and experiences based on the taxpayer's interests, concerns, and values. Furthermore, selection algorithms such as clustering algorithms and classification algorithms could be used to suggest the most suitable regions and experiences. This ensures that optimal suggestions are made based on the analysis results.

[0033] The Guide Department can introduce local tourist attractions, recommended restaurants, and events for interacting with locals, based on the information proposed by the Proposal Department. Tourist attractions include, but are not limited to, historical buildings, natural landscapes, and theme parks. Recommended restaurants include, but are not limited to, local renowned establishments and specific cuisine genres. Interaction events include, but are not limited to, local festivals, workshops, and social gatherings. This allows for the provision of a detailed guide to the region based on the proposed information.

[0034] The Information Provision Department can provide information on local specialties and events proposed by the Proposal Department. Local specialties include, but are not limited to, regional products, handicrafts, and food products. Event information includes, but is not limited to, dates, times, locations, and participation methods. This ensures that proposed information on local specialties and events is provided.

[0035] The input unit can analyze the taxpayer's past input history and select the optimal input method. This optimal method includes, but is not limited to, input interfaces and usability test results. For example, it can automatically display information that the taxpayer has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the taxpayer has used in the past. Furthermore, it can predict and suggest information that the taxpayer will use at specific times based on their past input history. This ensures that the optimal input method is selected based on past input history.

[0036] The input unit can filter the information based on the taxpayer's current living situation and areas of interest during input. Examples of living situation include, but are not limited to, occupation, family environment, and health status. Examples of areas of interest include, but are not limited to, hobbies, research topics, and social issues. For example, when a taxpayer inputs their current living situation, only relevant information is displayed. Furthermore, the information to be input is filtered based on the taxpayer's areas of interest, and only highly relevant information is displayed. Furthermore, the priority of the information to be input is determined based on the taxpayer's living situation and areas of interest. As a result, the input information is filtered based on the taxpayer's living situation and areas of interest.

[0037] The input unit can prioritize input of highly relevant information during input, taking into account the taxpayer's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. Examples of highly relevant information include, but are not limited to, the user's current location and past activity history. For example, when the taxpayer inputs their current location, information about related areas is displayed preferentially. Furthermore, the information to be input is filtered based on the taxpayer's geographical location information, and only highly relevant information is displayed. Furthermore, the priority of the information to be input is determined based on the taxpayer's geographical location information. As a result, highly relevant information is input preferentially based on the geographical location information.

[0038] The input unit can analyze the taxpayer's social media activity at the time of input and input relevant information. Social media activity includes, but is not limited to, the content of posts, the number of likes, the number of followers, etc. For example, the input unit can analyze the taxpayer's interests and input relevant information based on the taxpayer's social media activity. The input unit can also filter the information to be input based on the taxpayer's social media activity and display only highly relevant information. Furthermore, the input unit can determine the priority of the information to be input based on the taxpayer's social media activity. As a result, relevant information is input based on the social media activity.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the input information during the analysis. The importance of the information includes, but is not limited to, factors such as user interest and novelty. The level of detail of the analysis includes, but is not limited to, factors such as the depth of the analysis and the amount of information displayed. For example, a detailed analysis is performed for important information, while a concise analysis is performed for less important information. Furthermore, the analysis priority is determined based on the importance of the input information. In addition, multiple analysis methods are applied to important information to provide detailed results. This adjusts the level of detail of the analysis based on the importance of the information.

[0040] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. Information categories include, but are not limited to, genre, theme, and application. Analysis algorithms include, but are not limited to, clustering algorithms and classification algorithms. For example, a natural environment analysis algorithm is applied to information about nature. A historical background analysis algorithm is applied to information about history. Furthermore, a food culture analysis algorithm is applied to information about food culture. This ensures that the appropriate analysis algorithm is applied according to the category of information.

[0041] The analysis unit can determine the priority of analysis based on the timing of information submission during the analysis process. The timing of information submission includes, but is not limited to, the submission date and time, and submission frequency. The priority of analysis includes, but is not limited to, factors such as importance, urgency, and relevance. For example, the unit prioritizes the analysis of the most recent information, while older information is postponed. Furthermore, the analysis priority is determined based on the submission timing. In addition, information with a recent submission date is analyzed quickly. This ensures that the analysis priority is determined based on the timing of information submission.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. Relevance of information includes, but is not limited to, user interest and novelty. The order of analysis includes, but is not limited to, importance, relevance, and submission timing. For example, highly relevant information can be analyzed preferentially, while less relevant information can be postponed. Furthermore, the order of analysis is determined based on the relevance of the information. In addition, a more detailed analysis is performed on highly relevant information. This adjusts the order of analysis based on the relevance of the information.

[0043] The proposal team can adjust the level of detail of a proposal based on the importance of the region or experience. The importance of a region or experience includes, but is not limited to, user interest levels and regional characteristics. The level of detail of a proposal includes, but is not limited to, the depth of the proposal and the amount of information displayed. For example, detailed proposals are provided for important regions or experiences, while concise proposals are provided for less important regions or experiences. Furthermore, the priority of proposals is determined based on the importance of the region or experience. In addition, multiple proposal methods are applied to important regions and experiences to provide detailed results. This adjusts the level of detail of proposals based on the importance of the region or experience.

[0044] The proposal department can apply different proposal algorithms depending on the region and experience category when submitting a proposal. These categories include, but are not limited to, genre, theme, and use. Proposal algorithms include, but are not limited to, clustering algorithms and classification algorithms. For example, a proposal related to nature would apply a natural environment proposal algorithm. A proposal related to history would apply a historical background proposal algorithm. Furthermore, a proposal related to food culture would apply a food culture proposal algorithm. This ensures that the appropriate proposal algorithm is applied according to the region and experience category.

[0045] At the time of proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the region and experience. The time of submission of the region and experience includes, for example, but is not limited to, the submission date and time, the submission frequency, etc. The priority of the proposal includes, for example, but is not limited to, the importance, the urgency, the relevance, etc. For example, the most recent region and experience are suggested first, and older information is put off. The priority of the proposal is also determined based on the time of submission. Furthermore, regions and experiences that are close to their submission date are quickly suggested. In this way, the priority of the proposal is determined based on the time of submission of the region and experience.

[0046] The suggestion unit can adjust the order of suggestions based on the relevance of regions and experiences when making suggestions. Examples of the relevance of regions and experiences include, but are not limited to, the user's level of interest and the novelty of the information. Examples of the order of suggestions include, but are not limited to, importance, relevance, and submission time. For example, highly relevant regions and experiences are suggested first, and less relevant information is put off. The order of suggestions is also determined based on the relevance of regions and experiences. Furthermore, detailed suggestions are made for highly relevant regions and experiences. In this way, the order of suggestions is adjusted based on the relevance of regions and experiences.

[0047] When displaying the guide, the guide unit can select the optimal display method by referring to the taxpayer's past travel history. Past travel history includes, for example, but is not limited to, places visited, travel purposes, and activities experienced. Optimal display methods include, for example, but are not limited to, display interfaces and usability test results. For example, based on places visited by the taxpayer in the past, related information may be displayed preferentially. The guide display method may also be customized based on the taxpayer's past travel history. Furthermore, the taxpayer's past travel history may be analyzed and the optimal guide display method may be proposed. In this way, the optimal guide display method may be selected based on the past travel history.

[0048] When displaying the guide, the guide unit can customize the content of the guide based on the taxpayer's current living situation. Examples of living situations include, but are not limited to, the taxpayer's occupation, family environment, and health condition. Examples of the content of the guide include, but are not limited to, the depth of information and the type of information to display. For example, the guide unit prioritizes displaying relevant information based on the taxpayer's current living situation. The guide unit also customizes the content of the guide based on the taxpayer's living situation. The guide unit further analyzes the taxpayer's living situation and suggests optimal guide content. In this way, the guide content is customized based on the taxpayer's current living situation.

[0049] When displaying the guide, the guide unit can select optimal guide content taking into account the taxpayer's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. Examples of optimal guide content include, but are not limited to, the user's current location and past activity history. For example, when the taxpayer inputs their current location, information about related areas is displayed preferentially. Furthermore, the guide content is filtered based on the taxpayer's geographical location information to display only highly relevant information. Furthermore, the priority of the guide content is determined based on the taxpayer's geographical location information. As a result, optimal guide content is selected based on the geographical location information.

[0050] When displaying a guide, the guide unit may analyze the taxpayer's social media activity to suggest guide content. Social media activity may include, but is not limited to, the content of posts, the number of likes, and the number of followers. Guide content may include, but is not limited to, the depth of information and the type of information to display. For example, the guide unit may analyze the taxpayer's social media activity to suggest relevant guide content. The guide unit may also filter the guide content based on the taxpayer's social media activity to display only highly relevant information. Furthermore, the guide content may be prioritized based on the taxpayer's social media activity. In this way, guide content is suggested based on social media activity.

[0051] When providing information, the information provision department can analyze the taxpayer's past consumption behavior and select the optimal information provision method. Past consumption behavior includes, but is not limited to, purchase history, consumption patterns, and expenditure amounts. The optimal information provision method includes, but is not limited to, the provision interface and usability test results. For example, relevant information is provided based on products purchased by the taxpayer in the past. The information provision method is also customized based on the taxpayer's past consumption behavior. Furthermore, the taxpayer's past consumption behavior is analyzed and the optimal information provision method is proposed. In this way, the optimal information provision method is selected based on the taxpayer's past consumption behavior.

[0052] When providing information, the information provision unit can customize the means of providing information based on the taxpayer's current living situation. Examples of living situation include, but are not limited to, occupation, family environment, and health condition. Examples of the means of providing information include, but are not limited to, the depth of information and the type of information to display. For example, based on the taxpayer's current living situation, relevant information is provided preferentially. The information provision means is also customized based on the taxpayer's living situation. Furthermore, the taxpayer's living situation is analyzed and the optimal information provision means is proposed. In this way, the means of providing information is customized based on the taxpayer's current living situation.

[0053] The information provision unit can select the optimal information provision method by taking into account the taxpayer's geographical location information when providing information. Examples of geographical location information include, but are not limited to, GPS data and location information services. Examples of optimal information provision methods include, but are not limited to, a provision interface and usability test results. For example, when a taxpayer enters their current location, information about related areas is provided preferentially. In addition, the information provision method is filtered based on the taxpayer's geographical location information to provide only highly relevant information. Furthermore, the information provision priority is determined based on the taxpayer's geographical location information. As a result, the optimal information provision method is selected based on the geographical location information.

[0054] When providing information, the Information Provision Department can analyze the taxpayer's social media activity and suggest information provision methods. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. Information provision methods include, but are not limited to, the depth of information and the type of information displayed. For example, the Information Provision Department can analyze the taxpayer's interests and concerns from the taxpayer's social media activity and suggest relevant information provision methods. The Information Provision Department can also filter information provision methods based on the taxpayer's social media activity and provide only highly relevant information. Furthermore, the Information Provision Department can determine information provision priorities based on the taxpayer's social media activity. As a result, information provision methods are suggested based on social media activity.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The donation destination suggestion system may further include a feedback unit. The feedback unit collects impressions and evaluations of suggested areas and experiences after the taxpayer actually visits them, and provides the collected impressions and evaluations to the analysis unit. For example, the taxpayer may input evaluations of tourist spots and restaurants in the area they visited. The taxpayer may also input impressions of events and social gatherings they attended. This allows the feedback unit to collect information based on the taxpayer's actual experiences, which the analysis unit can reflect in the next proposal. Furthermore, the feedback unit can improve the accuracy of proposals based on the taxpayer's evaluations. For example, the feedback unit may prioritize the suggestions of areas and experiences with high ratings. Furthermore, the feedback unit may suggest areas for improvement for areas and experiences with low ratings. This allows the donation destination suggestion system to utilize taxpayer feedback to make more accurate proposals.

[0057] The input section can customize the input content based on the taxpayer's health condition and fitness level. For example, if the taxpayer is health-conscious, health-related areas and experiences can be prioritized. Also, if the taxpayer has a specific fitness level, activities and experiences appropriate for that level can be suggested. Furthermore, based on the taxpayer's health condition, the input content can be filtered to display only the most relevant information. This allows for more appropriate suggestions based on the taxpayer's health condition and fitness level.

[0058] The donation suggestion system may further include a community section. The community section allows taxpayers to share information about suggested areas and experiences with other taxpayers. For example, taxpayers can post photos and impressions of areas they have visited. They can also view other taxpayers' posts and make comments and ratings. Furthermore, the community section may promote interaction between taxpayers and provide a forum for sharing information about areas. This allows taxpayers to share information with other taxpayers and deepen their knowledge about the area.

[0059] The donation destination suggestion system can also include a customization section. This section allows taxpayers to customize suggested regions and experiences to their own preferences. For example, they can select tourist attractions and restaurants in suggested regions and create a customized travel plan. They can also adjust suggested experiences to fit their own schedule. Furthermore, the customization section can filter suggestions based on the taxpayer's preferences, displaying only highly relevant information. This ensures that taxpayers receive suggestions tailored to their individual tastes.

[0060] The donation suggestion system can also include a notification function. This notification function can inform taxpayers of the latest information regarding suggested regions and experiences. For example, it can notify them of new events in a suggested region. It can also notify them of special offers and discounts related to suggested experiences. Furthermore, the notification function can adjust the timing of notifications based on the taxpayer's schedule, ensuring that taxpayers receive the latest information in a timely manner.

[0061] The donation destination suggestion system can also include an evaluation section. This evaluation section allows taxpayers to evaluate suggested regions and experiences. For example, taxpayers can input ratings for tourist attractions and restaurants in regions they have visited. They can also input their impressions of events and social gatherings they have experienced. Furthermore, the evaluation section can improve the accuracy of suggestions based on taxpayer evaluations. For example, it can prioritize suggesting regions and experiences with high ratings. It can also suggest improvements for regions and experiences with low ratings. This allows the donation destination suggestion system to make more accurate suggestions by utilizing taxpayer evaluations.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The input section is used to enter the taxpayer's interests or concerns and values. Taxpayer interests or concerns include, but are not limited to, hobbies, specific themes, or activities. Values ​​include, but are not limited to, environmental protection, social responsibility, or cultural values. Step 2: The analysis unit analyzes the information input by the input unit. The analysis may include, but is not limited to, methods such as data mining, statistical analysis, and machine learning. Step 3: The proposal unit proposes a region or experience based on the results analyzed by the analysis unit. The proposal may include, but is not limited to, a proposal algorithm or evaluation criteria. Step 4: The guide unit introduces local tourist spots, recommended restaurants, and events for interacting with local people based on the information proposed by the suggestion unit. Tourist spots include, but are not limited to, historical buildings, natural landscapes, and theme parks. Recommended restaurants include, but are not limited to, famous local restaurants and specific cuisine genres. Interaction events include, but are not limited to, local festivals, workshops, and social gatherings. Step 5: The information providing unit provides information about the local specialties or events proposed by the suggestion unit. Examples of specialties include, but are not limited to, local specialty products, handicrafts, and food. Examples of event information include, but are not limited to, the date and time of the event, the location, and how to participate.

[0064] (Example 2) A donation suggestion system according to an embodiment of the present invention helps taxpayers decide on donation destinations based on their "discovery" of their desired future, the people they want to meet, and the appeal of their local area. This donation suggestion system allows taxpayers to input their interests, concerns, and values, and analyzes this information to suggest areas, people, and experiences that are most suitable for each taxpayer. For example, it suggests areas rich in nature for nature lovers, and areas rich in historical sites for history buffs. It also provides information on local specialties and events, allowing taxpayers to gain a deeper understanding of the area's appeal. Furthermore, the donation suggestion system also functions as a guide for taxpayers when they actually visit the area. For example, it introduces local tourist spots, recommended restaurants, and events where people can interact with locals. This allows taxpayers to not only receive gifts in return, but also to develop a deeper connection with the local area. This system proposes a new form of hometown tax donation by allowing taxpayers to select donation destinations based on their own values ​​and interests. Taxpayers can enjoy connecting with the local area and making new discoveries, rather than simply receiving goods. The donation suggestion system allows taxpayers to select donation destinations based on their own values ​​and interests, allowing them to enjoy connecting with the local area and making new discoveries.

[0065] The donation recipient suggestion system according to this embodiment comprises an input unit, an analysis unit, a suggestion unit, a guide unit, and an information provision unit. The input unit inputs the taxpayer's interests or concerns and values. The taxpayer's interests or concerns include, but are not limited to, hobbies, specific themes, and activities. Values ​​include, but are not limited to, environmental protection, social contribution, and cultural values. The analysis unit analyzes the information input by the input unit. For analysis, methods such as data mining, statistical analysis, and machine learning are used, but are not limited to, such methods. The suggestion unit suggests regions or experiences based on the results analyzed by the analysis unit. For suggestions, algorithms for suggestions and evaluation criteria are used, but are not limited to, such methods. The guide unit introduces local tourist spots, recommended restaurants, and events for interacting with local people based on the information suggested by the suggestion unit. Tourist spots include, but are not limited to, historical buildings, natural landscapes, and theme parks. Recommended restaurants include, but are not limited to, local famous restaurants and specific cuisine genres. Examples of social events include, but are not limited to, local festivals, workshops, and social gatherings. The information providing unit provides information on local specialties or events proposed by the suggestion unit. Examples of specialties include, but are not limited to, local specialty products, handicrafts, and food. Examples of event information include, but are not limited to, the date and time of the event, the location, and how to participate. As a result, the donation suggestion system according to the embodiment allows taxpayers to select donation destinations based on their own values ​​and interests, allowing them to enjoy connecting with the community and making new discoveries.

[0066] The input section allows taxpayers to enter information about their hobbies or interests, past travel experiences, and favorite foods or cultures. Hobbies or interests include, but are not limited to, sports, music, and art. Past travel experiences include, but are not limited to, places visited, purposes of travel, and activities experienced. Favorite foods or cultures include, but are not limited to, specific cuisines, traditional culture, and modern culture. This allows for more accurate suggestions by providing detailed information about the taxpayer.

[0067] The analysis unit can analyze information input by the input unit using AI. AI includes, but is not limited to, technologies such as machine learning, deep learning, and natural language processing. For example, machine learning can be used to classify taxpayers' interests and concerns, and deep learning can be used to analyze taxpayers' values. Furthermore, natural language processing can be used to analyze taxpayers' input and provide optimal suggestions. This improves the accuracy of information analysis through the use of AI.

[0068] The proposal department can suggest the most suitable regions and experiences to taxpayers based on the results analyzed by the analysis department. These optimal regions and experiences may include, but are not limited to, evaluation criteria and selection algorithms. For example, evaluation criteria could include selecting regions and experiences based on the taxpayer's interests, concerns, and values. Furthermore, selection algorithms such as clustering algorithms and classification algorithms could be used to suggest the most suitable regions and experiences. This ensures that optimal suggestions are made based on the analysis results.

[0069] The Guide Department can introduce local tourist attractions, recommended restaurants, and events for interacting with locals, based on the information proposed by the Proposal Department. Tourist attractions include, but are not limited to, historical buildings, natural landscapes, and theme parks. Recommended restaurants include, but are not limited to, local renowned establishments and specific cuisine genres. Interaction events include, but are not limited to, local festivals, workshops, and social gatherings. This allows for the provision of a detailed guide to the region based on the proposed information.

[0070] The Information Provision Department can provide information on local specialties and events proposed by the Proposal Department. Local specialties include, but are not limited to, regional products, handicrafts, and food products. Event information includes, but is not limited to, dates, times, locations, and participation methods. This ensures that proposed information on local specialties and events is provided.

[0071] The input unit can estimate the taxpayer's emotions and adjust the timing of input based on the estimated emotions. Emotions include, but are not limited to, emotion classifications such as joy, sadness, and surprise. Emotion estimation is performed using, for example, an emotion estimation algorithm. For example, if the taxpayer is stressed, the input is simplified and only minimal information is required. If the taxpayer is relaxed, detailed input options are provided, allowing the taxpayer to freely input information. Furthermore, if the taxpayer is in a hurry, voice input is prioritized, allowing the taxpayer to input information quickly. In this way, the timing of input is adjusted according to the taxpayer's emotions.

[0072] The input unit can analyze the taxpayer's past input history and select the optimal input method. This optimal method includes, but is not limited to, input interfaces and usability test results. For example, it can automatically display information that the taxpayer has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the taxpayer has used in the past. Furthermore, it can predict and suggest information that the taxpayer will use at specific times based on their past input history. This ensures that the optimal input method is selected based on past input history.

[0073] The input unit can filter the information based on the taxpayer's current living situation and areas of interest during input. Examples of living situation include, but are not limited to, occupation, family environment, and health status. Examples of areas of interest include, but are not limited to, hobbies, research topics, and social issues. For example, when a taxpayer inputs their current living situation, only relevant information is displayed. Furthermore, the information to be input is filtered based on the taxpayer's areas of interest, and only highly relevant information is displayed. Furthermore, the priority of the information to be input is determined based on the taxpayer's living situation and areas of interest. As a result, the input information is filtered based on the taxpayer's living situation and areas of interest.

[0074] The input unit can estimate the taxpayer's emotions and determine the priority of the information to be entered based on the estimated emotions. Emotions include, but are not limited to, emotional classifications such as joy, sadness, and surprise. For example, an emotion estimation algorithm can be used to estimate emotions. Information prioritization includes, but is not limited to, importance, urgency, and relevance. For example, if the taxpayer is stressed, important information will be prioritized for input. If the taxpayer is relaxed, detailed information will be input. Furthermore, if the taxpayer is in a hurry, only minimal information will be input. In this way, the priority of input information is determined according to the taxpayer's emotions.

[0075] The input unit can prioritize input of highly relevant information during input, taking into account the taxpayer's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. Examples of highly relevant information include, but are not limited to, the user's current location and past activity history. For example, when the taxpayer inputs their current location, information about related areas is displayed preferentially. Furthermore, the information to be input is filtered based on the taxpayer's geographical location information, and only highly relevant information is displayed. Furthermore, the priority of the information to be input is determined based on the taxpayer's geographical location information. As a result, highly relevant information is input preferentially based on the geographical location information.

[0076] The input unit can analyze the taxpayer's social media activity at the time of input and input relevant information. Social media activity includes, but is not limited to, the content of posts, the number of likes, the number of followers, etc. For example, the input unit can analyze the taxpayer's interests and input relevant information based on the taxpayer's social media activity. The input unit can also filter the information to be input based on the taxpayer's social media activity and display only highly relevant information. Furthermore, the input unit can determine the priority of the information to be input based on the taxpayer's social media activity. As a result, relevant information is input based on the social media activity.

[0077] The analysis unit can estimate the taxpayer's emotions and adjust the presentation of the analysis based on the estimated emotions. Emotions include, but are not limited to, emotional classifications such as joy, sadness, and surprise. Emotion estimation can be performed using, for example, an emotion estimation algorithm. Presentation of the analysis includes, but is not limited to, graphs, text displays, and interactive displays. For example, if the taxpayer is relaxed, detailed analysis results are provided. If the taxpayer is in a hurry, concise analysis results are provided. Furthermore, if the taxpayer is excited, visually stimulating analysis results are provided. In this way, the presentation of the analysis is adjusted according to the taxpayer's emotions.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the input information during the analysis. The importance of the information includes, but is not limited to, factors such as user interest and novelty. The level of detail of the analysis includes, but is not limited to, factors such as the depth of the analysis and the amount of information displayed. For example, a detailed analysis is performed for important information, while a concise analysis is performed for less important information. Furthermore, the analysis priority is determined based on the importance of the input information. In addition, multiple analysis methods are applied to important information to provide detailed results. This adjusts the level of detail of the analysis based on the importance of the information.

[0079] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. Information categories include, but are not limited to, genre, theme, and application. Analysis algorithms include, but are not limited to, clustering algorithms and classification algorithms. For example, a natural environment analysis algorithm is applied to information about nature. A historical background analysis algorithm is applied to information about history. Furthermore, a food culture analysis algorithm is applied to information about food culture. This ensures that the appropriate analysis algorithm is applied according to the category of information.

[0080] The analysis unit can estimate the taxpayer's emotions and adjust the length of the analysis based on the estimated emotions. Emotions include, but are not limited to, emotion categories such as joy, sadness, and surprise. An emotion estimation algorithm is used to estimate emotions. The length of the analysis includes, but is not limited to, the analysis time and the amount of information to display. For example, if the taxpayer is in a hurry, a short and concise analysis result is provided. If the taxpayer is relaxed, a detailed analysis result is provided. If the taxpayer is excited, a visually stimulating analysis result is provided. In this way, the length of the analysis is adjusted according to the taxpayer's emotions.

[0081] The analysis unit can determine the priority of analysis based on the timing of information submission during the analysis process. The timing of information submission includes, but is not limited to, the submission date and time, and submission frequency. The priority of analysis includes, but is not limited to, factors such as importance, urgency, and relevance. For example, the unit prioritizes the analysis of the most recent information, while older information is postponed. Furthermore, the analysis priority is determined based on the submission timing. In addition, information with a recent submission date is analyzed quickly. This ensures that the analysis priority is determined based on the timing of information submission.

[0082] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. Relevance of information includes, but is not limited to, user interest and novelty. The order of analysis includes, but is not limited to, importance, relevance, and submission timing. For example, highly relevant information can be analyzed preferentially, while less relevant information can be postponed. Furthermore, the order of analysis is determined based on the relevance of the information. In addition, a more detailed analysis is performed on highly relevant information. This adjusts the order of analysis based on the relevance of the information.

[0083] The proposal section can estimate the taxpayer's emotions and adjust the presentation of the proposal based on those emotions. Emotions include, but are not limited to, categories such as joy, sadness, and surprise. Emotion estimation can be performed using, for example, an emotion estimation algorithm. Presentation methods for the proposal include, but are not limited to, graphs, text displays, and interactive displays. For example, if the taxpayer is relaxed, a detailed proposal is provided. If the taxpayer is in a hurry, a concise proposal is provided. Furthermore, if the taxpayer is excited, a visually stimulating proposal is provided. In this way, the presentation of the proposal is adjusted according to the taxpayer's emotions.

[0084] The proposal team can adjust the level of detail of a proposal based on the importance of the region or experience. The importance of a region or experience includes, but is not limited to, user interest levels and regional characteristics. The level of detail of a proposal includes, but is not limited to, the depth of the proposal and the amount of information displayed. For example, detailed proposals are provided for important regions or experiences, while concise proposals are provided for less important regions or experiences. Furthermore, the priority of proposals is determined based on the importance of the region or experience. In addition, multiple proposal methods are applied to important regions and experiences to provide detailed results. This adjusts the level of detail of proposals based on the importance of the region or experience.

[0085] The proposal department can apply different proposal algorithms depending on the region and experience category when submitting a proposal. These categories include, but are not limited to, genre, theme, and use. Proposal algorithms include, but are not limited to, clustering algorithms and classification algorithms. For example, a proposal related to nature would apply a natural environment proposal algorithm. A proposal related to history would apply a historical background proposal algorithm. Furthermore, a proposal related to food culture would apply a food culture proposal algorithm. This ensures that the appropriate proposal algorithm is applied according to the region and experience category.

[0086] The suggestion unit can estimate the taxpayer's emotion and adjust the length of the suggestion based on the estimated emotion of the taxpayer. Emotions include, but are not limited to, emotion classifications such as joy, sadness, and surprise. Emotion estimation can be performed using, for example, an emotion estimation algorithm. The length of the suggestion can include, but is not limited to, the duration of the suggestion and the amount of information to display. For example, if the taxpayer is in a hurry, a short and concise suggestion can be provided. If the taxpayer is relaxed, a detailed suggestion can be provided. If the taxpayer is excited, a visually stimulating suggestion can be provided. In this way, the length of the suggestion can be adjusted according to the taxpayer's emotion.

[0087] At the time of proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the region and experience. The time of submission of the region and experience includes, for example, but is not limited to, the submission date and time, the submission frequency, etc. The priority of the proposal includes, for example, but is not limited to, the importance, the urgency, the relevance, etc. For example, the most recent region and experience are suggested first, and older information is put off. The priority of the proposal is also determined based on the time of submission. Furthermore, regions and experiences that are close to their submission date are quickly suggested. In this way, the priority of the proposal is determined based on the time of submission of the region and experience.

[0088] The suggestion unit can adjust the order of suggestions based on the relevance of regions and experiences when making suggestions. Examples of the relevance of regions and experiences include, but are not limited to, the user's level of interest and the novelty of the information. Examples of the order of suggestions include, but are not limited to, importance, relevance, and submission time. For example, highly relevant regions and experiences are suggested first, and less relevant information is put off. The order of suggestions is also determined based on the relevance of regions and experiences. Furthermore, detailed suggestions are made for highly relevant regions and experiences. In this way, the order of suggestions is adjusted based on the relevance of regions and experiences.

[0089] The guide unit can estimate the taxpayer's emotions and adjust the guide's display method based on the estimated emotions. Emotions include, but are not limited to, emotional classifications such as joy, sadness, and surprise. Emotion estimation can be performed using, for example, an emotion estimation algorithm. Guide display methods include, but are not limited to, text display, image display, and interactive display. For example, if the taxpayer is stressed, a calm color scheme interface can be provided to reduce visual stress. If the taxpayer is enjoying themselves, a bright color scheme interface can be provided to make the guided work more enjoyable. Furthermore, if the taxpayer is tired, a simple and highly visible interface can be provided to facilitate the guided work. In this way, the guide's display method is adjusted according to the taxpayer's emotions.

[0090] When displaying the guide, the guide unit can select the optimal display method by referring to the taxpayer's past travel history. Past travel history includes, for example, but is not limited to, places visited, travel purposes, and activities experienced. Optimal display methods include, for example, but are not limited to, display interfaces and usability test results. For example, based on places visited by the taxpayer in the past, related information may be displayed preferentially. The guide display method may also be customized based on the taxpayer's past travel history. Furthermore, the taxpayer's past travel history may be analyzed and the optimal guide display method may be proposed. In this way, the optimal guide display method may be selected based on the past travel history.

[0091] When displaying the guide, the guide unit can customize the content of the guide based on the taxpayer's current living situation. Examples of living situations include, but are not limited to, the taxpayer's occupation, family environment, and health condition. Examples of the content of the guide include, but are not limited to, the depth of information and the type of information to display. For example, the guide unit prioritizes displaying relevant information based on the taxpayer's current living situation. The guide unit also customizes the content of the guide based on the taxpayer's living situation. The guide unit further analyzes the taxpayer's living situation and suggests optimal guide content. In this way, the guide content is customized based on the taxpayer's current living situation.

[0092] The guide unit can estimate the taxpayer's emotions and determine the priority of guides based on the estimated emotions of the taxpayer. Emotions include, but are not limited to, emotion classifications such as joy, sadness, and surprise. An emotion estimation algorithm, for example, is used to estimate emotions. Guide priorities include, but are not limited to, importance, urgency, and relevance. For example, if the taxpayer is stressed, important information is displayed first. If the taxpayer is relaxed, detailed information is displayed. If the taxpayer is in a hurry, only minimal information is displayed. In this way, the priority of guides is determined according to the taxpayer's emotions.

[0093] When displaying the guide, the guide unit can select optimal guide content taking into account the taxpayer's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. Examples of optimal guide content include, but are not limited to, the user's current location and past activity history. For example, when the taxpayer inputs their current location, information about related areas is displayed preferentially. Furthermore, the guide content is filtered based on the taxpayer's geographical location information to display only highly relevant information. Furthermore, the priority of the guide content is determined based on the taxpayer's geographical location information. As a result, optimal guide content is selected based on the geographical location information.

[0094] When displaying a guide, the guide unit may analyze the taxpayer's social media activity to suggest guide content. Social media activity may include, but is not limited to, the content of posts, the number of likes, and the number of followers. Guide content may include, but is not limited to, the depth of information and the type of information to display. For example, the guide unit may analyze the taxpayer's social media activity to suggest relevant guide content. The guide unit may also filter the guide content based on the taxpayer's social media activity to display only highly relevant information. Furthermore, the guide content may be prioritized based on the taxpayer's social media activity. In this way, guide content is suggested based on social media activity.

[0095] The information provision unit can estimate the taxpayer's emotions and adjust the method of providing information based on the estimated emotions of the taxpayer. Emotions include, but are not limited to, emotion classifications such as joy, sadness, and surprise. Emotion estimation can be performed using, for example, an emotion estimation algorithm. Information provision methods include, but are not limited to, text display, image display, and interactive display. For example, if the taxpayer is relaxed, detailed information can be provided. If the taxpayer is in a hurry, concise information that focuses on the main points can be provided. Furthermore, if the taxpayer is excited, visually stimulating information can be provided. In this way, the method of providing information can be adjusted according to the taxpayer's emotions.

[0096] When providing information, the information provision department can analyze the taxpayer's past consumption behavior and select the optimal information provision method. Past consumption behavior includes, but is not limited to, purchase history, consumption patterns, and expenditure amounts. The optimal information provision method includes, but is not limited to, the provision interface and usability test results. For example, relevant information is provided based on products purchased by the taxpayer in the past. The information provision method is also customized based on the taxpayer's past consumption behavior. Furthermore, the taxpayer's past consumption behavior is analyzed and the optimal information provision method is proposed. In this way, the optimal information provision method is selected based on the taxpayer's past consumption behavior.

[0097] When providing information, the information provision unit can customize the means of providing information based on the taxpayer's current living situation. Examples of living situation include, but are not limited to, occupation, family environment, and health condition. Examples of the means of providing information include, but are not limited to, the depth of information and the type of information to display. For example, based on the taxpayer's current living situation, relevant information is provided preferentially. The information provision means is also customized based on the taxpayer's living situation. Furthermore, the taxpayer's living situation is analyzed and the optimal information provision means is proposed. In this way, the means of providing information is customized based on the taxpayer's current living situation.

[0098] The information provision unit can estimate the taxpayer's emotions and determine the priority of information provision based on the estimated emotions of the taxpayer. Emotions include, but are not limited to, emotion classifications such as joy, sadness, and surprise. An emotion estimation algorithm, for example, is used to estimate emotions. Priorities for information provision include, but are not limited to, importance, urgency, and relevance. For example, if the taxpayer is stressed, important information is provided first. If the taxpayer is relaxed, detailed information is provided. If the taxpayer is in a hurry, only minimal information is provided. In this way, the priority of information provision is determined according to the taxpayer's emotions.

[0099] The information provision unit can select the optimal information provision method by taking into account the taxpayer's geographical location information when providing information. Examples of geographical location information include, but are not limited to, GPS data and location information services. Examples of optimal information provision methods include, but are not limited to, a provision interface and usability test results. For example, when a taxpayer enters their current location, information about related areas is provided preferentially. In addition, the information provision method is filtered based on the taxpayer's geographical location information to provide only highly relevant information. Furthermore, the information provision priority is determined based on the taxpayer's geographical location information. As a result, the optimal information provision method is selected based on the geographical location information.

[0100] When providing information, the Information Provision Department can analyze the taxpayer's social media activity and suggest information provision methods. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. Information provision methods include, but are not limited to, the depth of information and the type of information displayed. For example, the Information Provision Department can analyze the taxpayer's interests and concerns from the taxpayer's social media activity and suggest relevant information provision methods. The Information Provision Department can also filter information provision methods based on the taxpayer's social media activity and provide only highly relevant information. Furthermore, the Information Provision Department can determine information provision priorities based on the taxpayer's social media activity. As a result, information provision methods are suggested based on social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, analysis unit, suggestion unit, guide unit, and information provision unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is implemented by the reception device 38 of the smart device 14 and inputs the taxpayer's interests and values. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and suggests areas and experiences based on the analysis results. The guide unit is implemented, for example, by the output device 40 of the smart device 14 and introduces tourist spots, restaurants, and social events based on the suggested information. The information provision unit is implemented, for example, by the output device 40 of the smart device 14 and provides information on local specialties and events. === Hard Collateral 1-2 === Each of the multiple elements described above, including the input unit, analysis unit, proposal unit, guide unit, and information provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the input unit is implemented by the microphone 238 of the smart glasses 214 and inputs the taxpayer's interests and values. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes regions and experiences based on the analysis results. The guide unit is implemented by the speaker 240 of the smart glasses 214 and introduces tourist spots, restaurants, and exchange events based on the proposed information. The information provision unit is implemented by the speaker 240 of the smart glasses 214 and provides information on local specialties and events. === Hard Collateral 1-3 === Each of the multiple elements described above, including the input unit, analysis unit, proposal unit, guide unit, and information provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the input unit is implemented by the microphone 238 of the headset terminal 314 and inputs the taxpayer's interests and values. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the input information. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes regions and experiences based on the analysis results. The guide unit is implemented by, for example, the display 343 of the headset terminal 314 and introduces tourist spots, restaurants, and exchange events based on the proposed information. The information provision unit is implemented by, for example, the display 343 of the headset terminal 314 and provides information on local specialties and events. === Hard Collateral 1-4 === Each of the multiple elements, including the input unit, analysis unit, suggestion unit, guide unit, and information provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the robot 414 and inputs the taxpayer's interests and values. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests areas and experiences based on the analysis results. The guide unit is realized, for example, by the speaker 240 of the robot 414 and introduces tourist spots, restaurants, and social events based on the suggested information. The information provision unit is realized, for example, by the speaker 240 of the robot 414 and provides information on local specialties and events.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The donation destination suggestion system may further include a feedback unit. The feedback unit collects impressions and evaluations of suggested areas and experiences after the taxpayer actually visits them, and provides the collected impressions and evaluations to the analysis unit. For example, the taxpayer may input evaluations of tourist spots and restaurants in the area they visited. The taxpayer may also input impressions of events and social gatherings they attended. This allows the feedback unit to collect information based on the taxpayer's actual experiences, which the analysis unit can reflect in the next proposal. Furthermore, the feedback unit can improve the accuracy of proposals based on the taxpayer's evaluations. For example, the feedback unit may prioritize the suggestions of areas and experiences with high ratings. Furthermore, the feedback unit may suggest areas for improvement for areas and experiences with low ratings. This allows the donation destination suggestion system to utilize taxpayer feedback to make more accurate proposals.

[0103] The input section can customize the input content based on the taxpayer's health condition and fitness level. For example, if the taxpayer is health-conscious, health-related areas and experiences can be prioritized. Also, if the taxpayer has a specific fitness level, activities and experiences appropriate for that level can be suggested. Furthermore, based on the taxpayer's health condition, the input content can be filtered to display only the most relevant information. This allows for more appropriate suggestions based on the taxpayer's health condition and fitness level.

[0104] The analysis unit can estimate the taxpayer's emotions and adjust the presentation of the analysis based on those emotions. For example, if the taxpayer is relaxed, it can provide detailed analysis results. If the taxpayer is in a hurry, it can provide concise analysis results that get straight to the point. Furthermore, if the taxpayer is excited, it can provide visually stimulating analysis results. In this way, the presentation of the analysis is adjusted according to the taxpayer's emotions.

[0105] The proposal department can estimate the taxpayer's emotions and adjust the way the proposal is presented based on those emotions. For example, if the taxpayer is relaxed, a detailed proposal can be provided. If the taxpayer is in a hurry, a concise proposal that gets straight to the point can be provided. Furthermore, if the taxpayer is excited, a visually stimulating proposal can be provided. In this way, the way the proposal is presented is adjusted according to the taxpayer's emotions.

[0106] The guide unit can estimate the taxpayer's emotions and adjust the guide's display method based on those estimates. For example, if the taxpayer is stressed, it can provide a calming color scheme interface to reduce visual stress. If the taxpayer is enjoying themselves, it can provide a bright color scheme interface to make the guided task more enjoyable. Furthermore, if the taxpayer is tired, it can provide a simple and highly visible interface to facilitate the guided task. In this way, the guide's display method is adjusted according to the taxpayer's emotions.

[0107] The information provision department can estimate the taxpayer's emotions and adjust the method of information provision based on those estimates. For example, if the taxpayer is relaxed, detailed information can be provided. If the taxpayer is in a hurry, concise information can be provided. Furthermore, if the taxpayer is excited, visually stimulating information can be provided. In this way, the method of information provision is adjusted according to the taxpayer's emotions.

[0108] The donation suggestion system may further include a community section. The community section allows taxpayers to share information about suggested areas and experiences with other taxpayers. For example, taxpayers can post photos and impressions of areas they have visited. They can also view other taxpayers' posts and make comments and ratings. Furthermore, the community section may promote interaction between taxpayers and provide a forum for sharing information about areas. This allows taxpayers to share information with other taxpayers and deepen their knowledge about the area.

[0109] The donation destination suggestion system can also include a customization section. This section allows taxpayers to customize suggested regions and experiences to their own preferences. For example, they can select tourist attractions and restaurants in suggested regions and create a customized travel plan. They can also adjust suggested experiences to fit their own schedule. Furthermore, the customization section can filter suggestions based on the taxpayer's preferences, displaying only highly relevant information. This ensures that taxpayers receive suggestions tailored to their individual tastes.

[0110] The donation suggestion system can also include a notification function. This notification function can inform taxpayers of the latest information regarding suggested regions and experiences. For example, it can notify them of new events in a suggested region. It can also notify them of special offers and discounts related to suggested experiences. Furthermore, the notification function can adjust the timing of notifications based on the taxpayer's schedule, ensuring that taxpayers receive the latest information in a timely manner.

[0111] The donation destination suggestion system can also include an evaluation section. This evaluation section allows taxpayers to evaluate suggested regions and experiences. For example, taxpayers can input ratings for tourist attractions and restaurants in regions they have visited. They can also input their impressions of events and social gatherings they have experienced. Furthermore, the evaluation section can improve the accuracy of suggestions based on taxpayer evaluations. For example, it can prioritize suggesting regions and experiences with high ratings. It can also suggest improvements for regions and experiences with low ratings. This allows the donation destination suggestion system to make more accurate suggestions by utilizing taxpayer evaluations.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The input section is used to enter the taxpayer's interests or concerns and values. Taxpayer interests or concerns include, but are not limited to, hobbies, specific themes, or activities. Values ​​include, but are not limited to, environmental protection, social responsibility, or cultural values. Step 2: The analysis unit analyzes the information input by the input unit. The analysis may include, but is not limited to, methods such as data mining, statistical analysis, and machine learning. Step 3: The proposal unit proposes a region or experience based on the results analyzed by the analysis unit. The proposal may include, but is not limited to, a proposal algorithm or evaluation criteria. Step 4: The guide unit introduces local tourist spots, recommended restaurants, and events for interacting with local people based on the information proposed by the suggestion unit. Tourist spots include, but are not limited to, historical buildings, natural landscapes, and theme parks. Recommended restaurants include, but are not limited to, famous local restaurants and specific cuisine genres. Interaction events include, but are not limited to, local festivals, workshops, and social gatherings. Step 5: The information providing unit provides information about the local specialties or events proposed by the suggestion unit. Examples of specialties include, but are not limited to, local specialty products, handicrafts, and food. Examples of event information include, but are not limited to, the date and time of the event, the location, and how to participate.

[0114] 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.

[0115] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system comprising an input unit for inputting the taxpayer's interests, concerns, and values; an analysis unit for analyzing the information input by the input unit; a proposal unit for suggesting regions or experiences based on the results of the analysis by the analysis unit; a guide unit for introducing local tourist spots, recommended restaurants, and events for interacting with local people based on the information proposed by the proposal unit; and an information provision unit for providing information on local specialties or events proposed by the proposal unit.

2. The system according to claim 1, characterized in that the input unit inputs information about the taxpayer's hobbies or interests, past travel experiences, favorite foods or culture.

3. The analysis unit Analyzing the information input by the input unit using AI 2. The system of claim 1.

4. The proposal unit Based on the results of the analysis by the analysis unit, the system proposes the most suitable areas and experiences for taxpayers.

2. The system of claim 1.

5. The aforementioned guide portion is Based on the information proposed by the proposal department, the system will introduce local tourist spots, recommended restaurants, and events for interacting with local people.

2. The system of claim 1.

6. The aforementioned information provision unit, Providing information on local specialties and events proposed by the proposal department 2. The system of claim 1.

7. The input unit Estimate taxpayer sentiment and adjust input timing based on estimated taxpayer sentiment 2. The system of claim 1.

8. The input unit Analyze the taxpayer's past data entry history and select the optimal data entry method 2. The system of claim 1.

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