system

Abstract

The system according to the embodiment aims to efficiently collect information about the city or town to which the user is moving and provide the information in accordance with the user's needs. [Solution] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a support unit. The collection unit collects information about a specified city or town. The analysis unit analyzes the information collected by the collection unit and organizes it to meet the user's needs. The provision unit provides the information organized by the analysis unit. The support unit provides support regarding important points or procedures related to moving.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently collect information about the city or town to which a user is moving and organize it to meet the user's needs.

[0005] The system according to the embodiment aims to efficiently collect information about the city or town to which the user is moving and provide the information in accordance with the user's needs. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a support unit. The collection unit collects information about a specified city or town. The analysis unit analyzes the information collected by the collection unit and organizes it according to the user's needs. The provision unit provides the information organized by the analysis unit. The support unit provides support regarding important points or procedures related to moving. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect information about the city or town to which the user is moving and provide it in accordance with the user's needs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A moving support system according to an embodiment of the present invention is a system that automatically collects and provides information about a city or town designated by a user. When a user designates a city or town as a potential destination, the moving support system automatically collects information about the designated city or town from the Internet. This information includes information about living environments, transportation access, educational institutions, medical facilities, shopping facilities, and public safety. The collected information is organized and provided according to the user's needs. For example, information about educational institutions and medical facilities is prioritized for users with children, while information about transportation access and shopping facilities is prioritized for single people. Support is also provided regarding important points and procedures related to the move. For example, the system provides information about procedures required for the move and information necessary for life in the new location (such as how to dispose of garbage and local rules). In this way, users can receive comprehensive support from selecting a destination to living after the move. By automatically collecting and providing information about the city or town designated by the user, the moving support system can provide comprehensive support from selecting a destination to living after the move.

[0029] A moving support system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a support unit. The collection unit collects information about a city or town specified by a user. The collection unit collects data from, for example, reliable sources on the Internet. For example, the collection unit can collect information from official government websites or certified databases. The collection unit can also analyze a user's past search history and select an optimal information collection method. For example, the collection unit prioritizes collecting information about cities or towns previously searched by the user. The collection unit can also filter information based on the user's current living situation and areas of interest. For example, for users of child-rearing age, information about educational institutions and medical facilities is prioritized. The analysis unit analyzes the information collected by the collection unit and organizes it according to the user's needs. For example, the analysis unit customizes the information based on a user profile. For example, the analysis unit organizes the information based on profile information such as the user's age, occupation, and interests. The analysis unit can also adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit analyzes information of high importance (such as public safety and medical facilities) in detail. The providing unit provides the information organized by the analyzing unit. The providing unit provides, for example, information required by the user in a predetermined format. For example, the providing unit can provide information in text format, graph format, interactive format, etc. The providing unit can also estimate the user's emotions and adjust the way information is presented based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method is provided. The support unit provides support regarding important points and procedures related to moving. The support unit provides, for example, information necessary for moving procedures and life in the new location. For example, the support unit provides information such as address change procedures, public service registration, garbage disposal instructions, and local rules. As a result, the moving support system according to the embodiment can automatically collect and provide information about the city or town specified by the user, thereby providing total support from selecting a moving destination to life after moving.

[0030] The collection unit may collect data from reliable sources. Examples of reliable sources include official government agency websites, certified databases, academic papers, and expert reports. For example, the collection unit may collect the latest demographic data from official government agency websites. The collection unit may also collect information about public services from certified databases. The collection unit may also collect information about educational institutions and medical facilities from academic papers and expert reports. For example, the collection unit may collect evaluation data on educational institutions in a specific city or town based on academic papers. This improves the reliability of the information provided by collecting data from reliable sources. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may efficiently collect information using an AI model for collecting data from online sources.

[0031] The analysis unit can customize information based on a user profile. The user profile includes information such as age, occupation, interests, family structure, and place of residence. The analysis unit customizes information based on, for example, the user's age. For example, to a young user, information emphasizing leisure facilities and event information is provided. The analysis unit can also customize information based on the user's occupation. For example, to a businessperson, information emphasizing transportation access and business facility information is provided. Furthermore, the analysis unit can customize information based on the user's interests. For example, to a user who enjoys the outdoors, information emphasizing natural environments and leisure facilities is provided. By customizing information based on the user profile, information tailored to the user's needs can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can efficiently organize information using an AI model for customizing information based on the user profile.

[0032] The providing unit can provide information required by a user in a predetermined format. Examples of the predetermined format include a text format, a graph format, and an interactive format. The providing unit can provide information in, for example, a text format. For example, the providing unit can display information about a city or town specified by a user in text format. The providing unit can also provide information in a graph format. For example, the providing unit can display demographic data and the usage status of public services in a graph format. The providing unit can also provide information in an interactive format. For example, the providing unit can provide an interactive interface that allows a user to check city or town information on a map. This facilitates the use of information by providing the information required by the user in an appropriate format. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can efficiently provide information using an AI model for providing information in a predetermined format.

[0033] The support unit can provide information necessary for procedures when moving and for life in the new location. Procedures necessary for moving include, for example, procedures for changing address, registration for public services, and procedures for transferring to a new school. The support unit can provide, for example, information regarding procedures for changing address. For example, the support unit can provide information on specific methods for procedures for changing address and necessary documents. The support unit can also provide information regarding registration for public services. For example, the support unit can provide information on how to register for public services such as water, electricity, and gas. The support unit can also provide information regarding procedures for transferring to a new school. For example, the support unit can provide information on specific methods for procedures for transferring a child to a new school and necessary documents. Information necessary for life in the new location includes, for example, nearby medical institutions, schools, public transportation, how to dispose of garbage, and local rules. The support unit can provide, for example, information regarding nearby medical institutions. For example, the support unit can provide information on hospitals and clinics near the new location. The support unit can also provide information on nearby schools. For example, the support unit can provide information on elementary and junior high schools near the new location. This allows the user to move smoothly by providing necessary procedures for moving and information necessary for life at the new location. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can efficiently provide information using an AI model for providing information about moving.

[0034] The collection unit can analyze the user's past search history and select a predetermined information collection method. For example, the collection unit prioritizes collecting information on cities and towns that the user has previously searched for. For example, the collection unit collects data from reliable information sources based on the information on cities and towns that the user has previously searched for. The collection unit can also collect reliable information based on information sources the user has previously used. For example, the collection unit analyzes information sources the user has previously used and selects an optimal information collection method. Furthermore, the collection unit can analyze the user's past search patterns and determine the optimal timing for collecting information. For example, the collection unit adjusts the timing of information collection based on the user's past search patterns. In this way, the optimal information collection method can be selected by analyzing the user's past search history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can efficiently collect information using an AI model for selecting an information collection method based on the user's past search history.

[0035] When collecting information, the collection unit can filter information based on the user's current living situation and areas of interest. For example, the collection unit prioritizes collecting information on educational institutions and medical facilities for users of child-rearing age. For example, the collection unit filters information on educational institutions and medical facilities based on profile information of users of child-rearing age. The collection unit can also prioritize collecting information on transportation access and shopping facilities for single people. For example, the collection unit filters information on transportation access and shopping facilities based on profile information of single people. The collection unit can also prioritize collecting information on medical facilities and public safety for elderly people. For example, the collection unit filters information on medical facilities and public safety based on profile information of elderly people. This allows for filtering information based on the user's current living situation and areas of interest, thereby providing useful information to the user. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can efficiently collect information using an AI model for filtering information based on user profile information.

[0036] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting information about municipalities close to the user's current location. For example, the collection unit collects information about municipalities close to the user's current location based on the user's geographical location information. Furthermore, if the user is interested in a particular area, the collection unit can prioritize collecting information about that area. For example, the collection unit collects information about a specific area based on the user's geographical location information. Furthermore, if the user is moving, the collection unit can collect optimal information based on the user's current location. For example, the collection unit collects optimal information for the user while moving based on the user's geographical location information. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can efficiently collect information using an AI model for collecting information based on the user's geographical location information.

[0037] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit prioritizes collecting information about cities, towns, and villages shared by the user on social media. For example, the collection unit collects information about the shared cities, towns, and villages based on the user's social media activities. The collection unit can also collect information about related cities, towns, and villages based on the content posted by accounts followed by the user. For example, the collection unit analyzes the content posted by accounts followed by the user and collects information about related cities, towns, and villages. The collection unit can also collect information about related cities, towns, and villages based on information about groups and communities in which the user participates. For example, the collection unit analyzes information about groups and communities in which the user participates and collects information about related cities, towns, and villages. In this way, related information can be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can efficiently collect information using an AI model for collecting information based on the user's social media activities.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The analysis unit, for example, analyzes information of high importance (such as public safety and medical facilities) in detail. For example, the analysis unit analyzes information related to public safety in detail based on the importance of the information. The analysis unit can also analyze information of low importance (such as leisure facility and event information) in a simplified manner. For example, the analysis unit analyzes leisure facility and event information in a simplified manner based on the importance of the information. Furthermore, the analysis unit can prioritize analysis of information of high importance according to the user's needs. For example, the analysis unit prioritizes analysis of information of high importance based on the user's profile information. In this way, important information can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can efficiently analyze information using an AI model for adjusting the level of detail of the analysis based on the importance of the information.

[0039] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies an analysis algorithm based on crime data to public safety information. For example, the analysis unit analyzes public safety information based on crime data. The analysis unit can also apply an analysis algorithm based on school evaluation data to educational institution information. For example, the analysis unit analyzes educational institution information based on school evaluation data. The analysis unit can also apply an analysis algorithm based on transportation data to transportation access information. For example, the analysis unit analyzes transportation access information based on transportation data. In this way, by applying different analysis algorithms depending on the category of information, optimal analysis can be performed for each category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can efficiently analyze information using an AI model for applying different analysis algorithms depending on the category of information.

[0040] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The analysis unit, for example, prioritizes analysis of the most recent information. For example, the analysis unit prioritizes analysis of the most recent information based on the time of submission of the information. The analysis unit can also briefly analyze older information. For example, the analysis unit briefly analyzes older information based on the time of submission of the information. Furthermore, the analysis unit can also determine the priority of analysis of information based on the time of submission in accordance with the needs of the user. For example, the analysis unit determines the priority of analysis of information based on the time of submission based on the user's profile information. In this way, by determining the priority of analysis based on the time of submission of the information, the most recent information can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can efficiently analyze information using an AI model for determining the priority of analysis based on the time of submission of the information.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit, for example, prioritizes analysis of information most relevant to the user's needs. For example, the analysis unit prioritizes analysis of information most relevant to the user's needs based on the relevance of the information. The analysis unit can also postpone less relevant information. For example, the analysis unit postpones less relevant information based on the relevance of the information. Furthermore, the analysis unit can also prioritize analysis of more relevant information based on a user profile. For example, the analysis unit prioritizes analysis of more relevant information based on the user's profile information. In this way, by adjusting the order of analysis based on the relevance of the information, more relevant information can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can efficiently analyze information using an AI model for adjusting the order of analysis based on the relevance of the information.

[0042] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. The providing unit, for example, provides detailed information of high importance (such as public safety and medical facilities). For example, the providing unit provides detailed information about public safety based on the importance of the information. The providing unit can also provide simplified information of low importance (such as leisure facility and event information). For example, the providing unit provides simplified information about leisure facilities and events based on the importance of the information. Furthermore, the providing unit can prioritize providing information of high importance according to the user's needs. For example, the providing unit prioritizes providing information of high importance based on the user's profile information. In this way, important information can be provided in detail by adjusting the level of detail of the information provided based on the importance of the information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can efficiently provide information using an AI model for adjusting the level of detail of the information provided based on the importance of the information.

[0043] The providing unit can apply different provision algorithms depending on the category of information when providing the information. For example, the providing unit applies a provision algorithm based on crime data to public safety information. For example, the providing unit provides public safety information based on crime data. The providing unit can also apply a provision algorithm based on school evaluation data to educational institution information. For example, the providing unit provides educational institution information based on school evaluation data. The providing unit can also apply a provision algorithm based on traffic data to transportation access information. For example, the providing unit provides transportation access information based on traffic data. In this way, by applying different provision algorithms depending on the category of information, optimal information can be provided for each category. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can efficiently provide information using an AI model for applying different provision algorithms depending on the category of information.

[0044] The providing unit can determine the priority of providing information based on the time of submission of the information at the time of providing the information. The providing unit, for example, prioritizes providing the latest information. For example, the providing unit prioritizes providing the latest information based on the time of submission of the information. The providing unit can also provide old information in a simplified manner. For example, the providing unit can provide old information in a simplified manner based on the time of submission of the information. Furthermore, the providing unit can determine the priority of providing information based on the time of submission in accordance with the needs of the user. For example, the providing unit determines the priority of providing information based on the time of submission based on the user's profile information. In this way, by determining the priority of providing information based on the time of submission of the information, the latest information can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can efficiently provide information using an AI model for determining the priority of providing information based on the time of submission of the information.

[0045] The providing unit can adjust the order of providing information based on the relevance of the information when providing the information. For example, the providing unit prioritizes providing information that is most relevant to the user's needs. For example, the providing unit prioritizes providing information that is most relevant to the user's needs based on the relevance of the information. The providing unit can also postpone less relevant information. For example, the providing unit postpones less relevant information based on the relevance of the information. Furthermore, the providing unit can also prioritize providing more relevant information based on a user profile. For example, the providing unit prioritizes providing more relevant information based on the user's profile information. In this way, by adjusting the order of providing information based on the relevance of the information, more relevant information can be prioritized. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can efficiently provide information using an AI model for adjusting the order of providing information based on the relevance of the information.

[0046] When providing support, the support unit can select a predetermined support method by referring to the user's past moving history. The support unit, for example, proposes an optimal support method based on the procedures used when the user moved in the past. For example, the support unit proposes an optimal support method based on the user's past moving history. The support unit can also predict necessary procedures based on the user's past moving history and provide support. For example, the support unit predicts necessary procedures based on the user's past moving history and provides support. The support unit can also analyze the user's past moving history and propose the most efficient support method. For example, the support unit proposes the most efficient support method based on the user's past moving history. In this way, the optimal support method can be selected by referring to the user's past moving history. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can provide efficient support using an AI model for selecting a support method based on the user's past moving history.

[0047] The support unit can customize the support content based on the user's current living situation when providing support. For example, the support unit provides support that emphasizes information on educational institutions and medical facilities to a user of child-rearing age. For example, the support unit provides support that emphasizes information on educational institutions and medical facilities based on the profile information of the user of child-rearing age. The support unit can also provide support that emphasizes information on transportation access and shopping facilities to a single person. For example, the support unit provides support that emphasizes information on transportation access and shopping facilities based on the profile information of the single person. The support unit can also provide support that emphasizes information on medical facilities and public safety to the elderly. For example, the support unit provides support that emphasizes information on medical facilities and public safety based on the profile information of the elderly person. This allows the support content to be customized based on the user's current living situation, thereby providing beneficial support to the user. Some or all of the above-described processing by the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can efficiently provide support using an AI model for customizing the support content based on the user's profile information.

[0048] When providing support, the support unit can select a predetermined support method by taking into account the user's geographical location information. For example, the support unit can prioritize providing information about cities, towns, and villages close to the user's current location. For example, the support unit can provide information about cities, towns, and villages close to the user's current location based on the user's geographical location information. Furthermore, if the user is interested in a particular area, the support unit can prioritize providing information about that area. For example, the support unit can provide information about a particular area based on the user's geographical location information. Furthermore, if the user is traveling, the support unit can provide an optimal support method based on the user's current location. For example, the support unit can provide an optimal support method for a traveling user based on the user's geographical location information. This allows the optimal support method to be selected by taking the user's geographical location information into account. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can efficiently provide support by using an AI model for selecting a support method based on the user's geographical location information.

[0049] When providing support, the support unit can analyze the user's social media activity and suggest support content. The support unit, for example, prioritizes providing information about cities and towns shared by the user on social media. For example, the support unit provides information about the shared cities and towns based on the user's social media activity. The support unit can also provide information about related cities and towns based on the content posted by accounts the user follows. For example, the support unit analyzes the content posted by accounts the user follows and provides information about related cities and towns. The support unit can also provide information about related cities and towns based on information about groups and communities the user participates in. For example, the support unit analyzes information about groups and communities the user participates in and provides information about related cities and towns. This makes it possible to suggest related support content by analyzing the user's social media activity. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can efficiently provide support using an AI model for suggesting support content based on the user's social media activity.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The collection unit can also analyze the user's past moving history and prioritize collecting information about candidate locations for moving. For example, the collection unit prioritizes collecting information about cities, towns, and villages with similar conditions based on information collected when the user moved in the past. The collection unit can also re-collect information about cities, towns, and villages that the user previously considered as moving destinations but ultimately did not select. Furthermore, the collection unit can collect new information about candidate locations for moving based on the user's past moving history. This makes it possible to provide more appropriate information by utilizing the user's past moving history.

[0052] The analysis unit can also analyze the user's social media activity and customize information based on the user's interests. For example, the analysis unit prioritizes organizing information about related cities and towns based on the content posted by accounts the user follows on social media. The analysis unit can also estimate the user's interests and customize information based on information about cities and towns shared by the user on social media. Furthermore, the analysis unit can organize information about related cities and towns based on information about groups and communities in which the user participates. This makes it possible to utilize the user's social media activity to provide more appropriate information.

[0053] The providing unit can also adjust the timing of providing information based on the user's geographical location information. For example, the providing unit can provide information about cities, towns, and villages that are close to the user's current location with priority. In addition, when the user moves to a specific area, the providing unit can provide information about that area in real time. Furthermore, when the user is moving, the providing unit can provide optimal information based on the user's current location. This makes it possible to provide information at a more appropriate time by utilizing the user's geographical location information.

[0054] The support unit can also provide advice about moving based on the user's past moving history. For example, the support unit can analyze problems the user experienced when moving in the past and provide advice to prevent similar problems from occurring. The support unit can also provide information about services and facilities that the user used in the past at their new home. Furthermore, the support unit can also provide information necessary for life at their new home based on the user's past moving history. This makes it possible to provide more appropriate support by utilizing the user's past moving history.

[0055] The support unit can also provide information about potential locations for moving based on the user's geographical location information. For example, the support unit can provide information about cities, towns, and villages that are close to the user's current location with priority. Also, if the user is interested in a particular area, the support unit can provide information about that area. Furthermore, if the user is traveling, the support unit can provide the optimal support method based on the user's current location. This makes it possible to provide more appropriate support by utilizing the user's geographical location information.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The collection unit collects information about the city or town specified by the user. The collection unit collects data from reliable sources on the Internet. For example, it collects information from official government websites and certified databases. The collection unit also analyzes the user's past search history and selects the optimal information collection method. For example, it may prioritize collecting information about cities or towns that the user has previously searched for. The collection unit also filters information based on the user's current living situation and areas of interest. For example, for users of child-rearing age, it may prioritize collecting information about educational institutions and medical facilities. Step 2: The analysis unit analyzes the information collected by the collection unit and organizes it to suit the user's needs. The analysis unit customizes the information based on the user profile. For example, it organizes the information based on profile information such as the user's age, occupation, and interests. The analysis unit also adjusts the level of detail of the analysis based on the importance of the information. For example, it analyzes information of high importance (such as public safety and medical facilities) in detail. Step 3: The providing unit provides the information organized by the analyzing unit. The providing unit provides the information the user needs in a predetermined format. For example, the information is provided in text format, graph format, interactive format, etc. The providing unit also estimates the user's emotions and adjusts the way the information is presented based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method is provided. Step 4: The support department will assist you with important points to note and procedures for moving. The support department will provide you with the necessary procedures for moving and information you will need for life in your new home. For example, they will provide information on changing your address, registering for public services, how to dispose of garbage, and local rules.

[0058] (Example 2) A moving support system according to an embodiment of the present invention is a system that automatically collects and provides information about a city or town designated by a user. When a user designates a city or town as a potential destination, the moving support system automatically collects information about the designated city or town from the Internet. This information includes information about living environments, transportation access, educational institutions, medical facilities, shopping facilities, and public safety. The collected information is organized and provided according to the user's needs. For example, information about educational institutions and medical facilities is prioritized for users with children, while information about transportation access and shopping facilities is prioritized for single people. Support is also provided regarding important points and procedures related to the move. For example, the system provides information about procedures required for the move and information necessary for life in the new location (such as how to dispose of garbage and local rules). In this way, users can receive comprehensive support from selecting a destination to living after the move. By automatically collecting and providing information about the city or town designated by the user, the moving support system can provide comprehensive support from selecting a destination to living after the move.

[0059] A moving support system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a support unit. The collection unit collects information about a city or town specified by a user. The collection unit collects data from, for example, reliable sources on the Internet. For example, the collection unit can collect information from official government websites or certified databases. The collection unit can also analyze a user's past search history and select an optimal information collection method. For example, the collection unit prioritizes collecting information about cities or towns previously searched by the user. The collection unit can also filter information based on the user's current living situation and areas of interest. For example, for users of child-rearing age, information about educational institutions and medical facilities is prioritized. The analysis unit analyzes the information collected by the collection unit and organizes it according to the user's needs. For example, the analysis unit customizes the information based on a user profile. For example, the analysis unit organizes the information based on profile information such as the user's age, occupation, and interests. The analysis unit can also adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit analyzes information of high importance (such as public safety and medical facilities) in detail. The providing unit provides the information organized by the analyzing unit. The providing unit provides, for example, information required by the user in a predetermined format. For example, the providing unit can provide information in text format, graph format, interactive format, etc. The providing unit can also estimate the user's emotions and adjust the way information is presented based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method is provided. The support unit provides support regarding important points and procedures related to moving. The support unit provides, for example, information necessary for moving procedures and life in the new location. For example, the support unit provides information such as address change procedures, public service registration, garbage disposal instructions, and local rules. As a result, the moving support system according to the embodiment can automatically collect and provide information about the city or town specified by the user, thereby providing total support from selecting a moving destination to life after moving.

[0060] The collection unit may collect data from reliable sources. Examples of reliable sources include official government agency websites, certified databases, academic papers, and expert reports. For example, the collection unit may collect the latest demographic data from official government agency websites. The collection unit may also collect information about public services from certified databases. The collection unit may also collect information about educational institutions and medical facilities from academic papers and expert reports. For example, the collection unit may collect evaluation data on educational institutions in a specific city or town based on academic papers. This improves the reliability of the information provided by collecting data from reliable sources. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may efficiently collect information using an AI model for collecting data from online sources.

[0061] The analysis unit can customize information based on a user profile. The user profile includes information such as age, occupation, interests, family structure, and place of residence. The analysis unit customizes information based on, for example, the user's age. For example, to a young user, information emphasizing leisure facilities and event information is provided. The analysis unit can also customize information based on the user's occupation. For example, to a businessperson, information emphasizing transportation access and business facility information is provided. Furthermore, the analysis unit can customize information based on the user's interests. For example, to a user who enjoys the outdoors, information emphasizing natural environments and leisure facilities is provided. By customizing information based on the user profile, information tailored to the user's needs can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can efficiently organize information using an AI model for customizing information based on the user profile.

[0062] The providing unit can provide information required by a user in a predetermined format. Examples of the predetermined format include a text format, a graph format, and an interactive format. The providing unit can provide information in, for example, a text format. For example, the providing unit can display information about a city or town specified by a user in text format. The providing unit can also provide information in a graph format. For example, the providing unit can display demographic data and the usage status of public services in a graph format. The providing unit can also provide information in an interactive format. For example, the providing unit can provide an interactive interface that allows a user to check city or town information on a map. This facilitates the use of information by providing the information required by the user in an appropriate format. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can efficiently provide information using an AI model for providing information in a predetermined format.

[0063] The support unit can provide information necessary for procedures when moving and for life in the new location. Procedures necessary for moving include, for example, procedures for changing address, registration for public services, and procedures for transferring to a new school. The support unit can provide, for example, information regarding procedures for changing address. For example, the support unit can provide information on specific methods for procedures for changing address and necessary documents. The support unit can also provide information regarding registration for public services. For example, the support unit can provide information on how to register for public services such as water, electricity, and gas. The support unit can also provide information regarding procedures for transferring to a new school. For example, the support unit can provide information on specific methods for procedures for transferring a child to a new school and necessary documents. Information necessary for life in the new location includes, for example, nearby medical institutions, schools, public transportation, how to dispose of garbage, and local rules. The support unit can provide, for example, information regarding nearby medical institutions. For example, the support unit can provide information on hospitals and clinics near the new location. The support unit can also provide information on nearby schools. For example, the support unit can provide information on elementary and junior high schools near the new location. This allows the user to move smoothly by providing necessary procedures for moving and information necessary for life at the new location. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can efficiently provide information using an AI model for providing information about moving.

[0064] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expressions. The collection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice and calculates an emotion score. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on heart rate fluctuations. This allows the timing of information collection to be adjusted according to the user's emotions, thereby providing information at the optimal timing for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0065] The collection unit can analyze the user's past search history and select a predetermined information collection method. For example, the collection unit prioritizes collecting information on cities and towns that the user has previously searched for. For example, the collection unit collects data from reliable information sources based on the information on cities and towns that the user has previously searched for. The collection unit can also collect reliable information based on information sources the user has previously used. For example, the collection unit analyzes information sources the user has previously used and selects an optimal information collection method. Furthermore, the collection unit can analyze the user's past search patterns and determine the optimal timing for collecting information. For example, the collection unit adjusts the timing of information collection based on the user's past search patterns. In this way, the optimal information collection method can be selected by analyzing the user's past search history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can efficiently collect information using an AI model for selecting an information collection method based on the user's past search history.

[0066] When collecting information, the collection unit can filter information based on the user's current living situation and areas of interest. For example, the collection unit prioritizes collecting information on educational institutions and medical facilities for users of child-rearing age. For example, the collection unit filters information on educational institutions and medical facilities based on profile information of users of child-rearing age. The collection unit can also prioritize collecting information on transportation access and shopping facilities for single people. For example, the collection unit filters information on transportation access and shopping facilities based on profile information of single people. The collection unit can also prioritize collecting information on medical facilities and public safety for elderly people. For example, the collection unit filters information on medical facilities and public safety based on profile information of elderly people. This allows for filtering information based on the user's current living situation and areas of interest, thereby providing useful information to the user. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can efficiently collect information using an AI model for filtering information based on user profile information.

[0067] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit prioritizes collecting information about public safety. For example, the collection unit prioritizes collecting information about public safety based on the user's emotion score. Furthermore, if the user is excited, the collection unit can also prioritize collecting information about leisure facilities and events. For example, the collection unit prioritizes collecting information about leisure facilities and events based on the user's emotion score. Furthermore, if the user is tired, the collection unit can also prioritize collecting information about conveniences in daily life. For example, the collection unit prioritizes collecting information about conveniences in daily life based on the user's emotion score. This allows the priority of information to be collected based on the user's emotions, thereby providing information that is important to the user preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0068] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting information about municipalities close to the user's current location. For example, the collection unit collects information about municipalities close to the user's current location based on the user's geographical location information. Furthermore, if the user is interested in a particular area, the collection unit can prioritize collecting information about that area. For example, the collection unit collects information about a specific area based on the user's geographical location information. Furthermore, if the user is moving, the collection unit can collect optimal information based on the user's current location. For example, the collection unit collects optimal information for the user while moving based on the user's geographical location information. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can efficiently collect information using an AI model for collecting information based on the user's geographical location information.

[0069] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit prioritizes collecting information about cities, towns, and villages shared by the user on social media. For example, the collection unit collects information about the shared cities, towns, and villages based on the user's social media activities. The collection unit can also collect information about related cities, towns, and villages based on the content posted by accounts followed by the user. For example, the collection unit analyzes the content posted by accounts followed by the user and collects information about related cities, towns, and villages. The collection unit can also collect information about related cities, towns, and villages based on information about groups and communities in which the user participates. For example, the collection unit analyzes information about groups and communities in which the user participates and collects information about related cities, towns, and villages. In this way, related information can be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can efficiently collect information using an AI model for collecting information based on the user's social media activities.

[0070] The analysis unit can estimate the user's emotions and adjust the information analysis method based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can analyze information about public safety in detail. For example, the analysis unit can analyze information about public safety in detail based on the user's emotion score. Furthermore, if the user is excited, the analysis unit can analyze information about leisure facilities and events in detail. For example, the analysis unit can analyze information about leisure facilities and events in detail based on the user's emotion score. Furthermore, if the user is tired, the analysis unit can analyze information about convenience of daily life in detail. For example, the analysis unit can analyze information about convenience of daily life in detail based on the user's emotion score. This allows the information analysis method to be adjusted according to the user's emotions, thereby providing optimal analysis results for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0071] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The analysis unit, for example, analyzes information of high importance (such as public safety and medical facilities) in detail. For example, the analysis unit analyzes information related to public safety in detail based on the importance of the information. The analysis unit can also analyze information of low importance (such as leisure facility and event information) in a simplified manner. For example, the analysis unit analyzes leisure facility and event information in a simplified manner based on the importance of the information. Furthermore, the analysis unit can prioritize analysis of information of high importance according to the user's needs. For example, the analysis unit prioritizes analysis of information of high importance based on the user's profile information. In this way, important information can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can efficiently analyze information using an AI model for adjusting the level of detail of the analysis based on the importance of the information.

[0072] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies an analysis algorithm based on crime data to public safety information. For example, the analysis unit analyzes public safety information based on crime data. The analysis unit can also apply an analysis algorithm based on school evaluation data to educational institution information. For example, the analysis unit analyzes educational institution information based on school evaluation data. The analysis unit can also apply an analysis algorithm based on transportation data to transportation access information. For example, the analysis unit analyzes transportation access information based on transportation data. In this way, by applying different analysis algorithms depending on the category of information, optimal analysis can be performed for each category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can efficiently analyze information using an AI model for applying different analysis algorithms depending on the category of information.

[0073] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, the analysis unit provides a simple, highly visible display method based on the user's emotion score. Furthermore, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, the analysis unit provides a display method that includes detailed information based on the user's emotion score. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. For example, the analysis unit provides a display method that focuses on the main points based on the user's emotion score. This allows the display method of the analysis results to be adjusted according to the user's emotions, thereby enabling a display that is easy for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0074] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The analysis unit, for example, prioritizes analysis of the most recent information. For example, the analysis unit prioritizes analysis of the most recent information based on the time of submission of the information. The analysis unit can also briefly analyze older information. For example, the analysis unit briefly analyzes older information based on the time of submission of the information. Furthermore, the analysis unit can also determine the priority of analysis of information based on the time of submission in accordance with the needs of the user. For example, the analysis unit determines the priority of analysis of information based on the time of submission based on the user's profile information. In this way, by determining the priority of analysis based on the time of submission of the information, the most recent information can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can efficiently analyze information using an AI model for determining the priority of analysis based on the time of submission of the information.

[0075] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit, for example, prioritizes analysis of information most relevant to the user's needs. For example, the analysis unit prioritizes analysis of information most relevant to the user's needs based on the relevance of the information. The analysis unit can also postpone less relevant information. For example, the analysis unit postpones less relevant information based on the relevance of the information. Furthermore, the analysis unit can also prioritize analysis of more relevant information based on a user profile. For example, the analysis unit prioritizes analysis of more relevant information based on the user's profile information. In this way, by adjusting the order of analysis based on the relevance of the information, more relevant information can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can efficiently analyze information using an AI model for adjusting the order of analysis based on the relevance of the information.

[0076] The providing unit can estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions. For example, if the user is nervous, the providing unit provides a simple, highly visible display method. For example, the providing unit provides a simple, highly visible display method based on the user's emotion score. Furthermore, if the user is relaxed, the providing unit can also provide a display method including detailed information. For example, the providing unit provides a display method including detailed information based on the user's emotion score. Furthermore, if the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. For example, the providing unit provides a display method that focuses on the main points based on the user's emotion score. This allows the information to be presented in a way that is easy for the user to understand by adjusting the way information is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0077] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. The providing unit, for example, provides detailed information of high importance (such as public safety and medical facilities). For example, the providing unit provides detailed information about public safety based on the importance of the information. The providing unit can also provide simplified information of low importance (such as leisure facility and event information). For example, the providing unit provides simplified information about leisure facilities and events based on the importance of the information. Furthermore, the providing unit can prioritize providing information of high importance according to the user's needs. For example, the providing unit prioritizes providing information of high importance based on the user's profile information. In this way, important information can be provided in detail by adjusting the level of detail of the information provided based on the importance of the information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can efficiently provide information using an AI model for adjusting the level of detail of the information provided based on the importance of the information.

[0078] The providing unit can apply different provision algorithms depending on the category of information when providing the information. For example, the providing unit applies a provision algorithm based on crime data to public safety information. For example, the providing unit provides public safety information based on crime data. The providing unit can also apply a provision algorithm based on school evaluation data to educational institution information. For example, the providing unit provides educational institution information based on school evaluation data. The providing unit can also apply a provision algorithm based on traffic data to transportation access information. For example, the providing unit provides transportation access information based on traffic data. In this way, by applying different provision algorithms depending on the category of information, optimal information can be provided for each category. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can efficiently provide information using an AI model for applying different provision algorithms depending on the category of information.

[0079] The providing unit can estimate the user's emotions and adjust the length of the information to be provided based on the estimated user emotions. For example, if the user is in a hurry, the providing unit can provide short, concise information. For example, the providing unit can provide short, concise information based on the user's emotion score. Furthermore, if the user is relaxed, the providing unit can provide longer information including detailed explanations. For example, the providing unit can provide longer information including detailed explanations based on the user's emotion score. Furthermore, if the user is excited, the providing unit can provide information with visually stimulating effects. For example, the providing unit can provide information with visually stimulating effects based on the user's emotion score. This allows the length of the information to be adjusted according to the user's emotions, thereby providing optimal information to the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0080] The providing unit can determine the priority of providing information based on the time of submission of the information at the time of providing the information. The providing unit, for example, prioritizes providing the latest information. For example, the providing unit prioritizes providing the latest information based on the time of submission of the information. The providing unit can also provide old information in a simplified manner. For example, the providing unit can provide old information in a simplified manner based on the time of submission of the information. Furthermore, the providing unit can determine the priority of providing information based on the time of submission in accordance with the needs of the user. For example, the providing unit determines the priority of providing information based on the time of submission based on the user's profile information. In this way, by determining the priority of providing information based on the time of submission of the information, the latest information can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can efficiently provide information using an AI model for determining the priority of providing information based on the time of submission of the information.

[0081] The providing unit can adjust the order of providing information based on the relevance of the information when providing the information. For example, the providing unit prioritizes providing information that is most relevant to the user's needs. For example, the providing unit prioritizes providing information that is most relevant to the user's needs based on the relevance of the information. The providing unit can also postpone less relevant information. For example, the providing unit postpones less relevant information based on the relevance of the information. Furthermore, the providing unit can also prioritize providing more relevant information based on a user profile. For example, the providing unit prioritizes providing more relevant information based on the user's profile information. In this way, by adjusting the order of providing information based on the relevance of the information, more relevant information can be prioritized. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can efficiently provide information using an AI model for adjusting the order of providing information based on the relevance of the information.

[0082] The support unit can estimate the user's emotions and adjust the support content based on the estimated user emotions. For example, if the user is feeling anxious, the support unit provides a detailed procedure guide. For example, the support unit provides a detailed procedure guide based on the user's emotion score. The support unit can also provide a concise procedure guide if the user is relaxed. For example, the support unit provides a concise procedure guide based on the user's emotion score. Furthermore, if the user is in a hurry, the support unit can provide a method for quickly completing the procedure. For example, the support unit provides a method for quickly completing the procedure based on the user's emotion score. This allows the support content to be adjusted according to the user's emotions, thereby providing optimal support for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or without AI. For example, the support department can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0083] When providing support, the support unit can select a predetermined support method by referring to the user's past moving history. The support unit, for example, proposes an optimal support method based on the procedures used when the user moved in the past. For example, the support unit proposes an optimal support method based on the user's past moving history. The support unit can also predict necessary procedures based on the user's past moving history and provide support. For example, the support unit predicts necessary procedures based on the user's past moving history and provides support. The support unit can also analyze the user's past moving history and propose the most efficient support method. For example, the support unit proposes the most efficient support method based on the user's past moving history. In this way, the optimal support method can be selected by referring to the user's past moving history. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can provide efficient support using an AI model for selecting a support method based on the user's past moving history.

[0084] The support unit can customize the support content based on the user's current living situation when providing support. For example, the support unit provides support that emphasizes information on educational institutions and medical facilities to a user of child-rearing age. For example, the support unit provides support that emphasizes information on educational institutions and medical facilities based on the profile information of the user of child-rearing age. The support unit can also provide support that emphasizes information on transportation access and shopping facilities to a single person. For example, the support unit provides support that emphasizes information on transportation access and shopping facilities based on the profile information of the single person. The support unit can also provide support that emphasizes information on medical facilities and public safety to the elderly. For example, the support unit provides support that emphasizes information on medical facilities and public safety based on the profile information of the elderly person. This allows the support content to be customized based on the user's current living situation, thereby providing beneficial support to the user. Some or all of the above-described processing by the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can efficiently provide support using an AI model for customizing the support content based on the user's profile information.

[0085] The support unit can estimate the user's emotions and determine the priority of support based on the estimated user emotions. For example, if the user is feeling anxious, the support unit can prioritize providing information about public safety. For example, the support unit can prioritize providing information about public safety based on the user's emotion score. Furthermore, if the user is excited, the support unit can prioritize providing information about leisure facilities and events. For example, the support unit can prioritize providing information about leisure facilities and events based on the user's emotion score. Furthermore, if the user is tired, the support unit can prioritize providing information about conveniences in daily life. For example, the support unit can prioritize providing information about conveniences in daily life based on the user's emotion score. In this way, by determining the priority of support according to the user's emotions, it is possible to prioritize providing support that is important to the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the support unit can be performed, for example, using AI or without AI. For example, the support department can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0086] When providing support, the support unit can select a predetermined support method by taking into account the user's geographical location information. For example, the support unit can prioritize providing information about cities, towns, and villages close to the user's current location. For example, the support unit can provide information about cities, towns, and villages close to the user's current location based on the user's geographical location information. Furthermore, if the user is interested in a particular area, the support unit can prioritize providing information about that area. For example, the support unit can provide information about a particular area based on the user's geographical location information. Furthermore, if the user is traveling, the support unit can provide an optimal support method based on the user's current location. For example, the support unit can provide an optimal support method for a traveling user based on the user's geographical location information. This allows the optimal support method to be selected by taking the user's geographical location information into account. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can efficiently provide support by using an AI model for selecting a support method based on the user's geographical location information.

[0087] When providing support, the support unit can analyze the user's social media activity and suggest support content. The support unit, for example, prioritizes providing information about cities and towns shared by the user on social media. For example, the support unit provides information about the shared cities and towns based on the user's social media activity. The support unit can also provide information about related cities and towns based on the content posted by accounts the user follows. For example, the support unit analyzes the content posted by accounts the user follows and provides information about related cities and towns. The support unit can also provide information about related cities and towns based on information about groups and communities the user participates in. For example, the support unit analyzes information about groups and communities the user participates in and provides information about related cities and towns. This makes it possible to suggest related support content by analyzing the user's social media activity. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can efficiently provide support using an AI model for suggesting support content based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and support unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information on the Internet via the control unit 46A of the smart device 14, and analyzes the user's past search history via the specific processing unit 290 of the data processing device 12. For example, the analysis unit organizes the information collected by the specific processing unit 290 of the data processing device 12 based on a user profile. For example, the provision unit provides the information organized by the control unit 46A of the smart device 14 in text format or graph format. For example, the support unit provides moving procedures and lifestyle information via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and support unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information on the Internet via the control unit 46A of the smart glasses 214, and analyzes the user's past search history via the specific processing unit 290 of the data processing device 12. For example, the analysis unit organizes the information collected by the specific processing unit 290 of the data processing device 12 based on a user profile. For example, the provision unit provides the information organized by the control unit 46A of the smart glasses 214 in text format or graph format. For example, the support unit provides moving-related procedures and lifestyle information via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and support unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects information on the Internet via the control unit 46A of the headset type terminal 314, and analyzes the user's past search history via the specific processing unit 290 of the data processing device 12. For example, the analysis unit organizes the information collected by the specific processing unit 290 of the data processing device 12 based on a user profile. For example, the provision unit provides the information organized by the control unit 46A of the headset type terminal 314 in text format or graph format. For example, the support unit provides moving-related procedures and lifestyle information via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and support unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information on the Internet using the control unit 46A of the robot 414, and analyzes the user's past search history using the specific processing unit 290 of the data processing device 12. For example, the analysis unit organizes the information collected by the specific processing unit 290 of the data processing device 12 based on a user profile. For example, the provision unit provides the information organized by the control unit 46A of the robot 414 in text format or graph format. For example, the support unit provides moving-related procedures and lifestyle information using the specific processing unit 290 of the data processing device 12.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The collection unit can also analyze the user's past moving history and prioritize collecting information about candidate locations for moving. For example, the collection unit prioritizes collecting information about cities, towns, and villages with similar conditions based on information collected when the user moved in the past. The collection unit can also re-collect information about cities, towns, and villages that the user previously considered as moving destinations but ultimately did not select. Furthermore, the collection unit can collect new information about candidate locations for moving based on the user's past moving history. This makes it possible to provide more appropriate information by utilizing the user's past moving history.

[0090] The analysis unit can also analyze the user's social media activity and customize information based on the user's interests. For example, the analysis unit prioritizes organizing information about related cities and towns based on the content posted by accounts the user follows on social media. The analysis unit can also estimate the user's interests and customize information based on information about cities and towns shared by the user on social media. Furthermore, the analysis unit can organize information about related cities and towns based on information about groups and communities in which the user participates. This makes it possible to utilize the user's social media activity to provide more appropriate information.

[0091] The providing unit can also adjust the timing of providing information based on the user's geographical location information. For example, the providing unit can provide information about cities, towns, and villages that are close to the user's current location with priority. In addition, when the user moves to a specific area, the providing unit can provide information about that area in real time. Furthermore, when the user is moving, the providing unit can provide optimal information based on the user's current location. This makes it possible to provide information at a more appropriate time by utilizing the user's geographical location information.

[0092] The support unit can also provide advice about moving based on the user's past moving history. For example, the support unit can analyze problems the user experienced when moving in the past and provide advice to prevent similar problems from occurring. The support unit can also provide information about services and facilities that the user used in the past at their new home. Furthermore, the support unit can also provide information necessary for life at their new home based on the user's past moving history. This makes it possible to provide more appropriate support by utilizing the user's past moving history.

[0093] The collection unit can also estimate the user's emotions and determine the priority of information collection based on the estimated user's emotions. For example, if the user feels anxious, the collection unit can prioritize collecting information about public safety. Also, if the user feels excited, the collection unit can prioritize collecting information about leisure facilities and events. Furthermore, if the user feels tired, the collection unit can prioritize collecting information about convenience in daily life. In this way, by determining the priority of information collection according to the user's emotions, it is possible to provide information that is important to the user with priority.

[0094] The analysis unit can also estimate the user's emotions and adjust the information analysis method based on the estimated user emotions. For example, if the user feels anxious, the analysis unit can analyze information about public safety in detail. If the user feels excited, the analysis unit can also analyze information about leisure facilities and events in detail. Furthermore, if the user feels tired, the analysis unit can analyze information about convenience in daily life in detail. In this way, by adjusting the information analysis method according to the user's emotions, it is possible to provide the user with the optimal analysis results.

[0095] The providing unit can also estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. In this way, by adjusting the way information is presented based on the user's emotions, it is possible to provide information that is easy for the user to understand.

[0096] The support unit can also estimate the user's emotions and adjust the support content based on the estimated user emotions. For example, if the user feels anxious, the support unit can provide detailed procedure guides. If the user feels relaxed, the support unit can also provide concise procedure guides. Furthermore, if the user is in a hurry, the support unit can provide a way to quickly complete the procedure. In this way, the support content can be adjusted according to the user's emotions, thereby providing optimal support for the user.

[0097] The providing unit can also estimate the user's emotions and adjust the length of the information to be provided based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can provide short, to-the-point information. If the user is relaxed, the providing unit can provide longer information including detailed explanations. Furthermore, if the user is excited, the providing unit can provide information with visually stimulating effects. In this way, by adjusting the length of the information to be provided according to the user's emotions, it is possible to provide optimal information to the user.

[0098] The support unit can also provide information about potential locations for moving based on the user's geographical location information. For example, the support unit can provide information about cities, towns, and villages that are close to the user's current location with priority. Also, if the user is interested in a particular area, the support unit can provide information about that area. Furthermore, if the user is traveling, the support unit can provide the optimal support method based on the user's current location. This makes it possible to provide more appropriate support by utilizing the user's geographical location information.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The collection unit collects information about the city or town specified by the user. The collection unit collects data from reliable sources on the Internet. For example, it collects information from official government websites and certified databases. The collection unit also analyzes the user's past search history and selects the optimal information collection method. For example, it may prioritize collecting information about cities or towns that the user has previously searched for. The collection unit also filters information based on the user's current living situation and areas of interest. For example, for users of child-rearing age, it may prioritize collecting information about educational institutions and medical facilities. Step 2: The analysis unit analyzes the information collected by the collection unit and organizes it to suit the user's needs. The analysis unit customizes the information based on the user profile. For example, it organizes the information based on profile information such as the user's age, occupation, and interests. The analysis unit also adjusts the level of detail of the analysis based on the importance of the information. For example, it analyzes information of high importance (such as public safety and medical facilities) in detail. Step 3: The providing unit provides the information organized by the analyzing unit. The providing unit provides the information the user needs in a predetermined format. For example, the information is provided in text format, graph format, interactive format, etc. The providing unit also estimates the user's emotions and adjusts the way the information is presented based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method is provided. Step 4: The support department will assist you with important points to note and procedures for moving. The support department will provide you with the necessary procedures for moving and information you will need for life in your new home. For example, they will provide information on changing your address, registering for public services, how to dispose of garbage, and local rules.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] 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.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit for collecting information on designated municipalities; an analysis unit that analyzes the information collected by the collection unit and organizes it according to user needs; a providing unit that provides the information organized by the analysis unit; A support department that provides support regarding moving precautions and procedures. A system characterized by:

2. The collecting unit Collect data from reliable sources 2. The system of claim 1.

3. The analysis unit Customize information based on user profile 2. The system of claim 1.

4. The providing unit Providing users with the information they need in a specific format 2. The system of claim 1.

5. The support portion is Providing necessary procedures for moving and information for life at the new location 2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit Analyze users' past search history and select a predetermined information collection method 2. The system of claim 1.

8. The collecting unit When collecting information, filter it based on the user's current life situation and areas of interest.

2. The system of claim 1.

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