system

Abstract

The system according to the embodiment aims to provide consistent support for managing utility bills, schedules, inventory, and the like at home or in a business. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, a customization unit, and a management unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides advice based on the analysis results obtained by the analysis unit. The customization unit customizes the system to meet the user's needs. The management unit supports administrative processing and household accounting management.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately provide a system that consistently supports household and business utility bills, schedules, inventory management, etc., and there is room for improvement.

[0005] The system according to the embodiment aims to provide consistent support for managing utility bills, schedules, inventory, and the like at home or in a business. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a customization unit, and a management unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides advice based on the analysis results obtained by the analysis unit. The customization unit customizes the data to meet the needs of the user. The management unit supports administrative processing and household accounting management. [Effects of the Invention]

[0007] The system according to the embodiment can consistently support the management of utility bills, schedules, inventory, etc. in homes and businesses. [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 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) The AI ​​support system according to an embodiment of the present invention provides consistent, targeted support and advice for managing and advising on household and business utility bills, schedules, inventory, and other items. This system offers comprehensive support, including advice and advice on household and business administrative tasks and household accounting. First, users input data on household and business utility bills, schedules, inventory, and other items. Next, AI analyzes this data and provides optimal management methods and advice. For example, it suggests ways to save on utility bills, optimize schedules, and efficiently manage inventory. AI can also learn from users' past data and provide future predictions and advice. Furthermore, AI supports household and business administrative tasks and household accounting. For example, it records expenses, manages expenditures, and predicts income, and provides appropriate advice to users. This allows users to efficiently manage their households and businesses. This service can be customized to meet user needs and provide comprehensive support for household and business operations. For example, it can address various needs, such as saving on utility bills and managing household accounts at home, and streamlining administrative tasks and optimizing inventory management at work. This allows the AI ​​support system to consistently support and advise on managing utility bills, schedules, inventory, and more for homes and businesses.

[0029] The AI ​​supporter system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a customization unit, and a management unit. The collection unit collects data such as household or business utility bills, schedules, and inventory. For example, the collection unit can collect utility bill data using a sensor. The collection unit can also collect schedule data manually entered by a user. The collection unit can also collect inventory data through an API. For example, the collection unit can collect data from an electric meter using a sensor and save it as utility bill data. The collection unit can also automatically collect schedule data entered by a user into a calendar app. The collection unit can also obtain inventory data from an inventory management system through an API. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze utility bill data using statistical analysis. The analysis unit can also analyze schedule data using a machine learning algorithm. The analysis unit can also analyze inventory data using data mining technology. For example, the analysis unit can statistically analyze utility bill data and suggest ways to save money. The analysis unit can also analyze schedule data using a machine learning algorithm and propose an optimal schedule. The analysis unit can also analyze inventory data using data mining technology and propose an efficient inventory management method. The provision unit provides advice based on the analysis results obtained by the analysis unit. The provision unit can, for example, propose ways to save on utility costs. The provision unit can also propose schedule optimization. The provision unit can also propose efficient inventory management methods. For example, the provision unit can propose ways to reduce electricity usage as a way to save on utility costs. The provision unit can also propose ways to prioritize tasks as a way to optimize the schedule. The provision unit can also propose ways to improve inventory turnover as a way to efficiently manage inventory. The customization unit adjusts the collection unit, analysis unit, and provision unit to meet the user's needs. The customization unit can, for example, adjust the data collection method of the collection unit based on the user's settings.The customization unit can also adjust the analysis method of the analysis unit based on past usage history. Furthermore, the customization unit can also adjust the advice method of the provision unit based on individual needs. For example, the customization unit can select, based on a user's settings, whether the collection unit will collect data using a sensor or manually input the data. The customization unit can also select, based on past usage history, whether the analysis unit will use statistical analysis or a machine learning algorithm. Furthermore, the customization unit can also select, based on individual needs, whether the provision unit will provide advice via text message or audio advice. The management unit records expenses, manages expenditures, predicts income, and provides appropriate advice to the user. For example, the management unit can scan receipts and convert them into digital data to record expenses. For example, the management unit can set a budget and track expenditures to manage expenditures. For example, the management unit can analyze past income data and predict future income to predict income. For example, the management unit can scan receipts and convert them into digital data to store them as expense records. For example, the management unit can manage expenditures by setting a budget and tracking expenditures. Furthermore, the management unit can predict income by analyzing past income data and predicting future income. This allows the AI ​​supporter system according to the embodiment to consistently support and advise on the management of household and business utility bills, schedules, inventory, etc.

[0030] The collection unit can collect data on household and business utility bills, schedules, and inventory. For example, to collect household utility bill data, the collection unit can collect data from electric meters and gas meters using sensors. The collection unit can also acquire data from a calendar app or task list to collect company schedule data. The collection unit can also acquire data from an inventory management system through an API to collect household and business inventory data. For example, the collection unit can collect data from an electric meter using sensors and save it as utility bill data. The collection unit can also automatically collect schedule data entered in a calendar app. The collection unit can also acquire inventory data from an inventory management system through an API and save it as inventory data. This allows for efficient collection of data such as household and business utility bills, schedules, and inventory. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can collect data from an electric meter using sensors and input the data into AI for analysis.

[0031] The analysis unit can analyze the data collected by the collection unit and provide management methods and advice. For example, the analysis unit can analyze utility bill data using statistical analysis and suggest ways to save money. The analysis unit can also analyze schedule data using machine learning algorithms and suggest optimal schedules. The analysis unit can also analyze inventory data using data mining technology and suggest efficient inventory management methods. For example, the analysis unit can statistically analyze utility bill data and suggest ways to reduce electricity use. The analysis unit can also analyze schedule data using machine learning algorithms and suggest ways to prioritize tasks. The analysis unit can also analyze inventory data using data mining technology and suggest ways to improve inventory turnover. This allows the collected data to be analyzed and optimal management methods and advice to be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the data collected by the collection unit into AI, which then performs analysis and provides management methods and advice.

[0032] The providing unit can suggest methods for saving on utility costs, managing schedules, and efficiently managing inventory based on the analysis results obtained by the analysis unit. For example, the providing unit can suggest a method for reducing electricity use as a method for saving on utility costs. The providing unit can also suggest a method for prioritizing tasks as a method for managing schedules. Furthermore, the providing unit can suggest a method for improving inventory turnover as a method for efficiently managing inventory. For example, the providing unit can suggest using energy-efficient home appliances to reduce electricity use. The providing unit can also suggest a method for classifying tasks based on importance and urgency to prioritize tasks. Furthermore, the providing unit can suggest a method for optimizing the timing of inventory ordering to improve inventory turnover. This makes it possible to provide specific advice based on the analysis results. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the analysis results obtained by the analysis unit into AI, which then generates advice and provides it to the user.

[0033] The customization unit can adjust the collection unit, analysis unit, and provision unit to suit the user's needs. For example, the customization unit can adjust the data collection method of the collection unit based on the user's settings. The customization unit can also adjust the analysis method of the analysis unit based on past usage history. Furthermore, the customization unit can adjust the advice method of the provision unit based on individual needs. For example, the customization unit can select whether the collection unit collects data using a sensor or manually inputs data based on the user's settings. The customization unit can also select whether the analysis unit uses statistical analysis or a machine learning algorithm based on past usage history. Furthermore, the customization unit can select whether the provision unit provides advice via text message or voice advice based on individual needs. This allows the system to be customized to suit the user's needs. Some or all of the above-mentioned processing in the customization unit may be performed using, for example, AI, or may be performed without AI. For example, the customization unit can input the user's settings and past usage history into AI, which can then suggest the optimal customization method.

[0034] The management unit can record expenses, manage expenditures, and predict income, and provide appropriate advice to users. For example, the management unit can scan receipts and convert them into digital data to record expenses. The management unit can also set budgets and track expenditures to manage expenses. The management unit can also analyze past income data and predict future income to predict income. For example, the management unit can scan receipts and convert them into digital data and save them as expense records. The management unit can also manage expenditures by setting budgets and tracking expenditures. The management unit can also predict income by analyzing past income data and predicting future income. This allows the system to record expenses, manage expenditures, predict income, and provide appropriate advice to users. Some or all of the above-described processing in the management unit may be performed using, or without, AI. For example, the management unit can input receipt data into AI, which can then record expenses, manage expenditures, and predict income.

[0035] The collection unit can analyze the user's past data collection history and select a collection method. For example, the collection unit can analyze time periods during which the user frequently collected data in the past and collect data during those time periods. The collection unit can also preferentially suggest collection methods (manual, voice input, etc.) that the user has used in the past. Furthermore, the collection unit can select the optimal collection method for a specific day of the week or time period based on the user's past data collection history. For example, the collection unit can analyze time periods during which the user frequently collected data in the past and collect data during those time periods. The collection unit can also preferentially suggest collection methods (manual, voice input, etc.) that the user has used in the past. Furthermore, the collection unit can select the optimal collection method for a specific day of the week or time period based on the user's past data collection history. In this way, the optimal collection method can be selected based on the user's past data collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past data collection history into AI, which can select the optimal collection method.

[0036] When collecting data, the collection unit can filter the data based on the user's current living situation or work situation. For example, if the user is at work, the collection unit can collect only work-related data. Furthermore, if the user is at home, the collection unit can collect only home-related data. Furthermore, if the user is traveling, the collection unit can collect only travel-related data. For example, if the user is at work, the collection unit can collect only work-related data. Furthermore, if the user is at home, the collection unit can collect only home-related data. Furthermore, if the user is traveling, the collection unit can collect only travel-related data. This allows data to be filtered according to the user's current situation. 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 current living situation or work situation into AI, and the AI ​​can filter the data.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the user's destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to the user's home. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the user's destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to the user's home. This allows highly relevant data to be prioritized based on the user's geographical location information. 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 the user's geographical location information into AI, which can then prioritize collecting highly relevant data.

[0038] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can collect related data based on information shared by the user on social media. The collection unit can also collect related data based on information about accounts the user follows on social media. The collection unit can also collect related data based on information about groups the user participates in on social media. For example, the collection unit collects related data based on information shared by the user on social media. The collection unit can also collect related data based on information about accounts the user follows on social media. The collection unit can also collect related data based on information about groups the user participates in on social media. This makes it possible to collect related data based on the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activities into AI, which then collects the related data.

[0039] 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 can perform a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. This makes it possible to adjust 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, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI, which can adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply an analysis algorithm that suggests saving methods to utility bill data. The analysis unit can also apply an analysis algorithm that suggests optimization to schedule data. The analysis unit can also apply an analysis algorithm that suggests efficient management methods to inventory data. For example, the analysis unit applies an analysis algorithm that suggests saving methods to utility bill data. The analysis unit can also apply an analysis algorithm that suggests optimization to schedule data. The analysis unit can also apply an analysis algorithm that suggests efficient management methods to inventory data. This allows the optimal analysis algorithm to be applied depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into AI, which then applies the optimal analysis algorithm.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit can, for example, prioritize analyzing the most recent data. The analysis unit can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can adjust the priority of analysis according to the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can adjust the priority of analysis according to the time when the data was collected. In this way, the priority of analysis can be determined 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, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into AI, and the AI ​​can determine the priority of analysis.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, 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. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data. In this way, the order of analysis can be adjusted based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, which can adjust the order of analysis.

[0043] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the analysis result. For example, the providing unit can provide detailed advice for analysis results with high importance. The providing unit can also provide brief advice for analysis results with low importance. Furthermore, the providing unit can determine the priority of the advice according to the importance of the analysis result. For example, the providing unit can provide detailed advice for analysis results with high importance. The providing unit can also provide brief advice for analysis results with low importance. Furthermore, the providing unit can determine the priority of the advice according to the importance of the analysis result. In this way, the level of detail of the advice can be adjusted according to the importance of the analysis result. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the importance of the analysis result to AI, and the AI ​​can adjust the level of detail of the advice.

[0044] When providing advice, the providing unit can apply different advice algorithms depending on the category of the analysis results. For example, the providing unit can apply an advice algorithm specialized for saving to a method of saving on utility bills. The providing unit can also apply an advice algorithm specialized for optimization to schedule optimization. Furthermore, the providing unit can apply an advice algorithm specialized for management to a method of efficiently managing inventory. For example, the providing unit applies an advice algorithm specialized for saving to a method of saving on utility bills. The providing unit can also apply an advice algorithm specialized for optimization to schedule optimization. Furthermore, the providing unit can apply an advice algorithm specialized for management to a method of efficiently managing inventory. This makes it possible to apply an optimal advice algorithm depending on the category of the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the category of the analysis results into AI, which can then apply the optimal advice algorithm.

[0045] When providing advice, the providing unit can determine the priority of the advice based on the time when the analysis results were collected. The providing unit can provide advice based on, for example, the latest analysis results. The providing unit can also provide the latest advice while referring to past analysis results. Furthermore, the providing unit can adjust the priority of the advice according to the time when the analysis results were collected. For example, the providing unit provides advice based on the latest analysis results. The providing unit can also provide the latest advice while referring to past analysis results. Furthermore, the providing unit can adjust the priority of the advice according to the time when the analysis results were collected. In this way, the priority of the advice can be determined based on the time when the analysis results were collected. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the time when the analysis results were collected into AI, and the AI ​​can determine the priority of the advice.

[0046] When providing advice, the providing unit can adjust the order of advice based on the relevance of the analysis results. The providing unit can provide advice based on, for example, a highly relevant analysis result. The providing unit can also postpone advice based on a less relevant analysis result. Furthermore, the providing unit can adjust the order of advice based on the relevance of the analysis results. For example, the providing unit provides advice based on a highly relevant analysis result. The providing unit can also postpone advice based on a less relevant analysis result. Furthermore, the providing unit can adjust the order of advice based on the relevance of the analysis results. In this way, the order of advice can be adjusted based on the relevance of the analysis results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the relevance of the analysis results to AI, and the AI ​​can adjust the order of advice.

[0047] During customization, the customization unit can analyze the user's past customization history to select the optimal customization method. For example, the customization unit can preferentially suggest customization methods that the user has used in the past. The customization unit can also select the optimal customization method from the user's past customization history. Furthermore, the customization unit can automatically display customization methods that the user has frequently used in the past as candidates. For example, the customization unit preferentially suggests customization methods that the user has used in the past. The customization unit can also select the optimal customization method from the user's past customization history. Furthermore, the customization unit can automatically display customization methods that the user has frequently used in the past as candidates. This allows the optimal customization method to be selected based on the user's past customization history. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's past customization history into AI, which can select the optimal customization method.

[0048] During customization, the customization unit can customize the customization means based on the user's current living situation or work situation. For example, when the user is at work, the customization unit can provide a work-related customization method. Furthermore, when the user is at home, the customization unit can provide a home-related customization method. Furthermore, when the user is traveling, the customization unit can provide a travel-related customization method. For example, when the user is at work, the customization unit can provide a work-related customization method. Furthermore, when the user is at home, the customization unit can provide a home-related customization method. Furthermore, when the user is traveling, the customization unit can provide a travel-related customization method. This makes it possible to adjust the customization means according to the user's current situation. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's current living situation or work situation into AI, and the AI ​​can adjust the customization means.

[0049] During customization, the customization unit can select a customization method taking into account the user's geographical location information. For example, when the user is in a specific area, the customization unit can provide a customization method related to the area. Furthermore, when the user is traveling, the customization unit can provide a customization method related to the destination. Furthermore, when the user is at home, the customization unit can provide a customization method related to the home. For example, when the user is in a specific area, the customization unit can provide a customization method related to the area. Furthermore, when the user is traveling, the customization unit can provide a customization method related to the destination. Furthermore, when the user is at home, the customization unit can provide a customization method related to the home. This makes it possible to select an optimal customization method based on the user's geographical location information. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's geographical location information into AI, which can select an optimal customization method.

[0050] During customization, the customization unit can analyze the user's social media activity and suggest customization methods. For example, the customization unit can suggest relevant customization methods based on information shared by the user on social media. The customization unit can also suggest relevant customization methods based on information about accounts the user follows on social media. Furthermore, the customization unit can also suggest relevant customization methods based on information about groups the user participates in on social media. For example, the customization unit can suggest relevant customization methods based on information shared by the user on social media. The customization unit can also suggest relevant customization methods based on information about accounts the user follows on social media. Furthermore, the customization unit can also suggest relevant customization methods based on information about groups the user participates in on social media. In this way, customization methods can be suggested based on the user's social media activity. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's social media activity into AI, which then suggests customization methods.

[0051] During management, the management unit can analyze the user's past management history and select the optimal management method. For example, the management unit can prioritize and suggest management methods that the user has used in the past. The management unit can also select the optimal management method from the user's past management history. Furthermore, the management unit can automatically display management methods that the user has frequently used in the past as candidates. For example, the management unit prioritizes and suggests management methods that the user has used in the past. The management unit can also select the optimal management method from the user's past management history. Furthermore, the management unit can automatically display management methods that the user has frequently used in the past as candidates. This allows the optimal management method to be selected based on the user's past management history. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the user's past management history into AI, which then selects the optimal management method.

[0052] During management, the management unit can customize the management means based on the user's current living situation or work situation. For example, when the user is at work, the management unit can provide a work-related management method. Furthermore, when the user is at home, the management unit can provide a home-related management method. Furthermore, when the user is traveling, the management unit can provide a travel-related management method. For example, when the user is at work, the management unit can provide a work-related management method. Furthermore, when the user is at home, the management unit can provide a home-related management method. Furthermore, when the user is traveling, the management unit can provide a travel-related management method. This allows the management means to be adjusted according to the user's current situation. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the user's current living situation or work situation into AI, and the AI ​​can adjust the management means.

[0053] During management, the management unit can select a management method taking into account the user's geographical location information. For example, if the user is in a specific area, the management unit can provide a management method related to that area. Furthermore, if the user is traveling, the management unit can provide a management method related to the user's destination. Furthermore, if the user is at home, the management unit can provide a management method related to the user's home. For example, if the user is in a specific area, the management unit can provide a management method related to that area. Furthermore, if the user is traveling, the management unit can provide a management method related to the user's destination. Furthermore, if the user is at home, the management unit can provide a management method related to the user's home. This allows the optimal management method to be selected based on the user's geographical location information. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the user's geographical location information into AI, which can select the optimal management method.

[0054] During management, the management unit can analyze the user's social media activity and suggest management measures. For example, the management unit can suggest relevant management methods based on information shared by the user on social media. The management unit can also suggest relevant management methods based on information about accounts the user follows on social media. Furthermore, the management unit can also suggest relevant management methods based on information about groups the user participates in on social media. For example, the management unit can suggest relevant management methods based on information shared by the user on social media. The management unit can also suggest relevant management methods based on information about accounts the user follows on social media. Furthermore, the management unit can also suggest relevant management methods based on information about groups the user participates in on social media. In this way, management measures can be suggested based on the user's social media activity. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the user's social media activity into AI, which then suggests management measures.

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

[0056] The analysis unit can select an analysis method based on the data collection source. For example, real-time analysis can be performed on data collected from a sensor. Batch processing can also be performed on manually entered data. Furthermore, statistical analysis can be performed on data acquired through an API. This allows the optimal analysis method to be selected depending on the data collection source. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the data collection source into AI, which can then select the optimal analysis method.

[0057] The providing unit can analyze the user's past advice history and select the optimal advice method. For example, it can preferentially provide advice formats that the user has preferred in the past. It can also select the optimal advice method for a specific situation from the user's past advice history. Furthermore, it can automatically display advice methods that the user has frequently used in the past as candidates. This makes it possible to select the optimal advice method based on the user's past advice history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's past advice history into AI, which can select the optimal advice method.

[0058] The customization unit can adjust the customization content taking into account the user's geographical location information. For example, if the user is in a specific area, customization tailored to the climate and culture of that area can be provided. Also, if the user is traveling, customization can be performed based on information about the user's destination. Furthermore, if the user is at home, customization optimized for the home environment can be provided. This makes it possible to provide optimal customization content based on the user's geographical location information. Some or all of the above-described processing in the customization unit may be performed using AI, or may be performed without using AI. For example, the customization unit can input the user's geographical location information into AI, which can select optimal customization content.

[0059] The management unit can analyze the user's social media activity and suggest relevant management methods. For example, the management unit can suggest relevant management methods based on information shared by the user on social media. The management unit can also suggest relevant management methods based on information about accounts the user follows on social media. The management unit can also suggest relevant management methods based on information about groups the user participates in on social media. In this way, management methods can be suggested based on the user's social media activity. Some or all of the above-mentioned processing in the management unit may be performed using AI, or may be performed without using AI. For example, the management unit can input the user's social media activity into AI, which then suggests management methods.

[0060] When collecting data, the collection unit can filter the data based on the user's current living situation or work situation. For example, if the user is at work, only work-related data can be collected. Also, if the user is at home, only home-related data can be collected. Furthermore, if the user is traveling, only travel-related data can be collected. This allows data to be filtered according to the user's current situation. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's current living situation or work situation into AI, which then filters the data.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The collection unit collects data such as utility bills, schedules, and inventory for homes and businesses. For example, it collects utility bill data using sensors, schedule data manually entered by users, and inventory data through APIs. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the utility bill data using statistical analysis, the schedule data using machine learning algorithms, and the inventory data using data mining techniques. Step 3: The provision unit provides advice based on the analysis results obtained by the analysis unit, such as suggestions for saving on utility costs, optimizing schedules, and efficiently managing inventory. Step 4: The customization unit adjusts the collection unit, analysis unit, and provision unit to suit the user's needs. For example, the customization unit adjusts the data collection method of the collection unit based on the user's settings, the analysis unit adjusts the analysis method based on past usage history, and the provision unit adjusts the advice method based on individual needs. Step 5: The management department records expenses, manages expenditures, forecasts income, and provides appropriate advice to users, such as scanning receipts and converting them into digital data, setting budgets, tracking expenses, and analyzing past income data to forecast future income.

[0063] (Example 2) The AI ​​support system according to an embodiment of the present invention provides consistent, targeted support and advice for managing and advising on household and business utility bills, schedules, inventory, and other items. This system offers comprehensive support, including advice and advice on household and business administrative tasks and household accounting. First, users input data on household and business utility bills, schedules, inventory, and other items. Next, AI analyzes this data and provides optimal management methods and advice. For example, it suggests ways to save on utility bills, optimize schedules, and efficiently manage inventory. AI can also learn from users' past data and provide future predictions and advice. Furthermore, AI supports household and business administrative tasks and household accounting. For example, it records expenses, manages expenditures, and predicts income, and provides appropriate advice to users. This allows users to efficiently manage their households and businesses. This service can be customized to meet user needs and provide comprehensive support for household and business operations. For example, it can address various needs, such as saving on utility bills and managing household accounts at home, and streamlining administrative tasks and optimizing inventory management at work. This allows the AI ​​support system to consistently support and advise on managing utility bills, schedules, inventory, and more for homes and businesses.

[0064] The AI ​​supporter system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a customization unit, and a management unit. The collection unit collects data such as household or business utility bills, schedules, and inventory. For example, the collection unit can collect utility bill data using a sensor. The collection unit can also collect schedule data manually entered by a user. The collection unit can also collect inventory data through an API. For example, the collection unit can collect data from an electric meter using a sensor and save it as utility bill data. The collection unit can also automatically collect schedule data entered by a user into a calendar app. The collection unit can also obtain inventory data from an inventory management system through an API. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze utility bill data using statistical analysis. The analysis unit can also analyze schedule data using a machine learning algorithm. The analysis unit can also analyze inventory data using data mining technology. For example, the analysis unit can statistically analyze utility bill data and suggest ways to save money. The analysis unit can also analyze schedule data using a machine learning algorithm and propose an optimal schedule. The analysis unit can also analyze inventory data using data mining technology and propose an efficient inventory management method. The provision unit provides advice based on the analysis results obtained by the analysis unit. The provision unit can, for example, propose ways to save on utility costs. The provision unit can also propose schedule optimization. The provision unit can also propose efficient inventory management methods. For example, the provision unit can propose ways to reduce electricity usage as a way to save on utility costs. The provision unit can also propose ways to prioritize tasks as a way to optimize the schedule. The provision unit can also propose ways to improve inventory turnover as a way to efficiently manage inventory. The customization unit adjusts the collection unit, analysis unit, and provision unit to meet the user's needs. The customization unit can, for example, adjust the data collection method of the collection unit based on the user's settings.The customization unit can also adjust the analysis method of the analysis unit based on past usage history. Furthermore, the customization unit can also adjust the advice method of the provision unit based on individual needs. For example, the customization unit can select, based on a user's settings, whether the collection unit will collect data using a sensor or manually input the data. The customization unit can also select, based on past usage history, whether the analysis unit will use statistical analysis or a machine learning algorithm. Furthermore, the customization unit can also select, based on individual needs, whether the provision unit will provide advice via text message or audio advice. The management unit records expenses, manages expenditures, predicts income, and provides appropriate advice to the user. For example, the management unit can scan receipts and convert them into digital data to record expenses. For example, the management unit can set a budget and track expenditures to manage expenditures. For example, the management unit can analyze past income data and predict future income to predict income. For example, the management unit can scan receipts and convert them into digital data to store them as expense records. For example, the management unit can manage expenditures by setting a budget and tracking expenditures. Furthermore, the management unit can predict income by analyzing past income data and predicting future income. This allows the AI ​​supporter system according to the embodiment to consistently support and advise on the management of household and business utility bills, schedules, inventory, etc.

[0065] The collection unit can collect data on household and business utility bills, schedules, and inventory. For example, to collect household utility bill data, the collection unit can collect data from electric meters and gas meters using sensors. The collection unit can also acquire data from a calendar app or task list to collect company schedule data. The collection unit can also acquire data from an inventory management system through an API to collect household and business inventory data. For example, the collection unit can collect data from an electric meter using sensors and save it as utility bill data. The collection unit can also automatically collect schedule data entered in a calendar app. The collection unit can also acquire inventory data from an inventory management system through an API and save it as inventory data. This allows for efficient collection of data such as household and business utility bills, schedules, and inventory. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can collect data from an electric meter using sensors and input the data into AI for analysis.

[0066] The analysis unit can analyze the data collected by the collection unit and provide management methods and advice. For example, the analysis unit can analyze utility bill data using statistical analysis and suggest ways to save money. The analysis unit can also analyze schedule data using machine learning algorithms and suggest optimal schedules. The analysis unit can also analyze inventory data using data mining technology and suggest efficient inventory management methods. For example, the analysis unit can statistically analyze utility bill data and suggest ways to reduce electricity use. The analysis unit can also analyze schedule data using machine learning algorithms and suggest ways to prioritize tasks. The analysis unit can also analyze inventory data using data mining technology and suggest ways to improve inventory turnover. This allows the collected data to be analyzed and optimal management methods and advice to be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the data collected by the collection unit into AI, which then performs analysis and provides management methods and advice.

[0067] The providing unit can suggest methods for saving on utility costs, managing schedules, and efficiently managing inventory based on the analysis results obtained by the analysis unit. For example, the providing unit can suggest a method for reducing electricity use as a method for saving on utility costs. The providing unit can also suggest a method for prioritizing tasks as a method for managing schedules. Furthermore, the providing unit can suggest a method for improving inventory turnover as a method for efficiently managing inventory. For example, the providing unit can suggest using energy-efficient home appliances to reduce electricity use. The providing unit can also suggest a method for classifying tasks based on importance and urgency to prioritize tasks. Furthermore, the providing unit can suggest a method for optimizing the timing of inventory ordering to improve inventory turnover. This makes it possible to provide specific advice based on the analysis results. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the analysis results obtained by the analysis unit into AI, which then generates advice and provides it to the user.

[0068] The customization unit can adjust the collection unit, analysis unit, and provision unit to suit the user's needs. For example, the customization unit can adjust the data collection method of the collection unit based on the user's settings. The customization unit can also adjust the analysis method of the analysis unit based on past usage history. Furthermore, the customization unit can adjust the advice method of the provision unit based on individual needs. For example, the customization unit can select whether the collection unit collects data using a sensor or manually inputs data based on the user's settings. The customization unit can also select whether the analysis unit uses statistical analysis or a machine learning algorithm based on past usage history. Furthermore, the customization unit can select whether the provision unit provides advice via text message or voice advice based on individual needs. This allows the system to be customized to suit the user's needs. Some or all of the above-mentioned processing in the customization unit may be performed using, for example, AI, or may be performed without AI. For example, the customization unit can input the user's settings and past usage history into AI, which can then suggest the optimal customization method.

[0069] The management unit can record expenses, manage expenditures, and predict income, and provide appropriate advice to users. For example, the management unit can scan receipts and convert them into digital data to record expenses. The management unit can also set budgets and track expenditures to manage expenses. The management unit can also analyze past income data and predict future income to predict income. For example, the management unit can scan receipts and convert them into digital data and save them as expense records. The management unit can also manage expenditures by setting budgets and tracking expenditures. The management unit can also predict income by analyzing past income data and predicting future income. This allows the system to record expenses, manage expenditures, predict income, and provide appropriate advice to users. Some or all of the above-described processing in the management unit may be performed using, or without, AI. For example, the management unit can input receipt data into AI, which can then record expenses, manage expenditures, and predict income.

[0070] 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 the collection timing to collect data when the user is relaxed. Furthermore, if the user is busy, the collection unit can adjust the collection timing to collect data when the user's schedule is free. Furthermore, if the user is relaxed, the collection unit can advance the collection timing to efficiently collect data. For example, if the user is feeling stressed, the collection unit delays the collection timing to collect data when the user is relaxed. Furthermore, if the user is busy, the collection unit can adjust the collection timing to collect data when the user's schedule is free. Furthermore, if the user is relaxed, the collection unit can advance the collection timing to efficiently collect data. This allows the timing of data collection to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's emotion data into AI, which may then adjust the collection timing.

[0071] The collection unit can analyze the user's past data collection history and select a collection method. For example, the collection unit can analyze time periods during which the user frequently collected data in the past and collect data during those time periods. The collection unit can also preferentially suggest collection methods (manual, voice input, etc.) that the user has used in the past. Furthermore, the collection unit can select the optimal collection method for a specific day of the week or time period based on the user's past data collection history. For example, the collection unit can analyze time periods during which the user frequently collected data in the past and collect data during those time periods. The collection unit can also preferentially suggest collection methods (manual, voice input, etc.) that the user has used in the past. Furthermore, the collection unit can select the optimal collection method for a specific day of the week or time period based on the user's past data collection history. In this way, the optimal collection method can be selected based on the user's past data collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past data collection history into AI, which can select the optimal collection method.

[0072] When collecting data, the collection unit can filter the data based on the user's current living situation or work situation. For example, if the user is at work, the collection unit can collect only work-related data. Furthermore, if the user is at home, the collection unit can collect only home-related data. Furthermore, if the user is traveling, the collection unit can collect only travel-related data. For example, if the user is at work, the collection unit can collect only work-related data. Furthermore, if the user is at home, the collection unit can collect only home-related data. Furthermore, if the user is traveling, the collection unit can collect only travel-related data. This allows data to be filtered according to the user's current situation. 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 current living situation or work situation into AI, and the AI ​​can filter the data.

[0073] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can postpone collection of less important data. Furthermore, if the user is relaxed, the collection unit can prioritize collection of more important data. Furthermore, if the user is busy, the collection unit can prioritize collection of only the minimum necessary data. For example, if the user is feeling stressed, the collection unit postpones collection of less important data. Furthermore, if the user is relaxed, the collection unit can prioritize collection of more important data. Furthermore, if the user is busy, the collection unit can prioritize collection of only the minimum necessary data. This allows data prioritization to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input user emotional data into the AI, which can then determine the priority of the data.

[0074] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the user's destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to the user's home. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the user's destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to the user's home. This allows highly relevant data to be prioritized based on the user's geographical location information. 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 the user's geographical location information into AI, which can then prioritize collecting highly relevant data.

[0075] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can collect related data based on information shared by the user on social media. The collection unit can also collect related data based on information about accounts the user follows on social media. The collection unit can also collect related data based on information about groups the user participates in on social media. For example, the collection unit collects related data based on information shared by the user on social media. The collection unit can also collect related data based on information about accounts the user follows on social media. The collection unit can also collect related data based on information about groups the user participates in on social media. This makes it possible to collect related data based on the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activities into AI, which then collects the related data.

[0076] 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 stressed, 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 busy, the analysis unit can provide a concise analysis result that focuses on the main points. For example, if the user is stressed, 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 busy, the analysis unit can provide a concise analysis result that focuses on the main points. This allows the way the analysis is presented to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotional data into the AI, which can then adjust how the analysis is presented.

[0077] 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 can perform a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. This makes it possible to adjust 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, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI, which can adjust the level of detail of the analysis.

[0078] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply an analysis algorithm that suggests saving methods to utility bill data. The analysis unit can also apply an analysis algorithm that suggests optimization to schedule data. The analysis unit can also apply an analysis algorithm that suggests efficient management methods to inventory data. For example, the analysis unit applies an analysis algorithm that suggests saving methods to utility bill data. The analysis unit can also apply an analysis algorithm that suggests optimization to schedule data. The analysis unit can also apply an analysis algorithm that suggests efficient management methods to inventory data. This allows the optimal analysis algorithm to be applied depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into AI, which then applies the optimal analysis algorithm.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the user is stressed, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is busy, the analysis unit can provide a concise analysis result. For example, if the user is stressed, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is busy, the analysis unit can provide a concise analysis result. This allows the length of the analysis to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into an AI, which can then adjust the length of the analysis.

[0080] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit can, for example, prioritize analyzing the most recent data. The analysis unit can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can adjust the priority of analysis according to the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can adjust the priority of analysis according to the time when the data was collected. In this way, the priority of analysis can be determined 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, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into AI, and the AI ​​can determine the priority of analysis.

[0081] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, 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. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data. In this way, the order of analysis can be adjusted based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, which can adjust the order of analysis.

[0082] The providing unit can estimate the user's emotions and adjust the way in which advice is presented based on the estimated user's emotions. For example, when the user is feeling stressed, the providing unit can provide simple, highly visible advice. Furthermore, when the user is relaxed, the providing unit can provide detailed advice. Furthermore, when the user is busy, the providing unit can provide concise advice that focuses on the main points. For example, when the user is feeling stressed, the providing unit can provide simple, highly visible advice. Furthermore, when the user is relaxed, the providing unit can provide detailed advice. Furthermore, when the user is busy, the providing unit can provide concise advice that focuses on the main points. This allows the way in which advice is presented to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the 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 emotional data into the AI, which can then adjust the way the advice is expressed.

[0083] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the analysis result. For example, the providing unit can provide detailed advice for analysis results with high importance. The providing unit can also provide brief advice for analysis results with low importance. Furthermore, the providing unit can determine the priority of the advice according to the importance of the analysis result. For example, the providing unit can provide detailed advice for analysis results with high importance. The providing unit can also provide brief advice for analysis results with low importance. Furthermore, the providing unit can determine the priority of the advice according to the importance of the analysis result. In this way, the level of detail of the advice can be adjusted according to the importance of the analysis result. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the importance of the analysis result to AI, and the AI ​​can adjust the level of detail of the advice.

[0084] When providing advice, the providing unit can apply different advice algorithms depending on the category of the analysis results. For example, the providing unit can apply an advice algorithm specialized for saving to a method of saving on utility bills. The providing unit can also apply an advice algorithm specialized for optimization to schedule optimization. Furthermore, the providing unit can apply an advice algorithm specialized for management to a method of efficiently managing inventory. For example, the providing unit applies an advice algorithm specialized for saving to a method of saving on utility bills. The providing unit can also apply an advice algorithm specialized for optimization to schedule optimization. Furthermore, the providing unit can apply an advice algorithm specialized for management to a method of efficiently managing inventory. This makes it possible to apply an optimal advice algorithm depending on the category of the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the category of the analysis results into AI, which can then apply the optimal advice algorithm.

[0085] The providing unit can estimate the user's emotions and adjust the length of advice based on the estimated user's emotions. For example, when the user is stressed, the providing unit can provide short, concise advice. Furthermore, when the user is relaxed, the providing unit can provide detailed advice. Furthermore, when the user is busy, the providing unit can provide concise advice. For example, when the user is stressed, the providing unit can provide short, concise advice. Furthermore, when the user is relaxed, the providing unit can provide detailed advice. Furthermore, when the user is busy, the providing unit can provide concise advice. This allows the length of advice to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into an AI, which can adjust the length of the advice.

[0086] When providing advice, the providing unit can determine the priority of the advice based on the time when the analysis results were collected. The providing unit can provide advice based on, for example, the latest analysis results. The providing unit can also provide the latest advice while referring to past analysis results. Furthermore, the providing unit can adjust the priority of the advice according to the time when the analysis results were collected. For example, the providing unit provides advice based on the latest analysis results. The providing unit can also provide the latest advice while referring to past analysis results. Furthermore, the providing unit can adjust the priority of the advice according to the time when the analysis results were collected. In this way, the priority of the advice can be determined based on the time when the analysis results were collected. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the time when the analysis results were collected into AI, and the AI ​​can determine the priority of the advice.

[0087] When providing advice, the providing unit can adjust the order of advice based on the relevance of the analysis results. The providing unit can provide advice based on, for example, a highly relevant analysis result. The providing unit can also postpone advice based on a less relevant analysis result. Furthermore, the providing unit can adjust the order of advice based on the relevance of the analysis results. For example, the providing unit provides advice based on a highly relevant analysis result. The providing unit can also postpone advice based on a less relevant analysis result. Furthermore, the providing unit can adjust the order of advice based on the relevance of the analysis results. In this way, the order of advice can be adjusted based on the relevance of the analysis results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the relevance of the analysis results to AI, and the AI ​​can adjust the order of advice.

[0088] The customization unit can estimate the user's emotion and adjust the customization method based on the estimated user's emotion. For example, when the user is stressed, the customization unit can provide a simple, highly visible customization method. Furthermore, when the user is relaxed, the customization unit can provide a detailed customization method. Furthermore, when the user is busy, the customization unit can provide a concise customization method that focuses on the main points. For example, when the user is stressed, the customization unit can provide a simple, highly visible customization method. Furthermore, when the user is relaxed, the customization unit can provide a detailed customization method. Furthermore, when the user is busy, the customization unit can provide a concise customization method that focuses on the main points. This allows the customization method to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the customization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the customization unit can input the user's emotional data into the AI, which can then adjust the customization method.

[0089] During customization, the customization unit can analyze the user's past customization history to select the optimal customization method. For example, the customization unit can preferentially suggest customization methods that the user has used in the past. The customization unit can also select the optimal customization method from the user's past customization history. Furthermore, the customization unit can automatically display customization methods that the user has frequently used in the past as candidates. For example, the customization unit preferentially suggests customization methods that the user has used in the past. The customization unit can also select the optimal customization method from the user's past customization history. Furthermore, the customization unit can automatically display customization methods that the user has frequently used in the past as candidates. This allows the optimal customization method to be selected based on the user's past customization history. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's past customization history into AI, which can select the optimal customization method.

[0090] During customization, the customization unit can customize the customization means based on the user's current living situation or work situation. For example, when the user is at work, the customization unit can provide a work-related customization method. Furthermore, when the user is at home, the customization unit can provide a home-related customization method. Furthermore, when the user is traveling, the customization unit can provide a travel-related customization method. For example, when the user is at work, the customization unit can provide a work-related customization method. Furthermore, when the user is at home, the customization unit can provide a home-related customization method. Furthermore, when the user is traveling, the customization unit can provide a travel-related customization method. This makes it possible to adjust the customization means according to the user's current situation. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's current living situation or work situation into AI, and the AI ​​can adjust the customization means.

[0091] The customization unit can estimate the user's emotions and determine the priority of customization based on the estimated user's emotions. For example, if the user is stressed, the customization unit can postpone customization of lower importance. Furthermore, if the user is relaxed, the customization unit can prioritize customization of higher importance. Furthermore, if the user is busy, the customization unit can prioritize only the minimum necessary customization. For example, if the user is stressed, the customization unit can postpone customization of lower importance. Furthermore, if the user is relaxed, the customization unit can prioritize customization of higher importance. Furthermore, if the user is busy, the customization unit can prioritize only the minimum necessary customization. This allows the priority of customization to be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the customization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the customization unit can input the user's emotional data into the AI, which can then determine the priorities for customization.

[0092] During customization, the customization unit can select a customization method taking into account the user's geographical location information. For example, when the user is in a specific area, the customization unit can provide a customization method related to the area. Furthermore, when the user is traveling, the customization unit can provide a customization method related to the destination. Furthermore, when the user is at home, the customization unit can provide a customization method related to the home. For example, when the user is in a specific area, the customization unit can provide a customization method related to the area. Furthermore, when the user is traveling, the customization unit can provide a customization method related to the destination. Furthermore, when the user is at home, the customization unit can provide a customization method related to the home. This makes it possible to select an optimal customization method based on the user's geographical location information. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's geographical location information into AI, which can select an optimal customization method.

[0093] During customization, the customization unit can analyze the user's social media activity and suggest customization methods. For example, the customization unit can suggest relevant customization methods based on information shared by the user on social media. The customization unit can also suggest relevant customization methods based on information about accounts the user follows on social media. Furthermore, the customization unit can also suggest relevant customization methods based on information about groups the user participates in on social media. For example, the customization unit can suggest relevant customization methods based on information shared by the user on social media. The customization unit can also suggest relevant customization methods based on information about accounts the user follows on social media. Furthermore, the customization unit can also suggest relevant customization methods based on information about groups the user participates in on social media. In this way, customization methods can be suggested based on the user's social media activity. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's social media activity into AI, which then suggests customization methods.

[0094] The management unit can estimate the user's emotions and adjust the management method based on the estimated user's emotions. For example, when the user is feeling stressed, the management unit can provide a simple, highly visible management method. Furthermore, when the user is relaxed, the management unit can provide a detailed management method. Furthermore, when the user is busy, the management unit can provide a concise management method that focuses on the main points. For example, when the user is feeling stressed, the management unit can provide a simple, highly visible management method. Furthermore, when the user is relaxed, the management unit can provide a detailed management method. Furthermore, when the user is busy, the management unit can provide a concise management method that focuses on the main points. This allows the management method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the management department can input user emotional data into the AI, which can then adjust its management methods.

[0095] During management, the management unit can analyze the user's past management history and select the optimal management method. For example, the management unit can prioritize and suggest management methods that the user has used in the past. The management unit can also select the optimal management method from the user's past management history. Furthermore, the management unit can automatically display management methods that the user has frequently used in the past as candidates. For example, the management unit prioritizes and suggests management methods that the user has used in the past. The management unit can also select the optimal management method from the user's past management history. Furthermore, the management unit can automatically display management methods that the user has frequently used in the past as candidates. This allows the optimal management method to be selected based on the user's past management history. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the user's past management history into AI, which then selects the optimal management method.

[0096] During management, the management unit can customize the management means based on the user's current living situation or work situation. For example, when the user is at work, the management unit can provide a work-related management method. Furthermore, when the user is at home, the management unit can provide a home-related management method. Furthermore, when the user is traveling, the management unit can provide a travel-related management method. For example, when the user is at work, the management unit can provide a work-related management method. Furthermore, when the user is at home, the management unit can provide a home-related management method. Furthermore, when the user is traveling, the management unit can provide a travel-related management method. This allows the management means to be adjusted according to the user's current situation. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the user's current living situation or work situation into AI, and the AI ​​can adjust the management means.

[0097] The management unit can estimate the user's emotions and determine management priorities based on the estimated user emotions. For example, if the user is feeling stressed, the management unit can postpone less important management. Furthermore, if the user is relaxed, the management unit can prioritize more important management. Furthermore, if the user is busy, the management unit can prioritize only the minimum necessary management. For example, if the user is feeling stressed, the management unit can postpone less important management. Furthermore, if the user is relaxed, the management unit can prioritize more important management. Furthermore, if the user is busy, the management unit can prioritize only the minimum necessary management. This allows management priorities to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the management department can input user emotional data into the AI, which can then determine management priorities.

[0098] During management, the management unit can select a management method taking into account the user's geographical location information. For example, if the user is in a specific area, the management unit can provide a management method related to that area. Furthermore, if the user is traveling, the management unit can provide a management method related to the user's destination. Furthermore, if the user is at home, the management unit can provide a management method related to the user's home. For example, if the user is in a specific area, the management unit can provide a management method related to that area. Furthermore, if the user is traveling, the management unit can provide a management method related to the user's destination. Furthermore, if the user is at home, the management unit can provide a management method related to the user's home. This allows the optimal management method to be selected based on the user's geographical location information. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the user's geographical location information into AI, which can select the optimal management method.

[0099] During management, the management unit can analyze the user's social media activity and suggest management measures. For example, the management unit can suggest relevant management methods based on information shared by the user on social media. The management unit can also suggest relevant management methods based on information about accounts the user follows on social media. Furthermore, the management unit can also suggest relevant management methods based on information about groups the user participates in on social media. For example, the management unit can suggest relevant management methods based on information shared by the user on social media. The management unit can also suggest relevant management methods based on information about accounts the user follows on social media. Furthermore, the management unit can also suggest relevant management methods based on information about groups the user participates in on social media. In this way, management measures can be suggested based on the user's social media activity. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the user's social media activity into AI, which then suggests management measures. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, customization unit, and management 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 can collect data using a sensor or a user input interface of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides advice based on the analysis results. The customization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and adjusts the operation of each unit to meet the user's needs. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records expenses, manages expenditures, and predicts income. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, customization unit, and management 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 can collect data using a sensor or a user input interface of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides advice based on the analysis results. The customization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and adjusts the operation of each unit according to the user's needs. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records expenses, manages expenditures, and predicts income. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, provision unit, customization unit, and management 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 can collect data using a sensor or a user input interface of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides advice based on the analysis results. The customization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and adjusts the operation of each unit to meet the user's needs. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records expenses, manages expenditures, and predicts income. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, provision unit, customization unit, and management 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 can collect data using sensors or a user input interface of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides advice based on the analysis results. The customization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and adjusts the operation of each unit to meet the user's needs. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records expenses, manages expenditures, and predicts income.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize analyzing important data, and if the user is relaxed, it can perform a detailed analysis. Also, if the user is busy, it can prioritize analyzing only the minimum amount of data necessary. This allows the analysis priority to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, or can be performed without AI. For example, the analysis unit can input the user's emotion data into AI, which can then determine the analysis priority.

[0102] The providing unit can estimate the user's emotions and adjust the timing of advice based on the estimated user's emotions. For example, if the user is feeling stressed, the providing of advice can be delayed to provide the advice when the user is relaxed. Also, if the user is busy, the providing of advice can be adjusted to provide the advice when the user's schedule is free. Furthermore, if the user is relaxed, the providing of advice can be accelerated to provide the advice efficiently. This allows the timing of advice to be adjusted according to the user's emotions. The estimation of emotions is realized using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into an AI, which can adjust the timing of advice.

[0103] The customization unit can estimate the user's emotions and adjust the frequency of customization based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of customization can be reduced so that customization can be performed when the user is relaxed. Also, if the user is busy, the frequency of customization can be adjusted so that customization can be performed when the user's schedule is free. Furthermore, if the user is relaxed, the frequency of customization can be increased so that customization can be performed efficiently. This allows the frequency of customization to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the customization unit can be performed using AI or without AI. For example, the customization unit can input user emotion data into AI, which can then adjust the frequency of customization.

[0104] The management unit can estimate the user's emotions and adjust the management notification method based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible notification method can be provided. If the user is relaxed, a detailed notification method can be provided. Furthermore, if the user is busy, a concise notification method that focuses on the main points can be provided. This allows the management notification method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the management unit can be performed using AI or without AI. For example, the management unit can input the user's emotion data into AI, which can then adjust the management notification method.

[0105] The providing unit can estimate the user's emotions and adjust the format of the advice based on the estimated user's emotions. For example, if the user is feeling stressed, the unit can provide advice using visually easy-to-understand graphs or charts. If the user is relaxed, the unit can provide detailed text-format advice. If the user is busy, the unit can provide advice in bullet-point format that focuses on the main points. This allows the format of the advice to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the providing unit may be performed using AI or without AI. For example, the providing unit can input the user's emotion data into AI, which can then adjust the format of the advice.

[0106] The analysis unit can select an analysis method based on the data collection source. For example, real-time analysis can be performed on data collected from a sensor. Batch processing can also be performed on manually entered data. Furthermore, statistical analysis can be performed on data acquired through an API. This allows the optimal analysis method to be selected depending on the data collection source. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the data collection source into AI, which can then select the optimal analysis method.

[0107] The providing unit can analyze the user's past advice history and select the optimal advice method. For example, it can preferentially provide advice formats that the user has preferred in the past. It can also select the optimal advice method for a specific situation from the user's past advice history. Furthermore, it can automatically display advice methods that the user has frequently used in the past as candidates. This makes it possible to select the optimal advice method based on the user's past advice history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's past advice history into AI, which can select the optimal advice method.

[0108] The customization unit can adjust the customization content taking into account the user's geographical location information. For example, if the user is in a specific area, customization tailored to the climate and culture of that area can be provided. Also, if the user is traveling, customization can be performed based on information about the user's destination. Furthermore, if the user is at home, customization optimized for the home environment can be provided. This makes it possible to provide optimal customization content based on the user's geographical location information. Some or all of the above-described processing in the customization unit may be performed using AI, or may be performed without using AI. For example, the customization unit can input the user's geographical location information into AI, which can select optimal customization content.

[0109] The management unit can analyze the user's social media activity and suggest relevant management methods. For example, the management unit can suggest relevant management methods based on information shared by the user on social media. The management unit can also suggest relevant management methods based on information about accounts the user follows on social media. The management unit can also suggest relevant management methods based on information about groups the user participates in on social media. In this way, management methods can be suggested based on the user's social media activity. Some or all of the above-mentioned processing in the management unit may be performed using AI, or may be performed without using AI. For example, the management unit can input the user's social media activity into AI, which then suggests management methods.

[0110] When collecting data, the collection unit can filter the data based on the user's current living situation or work situation. For example, if the user is at work, only work-related data can be collected. Also, if the user is at home, only home-related data can be collected. Furthermore, if the user is traveling, only travel-related data can be collected. This allows data to be filtered according to the user's current situation. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's current living situation or work situation into AI, which then filters the data.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The collection unit collects data such as utility bills, schedules, and inventory for homes and businesses. For example, it collects utility bill data using sensors, schedule data manually entered by users, and inventory data through APIs. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the utility bill data using statistical analysis, the schedule data using machine learning algorithms, and the inventory data using data mining techniques. Step 3: The provision unit provides advice based on the analysis results obtained by the analysis unit, such as suggestions for saving on utility costs, optimizing schedules, and efficiently managing inventory. Step 4: The customization unit adjusts the collection unit, analysis unit, and provision unit to suit the user's needs. For example, the customization unit adjusts the data collection method of the collection unit based on the user's settings, the analysis unit adjusts the analysis method based on past usage history, and the provision unit adjusts the advice method based on individual needs. Step 5: The management department records expenses, manages expenditures, forecasts income, and provides appropriate advice to users, such as scanning receipts and converting them into digital data, setting budgets, tracking expenses, and analyzing past income data to forecast future income.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 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 data; an analysis unit that analyzes the data collected by the collection unit; a providing unit that provides advice based on the analysis result obtained by the analyzing unit; A customization section that customizes the system to meet the user's needs; and a management department that supports administrative processing and household account book management. A system characterized by:

2. The collecting unit Collect home and business utility bill, schedule, and inventory data 2. The system of claim 1.

3. The analysis unit Analyzing the data collected by the collection unit and providing management methods and advice 2. The system of claim 1.

4. The providing unit Based on the analysis results obtained by the analysis unit, methods for saving on utility costs, schedule management, and efficient inventory management methods are proposed.

2. The system of claim 1.

5. The customization unit Adjusting the collection unit, analysis unit, and provision unit to suit the needs of the user 2. The system of claim 1.

6. The management unit Record expenses, manage expenditures, forecast income, and provide appropriate advice to users 2. The system of claim 1.

7. The collecting unit Estimate user emotions and adjust data collection timing 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 collection method 2. The system of claim 1.

9. The collecting unit As data is collected, it is filtered based on the user's current life and work situation.

2. The system of claim 1.

10. The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions 2. The system of claim 1.

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