System

Abstract

A system according to an embodiment analyzes multimodal data of a user and proposes an optimal lifestyle.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects multimodal data of a user. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a lifestyle based on the analysis result obtained by the analysis unit. The providing unit provides specific information based on the lifestyle proposed by the proposing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to propose optimal lifestyles for individual users, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze multimodal data of a user and propose an optimal lifestyle. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects multimodal data of a user. The analysis unit analyzes the data collected by the collection unit. The suggestion unit suggests a lifestyle based on the analysis results obtained by the analysis unit. The provision unit provides specific information based on the lifestyle suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze multimodal data of a user and propose an optimal lifestyle. [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 lifestyle suggestion system according to an embodiment of the present invention collects multimodal data from users, analyzes it using a generation AI, proposes optimal lifestyles, and provides specific information. This lifestyle suggestion system collects multimodal data, such as user videos, drawings, family structure, income, and commute destinations, and the generation AI analyzes this data to propose optimal lifestyles for users. For example, if a user's income and family structure suggest that living in a rural area is suitable, information about housing and jobs in that area is provided. Similarly, if living in an area close to the user's commute destination is suitable, information about housing and transportation options in that area is provided. This system allows users to find their optimal lifestyle and alleviates population concentration in urban areas. Furthermore, it is expected to revitalize rural areas and promote efficient energy use. This allows the lifestyle suggestion system to decentralize users' lifestyles and revitalize society as a whole.

[0029] A lifestyle suggestion system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects multimodal data of a user. The multimodal data of a user includes, but is not limited to, videos, drawings, family structure, income, and commuter destination. For example, the collection unit can capture the user's video with a camera, scan drawings, and collect family structure information in the form of a questionnaire. The collection unit can also obtain income data from pay slips and tax returns and collect commuter destination information from user input. The analysis unit analyzes the data collected by the collection unit. The analysis unit uses a generation AI to analyze the collected data and suggest an optimal lifestyle for the user. For example, the generation AI can determine whether living in a rural area is suitable based on the user's income and family structure. The generation AI can also suggest areas that can shorten commute times based on the user's commuter destination. The suggestion unit suggests a lifestyle based on the analysis results obtained by the analysis unit. For example, if living in a rural area is suitable based on the user's income and family structure, the suggestion unit provides information on housing and jobs in that area. Furthermore, if living in an area close to the commute destination is suitable, the suggestion unit can also provide information on housing and transportation in that area. The provision unit provides specific information based on the lifestyle suggested by the suggestion unit. For example, the provision unit provides the user with information on housing, work, and transportation in the suggested area. In this way, the lifestyle suggestion system according to the embodiment can diversify users' lifestyles and revitalize society as a whole.

[0030] The collection unit can collect multimodal data such as the user's video, drawings, family structure, income, and commute destination. For example, the collection unit can capture the user's video with a camera, scan the drawings, and collect family structure information in the form of a questionnaire. The collection unit can also obtain income data from pay slips and tax returns and collect commute destination information from user input. This allows for the collection of diverse user data, enabling more accurate lifestyle suggestions. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's video data into a generation AI and cause the generation AI to execute a process to extract necessary information from the video data.

[0031] The analysis unit can analyze the collected data and use the generation AI to suggest a lifestyle to the user. The analysis unit uses the generation AI to analyze the collected data and suggest an optimal lifestyle to the user. For example, the generation AI can determine whether living in a rural area is suitable based on the user's income and family structure. The generation AI can also suggest areas that can shorten the user's commute time based on the user's commute destination. This improves the accuracy of suggesting an optimal lifestyle to the user by using the generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the analysis unit can input the collected data into the generation AI and have the generation AI suggest an optimal lifestyle.

[0032] If living in a rural area is recommended based on the user's income and family structure, the suggestion unit can provide information on housing and jobs in that area. If living in a rural area is suitable based on the user's income and family structure, the suggestion unit provides information on housing and jobs in that area. For example, if the user has a medium income and a family of four, the suggestion unit suggests living in a rural area and provides information on housing, jobs, and educational institutions in that area. The suggestion unit can also determine whether living in a rural area is suitable based on the user's income and family structure and provide appropriate information. This promotes living in a rural area by providing information on appropriate areas based on the user's income and family structure. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data on the user's income and family structure into the generation AI and cause the generation AI to execute information on appropriate areas.

[0033] If living in an area close to the user's commute destination is recommended, the suggestion unit can provide information on housing and transportation options in that area. If living in an area close to the user's commute destination is suitable, the suggestion unit provides information on housing and transportation options in that area. For example, the suggestion unit can suggest an area where commute time can be shortened based on the user's commute destination, and provide information on housing and transportation options in that area. The suggestion unit can also suggest an area where commute time can be shortened based on the user's commute destination. In this way, by providing information on areas close to the user's commute destination, the user's commute time can be shortened and their quality of life improved. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data on the user's commute destination into the generation AI and cause the generation AI to execute information on appropriate areas.

[0034] The providing unit can provide specific information about a residence and a job based on the proposed lifestyle. The providing unit provides specific information about a residence and a job based on the proposed lifestyle. For example, the providing unit provides the user with information about a residence, a job, and transportation in the proposed area. The providing unit can also provide specific information about a residence and a job based on the proposed lifestyle. This makes it easier for the user to realize the proposed lifestyle by providing specific information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information about the proposed lifestyle to a generation AI and cause the generation AI to provide specific information.

[0035] The collection unit can analyze the user's past data collection history and select the optimal collection method. For example, if the user has frequently used voice input in the past, the collection unit can prioritize voice input. Furthermore, if the user has provided a lot of image data in the past, the collection unit can strengthen the collection of image data. Furthermore, if the user has preferred text input in the past, the collection unit can prioritize text input. In this way, by analyzing the past data collection history, the optimal collection method for the user can be selected. Some or all of the above-mentioned 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 input the user's past data collection history into the generation AI and cause the generation AI to select the optimal collection method.

[0036] The collection unit can perform filtering based on the user's current living situation and areas of interest when collecting data. For example, if the user is currently raising a child, the collection unit can prioritize collecting data related to childcare. Also, if the user is currently looking for a new job, the collection unit can also prioritize collecting data related to changing jobs. Also, if the user is currently interested in health, the collection unit can also prioritize collecting data related to health. In this way, by filtering data based on the user's living situation and areas of interest, more relevant data can be collected. One of the above-mentioned processes in the collection unit is Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input data on the user's living situation and areas of interest to the generation AI and have the generation AI perform filtering.

[0037] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user selects voice input, the collection unit can prioritize collecting voice data. Furthermore, if the user selects text input, the collection unit can also prioritize collecting text data. Furthermore, if the user selects image input, the collection unit can also prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned 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 input data on the user's input method into the generation AI and have the generation AI select the optimal collection means.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting weather information for the area where the user is currently located. The collection unit can also prioritize collecting traffic information for the area where the user is currently located. The collection unit can also prioritize collecting event information for the area where the user is currently located. In this way, highly relevant data can be collected preferentially 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 AI, for example, or may be performed without using AI. For example, the collection unit can input data of the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0039] During data collection, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects data related to locations where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing the user's social media activities. 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 input data on the user's social media activities into the generation AI and cause the generation AI to collect related data.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit can adjust the collection method based on, for example, feedback provided by the user in the past. The collection unit can also preferentially adopt data collection methods that the user has previously preferred. The collection unit can also eliminate data collection methods that the user has previously avoided. This allows the collection method to be customized by reflecting the user's past feedback, enabling more appropriate data collection. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data of the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on data of low importance. The analysis unit can also perform a detailed analysis on data of high interest to the user. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input information on the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply an economic analysis algorithm to income data. The analysis unit can also apply a sociological analysis algorithm to family structure data. The analysis unit can also apply a geographic analysis algorithm to commute destination data. This enables more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI, for example. For example, the analysis unit can input information on the data category into the generation AI and cause the generation AI to apply different analysis algorithms.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the current analysis based on the user's past analysis results. The analysis unit can also improve the analysis algorithm by referring to the user's past feedback. The analysis unit can also improve the accuracy of the analysis based on the user's past behavioral patterns. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data of the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0044] The analysis unit can determine the priority of the analysis based on the time of data collection during the analysis. For example, the analysis unit prioritizes the analysis of the most recent data. In addition, the analysis unit can also refer to past data and The analysis unit can also prioritize the most recent data. The analysis unit can also prioritize analysis of data from a period specified by the user. This allows the most recent data to be analyzed with priority by determining the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information about the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also prioritize analysis of data that is of great interest to the user. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI, for example. For example, the analysis unit can input information on the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can also avoid technical terminology. Furthermore, the analysis unit can adjust the use of technical terminology according to the user's level of understanding. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, it is possible to provide an analysis result that is easy to understand. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input information about the user's level of expertise into the generation AI and have the generation AI adjust the use of technical terminology.

[0047] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the lifestyle when making a suggestion. For example, the suggestion unit makes detailed suggestions for important lifestyles. The suggestion unit can also make simple suggestions for lifestyles with low importance. The suggestion unit can also make detailed suggestions for lifestyles in which the user is highly interested. This enables efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the lifestyle. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input information on the importance of lifestyles to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0048] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the lifestyle category. For example, the suggestion unit can apply an economic algorithm to suggestions related to income. The suggestion unit can also apply a sociological algorithm to suggestions related to family structure. The suggestion unit can also apply a geographic algorithm to suggestions related to commute destinations. In this way, applying different suggestion algorithms depending on the lifestyle category enables more accurate suggestions. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, the generation AI, for example. For example, the suggestion unit can input information about lifestyle categories into the generation AI and cause the generation AI to apply different suggestion algorithms.

[0049] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, adjusts the current suggestion based on the user's past suggestion results. The suggestion unit can also improve the suggestion algorithm by referring to the user's past feedback. The suggestion unit can also improve the accuracy of the suggestion based on the user's past behavioral patterns. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input data of the user's past suggestion results into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0050] When making a suggestion, the suggestion unit can determine the priority of the suggestion based on the time when the lifestyle data was collected. The suggestion unit, for example, prioritizes the most recent lifestyle data. The suggestion unit can also refer to past lifestyle data while placing emphasis on the most recent data. The suggestion unit can also prioritize the suggestion of lifestyle data from a period specified by the user. In this way, by determining the priority of the suggestion based on the time when the lifestyle data was collected, the most recent data can be prioritized. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input information on the time when the lifestyle data was collected into the generation AI and cause the generation AI to determine the priority of the suggestions.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of the lifestyles when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant lifestyles. The suggestion unit can also postpone suggesting less relevant lifestyles. The suggestion unit can also prioritize suggesting lifestyles that the user is most interested in. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of lifestyles. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input information on the relevance of lifestyles to the generation AI and cause the generation AI to adjust the order of suggestions.

[0052] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit uses a lot of technical terminology. Also, if the user does not have technical expertise, the suggestion unit can avoid technical terminology. Also, the suggestion unit can adjust the use of technical terminology according to the user's level of understanding. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, it is possible to provide a proposal that is easy to understand. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input information about the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0053] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit provides detailed information for important information. The providing unit can also provide simplified information for less important information. The providing unit can also provide detailed information for information that is of great interest to the user. This enables efficient information provision 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 AI, for example, or may be performed without using AI. For example, the providing unit can input information on the importance of the information to the generating AI and cause the generating AI to adjust the level of detail of the information provided.

[0054] The providing unit can apply different providing algorithms depending on the category of information when providing the information. For example, the providing unit can apply a real estate algorithm to housing information. The providing unit can also apply a recruitment algorithm to job information. The providing unit can also apply a transportation algorithm to transportation information. By applying different providing algorithms depending on the category of information, more accurate information can be provided. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input information on the category of information to the generating AI and cause the generating AI to apply different providing algorithms.

[0055] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the information. The providing unit can, for example, adjust the current provision based on the user's past provision results. The providing unit can also improve the provision algorithm by referring to the user's past feedback. The providing unit can also improve the accuracy of the provision based on the user's past behavioral patterns. In this way, the accuracy of the provision can be improved by referring to the user's past provision results. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input data of the user's past provision results into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0056] The providing unit can determine the priority of provision based on the time when the information was collected at the time of provision. The providing unit, for example, can provide the latest information preferentially. The providing unit can also emphasize the latest information while referring to past information. The providing unit can also provide information from a period specified by the user preferentially. In this way, by determining the priority of provision based on the time when the information was collected, the latest information can be provided preferentially. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information on the time when the information was collected to the generation AI and cause the generation AI to determine the priority of provision.

[0057] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. For example, the providing unit can provide highly relevant information preferentially. The providing unit can also postpone information with low relevance. The providing unit can also provide information that is of high interest to the user preferentially. This enables efficient information provision by adjusting the order of provision based on the relevance of the information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information on the relevance of the information to the generating AI and cause the generating AI to adjust the order of provision.

[0058] The providing unit can adjust the use of technical terminology provided according to the user's level of expertise when providing the information. For example, if the user has technical expertise, the providing unit uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can avoid technical terminology. Furthermore, the providing unit can adjust the use of technical terminology according to the user's level of understanding. In this way, by adjusting the use of technical terminology provided according to the user's level of expertise, it is possible to provide information that is easy to understand. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information on the user's level of expertise to the generating AI and cause the generating AI to adjust the use of technical terminology.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The analysis unit can collect the user's health data and suggest a lifestyle based on the user's health condition. For example, it can analyze the user's exercise habits and dietary habits to suggest a healthy lifestyle. The analysis unit can also analyze the user's sleep patterns to suggest optimal sleeping environments and habits. Furthermore, the analysis unit can analyze the user's stress level and suggest lifestyles to reduce stress. This makes it possible to suggest more personalized lifestyles based on the user's health condition.

[0061] The suggestion unit can also suggest lifestyles based on the user's hobbies and interests. For example, if the user likes outdoor activities, the suggestion unit can suggest living in an area rich in nature. If the user is interested in cultural activities, the suggestion unit can suggest living in an area with plenty of cultural facilities. Furthermore, if the user likes sports, the suggestion unit can suggest living in an area with plenty of sports facilities. This makes it possible to suggest a more fulfilling lifestyle based on the user's hobbies and interests.

[0062] The providing unit can collect feedback on the user's lifestyle and improve the accuracy of the suggestions. For example, the providing unit can collect the user's thoughts and points for improvement after practicing the suggested lifestyle. The providing unit can also evaluate the user's satisfaction with the suggested lifestyle. Furthermore, the providing unit can improve the suggestion algorithm based on the user's feedback. This allows for more accurate lifestyle suggestions by reflecting the user's feedback.

[0063] The collection unit can collect the user's social network data and use it to make lifestyle suggestions. For example, it can refer to the lifestyles of the user's friends and family. The collection unit can also analyze the user's social media activity to understand their interests and concerns. Furthermore, the collection unit can collect information on communities and groups in which the user participates and reflect this in lifestyle suggestions. This makes it possible to make more relevant lifestyle suggestions by utilizing the user's social network.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects the user's multimodal data. The user's multimodal data includes video, drawings, family structure, income, and commuter destination. For example, the collection unit may capture the user's video with a camera, scan the drawings, and collect family structure information in the form of a questionnaire. In addition, income data may be obtained from pay slips or tax returns, and commuter destination information may be collected from user input. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses the generation AI to analyze the collected data and suggest the optimal lifestyle for the user. For example, the generation AI may determine whether living in a rural area is suitable based on the user's income and family structure. The generation AI may also suggest areas where commuting time can be shortened based on the user's commute destination. Step 3: The suggestion unit suggests lifestyles based on the analysis results obtained by the analysis unit. For example, if a rural area is suitable based on the user's income and family structure, the suggestion unit provides information on housing and jobs in that area. Also, if a region close to the user's commute is suitable, the suggestion unit can provide information on housing and transportation options in that area. Step 4: The providing unit provides specific information based on the lifestyle proposed by the proposing unit. For example, the providing unit provides the user with information on housing, work, and transportation in the proposed area.

[0066] (Example 2) A lifestyle suggestion system according to an embodiment of the present invention collects multimodal data from users, analyzes it using a generation AI, proposes optimal lifestyles, and provides specific information. This lifestyle suggestion system collects multimodal data, such as user videos, drawings, family structure, income, and commute destinations, and the generation AI analyzes this data to propose optimal lifestyles for users. For example, if a user's income and family structure suggest that living in a rural area is suitable, information about housing and jobs in that area is provided. Similarly, if living in an area close to the user's commute destination is suitable, information about housing and transportation options in that area is provided. This system allows users to find their optimal lifestyle and alleviates population concentration in urban areas. Furthermore, it is expected to revitalize rural areas and promote efficient energy use. This allows the lifestyle suggestion system to decentralize users' lifestyles and revitalize society as a whole.

[0067] A lifestyle suggestion system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects multimodal data of a user. The multimodal data of a user includes, but is not limited to, videos, drawings, family structure, income, and commuter destination. For example, the collection unit can capture the user's video with a camera, scan drawings, and collect family structure information in the form of a questionnaire. The collection unit can also obtain income data from pay slips and tax returns and collect commuter destination information from user input. The analysis unit analyzes the data collected by the collection unit. The analysis unit uses a generation AI to analyze the collected data and suggest an optimal lifestyle for the user. For example, the generation AI can determine whether living in a rural area is suitable based on the user's income and family structure. The generation AI can also suggest areas that can shorten commute times based on the user's commuter destination. The suggestion unit suggests a lifestyle based on the analysis results obtained by the analysis unit. For example, if living in a rural area is suitable based on the user's income and family structure, the suggestion unit provides information on housing and jobs in that area. Furthermore, if living in an area close to the commute destination is suitable, the suggestion unit can also provide information on housing and transportation in that area. The provision unit provides specific information based on the lifestyle suggested by the suggestion unit. For example, the provision unit provides the user with information on housing, work, and transportation in the suggested area. In this way, the lifestyle suggestion system according to the embodiment can diversify users' lifestyles and revitalize society as a whole.

[0068] The collection unit can collect multimodal data such as the user's video, drawings, family structure, income, and commute destination. For example, the collection unit can capture the user's video with a camera, scan the drawings, and collect family structure information in the form of a questionnaire. The collection unit can also obtain income data from pay slips and tax returns and collect commute destination information from user input. This allows for the collection of diverse user data, enabling more accurate lifestyle suggestions. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's video data into a generation AI and cause the generation AI to execute a process to extract necessary information from the video data.

[0069] The analysis unit can analyze the collected data and use the generation AI to suggest a lifestyle to the user. The analysis unit uses the generation AI to analyze the collected data and suggest an optimal lifestyle to the user. For example, the generation AI can determine whether living in a rural area is suitable based on the user's income and family structure. The generation AI can also suggest areas that can shorten the user's commute time based on the user's commute destination. This improves the accuracy of suggesting an optimal lifestyle to the user by using the generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the analysis unit can input the collected data into the generation AI and have the generation AI suggest an optimal lifestyle.

[0070] If living in a rural area is recommended based on the user's income and family structure, the suggestion unit can provide information on housing and jobs in that area. If living in a rural area is suitable based on the user's income and family structure, the suggestion unit provides information on housing and jobs in that area. For example, if the user has a medium income and a family of four, the suggestion unit suggests living in a rural area and provides information on housing, jobs, and educational institutions in that area. The suggestion unit can also determine whether living in a rural area is suitable based on the user's income and family structure and provide appropriate information. This promotes living in a rural area by providing information on appropriate areas based on the user's income and family structure. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data on the user's income and family structure into the generation AI and cause the generation AI to execute information on appropriate areas.

[0071] If living in an area close to the user's commute destination is recommended, the suggestion unit can provide information on housing and transportation options in that area. If living in an area close to the user's commute destination is suitable, the suggestion unit provides information on housing and transportation options in that area. For example, the suggestion unit can suggest an area where commute time can be shortened based on the user's commute destination, and provide information on housing and transportation options in that area. The suggestion unit can also suggest an area where commute time can be shortened based on the user's commute destination. In this way, by providing information on areas close to the user's commute destination, the user's commute time can be shortened and their quality of life improved. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data on the user's commute destination into the generation AI and cause the generation AI to execute information on appropriate areas.

[0072] The providing unit can provide specific information about a residence and a job based on the proposed lifestyle. The providing unit provides specific information about a residence and a job based on the proposed lifestyle. For example, the providing unit provides the user with information about a residence, a job, and transportation in the proposed area. The providing unit can also provide specific information about a residence and a job based on the proposed lifestyle. This makes it easier for the user to realize the proposed lifestyle by providing specific information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information about the proposed lifestyle to a generation AI and cause the generation AI to provide specific information.

[0073] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can delay data collection until the user is relaxed. Furthermore, if the user is relaxed, the collection unit can immediately start data collection. Furthermore, if the user is in a hurry, the collection unit can prioritize collection of the most important data. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0074] The collection unit can analyze the user's past data collection history and select the optimal collection method. For example, if the user has frequently used voice input in the past, the collection unit can prioritize voice input. Furthermore, if the user has provided a lot of image data in the past, the collection unit can strengthen the collection of image data. Furthermore, if the user has preferred text input in the past, the collection unit can prioritize text input. In this way, by analyzing the past data collection history, the optimal collection method for the user can be selected. Some or all of the above-mentioned 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 input the user's past data collection history into the generation AI and cause the generation AI to select the optimal collection method.

[0075] The collection unit can perform filtering based on the user's current living situation and areas of interest when collecting data. For example, if the user is currently raising a child, the collection unit can prioritize collecting data related to childcare. Also, if the user is currently looking for a new job, the collection unit can also prioritize collecting data related to changing jobs. Also, if the user is currently interested in health, the collection unit can also prioritize collecting data related to health. In this way, by filtering data based on the user's living situation and areas of interest, more relevant data can be collected. One of the above-mentioned processes in the collection unit is Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input data on the user's living situation and areas of interest to the generation AI and have the generation AI perform filtering.

[0076] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user selects voice input, the collection unit can prioritize collecting voice data. Furthermore, if the user selects text input, the collection unit can also prioritize collecting text data. Furthermore, if the user selects image input, the collection unit can also prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned 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 input data on the user's input method into the generation AI and have the generation AI select the optimal collection means.

[0077] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data related to stress reduction. Furthermore, if the user is relaxed, the collection unit can prioritize collecting data based on the user's interests. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting the most important data. Thus, by prioritizing data based on the user's emotions, more important data can be collected preferentially. 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 collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0078] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting weather information for the area where the user is currently located. The collection unit can also prioritize collecting traffic information for the area where the user is currently located. The collection unit can also prioritize collecting event information for the area where the user is currently located. In this way, highly relevant data can be collected preferentially 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 AI, for example, or may be performed without using AI. For example, the collection unit can input data of the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0079] During data collection, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects data related to locations where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing the user's social media activities. 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 input data on the user's social media activities into the generation AI and cause the generation AI to collect related data.

[0080] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit can adjust the collection method based on, for example, feedback provided by the user in the past. The collection unit can also preferentially adopt data collection methods that the user has previously preferred. The collection unit can also eliminate data collection methods that the user has previously avoided. This allows the collection method to be customized by reflecting the user's past feedback, enabling more appropriate data collection. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data of the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0081] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. By adjusting the way the analysis is presented based on the user's emotions, more appropriate analysis results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0082] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on data of low importance. The analysis unit can also perform a detailed analysis on data of high interest to the user. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input information on the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0083] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply an economic analysis algorithm to income data. The analysis unit can also apply a sociological analysis algorithm to family structure data. The analysis unit can also apply a geographic analysis algorithm to commute destination data. This enables more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI, for example. For example, the analysis unit can input information on the data category into the generation AI and cause the generation AI to apply different analysis algorithms.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the current analysis based on the user's past analysis results. The analysis unit can also improve the analysis algorithm by referring to the user's past feedback. The analysis unit can also improve the accuracy of the analysis based on the user's past behavioral patterns. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data of the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0086] The analysis unit can determine the priority of the analysis based on the time of data collection during the analysis. For example, the analysis unit prioritizes the analysis of the most recent data. In addition, the analysis unit can also refer to past data and The analysis unit can also prioritize the most recent data. The analysis unit can also prioritize analysis of data from a period specified by the user. This allows the most recent data to be analyzed with priority by determining the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information about the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0087] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also prioritize analysis of data that is of great interest to the user. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI, for example. For example, the analysis unit can input information on the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0088] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can also avoid technical terminology. Furthermore, the analysis unit can adjust the use of technical terminology according to the user's level of understanding. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, it is possible to provide an analysis result that is easy to understand. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input information about the user's level of expertise into the generation AI and have the generation AI adjust the use of technical terminology.

[0089] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that focus on the main points. By adjusting the way suggestions are expressed based on the user's emotions, more appropriate suggestions can be provided. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way suggestions are expressed.

[0090] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the lifestyle when making a suggestion. For example, the suggestion unit makes detailed suggestions for important lifestyles. The suggestion unit can also make simple suggestions for lifestyles with low importance. The suggestion unit can also make detailed suggestions for lifestyles in which the user is highly interested. This enables efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the lifestyle. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input information on the importance of lifestyles to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0091] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the lifestyle category. For example, the suggestion unit can apply an economic algorithm to suggestions related to income. The suggestion unit can also apply a sociological algorithm to suggestions related to family structure. The suggestion unit can also apply a geographic algorithm to suggestions related to commute destinations. In this way, applying different suggestion algorithms depending on the lifestyle category enables more accurate suggestions. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, the generation AI, for example. For example, the suggestion unit can input information about lifestyle categories into the generation AI and cause the generation AI to apply different suggestion algorithms.

[0092] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, adjusts the current suggestion based on the user's past suggestion results. The suggestion unit can also improve the suggestion algorithm by referring to the user's past feedback. The suggestion unit can also improve the accuracy of the suggestion based on the user's past behavioral patterns. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input data of the user's past suggestion results into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0093] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short and to-the-point suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is excited, the suggestion unit can also provide visually stimulating suggestions. By adjusting the length of the suggestions based on the user's emotions, more appropriate suggestions can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0094] When making a suggestion, the suggestion unit can determine the priority of the suggestion based on the time when the lifestyle data was collected. The suggestion unit, for example, prioritizes the most recent lifestyle data. The suggestion unit can also refer to past lifestyle data while placing emphasis on the most recent data. The suggestion unit can also prioritize the suggestion of lifestyle data from a period specified by the user. In this way, by determining the priority of the suggestion based on the time when the lifestyle data was collected, the most recent data can be prioritized. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input information on the time when the lifestyle data was collected into the generation AI and cause the generation AI to determine the priority of the suggestions.

[0095] The suggestion unit can adjust the order of suggestions based on the relevance of the lifestyles when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant lifestyles. The suggestion unit can also postpone suggesting less relevant lifestyles. The suggestion unit can also prioritize suggesting lifestyles that the user is most interested in. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of lifestyles. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input information on the relevance of lifestyles to the generation AI and cause the generation AI to adjust the order of suggestions.

[0096] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit uses a lot of technical terminology. Also, if the user does not have technical expertise, the suggestion unit can avoid technical terminology. Also, the suggestion unit can adjust the use of technical terminology according to the user's level of understanding. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, it is possible to provide a proposal that is easy to understand. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input information about the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0097] The providing unit can estimate the user's emotion and adjust the way in which the information to be provided is presented based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides simple, highly visible information. When the user is relaxed, the providing unit can provide detailed information. The providing unit can also provide information that is relevant to the user. Furthermore, if the user is in a hurry, the providing unit can provide information that focuses on the main points. This allows the user to provide more appropriate information by adjusting the way information is expressed based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the way information is expressed.

[0098] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit provides detailed information for important information. The providing unit can also provide simplified information for less important information. The providing unit can also provide detailed information for information that is of great interest to the user. This enables efficient information provision 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 AI, for example, or may be performed without using AI. For example, the providing unit can input information on the importance of the information to the generating AI and cause the generating AI to adjust the level of detail of the information provided.

[0099] The providing unit can apply different providing algorithms depending on the category of information when providing the information. For example, the providing unit can apply a real estate algorithm to housing information. The providing unit can also apply a recruitment algorithm to job information. The providing unit can also apply a transportation algorithm to transportation information. By applying different providing algorithms depending on the category of information, more accurate information can be provided. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input information on the category of information to the generating AI and cause the generating AI to apply different providing algorithms.

[0100] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the information. The providing unit can, for example, adjust the current provision based on the user's past provision results. The providing unit can also improve the provision algorithm by referring to the user's past feedback. The providing unit can also improve the accuracy of the provision based on the user's past behavioral patterns. In this way, the accuracy of the provision can be improved by referring to the user's past provision results. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input data of the user's past provision results into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0101] The providing unit can 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. The providing unit can also provide detailed information if the user is relaxed. The providing unit can also provide visually stimulating information if the user is excited. By adjusting the length of information based on the user's emotions, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, for example, with 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 adjust the length of the information.

[0102] The providing unit can determine the priority of provision based on the time when the information was collected at the time of provision. The providing unit, for example, can provide the latest information preferentially. The providing unit can also emphasize the latest information while referring to past information. The providing unit can also provide information from a period specified by the user preferentially. In this way, by determining the priority of provision based on the time when the information was collected, the latest information can be provided preferentially. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information on the time when the information was collected to the generation AI and cause the generation AI to determine the priority of provision.

[0103] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. For example, the providing unit can provide highly relevant information preferentially. The providing unit can also postpone information with low relevance. The providing unit can also provide information that is of high interest to the user preferentially. This enables efficient information provision by adjusting the order of provision based on the relevance of the information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information on the relevance of the information to the generating AI and cause the generating AI to adjust the order of provision.

[0104] The providing unit can adjust the use of technical terminology provided according to the user's level of expertise when providing the information. For example, if the user has technical expertise, the providing unit uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can avoid technical terminology. Furthermore, the providing unit can adjust the use of technical terminology according to the user's level of understanding. In this way, by adjusting the use of technical terminology provided according to the user's level of expertise, it is possible to provide information that is easy to understand. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information on the user's level of expertise to the generating AI and cause the generating AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and provision 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 video and audio of the user using the camera 42 and microphone 38B of the smart device 14 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal lifestyle based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the user with specific information based on the suggested lifestyle. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and provision 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 video and audio of the user using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal lifestyle based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the user with specific information based on the suggested lifestyle. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and provision 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 video and audio of the user using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal lifestyle based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the user with specific information based on the suggested lifestyle. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects video and audio of the user using the camera 42 and microphone 238 of the robot 414 and transmits the video and audio to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal lifestyle based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the user with specific information based on the suggested lifestyle.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The analysis unit can collect the user's health data and suggest a lifestyle based on the user's health condition. For example, it can analyze the user's exercise habits and dietary habits to suggest a healthy lifestyle. The analysis unit can also analyze the user's sleep patterns to suggest optimal sleeping environments and habits. Furthermore, the analysis unit can analyze the user's stress level and suggest lifestyles to reduce stress. This makes it possible to suggest more personalized lifestyles based on the user's health condition.

[0107] The suggestion unit can also suggest lifestyles based on the user's hobbies and interests. For example, if the user likes outdoor activities, the suggestion unit can suggest living in an area rich in nature. If the user is interested in cultural activities, the suggestion unit can suggest living in an area with plenty of cultural facilities. Furthermore, if the user likes sports, the suggestion unit can suggest living in an area with plenty of sports facilities. This makes it possible to suggest a more fulfilling lifestyle based on the user's hobbies and interests.

[0108] The providing unit can collect feedback on the user's lifestyle and improve the accuracy of the suggestions. For example, the providing unit can collect the user's thoughts and points for improvement after practicing the suggested lifestyle. The providing unit can also evaluate the user's satisfaction with the suggested lifestyle. Furthermore, the providing unit can improve the suggestion algorithm based on the user's feedback. This allows for more accurate lifestyle suggestions by reflecting the user's feedback.

[0109] The collection unit can collect the user's social network data and use it to make lifestyle suggestions. For example, it can refer to the lifestyles of the user's friends and family. The collection unit can also analyze the user's social media activity to understand their interests and concerns. Furthermore, the collection unit can collect information on communities and groups in which the user participates and reflect this in lifestyle suggestions. This makes it possible to make more relevant lifestyle suggestions by utilizing the user's social network.

[0110] The analysis unit can estimate the user's emotions and adjust lifestyle suggestions based on the estimated user emotions. For example, if the user is feeling stressed, a relaxing lifestyle can be suggested. If the user is excited, an active lifestyle can be suggested. Furthermore, if the user is sad, a lifestyle to lift their spirits can be suggested. This makes it possible to suggest more appropriate lifestyles according to the user's emotions.

[0111] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is relaxed, suggestions can be made immediately. If the user is busy, suggestions can be postponed. Furthermore, if the user is emotionally unstable, suggestions can be refrained from. This allows lifestyle suggestions to be made at the optimal timing according to the user's emotions.

[0112] The providing unit can estimate the user's emotion and adjust the format of the information to be provided based on the estimated user's emotion. For example, if the user prefers visual information, the information can be provided in a graphical format. If the user prefers text information, the information can be provided in a detailed text format. Furthermore, if the user prefers audio information, the information can be provided in an audio format. In this way, information can be provided in the optimal format according to the user's emotion.

[0113] The analysis unit can estimate the user's emotions and adjust the frequency of analysis based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of analysis can be reduced. Also, if the user is relaxed, the frequency of analysis can be increased. Furthermore, if the user is in a hurry, the frequency of analysis can be temporarily stopped. In this way, by adjusting the frequency of analysis according to the user's emotions, more appropriate lifestyle suggestions can be made.

[0114] The suggestion unit can estimate the user's emotions and customize the content of the suggestions based on the estimated user emotions. For example, if the user is happy, a positive lifestyle can be suggested. If the user is feeling anxious, a lifestyle that gives a sense of security can be suggested. Furthermore, if the user is excited, an energetic lifestyle can be suggested. This makes it possible to suggest more appropriate lifestyles according to the user's emotions.

[0115] The providing unit can estimate the user's emotions and adjust the amount of information to be provided based on the estimated user's emotions. For example, if the user is relaxed, detailed information can be provided. If the user is in a hurry, short information that focuses on the main points can be provided. Furthermore, if the user is feeling stressed, the amount of information can be reduced. In this way, the optimal amount of information can be provided according to the user's emotions.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The collection unit collects the user's multimodal data. The user's multimodal data includes video, drawings, family structure, income, and commuter destination. For example, the collection unit may capture the user's video with a camera, scan the drawings, and collect family structure information in the form of a questionnaire. In addition, income data may be obtained from pay slips or tax returns, and commuter destination information may be collected from user input. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses the generation AI to analyze the collected data and suggest the optimal lifestyle for the user. For example, the generation AI may determine whether living in a rural area is suitable based on the user's income and family structure. The generation AI may also suggest areas where commuting time can be shortened based on the user's commute destination. Step 3: The suggestion unit suggests lifestyles based on the analysis results obtained by the analysis unit. For example, if a rural area is suitable based on the user's income and family structure, the suggestion unit provides information on housing and jobs in that area. Also, if a region close to the user's commute is suitable, the suggestion unit can provide information on housing and transportation options in that area. Step 4: The providing unit provides specific information based on the lifestyle proposed by the proposing unit. For example, the providing unit provides the user with information on housing, work, and transportation in the proposed area.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[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 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.

[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 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.

[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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

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

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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 that collects multimodal data of a user; an analysis unit that analyzes the data collected by the collection unit; a suggestion unit that suggests a lifestyle based on the analysis results obtained by the analysis unit; a providing unit that provides specific information based on the lifestyle suggested by the suggesting unit. A system characterized by:

2. The collecting unit Collect multimodal data such as user video, drawings, family structure, income, and commute destination 2. The system of claim 1.

3. The analysis unit Analyze the collected data and generate lifestyle suggestions for users using AI.

2. The system of claim 1.

4. The proposal unit If living in a rural area is recommended based on the user's income and family structure, provide information about housing and jobs in that area.

2. The system of claim 1.

5. The proposal unit If living close to your commute is recommended, provide information about housing and transportation options in that area.

2. The system of claim 1.

6. The providing unit Providing specific housing and job information based on the proposed lifestyle 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze the user's past data collection history and select the optimal collection method 2. The system of claim 1.

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