system

Abstract

The system according to this embodiment aims to provide a platform that allows users to obtain real-world information from actual residents. [Solution] The system according to this embodiment comprises a posting unit, a categorization unit, a search unit, an evaluation unit, and an AI evaluation unit. The posting unit allows users to post information about their town. The categorization unit categorizes the information posted by the posting unit by municipality. The search unit searches and views the information categorized by the categorization unit. The evaluation unit evaluates the reliability of the information searched and viewed by the search unit. The AI ​​evaluation unit automatically evaluates the town based on the information evaluated by the evaluation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to obtain real information from actual residents in selecting the location of a residence or a store opening.

[0005] The system according to the embodiment aims to provide a platform on which a user can obtain real information from actual residents.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a posting unit, a categorization unit, a search unit, an evaluation unit, and an AI evaluation unit. The posting unit allows users to post information about their town. The categorization unit categorizes the information posted by the posting unit at the municipal level. The search unit searches and views the information categorized by the categorization unit. The evaluation unit evaluates the reliability of the information searched and viewed by the search unit. The AI ​​evaluation unit automatically evaluates the town based on the information evaluated by the evaluation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide a platform that allows users to obtain real-world information from actual residents. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. As an example of the communication standard applied to the communication I / F, wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark) are included.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An information provision system according to an embodiment of the present invention is a platform that allows people choosing a location for a residence or store to obtain information about a town. This information provision system allows users to post information about towns they live in or are familiar with. The posted information is categorized by municipality and can be searched and viewed by other users. For example, it includes information such as living environment, safety, transportation convenience, and nearby facilities. By using this platform, the lack of information when choosing a location can be resolved, and costs due to incorrect selection can be reduced. Furthermore, it is conceivable to add an evaluation system to assess the reliability of posted content and a review function for posted content. It is also effective to use AI to analyze and evaluate posted content. Adding a function that automatically evaluates towns based on user-submitted information using AI and displays it in a ranking format would be beneficial. This would allow users to easily compare and consider options. Furthermore, adding a function that allows users to specify certain conditions (e.g., good safety, good transportation convenience) for searching would further improve usability. In this way, the information provision system allows users to post town information, categorize it, search, evaluate it, and perform AI evaluations, thereby resolving the lack of information when choosing a location and reducing costs due to incorrect selection.

[0029] The information provision system according to this embodiment comprises a posting unit, a categorization unit, a search unit, an evaluation unit, and an AI evaluation unit. The posting unit allows users to post information about their town. The posting unit can post information in formats such as text, images, and videos. The posting unit can also provide an interface for users to select the type of information they want to post. For example, the posting unit provides a text box for users to input text information. The posting unit can also provide buttons for uploading images and videos. Furthermore, the posting unit provides a preview function for posted content, allowing users to review the content before posting. The categorization unit categorizes the information posted by the posting unit by municipality. The categorization unit classifies information based on, for example, the definition of a municipality. The categorization unit can set the algorithm to be used and automatically classify the information. For example, the categorization unit analyzes the posted content using natural language processing technology and classifies it by municipality. The categorization unit can also provide an interface for users to manually classify the information. The search unit searches and views information categorized by the categorization unit. The search unit provides features such as keyword search and filtering. The search unit allows users to search for information by specifying specific conditions. For example, the search unit provides an interface for users to search for information by specifying conditions such as safety and transportation convenience. The search unit can also set the order in which search results are displayed. For example, the search unit prioritizes displaying highly relevant information. The evaluation unit assesses the reliability of information searched and viewed by the search unit. The evaluation unit assesses the reliability of information based on user ratings and the number of reviews, for example. The evaluation unit provides an interface for users to evaluate information. For example, the evaluation unit provides functions for users to post star ratings and comments. The evaluation unit can also set an algorithm to evaluate the reliability of posters. The AI ​​evaluation unit automatically evaluates the city based on the information evaluated by the evaluation unit. The AI ​​evaluation unit, for example, sets the algorithm to be used, analyzes the information, and evaluates the city.The AI ​​evaluation unit provides a function that automatically evaluates cities based on user-submitted information and displays the results in a ranking format. For example, the AI ​​evaluation unit creates rankings based on evaluation items such as the safety and accessibility of the city. As a result, the information provision system according to this embodiment allows users to submit city information, which is then categorized, searched, evaluated, and evaluated by AI, thereby eliminating the lack of information when choosing a location and reducing costs caused by poor selection.

[0030] The posting section allows users to post information about the city. Information can be posted in various formats, such as text, images, and videos. Specifically, users can enter detailed descriptions and comments using text boxes, and add visual information by clicking buttons to upload images and videos. The posting section can also provide an interface that allows users to select the type of information they want to post. For example, it can provide a text box for users to enter text information, and buttons for uploading images and videos. Furthermore, the posting section offers a preview function, allowing users to review their posts before submitting. This preview function allows users to check the content before posting and make corrections as needed, preventing the posting of incorrect or inappropriate information. The posting section also provides a tagging function to facilitate organization and searching of information. For example, users can tag posts with terms like "safety," "transportation," and "restaurants," making it easier for other users to find specific information. Additionally, the posting section can automatically retrieve location information and associate it with posts. This allows users to clearly see which areas their posted information relates to. The posting section can also provide guidelines and tips to improve the quality of information posted by users. For example, the posting section can display messages to users requesting specific information and detailed explanations, encouraging them to enrich their posts. In this way, the posting section can provide an environment where users can easily and effectively post information about the city, thereby improving the overall quality of information on the system.

[0031] The categorization unit categorizes information submitted by the posting unit at the municipal level. For example, the categorization unit classifies information based on the definition of a municipality. Specifically, it analyzes place names and address information included in the submitted content and automatically classifies them into the appropriate municipality. The categorization unit can set the algorithm to be used and automatically classify information. For example, the categorization unit can analyze submitted content using natural language processing technology and classify it at the municipal level. Natural language processing technology can extract place names and related keywords from the text of the submitted content and classify them into the appropriate category. Furthermore, the categorization unit can also provide an interface for users to manually classify information. For example, it can provide a dropdown menu for users to select a municipality when submitting a post, allowing for manual classification. This enables the categorization unit to achieve accurate classification based on the user's intent. Additionally, the categorization unit can periodically review the categories of submitted content and reclassify them as needed. For example, if a new municipality is added or the boundaries of an existing municipality are changed, the categorization unit automatically updates the information to maintain the latest classification. This ensures that the categorization unit always provides accurate and up-to-date information, creating an environment where users can quickly find the information they need.

[0032] The search function searches and displays information categorized by the categorization function. The search function provides features such as keyword search and filtering. Specifically, users can enter keywords into the search box to quickly find relevant information. The search function also allows users to search for information by specifying specific conditions. For example, it provides an interface for users to search for information by specifying conditions such as safety and transportation convenience. Furthermore, the search function allows users to set the order in which search results are displayed. For example, it prioritizes displaying highly relevant information. In addition, the search function can provide individually customized search results based on the user's search and browsing history. This allows users to efficiently find information based on their interests. The search function also provides a map display function, allowing search results to be displayed on a map. This allows users to visually confirm information related to a specific region. For example, if a user searches for a specific city or town, posts related to that region will be displayed as pins on the map, and detailed information can be viewed by clicking on it. Finally, the search function provides a search result filtering function, allowing users to narrow down search results based on specific conditions. For example, search results can be filtered by specifying conditions such as posting date and time, rating score, and the author's trustworthiness. This allows the search engine to provide a powerful tool for users to quickly and accurately find the information they need, improving the overall usability of the system.

[0033] The evaluation unit assesses the reliability of information searched and viewed by the search unit. For example, the evaluation unit evaluates the reliability of information based on user ratings and the number of reviews. Specifically, users can post star ratings and comments on information, which other users can use as a reference to judge the reliability of the information. The evaluation unit provides an interface for users to evaluate information. For example, the evaluation unit provides functions for users to post star ratings and comments. The evaluation unit can also set up algorithms to evaluate the reliability of posters. For example, it can calculate a poster's reliability score based on their past posting history and ratings from other users. This allows the evaluation unit to prioritize displaying reliable information and provide an environment where users can use information with peace of mind. Furthermore, the evaluation unit can continuously monitor the reliability of information and update ratings as needed. For example, if new ratings or comments are added, the evaluation unit automatically recalculates the reliability score to reflect the latest ratings. This allows the evaluation unit to always provide reliability ratings based on the latest information and support users in obtaining accurate information.

[0034] The AI ​​Evaluation Department automatically evaluates cities based on information assessed by the Evaluation Department. For example, the AI ​​Evaluation Department sets the algorithm to be used, analyzes the information, and evaluates cities. Specifically, the AI ​​Evaluation Department provides a function that automatically evaluates cities based on information posted by users and displays it in a ranking format. For example, the AI ​​Evaluation Department creates rankings based on evaluation items such as the safety and accessibility of the city. The AI ​​analyzes the posted content using natural language processing and machine learning techniques and calculates a score for each evaluation item. This allows the AI ​​Evaluation Department to provide objective and highly accurate city evaluations. Furthermore, the AI ​​Evaluation Department can continuously update the evaluation results to reflect the latest information. For example, if new posts or evaluations are added, the AI ​​Evaluation Department automatically recalculates the evaluation results and displays the latest ranking. In addition, the AI ​​Evaluation Department can improve the algorithm based on user feedback to improve the accuracy of the evaluation. As a result, the AI ​​Evaluation Department can always provide highly accurate city evaluations based on the latest information, providing reliable information for users to refer to when choosing a location.

[0035] The search unit includes a condition specification unit that allows users to specify specific conditions for their search. The search unit provides an interface for users to search for information by specifying conditions such as safety and transportation convenience. When a user searches by specifying conditions, the search unit can use the condition specification unit to set search conditions. For example, the search unit provides a condition specification unit for users to search for areas with good safety. The search unit can also provide a condition specification unit for users to search for areas with good transportation convenience. This makes the system more user-friendly by allowing users to search by specifying specific conditions. Some or all of the above-described processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the conditions specified by the user into the AI, and the AI ​​can provide the best search results based on the conditions.

[0036] The evaluation unit includes a review unit that provides a function for reviewing posted content. The evaluation unit provides, for example, an interface for users to review posted content. When a user posts a review, the evaluation unit can input the review content using the review unit. For example, the evaluation unit provides a review unit for users to post star ratings and comments. The evaluation unit can also provide a review unit for users to provide feedback on posted content. By providing a review function for posted content, the reliability of the posted content can be evaluated. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input the review content posted by a user into an AI, which can then analyze the review content and evaluate its reliability.

[0037] The posting function can analyze a user's past posting history and suggest the optimal posting format when a post is made. For example, the posting function can automatically suggest formats that the user has frequently used in the past. The posting function can also suggest the optimal format based on the user's past posting content. Furthermore, the posting function can prioritize suggesting specific formats based on the user's past posting history. In this way, the optimal posting format can be suggested by analyzing the user's past posting history. Some or all of the above processing in the posting function may be performed using AI, for example, or not using AI. For example, the posting function can input the user's past posting history data into a generating AI, which can then suggest the optimal posting format.

[0038] The posting function can automatically acquire the user's current location information when posting and reflect it in the post content. For example, when a user starts posting, the posting function can automatically acquire their current location and reflect it in the post content. When a user enters post content, the posting function can also suggest optimal information considering the distance from their current location. Furthermore, if a user posts while on the move, the posting function can update their current location in real time and reflect it in the post content. This allows the posting function to automatically acquire the user's current location information and reflect it in the post content. Some or all of the above processing in the posting function may be performed using AI, for example, or without AI. For example, the posting function can input the user's location information data into a generating AI, which can then analyze the location information and reflect it in the post content.

[0039] The posting function can automatically suggest post content by referencing the user's past travel history when a post is made. For example, the posting function can automatically suggest places the user has frequently visited in the past as post content. The posting function can also predict places the user will visit on specific days of the week or times of day and suggest them as post content. Furthermore, the posting function can analyze the user's past travel patterns and suggest the most suitable post content. This allows the system to automatically suggest the most suitable post content by referencing the user's past travel history. Some or all of the above processing in the posting function may be performed using AI, for example, or without AI. For example, the posting function can input the user's travel history data into a generating AI, which can then analyze the travel history and suggest the most suitable post content.

[0040] The posting function can suggest post content based on the user's schedule by referring to the user's calendar information when posting. For example, the posting function can automatically suggest post content by referring to the schedule registered in the user's calendar. The posting function can also suggest post content related to a specific event from the user's calendar information. Furthermore, the posting function can suggest the most suitable post content based on the schedule, based on the user's calendar information. In this way, by referring to the user's calendar information, the posting function can suggest the most suitable post content based on the schedule. Some or all of the above processing in the posting function may be performed using AI, for example, or not using AI. For example, the posting function can input the user's calendar information into a generating AI, and the generating AI can suggest the most suitable post content based on the schedule.

[0041] The categorization unit can improve the accuracy of categorization by considering the interrelationships of the posted content during the categorization process. For example, the categorization unit can analyze the relationships between posted content and classify related content into the same category. The categorization unit can also eliminate duplicate information by considering the interrelationships of the posted content. Furthermore, the categorization unit can propose the optimal categorization method based on the interrelationships of the posted content. In this way, the accuracy of categorization can be improved by considering the interrelationships of the posted content. Some or all of the above processing in the categorization unit may be performed using AI, for example, or without AI. For example, the categorization unit can input data on the interrelationships of the posted content into a generating AI, which can then analyze the relationships to improve the accuracy of categorization.

[0042] The categorization unit can categorize content while considering the poster's attribute information. For example, the categorization unit can propose the optimal categorization method by considering the poster's age and gender. The categorization unit can also classify content into relevant categories by considering the poster's occupation and hobbies. Furthermore, the categorization unit can provide optimal categorization criteria based on the poster's attribute information. This allows for more appropriate categorization by considering the poster's attribute information. Some or all of the above-described processes in the categorization unit may be performed using AI, for example, or without AI. For example, the categorization unit can input the poster's attribute information data into a generating AI, which can then analyze the attribute information and perform categorization.

[0043] The categorization unit can categorize content while considering its geographical distribution. For example, the categorization unit can analyze the geographical distribution of content and categorize it by region. The categorization unit can also provide relevant regional information while considering the geographical distribution of content. Furthermore, the categorization unit can propose the optimal categorization method based on the geographical distribution of content. This allows for more appropriate categorization by considering the geographical distribution of content. Some or all of the above processing in the categorization unit may be performed using AI, for example, or without AI. For example, the categorization unit can input geographical distribution data of content into a generating AI, which can then analyze the geographical distribution and perform categorization.

[0044] The categorization unit can improve the accuracy of categorization by referring to relevant literature for the submitted content during the categorization process. For example, the categorization unit can automatically search for literature related to the submitted content and use it as a reference for categorization. The categorization unit can also propose the optimal categorization method based on the relevant literature for the submitted content. Furthermore, the categorization unit can eliminate duplicate information by referring to relevant literature for the submitted content. In this way, the accuracy of categorization can be improved by referring to relevant literature for the submitted content. Some or all of the above processes in the categorization unit may be performed using AI, for example, or not using AI. For example, the categorization unit can input data on relevant literature for the submitted content into a generating AI, and the generating AI can analyze the relevant literature to improve the accuracy of categorization.

[0045] The search unit can optimize current search results by referring to past search data during a search. For example, the search unit can prioritize displaying highly relevant search results based on the user's past search history. The search unit can also analyze the user's past search data and suggest the most suitable search results. Furthermore, the search unit can display search results relevant to a specific time period based on the user's past search history. This allows for the optimization of current search results by referring to past search data. Some or all of the above-described processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input past search data into a generating AI, which can then analyze the data to provide the most suitable search results.

[0046] The search unit can prioritize displaying relevant search results based on the user's search history during a search. For example, the search unit can prioritize displaying highly relevant search results based on the user's past search history. The search unit can also analyze the user's search history and suggest the most suitable search results. Furthermore, the search unit can display search results related to specific keywords from the user's search history. This allows for the provision of more appropriate information by prioritizing relevant search results based on the user's search history. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the user's search history data into a generating AI, which can then analyze the data and provide relevant search results.

[0047] The search unit can prioritize displaying highly relevant search results by considering the user's geographical location information during a search. For example, the search unit can prioritize displaying highly relevant search results based on the user's current location. The search unit can also suggest optimal search results by considering the user's geographical location information. Furthermore, the search unit can display search results related to a specific region based on the user's geographical location information. This allows for the priority display of highly relevant search results by considering the user's geographical location information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the user's geographical location information data into a generating AI, which can then analyze the data to provide highly relevant search results.

[0048] The search unit can analyze the user's social media activity during a search and display relevant search results. For example, the search unit can display highly relevant search results based on the user's social media activity. The search unit can also analyze the user's social media activity and suggest the most suitable search results. Furthermore, the search unit can display search results related to specific keywords from the user's social media activity. In this way, by analyzing the user's social media activity, highly relevant search results can be displayed. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the user's social media activity data into a generating AI, which can then analyze the data and provide relevant search results.

[0049] The evaluation unit can optimize the current evaluation by referring to past evaluation data during the evaluation process. For example, the evaluation unit can prioritize displaying highly relevant evaluations based on the user's past evaluation data. The evaluation unit can also analyze the user's past evaluation data and suggest the optimal evaluation. Furthermore, the evaluation unit can display evaluations related to a specific time period from the user's past evaluation data. This allows for the optimization of the current evaluation by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input past evaluation data into a generating AI, which can then analyze the data and provide the optimal evaluation.

[0050] The evaluation unit can perform evaluations while considering the poster's attribute information. For example, the evaluation unit can propose the optimal evaluation method by considering the poster's age and gender. The evaluation unit can also provide relevant evaluations by considering the poster's occupation and hobbies. Furthermore, the evaluation unit can provide optimal evaluation criteria based on the poster's attribute information. This makes it possible to perform more appropriate evaluations by considering the poster's attribute information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the poster's attribute information data into a generating AI, and the generating AI can analyze the data and perform the evaluation.

[0051] The evaluation unit can perform evaluations while considering the geographical distribution of the submitted content. For example, the evaluation unit can analyze the geographical distribution of the submitted content and perform evaluations for each region. The evaluation unit can also provide relevant regional information while considering the geographical distribution of the submitted content. Furthermore, the evaluation unit can propose the optimal evaluation method based on the geographical distribution of the submitted content. This makes it possible to perform more appropriate evaluations by considering the geographical distribution of the submitted content. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input geographical distribution data of the submitted content into a generating AI, and the generating AI can analyze the data and perform evaluations.

[0052] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature related to the submitted content during the evaluation process. For example, the evaluation unit can automatically search for literature related to the submitted content and use it as a reference for evaluation. The evaluation unit can also propose the optimal evaluation method based on the relevant literature related to the submitted content. Furthermore, the evaluation unit can eliminate duplicate information by referring to relevant literature related to the submitted content. In this way, the accuracy of the evaluation can be improved by referring to relevant literature related to the submitted content. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input data on relevant literature related to the submitted content into a generating AI, and the generating AI can analyze the data to improve the accuracy of the evaluation.

[0053] The AI ​​evaluation unit can optimize the current evaluation by referring to past evaluation data during the AI ​​evaluation process. For example, the AI ​​evaluation unit can prioritize displaying highly relevant AI evaluations based on the user's past evaluation data. The AI ​​evaluation unit can also analyze the user's past evaluation data and propose the optimal AI evaluation. Furthermore, the AI ​​evaluation unit can display AI evaluations related to a specific time period from the user's past evaluation data. This allows for the optimization of the current evaluation by referring to past evaluation data. Some or all of the above processes in the AI ​​evaluation unit may be performed using AI, for example, or without AI. For example, the AI ​​evaluation unit can input past evaluation data into a generating AI, which can then analyze the data and provide the optimal evaluation.

[0054] The AI ​​evaluation unit can perform evaluations while considering the poster's attribute information. For example, the AI ​​evaluation unit can propose the optimal AI evaluation method by considering the poster's age and gender. The AI ​​evaluation unit can also provide relevant AI evaluations by considering the poster's occupation and hobbies. Furthermore, the AI ​​evaluation unit can provide optimal AI evaluation criteria based on the poster's attribute information. This makes it possible to perform more appropriate evaluations by considering the poster's attribute information. Some or all of the above processing in the AI ​​evaluation unit may be performed using AI, for example, or without AI. For example, the AI ​​evaluation unit can input the poster's attribute information data into a generating AI, and the generating AI can analyze the data and perform the evaluation.

[0055] The AI ​​evaluation unit can perform evaluations while considering the geographical distribution of the posted content. For example, the AI ​​evaluation unit can analyze the geographical distribution of the posted content and perform AI evaluations for each region. The AI ​​evaluation unit can also provide relevant regional information while considering the geographical distribution of the posted content. Furthermore, the AI ​​evaluation unit can propose the optimal AI evaluation method based on the geographical distribution of the posted content. This makes it possible to perform more appropriate evaluations by considering the geographical distribution of the posted content. Some or all of the above processing in the AI ​​evaluation unit may be performed using AI, for example, or without AI. For example, the AI ​​evaluation unit can input geographical distribution data of the posted content into a generating AI, and the generating AI can analyze the data and perform evaluations.

[0056] The AI ​​evaluation unit can improve the accuracy of its evaluation by referring to relevant literature related to the submitted content during the AI ​​evaluation process. For example, the AI ​​evaluation unit can automatically search for literature related to the submitted content and use it as a reference for the AI ​​evaluation. The AI ​​evaluation unit can also propose the optimal AI evaluation method based on the relevant literature related to the submitted content. Furthermore, the AI ​​evaluation unit can eliminate duplicate information by referring to relevant literature related to the submitted content. In this way, the accuracy of the evaluation can be improved by referring to relevant literature related to the submitted content. Some or all of the above processes in the AI ​​evaluation unit may be performed using AI, for example, or without using AI. For example, the AI ​​evaluation unit can input data on relevant literature related to the submitted content into a generating AI, and the generating AI can analyze the data to improve the accuracy of the evaluation.

[0057] The condition specification unit can optimize the current condition specification by referring to past search data when specifying conditions. For example, the condition specification unit can prioritize displaying highly relevant condition specifications based on the user's past search data. The condition specification unit can also analyze the user's past search data and suggest the optimal condition specification. Furthermore, the condition specification unit can display condition specifications related to a specific time period from the user's past search data. This allows for the optimization of the current condition specification by referring to past search data. Some or all of the above processing in the condition specification unit may be performed using AI, for example, or without AI. For example, the condition specification unit can input past search data into a generating AI, which can then analyze the data and provide the optimal condition specification.

[0058] The condition specification unit can prioritize displaying highly relevant conditions by considering the user's geographical location information when conditions are specified. For example, the condition specification unit can prioritize displaying highly relevant conditions based on the user's current location. The condition specification unit can also suggest optimal conditions by considering the user's geographical location information. Furthermore, the condition specification unit can display conditions related to a specific region based on the user's geographical location information. This allows for the priority display of highly relevant conditions by considering the user's geographical location information. Some or all of the above processing in the condition specification unit may be performed using AI, for example, or without AI. For example, the condition specification unit can input the user's geographical location information data into a generating AI, which can then analyze the data and provide highly relevant conditions.

[0059] The review unit can optimize the current review by referring to past review history during the review process. For example, the review unit can prioritize displaying highly relevant reviews based on the user's past review history. The review unit can also analyze the user's past review history and suggest the most suitable review. Furthermore, the review unit can display reviews related to a specific time period from the user's past review history. This allows for the optimization of the current review by referring to past review history. Some or all of the above processes in the review unit may be performed using AI, for example, or without AI. For example, the review unit can input past review history data into a generating AI, which can then analyze the data and provide the most suitable review.

[0060] The review unit can prioritize displaying highly relevant reviews by considering the user's geographical location during the review process. For example, the review unit can prioritize displaying highly relevant reviews based on the user's current location. The review unit can also suggest the most relevant reviews by considering the user's geographical location. Furthermore, the review unit can display reviews related to a specific region based on the user's geographical location. This allows for the priority display of highly relevant reviews by considering the user's geographical location. Some or all of the above processing in the review unit may be performed using AI, for example, or without AI. For example, the review unit can input the user's geographical location data into a generating AI, which can then analyze the data and provide highly relevant reviews.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The information provision system may further include a hobby / interest section that provides information based on the user's hobbies and interests. For example, if the user is interested in music, it may suggest nearby live music venues or music events. If the user is interested in cooking, it may suggest nearby cooking classes or restaurants. If the user is interested in outdoor activities, it may suggest nearby hiking trails or campsites. This makes it possible to provide information tailored to the user's hobbies and interests. Some or all of the above processing in the hobby / interest section may be performed using AI, for example, or not. For example, the hobby / interest section can input the user's hobby data into a generating AI, which can then analyze the data and provide optimal information.

[0063] The information provision system may further include a social networking section that analyzes the user's social network and provides information based on the ratings of friends and acquaintances. For example, it could suggest restaurants and cafes that the user's friends have given high ratings to. It could also suggest tourist spots that the user's acquaintances have visited. Furthermore, it could suggest events that the user's friends are participating in. This enables the provision of information based on the user's social network. Some or all of the above processing in the social networking section may be performed using AI, for example, or not using AI. For example, the social networking section can input the user's social data into a generating AI, which can then analyze the data and provide optimal information.

[0064] The information provision system may further include a behavioral history unit that analyzes the user's past behavioral history and provides information based on behavioral patterns. For example, it may suggest new nearby spots based on places the user has frequently visited in the past. It can also predict places the user will visit on specific days of the week or times of day and provide optimal information. Furthermore, it can analyze the user's past behavioral patterns and suggest optimal routes and schedules. This enables the provision of information based on the user's behavioral patterns. Some or all of the above-described processing in the behavioral history unit may be performed using AI, for example, or without AI. For example, the behavioral history unit can input user behavioral data into a generating AI, which can then analyze the data and provide optimal information.

[0065] The information provision system may further include a purchase history section that analyzes the user's purchase history and provides information based on their purchase patterns. For example, it can suggest related products and services based on products the user has purchased in the past. It can also predict products the user will purchase during specific seasons or events and provide optimal information. Furthermore, it can analyze the user's purchase patterns and suggest optimal campaigns and discount information. This enables the provision of information based on the user's purchase patterns. Some or all of the above-described processes in the purchase history section may be performed using AI, for example, or without AI. For example, the purchase history section can input the user's purchase data into a generating AI, which can then analyze the data and provide optimal information.

[0066] The information provision system may further include a feedback collection unit that collects user feedback and improves the system based on that feedback. For example, it may provide an interface that allows users to provide feedback on the usability of the system. Users can also post opinions and requests regarding specific functions. Furthermore, it may analyze user feedback and identify areas for system improvement. This enables system improvement based on user feedback. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input user feedback data into a generating AI, which can then analyze the data to identify areas for system improvement.

[0067] The information provision system may further include a real-time location information unit that provides information in real time based on the user's location. For example, it can suggest nearby restaurants and cafes based on the user's current location. If the user is on the move, it can also provide optimal routes and traffic information. Furthermore, if the user is participating in a specific event, it can suggest nearby tourist attractions and activities. This enables the provision of real-time information based on the user's location. Some or all of the above processing in the real-time location information unit may be performed using AI, for example, or without AI. For example, the real-time location information unit can input the user's location data into a generating AI, which can then analyze the data and provide optimal information.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The posting section allows users to post information about the city. The posting section allows users to post information in various formats such as text, images, and videos, and provides an interface for users to select the type of information they want to post. For example, it provides a text box for entering text information, buttons for uploading images and videos, and a preview function for the posted content. Step 2: The categorization unit categorizes the information submitted by the submission unit at the municipal level. The categorization unit classifies the information based on the definition of a municipality, analyzes the content of the submission using natural language processing technology, and classifies it at the municipal level. It also provides an interface for users to manually classify the information. Step 3: The search unit searches and views information categorized by the categorization unit. The search unit provides keyword search and filtering functions, and offers an interface for users to search for information by specifying specific conditions. The order in which search results are displayed can also be set, prioritizing the display of highly relevant information. Step 4: The evaluation unit assesses the reliability of the information retrieved and viewed by the search unit. The evaluation unit evaluates the reliability of the information based on user ratings and the number of reviews, and provides an interface for users to evaluate the information. For example, it sets up functions for posting star ratings and comments, and an algorithm for evaluating the reliability of the poster. Step 5: The AI ​​evaluation unit automatically evaluates the city based on the information evaluated by the evaluation unit. The AI ​​evaluation unit sets the algorithm to be used, analyzes the information to evaluate the city, and provides a function to display it in a ranking format. For example, it creates rankings based on evaluation items such as the safety and convenience of the city.

[0070] (Example of form 2) An information provision system according to an embodiment of the present invention is a platform that allows people choosing a location for a residence or store to obtain information about a town. This information provision system allows users to post information about towns they live in or are familiar with. The posted information is categorized by municipality and can be searched and viewed by other users. For example, it includes information such as living environment, safety, transportation convenience, and nearby facilities. By using this platform, the lack of information when choosing a location can be resolved, and costs due to incorrect selection can be reduced. Furthermore, it is conceivable to add an evaluation system to assess the reliability of posted content and a review function for posted content. It is also effective to use AI to analyze and evaluate posted content. Adding a function that automatically evaluates towns based on user-submitted information using AI and displays it in a ranking format would be beneficial. This would allow users to easily compare and consider options. Furthermore, adding a function that allows users to specify certain conditions (e.g., good safety, good transportation convenience) for searching would further improve usability. In this way, the information provision system allows users to post town information, categorize it, search, evaluate it, and perform AI evaluations, thereby resolving the lack of information when choosing a location and reducing costs due to incorrect selection.

[0071] The information provision system according to this embodiment comprises a posting unit, a categorization unit, a search unit, an evaluation unit, and an AI evaluation unit. The posting unit allows users to post information about their town. The posting unit can post information in formats such as text, images, and videos. The posting unit can also provide an interface for users to select the type of information they want to post. For example, the posting unit provides a text box for users to input text information. The posting unit can also provide buttons for uploading images and videos. Furthermore, the posting unit provides a preview function for posted content, allowing users to review the content before posting. The categorization unit categorizes the information posted by the posting unit by municipality. The categorization unit classifies information based on, for example, the definition of a municipality. The categorization unit can set the algorithm to be used and automatically classify the information. For example, the categorization unit analyzes the posted content using natural language processing technology and classifies it by municipality. The categorization unit can also provide an interface for users to manually classify the information. The search unit searches and views information categorized by the categorization unit. The search unit provides features such as keyword search and filtering. The search unit allows users to search for information by specifying specific conditions. For example, the search unit provides an interface for users to search for information by specifying conditions such as safety and transportation convenience. The search unit can also set the order in which search results are displayed. For example, the search unit prioritizes displaying highly relevant information. The evaluation unit assesses the reliability of information searched and viewed by the search unit. The evaluation unit assesses the reliability of information based on user ratings and the number of reviews, for example. The evaluation unit provides an interface for users to evaluate information. For example, the evaluation unit provides functions for users to post star ratings and comments. The evaluation unit can also set an algorithm to evaluate the reliability of posters. The AI ​​evaluation unit automatically evaluates the city based on the information evaluated by the evaluation unit. The AI ​​evaluation unit, for example, sets the algorithm to be used, analyzes the information, and evaluates the city.The AI ​​evaluation unit provides a function that automatically evaluates cities based on user-submitted information and displays the results in a ranking format. For example, the AI ​​evaluation unit creates rankings based on evaluation items such as the safety and accessibility of the city. As a result, the information provision system according to this embodiment allows users to submit city information, which is then categorized, searched, evaluated, and evaluated by AI, thereby eliminating the lack of information when choosing a location and reducing costs caused by poor selection.

[0072] The posting section allows users to post information about the city. Information can be posted in various formats, such as text, images, and videos. Specifically, users can enter detailed descriptions and comments using text boxes, and add visual information by clicking buttons to upload images and videos. The posting section can also provide an interface that allows users to select the type of information they want to post. For example, it can provide a text box for users to enter text information, and buttons for uploading images and videos. Furthermore, the posting section offers a preview function, allowing users to review their posts before submitting. This preview function allows users to check the content before posting and make corrections as needed, preventing the posting of incorrect or inappropriate information. The posting section also provides a tagging function to facilitate organization and searching of information. For example, users can tag posts with terms like "safety," "transportation," and "restaurants," making it easier for other users to find specific information. Additionally, the posting section can automatically retrieve location information and associate it with posts. This allows users to clearly see which areas their posted information relates to. The posting section can also provide guidelines and tips to improve the quality of information posted by users. For example, the posting section can display messages to users requesting specific information and detailed explanations, encouraging them to enrich their posts. In this way, the posting section can provide an environment where users can easily and effectively post information about the city, thereby improving the overall quality of information on the system.

[0073] The categorization unit categorizes information submitted by the posting unit at the municipal level. For example, the categorization unit classifies information based on the definition of a municipality. Specifically, it analyzes place names and address information included in the submitted content and automatically classifies them into the appropriate municipality. The categorization unit can set the algorithm to be used and automatically classify information. For example, the categorization unit can analyze submitted content using natural language processing technology and classify it at the municipal level. Natural language processing technology can extract place names and related keywords from the text of the submitted content and classify them into the appropriate category. Furthermore, the categorization unit can also provide an interface for users to manually classify information. For example, it can provide a dropdown menu for users to select a municipality when submitting a post, allowing for manual classification. This enables the categorization unit to achieve accurate classification based on the user's intent. Additionally, the categorization unit can periodically review the categories of submitted content and reclassify them as needed. For example, if a new municipality is added or the boundaries of an existing municipality are changed, the categorization unit automatically updates the information to maintain the latest classification. This ensures that the categorization unit always provides accurate and up-to-date information, creating an environment where users can quickly find the information they need.

[0074] The search function searches and displays information categorized by the categorization function. The search function provides features such as keyword search and filtering. Specifically, users can enter keywords into the search box to quickly find relevant information. The search function also allows users to search for information by specifying specific conditions. For example, it provides an interface for users to search for information by specifying conditions such as safety and transportation convenience. Furthermore, the search function allows users to set the order in which search results are displayed. For example, it prioritizes displaying highly relevant information. In addition, the search function can provide individually customized search results based on the user's search and browsing history. This allows users to efficiently find information based on their interests. The search function also provides a map display function, allowing search results to be displayed on a map. This allows users to visually confirm information related to a specific region. For example, if a user searches for a specific city or town, posts related to that region will be displayed as pins on the map, and detailed information can be viewed by clicking on it. Finally, the search function provides a search result filtering function, allowing users to narrow down search results based on specific conditions. For example, search results can be filtered by specifying conditions such as posting date and time, rating score, and the author's trustworthiness. This allows the search engine to provide a powerful tool for users to quickly and accurately find the information they need, improving the overall usability of the system.

[0075] The evaluation unit assesses the reliability of information searched and viewed by the search unit. For example, the evaluation unit evaluates the reliability of information based on user ratings and the number of reviews. Specifically, users can post star ratings and comments on information, which other users can use as a reference to judge the reliability of the information. The evaluation unit provides an interface for users to evaluate information. For example, the evaluation unit provides functions for users to post star ratings and comments. The evaluation unit can also set up algorithms to evaluate the reliability of posters. For example, it can calculate a poster's reliability score based on their past posting history and ratings from other users. This allows the evaluation unit to prioritize displaying reliable information and provide an environment where users can use information with peace of mind. Furthermore, the evaluation unit can continuously monitor the reliability of information and update ratings as needed. For example, if new ratings or comments are added, the evaluation unit automatically recalculates the reliability score to reflect the latest ratings. This allows the evaluation unit to always provide reliability ratings based on the latest information and support users in obtaining accurate information.

[0076] The AI ​​Evaluation Department automatically evaluates cities based on information assessed by the Evaluation Department. For example, the AI ​​Evaluation Department sets the algorithm to be used, analyzes the information, and evaluates cities. Specifically, the AI ​​Evaluation Department provides a function that automatically evaluates cities based on information posted by users and displays it in a ranking format. For example, the AI ​​Evaluation Department creates rankings based on evaluation items such as the safety and accessibility of the city. The AI ​​analyzes the posted content using natural language processing and machine learning techniques and calculates a score for each evaluation item. This allows the AI ​​Evaluation Department to provide objective and highly accurate city evaluations. Furthermore, the AI ​​Evaluation Department can continuously update the evaluation results to reflect the latest information. For example, if new posts or evaluations are added, the AI ​​Evaluation Department automatically recalculates the evaluation results and displays the latest ranking. In addition, the AI ​​Evaluation Department can improve the algorithm based on user feedback to improve the accuracy of the evaluation. As a result, the AI ​​Evaluation Department can always provide highly accurate city evaluations based on the latest information, providing reliable information for users to refer to when choosing a location.

[0077] The search unit includes a condition specification unit that allows users to specify specific conditions for their search. The search unit provides an interface for users to search for information by specifying conditions such as safety and transportation convenience. When a user searches by specifying conditions, the search unit can use the condition specification unit to set search conditions. For example, the search unit provides a condition specification unit for users to search for areas with good safety. The search unit can also provide a condition specification unit for users to search for areas with good transportation convenience. This makes the system more user-friendly by allowing users to search by specifying specific conditions. Some or all of the above-described processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the conditions specified by the user into the AI, and the AI ​​can provide the best search results based on the conditions.

[0078] The evaluation unit includes a review unit that provides a function for reviewing posted content. The evaluation unit provides, for example, an interface for users to review posted content. When a user posts a review, the evaluation unit can input the review content using the review unit. For example, the evaluation unit provides a review unit for users to post star ratings and comments. The evaluation unit can also provide a review unit for users to provide feedback on posted content. By providing a review function for posted content, the reliability of the posted content can be evaluated. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input the review content posted by a user into an AI, which can then analyze the review content and evaluate its reliability.

[0079] The posting section can estimate the user's emotions and adjust the display order of posts based on the estimated emotions. For example, if the user is stressed, the posting section can prioritize displaying relaxing content. If the user is excited, the posting section can also prioritize displaying interesting content. Furthermore, if the user is tired, the posting section can prioritize displaying concise and easy-to-understand content. By adjusting the display order of posts according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the posting section may be performed using AI, or not using AI. For example, the posting section can input user emotion data into a generative AI, which can estimate the emotions and adjust the display order.

[0080] The posting function can analyze a user's past posting history and suggest the optimal posting format when a post is made. For example, the posting function can automatically suggest formats that the user has frequently used in the past. The posting function can also suggest the optimal format based on the user's past posting content. Furthermore, the posting function can prioritize suggesting specific formats based on the user's past posting history. In this way, the optimal posting format can be suggested by analyzing the user's past posting history. Some or all of the above processing in the posting function may be performed using AI, for example, or not using AI. For example, the posting function can input the user's past posting history data into a generating AI, which can then suggest the optimal posting format.

[0081] The posting function can automatically acquire the user's current location information when posting and reflect it in the post content. For example, when a user starts posting, the posting function can automatically acquire their current location and reflect it in the post content. When a user enters post content, the posting function can also suggest optimal information considering the distance from their current location. Furthermore, if a user posts while on the move, the posting function can update their current location in real time and reflect it in the post content. This allows the posting function to automatically acquire the user's current location information and reflect it in the post content. Some or all of the above processing in the posting function may be performed using AI, for example, or without AI. For example, the posting function can input the user's location information data into a generating AI, which can then analyze the location information and reflect it in the post content.

[0082] The posting section can estimate the user's emotions and adjust the design of the posting interface based on the estimated emotions. For example, if the user is stressed, the posting section can provide an interface with calming colors to reduce visual stress. If the user is having fun, the posting section can provide an interface with bright colors to make the posting process more enjoyable. Furthermore, if the user is tired, the posting section can provide a simple and highly visible interface to make the posting process easier. In this way, by adjusting the design of the posting interface according to the user's emotions, a more user-friendly interface can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the posting section may be performed using AI, or not using AI. For example, the posting section can input user emotion data into a generative AI, which can estimate the emotions and adjust the interface design.

[0083] The posting function can automatically suggest post content by referencing the user's past travel history when a post is made. For example, the posting function can automatically suggest places the user has frequently visited in the past as post content. The posting function can also predict places the user will visit on specific days of the week or times of day and suggest them as post content. Furthermore, the posting function can analyze the user's past travel patterns and suggest the most suitable post content. This allows the system to automatically suggest the most suitable post content by referencing the user's past travel history. Some or all of the above processing in the posting function may be performed using AI, for example, or without AI. For example, the posting function can input the user's travel history data into a generating AI, which can then analyze the travel history and suggest the most suitable post content.

[0084] The posting function can suggest post content based on the user's schedule by referring to the user's calendar information when posting. For example, the posting function can automatically suggest post content by referring to the schedule registered in the user's calendar. The posting function can also suggest post content related to a specific event from the user's calendar information. Furthermore, the posting function can suggest the most suitable post content based on the schedule, based on the user's calendar information. In this way, by referring to the user's calendar information, the posting function can suggest the most suitable post content based on the schedule. Some or all of the above processing in the posting function may be performed using AI, for example, or not using AI. For example, the posting function can input the user's calendar information into a generating AI, and the generating AI can suggest the most suitable post content based on the schedule.

[0085] The categorization unit can estimate the user's emotions and adjust the categorization criteria based on the estimated emotions. For example, if the user is relaxed, the categorization unit can provide detailed categorization criteria. If the user is in a hurry, the categorization unit can also provide concise categorization criteria. Furthermore, if the user is excited, the categorization unit can provide visually stimulating categorization criteria. This allows for more appropriate categorization by adjusting the categorization criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the categorization unit may be performed using AI or not using AI. For example, the categorization unit can input user emotion data into a generative AI, which can estimate emotions and adjust the categorization criteria.

[0086] The categorization unit can improve the accuracy of categorization by considering the interrelationships of the posted content during the categorization process. For example, the categorization unit can analyze the relationships between posted content and classify related content into the same category. The categorization unit can also eliminate duplicate information by considering the interrelationships of the posted content. Furthermore, the categorization unit can propose the optimal categorization method based on the interrelationships of the posted content. In this way, the accuracy of categorization can be improved by considering the interrelationships of the posted content. Some or all of the above processing in the categorization unit may be performed using AI, for example, or without AI. For example, the categorization unit can input data on the interrelationships of the posted content into a generating AI, which can then analyze the relationships to improve the accuracy of categorization.

[0087] The categorization unit can categorize content while considering the poster's attribute information. For example, the categorization unit can propose the optimal categorization method by considering the poster's age and gender. The categorization unit can also classify content into relevant categories by considering the poster's occupation and hobbies. Furthermore, the categorization unit can provide optimal categorization criteria based on the poster's attribute information. This allows for more appropriate categorization by considering the poster's attribute information. Some or all of the above-described processes in the categorization unit may be performed using AI, for example, or without AI. For example, the categorization unit can input the poster's attribute information data into a generating AI, which can then analyze the attribute information and perform categorization.

[0088] The categorization unit can estimate the user's emotions and adjust the order in which the categorization results are displayed based on the estimated emotions. For example, if the user is relaxed, the categorization unit may prioritize displaying detailed categorization results. If the user is in a hurry, the categorization unit may also prioritize displaying concise categorization results. Furthermore, if the user is excited, the categorization unit may prioritize displaying visually stimulating categorization results. This allows for the provision of more appropriate information by adjusting the order in which the categorization results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the categorization unit may be performed using AI, or not using AI. For example, the categorization unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the display order.

[0089] The categorization unit can categorize content while considering its geographical distribution. For example, the categorization unit can analyze the geographical distribution of content and categorize it by region. The categorization unit can also provide relevant regional information while considering the geographical distribution of content. Furthermore, the categorization unit can propose the optimal categorization method based on the geographical distribution of content. This allows for more appropriate categorization by considering the geographical distribution of content. Some or all of the above processing in the categorization unit may be performed using AI, for example, or without AI. For example, the categorization unit can input geographical distribution data of content into a generating AI, which can then analyze the geographical distribution and perform categorization.

[0090] The categorization unit can improve the accuracy of categorization by referring to relevant literature for the submitted content during the categorization process. For example, the categorization unit can automatically search for literature related to the submitted content and use it as a reference for categorization. The categorization unit can also propose the optimal categorization method based on the relevant literature for the submitted content. Furthermore, the categorization unit can eliminate duplicate information by referring to relevant literature for the submitted content. In this way, the accuracy of categorization can be improved by referring to relevant literature for the submitted content. Some or all of the above processes in the categorization unit may be performed using AI, for example, or not using AI. For example, the categorization unit can input data on relevant literature for the submitted content into a generating AI, and the generating AI can analyze the relevant literature to improve the accuracy of categorization.

[0091] The search unit can estimate the user's emotions and adjust how search results are displayed based on the estimated emotions. For example, if the user is relaxed, the search unit can display detailed search results. If the user is in a hurry, the search unit can also display concise search results. Furthermore, if the user is excited, the search unit can display visually stimulating search results. By adjusting how search results are displayed according to the user's emotions, more relevant information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, or not using AI. For example, the search unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the display method.

[0092] The search unit can optimize current search results by referring to past search data during a search. For example, the search unit can prioritize displaying highly relevant search results based on the user's past search history. The search unit can also analyze the user's past search data and suggest the most suitable search results. Furthermore, the search unit can display search results relevant to a specific time period based on the user's past search history. This allows for the optimization of current search results by referring to past search data. Some or all of the above-described processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input past search data into a generating AI, which can then analyze the data to provide the most suitable search results.

[0093] The search unit can prioritize displaying relevant search results based on the user's search history during a search. For example, the search unit can prioritize displaying highly relevant search results based on the user's past search history. The search unit can also analyze the user's search history and suggest the most suitable search results. Furthermore, the search unit can display search results related to specific keywords from the user's search history. This allows for the provision of more appropriate information by prioritizing relevant search results based on the user's search history. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the user's search history data into a generating AI, which can then analyze the data and provide relevant search results.

[0094] The search unit can estimate the user's emotions and prioritize search results based on those emotions. For example, if the user is relaxed, the search unit may prioritize detailed search results. If the user is in a hurry, the search unit may prioritize concise search results. If the user is excited, the search unit may prioritize visually stimulating search results. By prioritizing search results according to the user's emotions, more relevant information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, or not using AI. For example, the search unit can input user emotion data into a generative AI, which can estimate the emotions and determine the prioritization.

[0095] The search unit can prioritize displaying highly relevant search results by considering the user's geographical location information during a search. For example, the search unit can prioritize displaying highly relevant search results based on the user's current location. The search unit can also suggest optimal search results by considering the user's geographical location information. Furthermore, the search unit can display search results related to a specific region based on the user's geographical location information. This allows for the priority display of highly relevant search results by considering the user's geographical location information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the user's geographical location information data into a generating AI, which can then analyze the data to provide highly relevant search results.

[0096] The search unit can analyze the user's social media activity during a search and display relevant search results. For example, the search unit can display highly relevant search results based on the user's social media activity. The search unit can also analyze the user's social media activity and suggest the most suitable search results. Furthermore, the search unit can display search results related to specific keywords from the user's social media activity. In this way, by analyzing the user's social media activity, highly relevant search results can be displayed. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the user's social media activity data into a generating AI, which can then analyze the data and provide relevant search results.

[0097] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is relaxed, the evaluation unit can provide detailed evaluation criteria. If the user is in a hurry, the evaluation unit can also provide concise evaluation criteria. Furthermore, if the user is excited, the evaluation unit can provide visually stimulating evaluation criteria. This allows for a more appropriate evaluation by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit can input user emotion data into a generative AI, which can estimate emotions and adjust the evaluation criteria.

[0098] The evaluation unit can optimize the current evaluation by referring to past evaluation data during the evaluation process. For example, the evaluation unit can prioritize displaying highly relevant evaluations based on the user's past evaluation data. The evaluation unit can also analyze the user's past evaluation data and suggest the optimal evaluation. Furthermore, the evaluation unit can display evaluations related to a specific time period from the user's past evaluation data. This allows for the optimization of the current evaluation by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input past evaluation data into a generating AI, which can then analyze the data and provide the optimal evaluation.

[0099] The evaluation unit can perform evaluations while considering the poster's attribute information. For example, the evaluation unit can propose the optimal evaluation method by considering the poster's age and gender. The evaluation unit can also provide relevant evaluations by considering the poster's occupation and hobbies. Furthermore, the evaluation unit can provide optimal evaluation criteria based on the poster's attribute information. This makes it possible to perform more appropriate evaluations by considering the poster's attribute information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the poster's attribute information data into a generating AI, and the generating AI can analyze the data and perform the evaluation.

[0100] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. For example, if the user is relaxed, the evaluation unit can display detailed evaluation results. If the user is in a hurry, the evaluation unit can also display concise evaluation results. Furthermore, if the user is excited, the evaluation unit can display visually stimulating evaluation results. This allows for the provision of more appropriate information by adjusting the display method of evaluation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the display method.

[0101] The evaluation unit can perform evaluations while considering the geographical distribution of the submitted content. For example, the evaluation unit can analyze the geographical distribution of the submitted content and perform evaluations for each region. The evaluation unit can also provide relevant regional information while considering the geographical distribution of the submitted content. Furthermore, the evaluation unit can propose the optimal evaluation method based on the geographical distribution of the submitted content. This makes it possible to perform more appropriate evaluations by considering the geographical distribution of the submitted content. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input geographical distribution data of the submitted content into a generating AI, and the generating AI can analyze the data and perform evaluations.

[0102] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature related to the submitted content during the evaluation process. For example, the evaluation unit can automatically search for literature related to the submitted content and use it as a reference for evaluation. The evaluation unit can also propose the optimal evaluation method based on the relevant literature related to the submitted content. Furthermore, the evaluation unit can eliminate duplicate information by referring to relevant literature related to the submitted content. In this way, the accuracy of the evaluation can be improved by referring to relevant literature related to the submitted content. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input data on relevant literature related to the submitted content into a generating AI, and the generating AI can analyze the data to improve the accuracy of the evaluation.

[0103] The AI ​​evaluation unit can estimate the user's emotions and adjust the AI ​​evaluation criteria based on the estimated emotions. For example, if the user is relaxed, the AI ​​evaluation unit can provide detailed AI evaluation criteria. If the user is in a hurry, the AI ​​evaluation unit can also provide concise AI evaluation criteria. Furthermore, if the user is excited, the AI ​​evaluation unit can provide visually stimulating AI evaluation criteria. This allows for more appropriate evaluations by adjusting the AI ​​evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the AI ​​evaluation unit may be performed using AI or not using AI. For example, the AI ​​evaluation unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the evaluation criteria.

[0104] The AI ​​evaluation unit can optimize the current evaluation by referring to past evaluation data during the AI ​​evaluation process. For example, the AI ​​evaluation unit can prioritize displaying highly relevant AI evaluations based on the user's past evaluation data. The AI ​​evaluation unit can also analyze the user's past evaluation data and propose the optimal AI evaluation. Furthermore, the AI ​​evaluation unit can display AI evaluations related to a specific time period from the user's past evaluation data. This allows for the optimization of the current evaluation by referring to past evaluation data. Some or all of the above processes in the AI ​​evaluation unit may be performed using AI, for example, or without AI. For example, the AI ​​evaluation unit can input past evaluation data into a generating AI, which can then analyze the data and provide the optimal evaluation.

[0105] The AI ​​evaluation unit can perform evaluations while considering the poster's attribute information. For example, the AI ​​evaluation unit can propose the optimal AI evaluation method by considering the poster's age and gender. The AI ​​evaluation unit can also provide relevant AI evaluations by considering the poster's occupation and hobbies. Furthermore, the AI ​​evaluation unit can provide optimal AI evaluation criteria based on the poster's attribute information. This makes it possible to perform more appropriate evaluations by considering the poster's attribute information. Some or all of the above processing in the AI ​​evaluation unit may be performed using AI, for example, or without AI. For example, the AI ​​evaluation unit can input the poster's attribute information data into a generating AI, and the generating AI can analyze the data and perform the evaluation.

[0106] The AI ​​evaluation unit can estimate the user's emotions and adjust the display method of the AI ​​evaluation results based on the estimated user emotions. For example, if the user is relaxed, the AI ​​evaluation unit can display detailed AI evaluation results. If the user is in a hurry, the AI ​​evaluation unit can also display concise AI evaluation results. Furthermore, if the user is excited, the AI ​​evaluation unit can display visually stimulating AI evaluation results. This allows for the provision of more appropriate information by adjusting the display method of the AI ​​evaluation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the AI ​​evaluation unit may be performed using AI, or not using AI. For example, the AI ​​evaluation unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the display method.

[0107] The AI ​​evaluation unit can perform evaluations while considering the geographical distribution of the posted content. For example, the AI ​​evaluation unit can analyze the geographical distribution of the posted content and perform AI evaluations for each region. The AI ​​evaluation unit can also provide relevant regional information while considering the geographical distribution of the posted content. Furthermore, the AI ​​evaluation unit can propose the optimal AI evaluation method based on the geographical distribution of the posted content. This makes it possible to perform more appropriate evaluations by considering the geographical distribution of the posted content. Some or all of the above processing in the AI ​​evaluation unit may be performed using AI, for example, or without AI. For example, the AI ​​evaluation unit can input geographical distribution data of the posted content into a generating AI, and the generating AI can analyze the data and perform evaluations.

[0108] The AI ​​evaluation unit can improve the accuracy of its evaluation by referring to relevant literature related to the submitted content during the AI ​​evaluation process. For example, the AI ​​evaluation unit can automatically search for literature related to the submitted content and use it as a reference for the AI ​​evaluation. The AI ​​evaluation unit can also propose the optimal AI evaluation method based on the relevant literature related to the submitted content. Furthermore, the AI ​​evaluation unit can eliminate duplicate information by referring to relevant literature related to the submitted content. In this way, the accuracy of the evaluation can be improved by referring to relevant literature related to the submitted content. Some or all of the above processes in the AI ​​evaluation unit may be performed using AI, for example, or without using AI. For example, the AI ​​evaluation unit can input data on relevant literature related to the submitted content into a generating AI, and the generating AI can analyze the data to improve the accuracy of the evaluation.

[0109] The condition specification unit can estimate the user's emotions and adjust the criteria for condition specification based on the estimated user emotions. For example, if the user is relaxed, the condition specification unit can provide detailed criteria for condition specification. If the user is in a hurry, the condition specification unit can also provide concise criteria for condition specification. Furthermore, if the user is excited, the condition specification unit can provide visually stimulating criteria for condition specification. This allows for more appropriate condition specification by adjusting the criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the condition specification unit may be performed using AI, for example, or not using AI. For example, the condition specification unit can input user emotion data into a generative AI, which can estimate emotions and adjust the criteria.

[0110] The condition specification unit can optimize the current condition specification by referring to past search data when specifying conditions. For example, the condition specification unit can prioritize displaying highly relevant condition specifications based on the user's past search data. The condition specification unit can also analyze the user's past search data and suggest the optimal condition specification. Furthermore, the condition specification unit can display condition specifications related to a specific time period from the user's past search data. This allows for the optimization of the current condition specification by referring to past search data. Some or all of the above processing in the condition specification unit may be performed using AI, for example, or without AI. For example, the condition specification unit can input past search data into a generating AI, which can then analyze the data and provide the optimal condition specification.

[0111] The condition specification unit can estimate the user's emotions and determine the priority of condition specifications based on the estimated user emotions. For example, if the user is relaxed, the condition specification unit may prioritize displaying detailed condition specifications. If the user is in a hurry, the condition specification unit may also prioritize displaying concise condition specifications. Furthermore, if the user is excited, the condition specification unit may also prioritize displaying visually stimulating condition specifications. This allows for the provision of more appropriate information by determining the priority of condition specifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the condition specification unit may be performed using AI, or not using AI. For example, the condition specification unit can input user emotion data into a generative AI, which can estimate emotions and determine priorities.

[0112] The condition specification unit can prioritize displaying highly relevant conditions by considering the user's geographical location information when conditions are specified. For example, the condition specification unit can prioritize displaying highly relevant conditions based on the user's current location. The condition specification unit can also suggest optimal conditions by considering the user's geographical location information. Furthermore, the condition specification unit can display conditions related to a specific region based on the user's geographical location information. This allows for the priority display of highly relevant conditions by considering the user's geographical location information. Some or all of the above processing in the condition specification unit may be performed using AI, for example, or without AI. For example, the condition specification unit can input the user's geographical location information data into a generating AI, which can then analyze the data and provide highly relevant conditions.

[0113] The review section can estimate the user's emotions and adjust how reviews are displayed based on the estimated emotions. For example, if the user is relaxed, the review section may display a detailed review. If the user is in a hurry, the review section may display a concise review. Furthermore, if the user is excited, the review section may display a visually stimulating review. This allows for the provision of more relevant information by adjusting how reviews are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the review section may be performed using AI or not. For example, the review section can input user emotion data into a generative AI, which can then estimate the emotions and adjust the display method.

[0114] The review unit can optimize the current review by referring to past review history during the review process. For example, the review unit can prioritize displaying highly relevant reviews based on the user's past review history. The review unit can also analyze the user's past review history and suggest the most suitable review. Furthermore, the review unit can display reviews related to a specific time period from the user's past review history. This allows for the optimization of the current review by referring to past review history. Some or all of the above processes in the review unit may be performed using AI, for example, or without AI. For example, the review unit can input past review history data into a generating AI, which can then analyze the data and provide the most suitable review.

[0115] The review section can estimate the user's emotions and prioritize reviews based on those emotions. For example, if the user is relaxed, the review section may prioritize displaying detailed reviews. If the user is in a hurry, the review section may also prioritize displaying concise reviews. Furthermore, if the user is excited, the review section may prioritize displaying visually stimulating reviews. This allows for the provision of more relevant information by prioritizing reviews according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the review section may be performed using AI or not. For example, the review section can input user emotion data into a generative AI, which can then estimate the emotions and determine the prioritization.

[0116] The review unit can prioritize displaying highly relevant reviews by considering the user's geographical location during the review process. For example, the review unit can prioritize displaying highly relevant reviews based on the user's current location. The review unit can also suggest the most relevant reviews by considering the user's geographical location. Furthermore, the review unit can display reviews related to a specific region based on the user's geographical location. This allows for the priority display of highly relevant reviews by considering the user's geographical location. Some or all of the above processing in the review unit may be performed using AI, for example, or without AI. For example, the review unit can input the user's geographical location data into a generating AI, which can then analyze the data and provide highly relevant reviews.

[0117] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0118] The information provision system may further include a health monitoring unit that monitors the user's health status and provides information based on that status. For example, if the user is feeling stressed, it may suggest places or activities where they can relax. If the user is tired, it may suggest nearby rest spots or cafes. If the user is not getting enough exercise, it may suggest nearby parks or gyms. This makes it possible to provide information tailored to the user's health status. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input the user's health data into a generating AI, which can then analyze the data and provide optimal information.

[0119] The information provision system may further include a hobby / interest section that provides information based on the user's hobbies and interests. For example, if the user is interested in music, it may suggest nearby live music venues or music events. If the user is interested in cooking, it may suggest nearby cooking classes or restaurants. If the user is interested in outdoor activities, it may suggest nearby hiking trails or campsites. This makes it possible to provide information tailored to the user's hobbies and interests. Some or all of the above processing in the hobby / interest section may be performed using AI, for example, or not. For example, the hobby / interest section can input the user's hobby data into a generating AI, which can then analyze the data and provide optimal information.

[0120] The information provision system may further include a social networking section that analyzes the user's social network and provides information based on the ratings of friends and acquaintances. For example, it could suggest restaurants and cafes that the user's friends have given high ratings to. It could also suggest tourist spots that the user's acquaintances have visited. Furthermore, it could suggest events that the user's friends are participating in. This enables the provision of information based on the user's social network. Some or all of the above processing in the social networking section may be performed using AI, for example, or not using AI. For example, the social networking section can input the user's social data into a generating AI, which can then analyze the data and provide optimal information.

[0121] The information provision system may further include a behavioral history unit that analyzes the user's past behavioral history and provides information based on behavioral patterns. For example, it may suggest new nearby spots based on places the user has frequently visited in the past. It can also predict places the user will visit on specific days of the week or times of day and provide optimal information. Furthermore, it can analyze the user's past behavioral patterns and suggest optimal routes and schedules. This enables the provision of information based on the user's behavioral patterns. Some or all of the above-described processing in the behavioral history unit may be performed using AI, for example, or without AI. For example, the behavioral history unit can input user behavioral data into a generating AI, which can then analyze the data and provide optimal information.

[0122] The information provision system may further include an ad display unit that estimates the user's emotions and adjusts the way ads are displayed based on the estimated emotions. For example, if the user is relaxed, a detailed ad may be displayed. If the user is in a hurry, a concise ad may be displayed. If the user is excited, a visually stimulating ad may be displayed. This allows for more effective ad display by adjusting the way ads are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ad display unit may be performed using AI, for example, or without AI. For example, the ad display unit can input user emotion data into a generative AI, which can estimate the emotion and adjust the way ads are displayed.

[0123] The information provision system may further include a notification adjustment unit that estimates the user's emotions and adjusts the timing of notifications based on the estimated emotions. For example, if the user is relaxed, the notification may be displayed immediately. If the user is in a hurry, the notification may be postponed. Also, if the user is excited, the notification may be made visually more prominent. This allows for more effective notifications by adjusting the timing of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification adjustment unit may be performed using AI, for example, or without AI. For example, the notification adjustment unit can input user emotion data into the generative AI, which can estimate the emotion and adjust the timing of notifications.

[0124] The information provision system may further include an interface customization unit that estimates the user's emotions and customizes the interface based on the estimated emotions. For example, if the user is relaxed, an interface with calming colors may be provided. If the user is in a hurry, a simple and highly visible interface may be provided. If the user is excited, a visually stimulating interface may be provided. In this way, a more user-friendly interface can be provided by customizing the interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interface customization unit may be performed using AI, or not using AI. For example, the interface customization unit can input user emotion data into the generative AI, which can estimate the emotions and customize the interface.

[0125] The information provision system may further include a purchase history section that analyzes the user's purchase history and provides information based on their purchase patterns. For example, it can suggest related products and services based on products the user has purchased in the past. It can also predict products the user will purchase during specific seasons or events and provide optimal information. Furthermore, it can analyze the user's purchase patterns and suggest optimal campaigns and discount information. This enables the provision of information based on the user's purchase patterns. Some or all of the above-described processes in the purchase history section may be performed using AI, for example, or without AI. For example, the purchase history section can input the user's purchase data into a generating AI, which can then analyze the data and provide optimal information.

[0126] The information provision system may further include a feedback collection unit that collects user feedback and improves the system based on that feedback. For example, it may provide an interface that allows users to provide feedback on the usability of the system. Users can also post opinions and requests regarding specific functions. Furthermore, it may analyze user feedback and identify areas for system improvement. This enables system improvement based on user feedback. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input user feedback data into a generating AI, which can then analyze the data to identify areas for system improvement.

[0127] The information provision system may further include a real-time location information unit that provides information in real time based on the user's location. For example, it can suggest nearby restaurants and cafes based on the user's current location. If the user is on the move, it can also provide optimal routes and traffic information. Furthermore, if the user is participating in a specific event, it can suggest nearby tourist attractions and activities. This enables the provision of real-time information based on the user's location. Some or all of the above processing in the real-time location information unit may be performed using AI, for example, or without AI. For example, the real-time location information unit can input the user's location data into a generating AI, which can then analyze the data and provide optimal information.

[0128] The following briefly describes the processing flow for example form 2.

[0129] Step 1: The posting section allows users to post information about the city. The posting section allows users to post information in various formats such as text, images, and videos, and provides an interface for users to select the type of information they want to post. For example, it provides a text box for entering text information, buttons for uploading images and videos, and a preview function for the posted content. Step 2: The categorization unit categorizes the information submitted by the submission unit at the municipal level. The categorization unit classifies the information based on the definition of a municipality, analyzes the content of the submission using natural language processing technology, and classifies it at the municipal level. It also provides an interface for users to manually classify the information. Step 3: The search unit searches and views information categorized by the categorization unit. The search unit provides keyword search and filtering functions, and offers an interface for users to search for information by specifying specific conditions. The order in which search results are displayed can also be set, prioritizing the display of highly relevant information. Step 4: The evaluation unit assesses the reliability of the information retrieved and viewed by the search unit. The evaluation unit evaluates the reliability of the information based on user ratings and the number of reviews, and provides an interface for users to evaluate the information. For example, it sets up functions for posting star ratings and comments, and an algorithm for evaluating the reliability of the poster. Step 5: The AI ​​evaluation unit automatically evaluates the city based on the information evaluated by the evaluation unit. The AI ​​evaluation unit sets the algorithm to be used, analyzes the information to evaluate the city, and provides a function to display it in a ranking format. For example, it creates rankings based on evaluation items such as the safety and convenience of the city.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0132] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the posting unit, categorization unit, search unit, evaluation unit, and AI evaluation unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the posting unit is implemented by the control unit 46A of the smart device 14, allowing users to post information in the form of text, images, videos, etc. The categorization unit is implemented by the specific processing unit 290 of the data processing unit 12, categorizing the posted information by municipality. The search unit is implemented by the control unit 46A of the smart device 14, for example, to search and view the categorized information. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to evaluate the reliability of the information. The AI ​​evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, allowing AI to automatically evaluate the city. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0134] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0135] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the posting unit, categorization unit, search unit, evaluation unit, and AI evaluation unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the posting unit is implemented by the control unit 46A of the smart glasses 214, allowing users to post information in the form of text, images, videos, etc. The categorization unit is implemented by the specific processing unit 290 of the data processing unit 12, categorizing the posted information by municipality. The search unit is implemented by the control unit 46A of the smart glasses 214, for example, to search and view the categorized information. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to evaluate the reliability of the information. The AI ​​evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, allowing AI to automatically evaluate the city. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0150] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0151] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the posting unit, categorization unit, search unit, evaluation unit, and AI evaluation unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the posting unit is implemented by the control unit 46A of the headset terminal 314, allowing users to post information in the form of text, images, videos, etc. The categorization unit is implemented by the specific processing unit 290 of the data processing unit 12, categorizing the posted information by municipality. The search unit is implemented by the control unit 46A of the headset terminal 314, for example, to search and view the categorized information. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to evaluate the reliability of the information. The AI ​​evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, allowing AI to automatically evaluate the city. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0166] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0167] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0168] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0169] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0170] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0172] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0173] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0174] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0175] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0176] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0177] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0178] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0179] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0181] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0182] Each of the multiple elements described above, including the posting unit, categorization unit, search unit, evaluation unit, and AI evaluation unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the posting unit is implemented by the control unit 46A of the robot 414, allowing users to post information in the form of text, images, videos, etc. The categorization unit is implemented by the specific processing unit 290 of the data processing unit 12, categorizing the posted information by municipality. The search unit is implemented by the control unit 46A of the robot 414, for example, to search and view the categorized information. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to evaluate the reliability of the information. The AI ​​evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, allowing the AI ​​to automatically evaluate the city. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0183] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0184] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0185] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0186] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0187] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0188] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0189] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0190] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0191] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0192] 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.

[0193] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0194] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0195] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0196] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0197] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0198] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0199] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0200] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0201] (Note 1) A posting section where users can submit information about the town, A categorization unit categorizes the information submitted by the aforementioned submission unit at the municipal level, A search unit that searches and views information categorized by the aforementioned categorization unit, An evaluation unit that evaluates the reliability of the information retrieved and viewed by the aforementioned search unit, The system comprises an AI evaluation unit that automatically evaluates the city based on the information evaluated by the aforementioned evaluation unit. A system characterized by the following features. (Note 2) The aforementioned search unit, It includes a condition specification section that allows you to search by specifying specific conditions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The evaluation unit, It includes a review section that provides a review function for posted content. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned submission section, It estimates the user's sentiment and adjusts the display order of posts based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned submission section, When a user posts, the system analyzes their past posting history and suggests the most suitable posting format. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned submission section, When posting, the system automatically retrieves the user's current location information and incorporates it into the post content. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned submission section, It estimates the user's emotions and adjusts the posting interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned submission section, When posting, the system automatically suggests post content based on the user's past browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned submission section, When posting, the system will refer to the user's calendar information to suggest post content based on their schedule. The system described in Appendix 1, characterized by the features described herein. (Note 10) The categorization section is, It estimates user sentiment and adjusts categorization criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The categorization section is, When categorizing, consider the interrelationships between posts to improve the accuracy of categorization. The system described in Appendix 1, characterized by the features described herein. (Note 12) The categorization section is, When categorizing, consider the attribute information of the poster. The system described in Appendix 1, characterized by the features described herein. (Note 13) The categorization section is, It estimates the user's emotions and adjusts the order in which the categorization results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The categorization section is, When categorizing, consider the geographical distribution of the submitted content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The categorization section is, When categorizing, refer to relevant literature related to the submitted content to improve the accuracy of categorization. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, When you search, we optimize the current search results by referring to past search data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned search unit, When searching, prioritize relevant search results based on the user's search history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned search unit, It estimates the user's emotions and determines the priority of search results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned search unit, When searching, the system prioritizes displaying more relevant search results by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned search unit, When you search, we analyze your social media activity and display relevant search results. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, During the evaluation process, past evaluation data is referenced to optimize the current evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, When evaluating a poster, the poster's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The evaluation unit, When evaluating submissions, the geographical distribution of the submitted content will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The evaluation unit, During the evaluation process, we refer to relevant literature related to the submitted content to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned AI evaluation unit, It estimates the user's emotions and adjusts the AI ​​evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned AI evaluation unit, During AI evaluation, past evaluation data is referenced to optimize the current evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned AI evaluation unit, When using AI evaluation, the AI ​​takes into account the poster's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned AI evaluation unit, It estimates the user's emotions and adjusts how the AI ​​evaluation results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned AI evaluation unit, During AI evaluation, the geographical distribution of the submitted content is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned AI evaluation unit, When using AI evaluation, we improve the accuracy of the evaluation by referring to relevant literature on the submitted content. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned condition specification unit is, It estimates the user's emotions and adjusts the criteria for specifying conditions based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned condition specification unit is, When specifying search conditions, the system optimizes the current conditions by referencing past search data. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned condition specification unit is, The system estimates the user's emotions and determines the priority of conditional specifications based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned condition specification unit is, When specifying conditions, the system prioritizes displaying the most relevant conditions, taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned review section, It estimates user sentiment and adjusts how reviews are displayed based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned review section, During the review process, refer to past review history to optimize the current review. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned review section, It estimates user sentiment and prioritizes reviews based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned review section, When users leave reviews, the system prioritizes displaying reviews that are more relevant, taking into account their geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0202] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A posting section where users can submit information about the town, A categorization unit categorizes the information submitted by the aforementioned submission unit at the municipal level, A search unit that searches and views information categorized by the aforementioned categorization unit, An evaluation unit that evaluates the reliability of the information retrieved and viewed by the search unit, The system includes an AI evaluation unit that automatically evaluates the city based on the information evaluated by the aforementioned evaluation unit. A system characterized by the following features.

2. The aforementioned search unit, It includes a condition specification section that allows you to search by specifying specific conditions. The system according to feature 1.

3. The evaluation unit, It includes a review section that provides a review function for posted content. The system according to feature 1.

4. The aforementioned submission section, It estimates the user's sentiment and adjusts the display order of posts based on the estimated user sentiment. The system according to feature 1.

5. The aforementioned submission section, When a user posts, the system analyzes their past posting history and suggests the most suitable posting format. The system according to feature 1.

6. The aforementioned submission section, When posting, the system automatically retrieves the user's current location information and incorporates it into the post content. The system according to feature 1.

7. The aforementioned submission section, It estimates the user's emotions and adjusts the posting interface design based on those estimated emotions. The system according to feature 1.

8. The aforementioned submission section, When posting, the system automatically suggests post content based on the user's past browsing history. The system according to feature 1.

9. The aforementioned submission section, When posting, the system will refer to the user's calendar information to suggest post content based on their schedule. The system according to feature 1.

10. The categorization section is, It estimates user sentiment and adjusts categorization criteria based on the estimated user sentiment. The system according to feature 1.

Images