System

Abstract

An object of the system according to the embodiment is to refer to long-term past data, perform personal statistical processing, and enable a user to freely operate data.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a statistical processing unit, a query generation unit, and a reminiscence information sharing unit. The collection unit collects long-term past data of the user. The analysis unit analyzes the data collected by the collection unit and grasps a behavior pattern and a tendency of the user. The statistical processing unit performs personal statistical processing based on the analysis result obtained by the analysis unit. The query generation unit analyzes a natural language instruction based on the result obtained by the statistical processing unit and generates a query. The reminiscence sharing unit organizes and presents reminiscences of the user based on the query generated by the query generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult to refer to long-term past data or perform personal statistical processing, and users cannot freely manipulate data.

[0005] The system according to the embodiment aims to enable users to refer to long-term historical data, perform personal statistical processing, and freely manipulate data. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a statistical processing unit, a query generation unit, and a memory sharing unit. The collection unit collects long-term historical data of the user. The analysis unit analyzes the data collected by the collection unit to understand the user's behavioral patterns and trends. The statistical processing unit performs personal statistical processing based on the analysis results obtained by the analysis unit. The query generation unit analyzes natural language commands based on the results obtained by the statistical processing unit and generates a query. The memory sharing unit organizes and presents the user's memories based on the query generated by the query generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can refer to long-term historical data, perform personal statistical processing, and allow users to freely manipulate the data. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses a generation AI to enable access to long-term historical data, enabling personal statistical processing and memory sharing. This system collects a user's long-term historical data, analyzes it using a generation AI, and identifies the user's behavioral patterns and trends. The generation AI then performs personal statistical processing based on the analysis results and analyzes natural language commands to generate appropriate queries. Furthermore, the generation AI organizes and presents the user's memories based on the queries generated. For example, the system can analyze a user's past expenditures and events attended to compile monthly spending trends and memories related to specific events. Furthermore, when a user inputs a natural language command such as "Show me photos from last year's trip," the generation AI generates an appropriate query and organizes and presents past travel photos and event records. This allows the user to reflect on their past behavior and use it as a reference for future planning. This allows the system to collect, analyze, statistically process, generate queries, and share memories. For example, the system can analyze a user's past expenditures and events attended to compile monthly spending trends and memories related to specific events. Additionally, when a user inputs a natural language command such as "Show me photos from last year's trip," the generative AI generates an appropriate query and presents organized photos and event records from past trips, allowing users to look back on their past actions and use them as reference when making future plans.

[0029] An information processing system according to an embodiment includes a collection unit, an analysis unit, a statistical processing unit, a query generation unit, and a memory sharing unit. The collection unit collects long-term historical data of a user. The collection unit can collect personal data, such as a household account book or diary. The collection unit can also efficiently collect the user's historical data using a generation AI. For example, the collection unit automatically collects data from the user's smartphone or PC. The collection unit can also collect data from cloud storage with the user's permission. The analysis unit analyzes the data collected by the collection unit to understand the user's behavioral patterns and trends. The analysis unit can analyze the data in detail using the generation AI. For example, the analysis unit analyzes the user's spending patterns and event participation history. The analysis unit can also analyze the user's behavioral patterns over time to understand long-term trends. The statistical processing unit performs personal statistical processing based on the analysis results obtained by the analysis unit. The statistical processing unit can efficiently perform statistical processing using the generation AI. For example, the statistical processing unit compiles monthly spending trends and memories related to specific events. The statistical processing unit can also provide reference information for making future plans based on the user's behavioral patterns. The query generation unit analyzes natural language commands based on the results obtained by the statistical processing unit and generates an appropriate query. The query generation unit can analyze natural language commands using a generation AI. For example, the query generation unit analyzes a command such as "Show me photos of last year's trip" and generates an appropriate query. The query generation unit can also acquire information from multiple data sources based on the user's command. The memory sharing unit organizes and presents the user's memories based on the query generated by the query generation unit. The memory sharing unit can efficiently organize the user's memories using a generation AI. For example, the memory sharing unit organizes photos and event records from past trips and presents them to the user. The memory sharing unit can also organize the user's memories in chronological order and visually display them. This enables the information processing system according to the embodiment to collect, analyze, statistically process, generate queries, and share memories of a user's long-term past data.

[0030] The collection unit can collect personal data from a household account book or diary. The collection unit, for example, collects household account book data. For example, the collection unit can collect expenditure and income data entered by the user. The collection unit can also collect diary data. For example, the collection unit can collect text data of diaries written by the user. The collection unit can also collect household account book or diary data from cloud storage with the user's permission. By collecting personal data, the user's behavioral patterns and tendencies can be more accurately understood. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can automatically collect data from the user's smartphone or computer and input the data into the generation AI for analysis.

[0031] The analysis unit can analyze the collected data to understand the user's behavioral patterns and trends. The analysis unit can, for example, analyze collected household accounting data to understand the user's spending patterns. For example, the analysis unit can analyze the user's monthly spending amount and spending categories. The analysis unit can also analyze collected diary data to understand the user's behavioral patterns. For example, the analysis unit can analyze the contents of the user's diary to understand the frequency of specific events or activities. The analysis unit can also analyze the collected data in chronological order to understand long-term trends. For example, the analysis unit can analyze changes in the user's spending patterns and behavioral patterns in chronological order. In this way, the user's behavioral patterns and trends can be understood by analyzing the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the collected data into the generation AI, which can analyze the data to understand the behavioral patterns and trends.

[0032] The statistical processing unit can compile monthly spending trends or memories related to specific events. For example, the statistical processing unit compiles monthly spending trends. For example, the statistical processing unit can aggregate a user's monthly spending amounts and spending categories and display them as graphs or charts. The statistical processing unit can also compile memories related to specific events. For example, the statistical processing unit can analyze a user's diary and photo data and organize memories related to specific events in chronological order. The statistical processing unit can also provide reference information for future planning based on the user's behavioral patterns. For example, the statistical processing unit can suggest future spending plans and event plans based on the user's spending patterns and event participation history. This allows the user to look back on their past behavior by compiling monthly spending trends and memories related to specific events. Some or all of the above-described processing in the statistical processing unit may be performed using or without the generation AI. For example, the statistical processing unit can input collected data into the generation AI, which then performs statistical processing and outputs the results.

[0033] The query generation unit can analyze natural language commands and generate an appropriate query. For example, the query generation unit can analyze natural language commands input by a user and generate an appropriate query. For example, the query generation unit can analyze a command such as "Show me photos from last year's trip" and generate a query to search for photos of past trips. The query generation unit can also generate queries to obtain information from multiple data sources. For example, the query generation unit generates queries to obtain data from a user's smartphone or cloud storage. The query generation unit can also efficiently analyze natural language commands using a generation AI. For example, the query generation unit uses natural language processing technology to analyze the commands and generate an appropriate query. This frees the user from the constraints of the app by analyzing the natural language commands and generating an appropriate query. Some or all of the above-described processing in the query generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the query generation unit can input a user's command into a generation AI, which then analyzes the command and generates a query.

[0034] The memory sharing unit can organize photos of past trips or records of events and present them to the user. For example, the memory sharing unit can organize photos of past trips and present them to the user. For example, the memory sharing unit can organize travel photos taken by the user in chronological order and display them in an album format. The memory sharing unit can also organize event records. For example, the memory sharing unit can organize photos and video clips of events the user attended and visually display them. The memory sharing unit can also efficiently organize the user's memories using a generation AI. For example, the memory sharing unit uses a generation AI to analyze the content of photos and video clips and automatically organize related memories. This allows memories to be shared by organizing photos of past trips and records of events and presenting them to the user. Some or all of the above-described processing in the memory sharing unit may be performed using or without the generation AI. For example, the memory sharing unit can input the user's photos and video clips into the generation AI, which then analyzes the content and organizes memories.

[0035] The collection unit can analyze the user's past data collection history and select a collection method. The collection unit, for example, analyzes the user's past data collection history and selects the optimal collection method. For example, the collection unit can prioritize the selection of a data collection method that the user has frequently used in the past. The collection unit can also allow the generation AI to select the most efficient collection method from the user's past data collection history. The collection unit can also analyze the user's past data collection history and customize the collection method. For example, the collection unit adjusts the collection method based on the user's past data collection history. In this way, the optimal collection method can be selected by analyzing the user's past data collection history. Some or all of the above-described processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's past data collection history into the generation AI, which can select the optimal collection method.

[0036] The collection unit can filter data based on the user's current living situation and areas of interest when collecting data. For example, the collection unit can filter data based on the user's current living situation and areas of interest when collecting data. For example, the collection unit can prioritize collecting data related to areas in which the user is currently interested. The collection unit can also filter and collect highly relevant data according to the user's living situation. The collection unit can also exclude unnecessary data and collect only necessary data based on the user's areas of interest. For example, the collection unit prioritizes collecting data related to the user's hobbies and topics of interest. This makes it possible to collect highly relevant data by filtering based on the user's current living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's current living situation and areas of interest into the generation AI, and the generation AI can perform filtering.

[0037] The collection unit can select a collection means according to the user's input method when collecting data. For example, the collection unit selects the optimal collection means according to the user's input method when collecting data. For example, when the user uses voice input, the collection unit can cause the generation AI to prioritize collecting voice data. Also, when the user uses text input, the collection unit can cause the generation AI to prioritize collecting text data. Also, when the user uses image input, the collection unit can cause the generation AI to prioritize collecting image data. For example, the collection unit collects image data taken by the user using a smartphone camera. This allows efficient data collection by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's input method to the generation AI, and the generation AI can select the optimal collection means.

[0038] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, if the user is in a specific area, the collection unit can prioritize collecting data related to that area. The collection unit can also filter and collect highly relevant data based on the user's current location. The collection unit can also exclude unnecessary data and collect only necessary data by taking into account the user's geographical location information. For example, if the user is traveling, the collection unit prioritizes collecting data related to the travel destination. This allows for efficient data collection by collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's geographical location information into the generation AI, which can select highly relevant data.

[0039] The collection unit can analyze the user's social media activities and collect relevant data when collecting data. For example, the collection unit can analyze the user's social media activities and collect relevant data when collecting data. For example, the collection unit can collect relevant data based on information shared by the user on social media. The collection unit can also analyze the user's social media activities and prioritize collection of highly relevant data. The collection unit can also collect relevant data by referring to the activities of the user's friends on social media. For example, the collection unit collects data related to posts that the user has "liked" on social media. In this way, highly relevant data can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's social media activity data into the generation AI, which can select relevant data.

[0040] The collection unit can adjust the collection method when collecting data by reflecting the user's past feedback. For example, the collection unit can adjust the collection method when collecting data by reflecting the user's past feedback. For example, the collection unit can adjust the collection method based on feedback provided by the user in the past. The collection unit can also customize the type of data to be collected by reflecting the user's past feedback. The collection unit can also optimize the collection method by referring to the user's past feedback. For example, if the user has previously given feedback that "this data is unnecessary," the collection unit adjusts the collection method so that the data is not collected. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's past feedback data into the generation AI, which can then adjust the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the data. For example, the analysis unit can have the generation AI perform a detailed analysis of data with high importance. The analysis unit can also have the generation AI perform a concise analysis of data with low importance. The analysis unit can also have the generation AI adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the importance of the data to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies different analysis algorithms depending on the data category. For example, the analysis unit can have the generation AI apply a specific analysis algorithm to household accounting data. The analysis unit can also have the generation AI apply a different analysis algorithm to diary data. The analysis unit can also have the generation AI select the optimal analysis algorithm depending on the data category. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the data category to the generation AI, and the generation AI can select the optimal analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the analysis by referring to, for example, the user's past analysis results. For example, the analysis unit can cause the generation AI to improve the accuracy of the analysis based on the user's past analysis results. The analysis unit can also cause the generation AI to adjust the analysis method by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and cause the generation AI to select the optimal analysis method. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's past analysis results into the generation AI, and the generation AI can improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time of data submission. The analysis unit determines the analysis priority based on, for example, the time of data submission. For example, the analysis unit can prioritize analysis of recently submitted data. The analysis unit can also postpone analysis of data that was submitted earlier. The analysis unit can also adjust the analysis priority based on the time of data submission. In this way, by determining the analysis priority based on the time of data submission, analysis can be performed efficiently. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the time of data submission to the generation AI, and the generation AI can determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit adjusts the order of analysis based on, for example, 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. The analysis unit can also adjust the order of analysis based on the relevance of the data. In this way, analysis can be performed efficiently by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the relevance of the data to the generation AI, and the generation AI can adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit can, for example, adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can cause the generation AI to provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can cause the generation AI to provide analysis results in simple language. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. This makes it possible to provide analysis results that are easier to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's level of expertise into the generation AI, and the generation AI can adjust the use of technical terms in the analysis.

[0047] During statistical processing, the statistical processing unit can analyze the user's past data and select a statistical processing method. The statistical processing unit, for example, analyzes the user's past data and selects the optimal statistical processing method. For example, the statistical processing unit can cause the generation AI to select the optimal statistical processing method based on the user's past data. The statistical processing unit can also analyze the user's past data and cause the generation AI to adjust the statistical processing method. The statistical processing unit can also cause the generation AI to select the optimal statistical processing method by referring to the user's past data. In this way, the optimal statistical processing method can be selected by analyzing the user's past data. Some or all of the above-mentioned processing in the statistical processing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the statistical processing unit can input the user's past data into the generation AI, and the generation AI can select the optimal statistical processing method.

[0048] During statistical processing, the statistical processing unit can customize the statistical processing means based on the user's current living situation. The statistical processing unit customizes the statistical processing means based on, for example, the user's current living situation. For example, the statistical processing unit allows the generation AI to customize the statistical processing means according to the user's current living situation. The statistical processing unit can also allow the generation AI to select the optimal statistical processing method based on the user's living situation. The statistical processing unit can also allow the generation AI to adjust the statistical processing means taking the user's living situation into consideration. In this way, more appropriate statistical processing can be performed by customizing the statistical processing means based on the user's current living situation. Some or all of the above-mentioned processing in the statistical processing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the statistical processing unit can input the user's current living situation into the generation AI, and the generation AI can customize the statistical processing means.

[0049] The statistical processing unit can improve the statistical processing method by reflecting user feedback during statistical processing. For example, the statistical processing unit can improve the statistical processing method by reflecting user feedback during statistical processing. For example, the statistical processing unit can cause the generation AI to improve the statistical processing method based on user feedback. The statistical processing unit can also cause the generation AI to adjust the statistical processing method by reflecting past user feedback. The statistical processing unit can also analyze user feedback and cause the generation AI to select the optimal statistical processing method. In this way, the statistical processing method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the statistical processing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the statistical processing unit can input user feedback into the generation AI, and the generation AI can improve the statistical processing method.

[0050] The statistical processing unit can select a statistical processing method taking into account the user's geographical location information during statistical processing. For example, the statistical processing unit selects the optimal statistical processing method taking into account the user's geographical location information during statistical processing. For example, if the user is in a specific area, the statistical processing unit can prioritize statistical processing related to that area. The statistical processing unit can also perform highly relevant statistical processing based on the user's current location. The statistical processing unit can also exclude unnecessary statistical processing and perform only necessary statistical processing taking into account the user's geographical location information. This makes it possible to select the optimal statistical processing method by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the statistical processing unit may be performed using or without the generation AI. For example, the statistical processing unit can input the user's geographical location information into the generation AI, which can select the optimal statistical processing method.

[0051] The statistical processing unit can analyze the user's social media activity and suggest statistical processing methods during statistical processing. For example, the statistical processing unit can analyze the user's social media activity and suggest statistical processing methods during statistical processing. For example, the statistical processing unit can perform relevant statistical processing based on information shared by the user on social media. The statistical processing unit can also analyze the user's social media activity and prioritize highly relevant statistical processing. The statistical processing unit can also perform relevant statistical processing with reference to the activity of the user's friends on social media. In this way, highly relevant statistical processing can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the statistical processing unit may be performed using or without the generation AI. For example, the statistical processing unit can input the user's social media activity data into the generation AI, which can suggest relevant statistical processing.

[0052] The statistical processing unit can adjust the statistical processing method by reflecting the user's past feedback during statistical processing. For example, the statistical processing unit adjusts the statistical processing method by reflecting the user's past feedback during statistical processing. For example, the statistical processing unit allows the generation AI to customize the statistical processing method based on the user's past feedback. The statistical processing unit can also allow the generation AI to adjust the statistical processing method by reflecting the user's past feedback. The statistical processing unit can also allow the generation AI to select the optimal statistical processing method by referring to the user's past feedback. In this way, the statistical processing method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the statistical processing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the statistical processing unit can input the user's past feedback into the generation AI, and the generation AI can adjust the statistical processing method.

[0053] The query generation unit can select a query generation method by analyzing the user's past query history when generating a query. For example, the query generation unit can analyze the user's past query history and select an optimal query generation method when generating a query. For example, the query generation unit can cause the generation AI to select an optimal query generation method based on the user's past query history. The query generation unit can also analyze the user's past query history and cause the generation AI to adjust the query generation method. The query generation unit can also cause the generation AI to select an optimal query generation method by referring to the user's past query history. In this way, the optimal query generation method can be selected by analyzing the user's past query history. Some or all of the above-described processing in the query generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the query generation unit can input the user's past query history into the generation AI, and the generation AI can select an optimal query generation method.

[0054] The query generation unit can customize the means for query generation based on the user's current living situation when generating a query. The query generation unit customizes the means for query generation based on the user's current living situation when generating a query. For example, the query generation unit can cause the generation AI to customize the means for query generation according to the user's current living situation. The query generation unit can also cause the generation AI to select an optimal query generation method based on the user's living situation. The query generation unit can also cause the generation AI to adjust the means for query generation taking the user's living situation into consideration. In this way, by customizing the means for query generation based on the user's current living situation, a more appropriate query can be generated. Some or all of the above-described processing in the query generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the query generation unit can input the user's current living situation into the generation AI, and the generation AI can customize the means for query generation.

[0055] The query generation unit can improve the query generation method by reflecting user feedback when generating a query. For example, the query generation unit can improve the query generation method by reflecting user feedback when generating a query. For example, the query generation unit can cause the generation AI to improve the query generation method based on user feedback. The query generation unit can also cause the generation AI to adjust the query generation method by reflecting past user feedback. The query generation unit can also analyze user feedback and cause the generation AI to select an optimal query generation method. In this way, the query generation method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the query generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the query generation unit can input user feedback into the generation AI, and the generation AI can improve the query generation method.

[0056] The query generation unit can select a query generation method by taking into account the user's geographical location information when generating a query. For example, the query generation unit selects the optimal query generation method by taking into account the user's geographical location information when generating a query. For example, if the user is in a specific area, the query generation unit can preferentially generate queries related to that area. The query generation unit can also generate highly relevant queries based on the user's current location. The query generation unit can also exclude unnecessary queries and generate only necessary queries by taking into account the user's geographical location information. In this way, the optimal query generation method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the query generation unit may be performed using a generation AI or may be performed without using a generation AI. For example, the query generation unit can input the user's geographical location information to the generation AI, which can then select the optimal query generation method.

[0057] The query generation unit can analyze the user's social media activity and suggest a means for generating a query when generating a query. For example, the query generation unit can analyze the user's social media activity and suggest a means for generating a query when generating a query. For example, the query generation unit can generate relevant queries based on information shared by the user on social media. The query generation unit can also analyze the user's social media activity and prioritize generating highly relevant queries. The query generation unit can also generate relevant queries by referring to the activity of the user's friends on social media. In this way, highly relevant queries can be generated by analyzing the user's social media activity. Some or all of the above-described processing in the query generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the query generation unit can input the user's social media activity data into the generation AI, which can then suggest relevant queries.

[0058] The query generation unit can adjust the query generation method by reflecting the user's past feedback when generating a query. For example, the query generation unit can adjust the query generation method by reflecting the user's past feedback when generating a query. For example, the query generation unit can customize the query generation method by the generation AI based on the user's past feedback. The query generation unit can also adjust the query generation method by reflecting the user's past feedback. The query generation unit can also select the optimal query generation method by referring to the user's past feedback. In this way, the query generation method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the query generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the query generation unit can input the user's past feedback into the generation AI, and the generation AI can adjust the query generation method.

[0059] The memory sharing unit can analyze the user's past memories and select a sharing method when sharing memories. For example, the memory sharing unit analyzes the user's past memories and selects the optimal sharing method when sharing memories. For example, the memory sharing unit can have the generation AI select the optimal sharing method based on the user's past memories. The memory sharing unit can also analyze the user's past memories and have the generation AI adjust the sharing method. The memory sharing unit can also select the optimal sharing method by referring to the user's past memories. In this way, the optimal sharing method can be selected by analyzing the user's past memories. Some or all of the above-mentioned processing in the memory sharing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the memory sharing unit can input the user's past memories into the generation AI, and the generation AI can select the optimal sharing method.

[0060] The memory sharing unit can customize the memory sharing means based on the user's current living situation when sharing memories. For example, the memory sharing unit customizes the memory sharing means based on the user's current living situation when sharing memories. For example, the memory sharing unit can have the generation AI customize the memory sharing means according to the user's current living situation. The memory sharing unit can also have the generation AI select the optimal memory sharing method based on the user's living situation. The memory sharing unit can also have the generation AI adjust the memory sharing means taking the user's living situation into consideration. In this way, more appropriate memories can be shared by customizing the memory sharing means based on the user's current living situation. Some or all of the above-described processing in the memory sharing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the memory sharing unit can input the user's current living situation into the generation AI, and the generation AI can customize the memory sharing means.

[0061] The memory sharing unit can improve the memory sharing method by reflecting user feedback when sharing memories. The memory sharing unit, for example, improves the memory sharing method by reflecting user feedback when sharing memories. For example, the memory sharing unit can cause the generation AI to improve the memory sharing method based on user feedback. The memory sharing unit can also cause the generation AI to adjust the memory sharing method by reflecting the user's past feedback. The memory sharing unit can also analyze the user's feedback and cause the generation AI to select the optimal memory sharing method. In this way, the memory sharing method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the memory sharing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the memory sharing unit can input user feedback into the generation AI, and the generation AI can improve the memory sharing method.

[0062] The memory sharing unit can select a memory sharing method taking into account the user's geographical location information when sharing memories. For example, the memory sharing unit selects the optimal memory sharing method taking into account the user's geographical location information when sharing memories. For example, if the user is in a specific area, the memory sharing unit can prioritize sharing memories related to that area. The memory sharing unit can also share highly relevant memories based on the user's current location. The memory sharing unit can also exclude unnecessary memories and share only necessary memories taking into account the user's geographical location information. In this way, the optimal memory sharing method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the memory sharing unit may be performed using or without the generation AI. For example, the memory sharing unit can input the user's geographical location information into the generation AI, which can then select the optimal memory sharing method.

[0063] The memory sharing unit can analyze the user's social media activity and suggest ways to share memories when sharing memories. For example, the memory sharing unit can analyze the user's social media activity and suggest ways to share memories when sharing memories. For example, the memory sharing unit can share related memories based on information the user has shared on social media. The memory sharing unit can also analyze the user's social media activity and prioritize sharing highly relevant memories. The memory sharing unit can also share related memories with reference to the activity of the user's friends on social media. In this way, highly relevant memories can be shared by analyzing the user's social media activity. Some or all of the above-described processing in the memory sharing unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the memory sharing unit can input the user's social media activity data into the generation AI, which can suggest related memories.

[0064] The memory sharing unit can adjust the memory sharing method by reflecting the user's past feedback when sharing memories. For example, the memory sharing unit can adjust the memory sharing method by reflecting the user's past feedback when sharing memories. For example, the memory sharing unit can have the generation AI customize the memory sharing method based on the user's past feedback. The memory sharing unit can also have the generation AI adjust the memory sharing method by reflecting the user's past feedback. The memory sharing unit can also have the generation AI select the optimal memory sharing method by referring to the user's past feedback. In this way, the memory sharing method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the memory sharing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the memory sharing unit can input the user's past feedback into the generation AI, and the generation AI can adjust the memory sharing method.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The collection unit can analyze the user's past data collection history and select a collection method. For example, the collection unit can prioritize the selection of a data collection method that the user has frequently used in the past. The collection unit can also allow the generation AI to select the most efficient collection method from the user's past data collection history. The collection unit can also analyze the user's past data collection history and customize the collection method. For example, the collection unit adjusts the collection method based on the user's past data collection history. In this way, the optimal collection method can be selected by analyzing the user's past data collection history. Some or all of the above-described processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's past data collection history into the generation AI, which can then select the optimal collection method.

[0067] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, the collection unit can prioritize collecting data related to areas in which the user is currently interested. The collection unit can also filter and collect highly relevant data according to the user's living situation. The collection unit can also exclude unnecessary data and collect only necessary data based on the user's areas of interest. For example, the collection unit prioritizes collecting data related to the user's hobbies and topics of interest. This makes it possible to collect highly relevant data by filtering based on the user's current living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's current living situation and areas of interest into the generation AI, which then performs the filtering.

[0068] When collecting data, the collection unit can select a collection means according to the user's input method. For example, when the user uses voice input, the collection unit can cause the generation AI to prioritize collecting voice data. Also, when the user uses text input, the collection unit can cause the generation AI to prioritize collecting text data. Also, when the user uses image input, the collection unit can cause the generation AI to prioritize collecting image data. For example, the collection unit collects image data taken by the user using a smartphone camera. This allows efficient data collection by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's input method to the generation AI, which can select the optimal collection means.

[0069] 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 have the generation AI perform a detailed analysis of data with high importance. The analysis unit can also have the generation AI perform a concise analysis of data with low importance. The analysis unit can also have the generation AI adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the importance of the data to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0070] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can have the generation AI apply a specific analysis algorithm to household accounting data. The analysis unit can also have the generation AI apply a different analysis algorithm to diary data. The analysis unit can also have the generation AI select the optimal analysis algorithm depending on the data category. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the data category to the generation AI, and the generation AI can select the optimal analysis algorithm.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The collection unit collects the user's long-term historical data. The collection unit can collect personal data such as a household account book or diary. The collection unit can also use generative AI to efficiently collect the user's historical data. For example, the collection unit can automatically collect data from the user's smartphone or computer. The collection unit can also collect data from cloud storage with the user's permission. Step 2: The analysis unit analyzes the data collected by the collection unit to understand the user's behavioral patterns and trends. The analysis unit can use generative AI to analyze the data in detail. For example, the analysis unit can analyze the user's spending patterns and event participation history. The analysis unit can also analyze the user's behavioral patterns over time to understand long-term trends. Step 3: The statistical processing unit performs personalized statistical processing based on the analysis results obtained by the analysis unit. The statistical processing unit can efficiently perform statistical processing using generative AI. For example, the statistical processing unit can compile monthly spending trends or memories related to specific events. The statistical processing unit can also provide reference information for future planning based on the user's behavioral patterns. Step 4: The query generation unit analyzes the natural language command based on the results obtained by the statistical processing unit and generates an appropriate query. The query generation unit can analyze the natural language command using generative AI. For example, the query generation unit analyzes a command such as "Show me photos from last year's trip" and generates an appropriate query. The query generation unit can also retrieve information from multiple data sources based on the user's command. Step 5: The memory sharing unit organizes and presents the user's memories based on the query generated by the query generation unit. The memory sharing unit can efficiently organize the user's memories using the generation AI. For example, the memory sharing unit organizes photos of past trips and records of events and presents them to the user. The memory sharing unit can also organize the user's memories in chronological order and display them visually.

[0073] (Example 2) A system according to an embodiment of the present invention uses a generation AI to enable access to long-term historical data, enabling personal statistical processing and memory sharing. This system collects a user's long-term historical data, analyzes it using a generation AI, and identifies the user's behavioral patterns and trends. The generation AI then performs personal statistical processing based on the analysis results and analyzes natural language commands to generate appropriate queries. Furthermore, the generation AI organizes and presents the user's memories based on the queries generated. For example, the system can analyze a user's past expenditures and events attended to compile monthly spending trends and memories related to specific events. Furthermore, when a user inputs a natural language command such as "Show me photos from last year's trip," the generation AI generates an appropriate query and organizes and presents past travel photos and event records. This allows the user to reflect on their past behavior and use it as a reference for future planning. This allows the system to collect, analyze, statistically process, generate queries, and share memories. For example, the system can analyze a user's past expenditures and events attended to compile monthly spending trends and memories related to specific events. Additionally, when a user inputs a natural language command such as "Show me photos from last year's trip," the generative AI generates an appropriate query and presents organized photos and event records from past trips, allowing users to look back on their past actions and use them as reference when making future plans.

[0074] An information processing system according to an embodiment includes a collection unit, an analysis unit, a statistical processing unit, a query generation unit, and a memory sharing unit. The collection unit collects long-term historical data of a user. The collection unit can collect personal data, such as a household account book or diary. The collection unit can also efficiently collect the user's historical data using a generation AI. For example, the collection unit automatically collects data from the user's smartphone or PC. The collection unit can also collect data from cloud storage with the user's permission. The analysis unit analyzes the data collected by the collection unit to understand the user's behavioral patterns and trends. The analysis unit can analyze the data in detail using the generation AI. For example, the analysis unit analyzes the user's spending patterns and event participation history. The analysis unit can also analyze the user's behavioral patterns over time to understand long-term trends. The statistical processing unit performs personal statistical processing based on the analysis results obtained by the analysis unit. The statistical processing unit can efficiently perform statistical processing using the generation AI. For example, the statistical processing unit compiles monthly spending trends and memories related to specific events. The statistical processing unit can also provide reference information for making future plans based on the user's behavioral patterns. The query generation unit analyzes natural language commands based on the results obtained by the statistical processing unit and generates an appropriate query. The query generation unit can analyze natural language commands using a generation AI. For example, the query generation unit analyzes a command such as "Show me photos of last year's trip" and generates an appropriate query. The query generation unit can also acquire information from multiple data sources based on the user's command. The memory sharing unit organizes and presents the user's memories based on the query generated by the query generation unit. The memory sharing unit can efficiently organize the user's memories using a generation AI. For example, the memory sharing unit organizes photos and event records from past trips and presents them to the user. The memory sharing unit can also organize the user's memories in chronological order and visually display them. This enables the information processing system according to the embodiment to collect, analyze, statistically process, generate queries, and share memories of a user's long-term past data.

[0075] The collection unit can collect personal data from a household account book or diary. The collection unit, for example, collects household account book data. For example, the collection unit can collect expenditure and income data entered by the user. The collection unit can also collect diary data. For example, the collection unit can collect text data of diaries written by the user. The collection unit can also collect household account book or diary data from cloud storage with the user's permission. By collecting personal data, the user's behavioral patterns and tendencies can be more accurately understood. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can automatically collect data from the user's smartphone or computer and input the data into the generation AI for analysis.

[0076] The analysis unit can analyze the collected data to understand the user's behavioral patterns and trends. The analysis unit can, for example, analyze collected household accounting data to understand the user's spending patterns. For example, the analysis unit can analyze the user's monthly spending amount and spending categories. The analysis unit can also analyze collected diary data to understand the user's behavioral patterns. For example, the analysis unit can analyze the contents of the user's diary to understand the frequency of specific events or activities. The analysis unit can also analyze the collected data in chronological order to understand long-term trends. For example, the analysis unit can analyze changes in the user's spending patterns and behavioral patterns in chronological order. In this way, the user's behavioral patterns and trends can be understood by analyzing the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the collected data into the generation AI, which can analyze the data to understand the behavioral patterns and trends.

[0077] The statistical processing unit can compile monthly spending trends or memories related to specific events. For example, the statistical processing unit compiles monthly spending trends. For example, the statistical processing unit can aggregate a user's monthly spending amounts and spending categories and display them as graphs or charts. The statistical processing unit can also compile memories related to specific events. For example, the statistical processing unit can analyze a user's diary and photo data and organize memories related to specific events in chronological order. The statistical processing unit can also provide reference information for future planning based on the user's behavioral patterns. For example, the statistical processing unit can suggest future spending plans and event plans based on the user's spending patterns and event participation history. This allows the user to look back on their past behavior by compiling monthly spending trends and memories related to specific events. Some or all of the above-described processing in the statistical processing unit may be performed using or without the generation AI. For example, the statistical processing unit can input collected data into the generation AI, which then performs statistical processing and outputs the results.

[0078] The query generation unit can analyze natural language commands and generate an appropriate query. For example, the query generation unit can analyze natural language commands input by a user and generate an appropriate query. For example, the query generation unit can analyze a command such as "Show me photos from last year's trip" and generate a query to search for photos of past trips. The query generation unit can also generate queries to obtain information from multiple data sources. For example, the query generation unit generates queries to obtain data from a user's smartphone or cloud storage. The query generation unit can also efficiently analyze natural language commands using a generation AI. For example, the query generation unit uses natural language processing technology to analyze the commands and generate an appropriate query. This frees the user from the constraints of the app by analyzing the natural language commands and generating an appropriate query. Some or all of the above-described processing in the query generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the query generation unit can input a user's command into a generation AI, which then analyzes the command and generates a query.

[0079] The memory sharing unit can organize photos of past trips or records of events and present them to the user. For example, the memory sharing unit can organize photos of past trips and present them to the user. For example, the memory sharing unit can organize travel photos taken by the user in chronological order and display them in an album format. The memory sharing unit can also organize event records. For example, the memory sharing unit can organize photos and video clips of events the user attended and visually display them. The memory sharing unit can also efficiently organize the user's memories using a generation AI. For example, the memory sharing unit uses a generation AI to analyze the content of photos and video clips and automatically organize related memories. This allows memories to be shared by organizing photos of past trips and records of events and presenting them to the user. Some or all of the above-described processing in the memory sharing unit may be performed using or without the generation AI. For example, the memory sharing unit can input the user's photos and video clips into the generation AI, which then analyzes the content and organizes memories.

[0080] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of data collection. For example, if the user is relaxed, the collection unit selects the timing for the generation AI to collect the user's past data. Furthermore, if the user is feeling stressed, the collection unit can cause the generation AI to temporarily stop data collection and resume it when the user has calmed down. Furthermore, if the user is busy, the collection unit can cause the generation AI to postpone data collection and collect data when the user has more time. This allows data to be collected at a more appropriate time by adjusting the timing of data collection based on 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-described processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotional data into the generation AI, which can then estimate the emotion and adjust the timing of data collection.

[0081] The collection unit can analyze the user's past data collection history and select a collection method. The collection unit, for example, analyzes the user's past data collection history and selects the optimal collection method. For example, the collection unit can prioritize the selection of a data collection method that the user has frequently used in the past. The collection unit can also allow the generation AI to select the most efficient collection method from the user's past data collection history. The collection unit can also analyze the user's past data collection history and customize the collection method. For example, the collection unit adjusts the collection method based on the user's past data collection history. In this way, the optimal collection method can be selected by analyzing the user's past data collection history. Some or all of the above-described processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's past data collection history into the generation AI, which can select the optimal collection method.

[0082] The collection unit can filter data based on the user's current living situation and areas of interest when collecting data. For example, the collection unit can filter data based on the user's current living situation and areas of interest when collecting data. For example, the collection unit can prioritize collecting data related to areas in which the user is currently interested. The collection unit can also filter and collect highly relevant data according to the user's living situation. The collection unit can also exclude unnecessary data and collect only necessary data based on the user's areas of interest. For example, the collection unit prioritizes collecting data related to the user's hobbies and topics of interest. This makes it possible to collect highly relevant data by filtering based on the user's current living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's current living situation and areas of interest into the generation AI, and the generation AI can perform filtering.

[0083] The collection unit can select a collection means according to the user's input method when collecting data. For example, the collection unit selects the optimal collection means according to the user's input method when collecting data. For example, when the user uses voice input, the collection unit can cause the generation AI to prioritize collecting voice data. Also, when the user uses text input, the collection unit can cause the generation AI to prioritize collecting text data. Also, when the user uses image input, the collection unit can cause the generation AI to prioritize collecting image data. For example, the collection unit collects image data taken by the user using a smartphone camera. This allows efficient data collection by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's input method to the generation AI, and the generation AI can select the optimal collection means.

[0084] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of data to be collected. For example, when the user is relaxed, the collection unit can cause the generation AI to prioritize collecting data of high importance. Furthermore, when the user is stressed, the collection unit can cause the generation AI to prioritize collecting data of low importance. Furthermore, when the user is busy, the collection unit can cause the generation AI to prioritize collecting data of high urgency. In this way, by determining the priority of data to be collected based on the user's emotions, important data can be collected preferentially. 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 collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI, which can then estimate the emotion and determine the priority of the data.

[0085] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, if the user is in a specific area, the collection unit can prioritize collecting data related to that area. The collection unit can also filter and collect highly relevant data based on the user's current location. The collection unit can also exclude unnecessary data and collect only necessary data by taking into account the user's geographical location information. For example, if the user is traveling, the collection unit prioritizes collecting data related to the travel destination. This allows for efficient data collection by collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's geographical location information into the generation AI, which can select highly relevant data.

[0086] The collection unit can analyze the user's social media activities and collect relevant data when collecting data. For example, the collection unit can analyze the user's social media activities and collect relevant data when collecting data. For example, the collection unit can collect relevant data based on information shared by the user on social media. The collection unit can also analyze the user's social media activities and prioritize collection of highly relevant data. The collection unit can also collect relevant data by referring to the activities of the user's friends on social media. For example, the collection unit collects data related to posts that the user has "liked" on social media. In this way, highly relevant data can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's social media activity data into the generation AI, which can select relevant data.

[0087] The collection unit can adjust the collection method when collecting data by reflecting the user's past feedback. For example, the collection unit can adjust the collection method when collecting data by reflecting the user's past feedback. For example, the collection unit can adjust the collection method based on feedback provided by the user in the past. The collection unit can also customize the type of data to be collected by reflecting the user's past feedback. The collection unit can also optimize the collection method by referring to the user's past feedback. For example, if the user has previously given feedback that "this data is unnecessary," the collection unit adjusts the collection method so that the data is not collected. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's past feedback data into the generation AI, which can then adjust the collection method.

[0088] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the way the analysis is presented. For example, if the user is relaxed, the analysis unit can cause the generation AI to provide detailed analysis results. If the user is stressed, the analysis unit can cause the generation AI to provide concise analysis results. If the user is busy, the analysis unit can cause the generation AI to provide analysis results that focus on the main points. This allows for more appropriate analysis results to be provided by adjusting the way the analysis is presented based on 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 the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then estimate the emotion and adjust the way the analysis is presented.

[0089] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the data. For example, the analysis unit can have the generation AI perform a detailed analysis of data with high importance. The analysis unit can also have the generation AI perform a concise analysis of data with low importance. The analysis unit can also have the generation AI adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the importance of the data to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0090] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies different analysis algorithms depending on the data category. For example, the analysis unit can have the generation AI apply a specific analysis algorithm to household accounting data. The analysis unit can also have the generation AI apply a different analysis algorithm to diary data. The analysis unit can also have the generation AI select the optimal analysis algorithm depending on the data category. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the data category to the generation AI, and the generation AI can select the optimal analysis algorithm.

[0091] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the analysis by referring to, for example, the user's past analysis results. For example, the analysis unit can cause the generation AI to improve the accuracy of the analysis based on the user's past analysis results. The analysis unit can also cause the generation AI to adjust the analysis method by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and cause the generation AI to select the optimal analysis method. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's past analysis results into the generation AI, and the generation AI can improve the accuracy of the analysis.

[0092] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis. For example, if the user is relaxed, the analysis unit can cause the generation AI to perform a detailed analysis. Furthermore, if the user is stressed, the analysis unit can cause the generation AI to perform a concise analysis. Furthermore, if the user is busy, the analysis unit can cause the generation AI to perform an analysis that focuses on the main points. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then estimate the emotion and adjust the length of the analysis.

[0093] During analysis, the analysis unit can determine the analysis priority based on the time of data submission. The analysis unit determines the analysis priority based on, for example, the time of data submission. For example, the analysis unit can prioritize analysis of recently submitted data. The analysis unit can also postpone analysis of data that was submitted earlier. The analysis unit can also adjust the analysis priority based on the time of data submission. In this way, by determining the analysis priority based on the time of data submission, analysis can be performed efficiently. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the time of data submission to the generation AI, and the generation AI can determine the analysis priority.

[0094] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit adjusts the order of analysis based on, for example, 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. The analysis unit can also adjust the order of analysis based on the relevance of the data. In this way, analysis can be performed efficiently by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the relevance of the data to the generation AI, and the generation AI can adjust the order of analysis.

[0095] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit can, for example, adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can cause the generation AI to provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can cause the generation AI to provide analysis results in simple language. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. This makes it possible to provide analysis results that are easier to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's level of expertise into the generation AI, and the generation AI can adjust the use of technical terms in the analysis.

[0096] The statistical processing unit can estimate the user's emotions and adjust the statistical processing method based on the estimated user's emotions. The statistical processing unit, for example, estimates the user's emotions and adjusts the statistical processing method. For example, if the user is relaxed, the statistical processing unit can cause the generation AI to perform detailed statistical processing. Furthermore, if the user is stressed, the statistical processing unit can cause the generation AI to perform concise statistical processing. Furthermore, if the user is busy, the statistical processing unit can cause the generation AI to perform statistical processing that focuses on the main points. This allows for more appropriate statistical processing by adjusting the statistical processing method based on 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 statistical processing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the statistical processing unit can input the user's emotion data into the generation AI, which can then estimate the emotion and adjust the statistical processing method.

[0097] During statistical processing, the statistical processing unit can analyze the user's past data and select a statistical processing method. The statistical processing unit, for example, analyzes the user's past data and selects the optimal statistical processing method. For example, the statistical processing unit can cause the generation AI to select the optimal statistical processing method based on the user's past data. The statistical processing unit can also analyze the user's past data and cause the generation AI to adjust the statistical processing method. The statistical processing unit can also cause the generation AI to select the optimal statistical processing method by referring to the user's past data. In this way, the optimal statistical processing method can be selected by analyzing the user's past data. Some or all of the above-mentioned processing in the statistical processing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the statistical processing unit can input the user's past data into the generation AI, and the generation AI can select the optimal statistical processing method.

[0098] During statistical processing, the statistical processing unit can customize the statistical processing means based on the user's current living situation. The statistical processing unit customizes the statistical processing means based on, for example, the user's current living situation. For example, the statistical processing unit allows the generation AI to customize the statistical processing means according to the user's current living situation. The statistical processing unit can also allow the generation AI to select the optimal statistical processing method based on the user's living situation. The statistical processing unit can also allow the generation AI to adjust the statistical processing means taking the user's living situation into consideration. In this way, more appropriate statistical processing can be performed by customizing the statistical processing means based on the user's current living situation. Some or all of the above-mentioned processing in the statistical processing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the statistical processing unit can input the user's current living situation into the generation AI, and the generation AI can customize the statistical processing means.

[0099] The statistical processing unit can improve the statistical processing method by reflecting user feedback during statistical processing. For example, the statistical processing unit can improve the statistical processing method by reflecting user feedback during statistical processing. For example, the statistical processing unit can cause the generation AI to improve the statistical processing method based on user feedback. The statistical processing unit can also cause the generation AI to adjust the statistical processing method by reflecting past user feedback. The statistical processing unit can also analyze user feedback and cause the generation AI to select the optimal statistical processing method. In this way, the statistical processing method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the statistical processing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the statistical processing unit can input user feedback into the generation AI, and the generation AI can improve the statistical processing method.

[0100] The statistical processing unit can estimate the user's emotions and determine the priority of statistical processing based on the estimated user's emotions. The statistical processing unit, for example, estimates the user's emotions and determines the priority of statistical processing. For example, when the user is relaxed, the statistical processing unit can cause the generation AI to prioritize statistical processing with high importance. Furthermore, when the user is stressed, the statistical processing unit can cause the generation AI to postpone statistical processing with low importance. Furthermore, when the user is busy, the statistical processing unit can cause the generation AI to prioritize statistical processing with high urgency. In this way, by determining the priority of statistical processing based on the user's emotions, important statistical processing can be performed preferentially. 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 statistical processing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the statistical processing unit can input user emotion data into the generation AI, which can estimate the emotion and determine the priority of statistical processing.

[0101] The statistical processing unit can select a statistical processing method taking into account the user's geographical location information during statistical processing. For example, the statistical processing unit selects the optimal statistical processing method taking into account the user's geographical location information during statistical processing. For example, if the user is in a specific area, the statistical processing unit can prioritize statistical processing related to that area. The statistical processing unit can also perform highly relevant statistical processing based on the user's current location. The statistical processing unit can also exclude unnecessary statistical processing and perform only necessary statistical processing taking into account the user's geographical location information. This makes it possible to select the optimal statistical processing method by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the statistical processing unit may be performed using or without the generation AI. For example, the statistical processing unit can input the user's geographical location information into the generation AI, which can select the optimal statistical processing method.

[0102] The statistical processing unit can analyze the user's social media activity and suggest statistical processing methods during statistical processing. For example, the statistical processing unit can analyze the user's social media activity and suggest statistical processing methods during statistical processing. For example, the statistical processing unit can perform relevant statistical processing based on information shared by the user on social media. The statistical processing unit can also analyze the user's social media activity and prioritize highly relevant statistical processing. The statistical processing unit can also perform relevant statistical processing with reference to the activity of the user's friends on social media. In this way, highly relevant statistical processing can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the statistical processing unit may be performed using or without the generation AI. For example, the statistical processing unit can input the user's social media activity data into the generation AI, which can suggest relevant statistical processing.

[0103] The statistical processing unit can adjust the statistical processing method by reflecting the user's past feedback during statistical processing. For example, the statistical processing unit adjusts the statistical processing method by reflecting the user's past feedback during statistical processing. For example, the statistical processing unit allows the generation AI to customize the statistical processing method based on the user's past feedback. The statistical processing unit can also allow the generation AI to adjust the statistical processing method by reflecting the user's past feedback. The statistical processing unit can also allow the generation AI to select the optimal statistical processing method by referring to the user's past feedback. In this way, the statistical processing method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the statistical processing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the statistical processing unit can input the user's past feedback into the generation AI, and the generation AI can adjust the statistical processing method.

[0104] The query generation unit can estimate the user's emotion and adjust the query generation method based on the estimated user's emotion. For example, the query generation unit estimates the user's emotion and adjusts the query generation method. For example, if the user is relaxed, the query generation unit can cause the generation AI to generate a detailed query. Also, if the user is stressed, the query generation unit can cause the generation AI to generate a concise query. Also, if the user is busy, the query generation unit can cause the generation AI to generate a query that focuses on the main points. This allows the query generation method to be adjusted based on the user's emotion, resulting in the generation of a more appropriate query. The emotion estimation is achieved using an emotion estimation function, such as 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 query generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the query generation unit can input the user's emotion data into the generation AI, which can then estimate the emotion and adjust the query generation method.

[0105] The query generation unit can select a query generation method by analyzing the user's past query history when generating a query. For example, the query generation unit can analyze the user's past query history and select an optimal query generation method when generating a query. For example, the query generation unit can cause the generation AI to select an optimal query generation method based on the user's past query history. The query generation unit can also analyze the user's past query history and cause the generation AI to adjust the query generation method. The query generation unit can also cause the generation AI to select an optimal query generation method by referring to the user's past query history. In this way, the optimal query generation method can be selected by analyzing the user's past query history. Some or all of the above-described processing in the query generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the query generation unit can input the user's past query history into the generation AI, and the generation AI can select an optimal query generation method.

[0106] The query generation unit can customize the means for query generation based on the user's current living situation when generating a query. The query generation unit customizes the means for query generation based on the user's current living situation when generating a query. For example, the query generation unit can cause the generation AI to customize the means for query generation according to the user's current living situation. The query generation unit can also cause the generation AI to select an optimal query generation method based on the user's living situation. The query generation unit can also cause the generation AI to adjust the means for query generation taking the user's living situation into consideration. In this way, by customizing the means for query generation based on the user's current living situation, a more appropriate query can be generated. Some or all of the above-described processing in the query generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the query generation unit can input the user's current living situation into the generation AI, and the generation AI can customize the means for query generation.

[0107] The query generation unit can improve the query generation method by reflecting user feedback when generating a query. For example, the query generation unit can improve the query generation method by reflecting user feedback when generating a query. For example, the query generation unit can cause the generation AI to improve the query generation method based on user feedback. The query generation unit can also cause the generation AI to adjust the query generation method by reflecting past user feedback. The query generation unit can also analyze user feedback and cause the generation AI to select an optimal query generation method. In this way, the query generation method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the query generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the query generation unit can input user feedback into the generation AI, and the generation AI can improve the query generation method.

[0108] The query generation unit can estimate the user's emotions and determine the priority of query generation based on the estimated user emotions. The query generation unit, for example, estimates the user's emotions and determines the priority of query generation. For example, when the user is relaxed, the query generation unit can cause the generation AI to prioritize generating queries with high importance. Furthermore, when the user is stressed, the query generation unit can cause the generation AI to postpone queries with low importance. Furthermore, when the user is busy, the query generation unit can cause the generation AI to prioritize generating queries with high urgency. In this way, by determining the priority of query generation based on the user's emotions, important queries can be generated with priority. 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 query generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the query generation unit can input the user's emotional data into the generation AI, which can then infer the emotions and determine the priority of query generation.

[0109] The query generation unit can select a query generation method by taking into account the user's geographical location information when generating a query. For example, the query generation unit selects the optimal query generation method by taking into account the user's geographical location information when generating a query. For example, if the user is in a specific area, the query generation unit can preferentially generate queries related to that area. The query generation unit can also generate highly relevant queries based on the user's current location. The query generation unit can also exclude unnecessary queries and generate only necessary queries by taking into account the user's geographical location information. In this way, the optimal query generation method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the query generation unit may be performed using a generation AI or may be performed without using a generation AI. For example, the query generation unit can input the user's geographical location information to the generation AI, which can then select the optimal query generation method.

[0110] The query generation unit can analyze the user's social media activity and suggest a means for generating a query when generating a query. For example, the query generation unit can analyze the user's social media activity and suggest a means for generating a query when generating a query. For example, the query generation unit can generate relevant queries based on information shared by the user on social media. The query generation unit can also analyze the user's social media activity and prioritize generating highly relevant queries. The query generation unit can also generate relevant queries by referring to the activity of the user's friends on social media. In this way, highly relevant queries can be generated by analyzing the user's social media activity. Some or all of the above-described processing in the query generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the query generation unit can input the user's social media activity data into the generation AI, which can then suggest relevant queries.

[0111] The query generation unit can adjust the query generation method by reflecting the user's past feedback when generating a query. For example, the query generation unit can adjust the query generation method by reflecting the user's past feedback when generating a query. For example, the query generation unit can customize the query generation method by the generation AI based on the user's past feedback. The query generation unit can also adjust the query generation method by reflecting the user's past feedback. The query generation unit can also select the optimal query generation method by referring to the user's past feedback. In this way, the query generation method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the query generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the query generation unit can input the user's past feedback into the generation AI, and the generation AI can adjust the query generation method.

[0112] The memory sharing unit can estimate the user's emotions and adjust the memory sharing method based on the estimated user emotions. For example, the memory sharing unit estimates the user's emotions and adjusts the memory sharing method. For example, if the user is relaxed, the memory sharing unit can cause the generation AI to share detailed memories. Also, if the user is stressed, the memory sharing unit can cause the generation AI to share concise memories. Also, if the user is busy, the memory sharing unit can cause the generation AI to share memories that focus on the main points. This allows for more appropriate memory sharing by adjusting the memory sharing method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the memory sharing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the memory sharing unit can input the user's emotion data into the generation AI, which can estimate the emotion and adjust the memory sharing method.

[0113] The memory sharing unit can analyze the user's past memories and select a sharing method when sharing memories. For example, the memory sharing unit analyzes the user's past memories and selects the optimal sharing method when sharing memories. For example, the memory sharing unit can have the generation AI select the optimal sharing method based on the user's past memories. The memory sharing unit can also analyze the user's past memories and have the generation AI adjust the sharing method. The memory sharing unit can also select the optimal sharing method by referring to the user's past memories. In this way, the optimal sharing method can be selected by analyzing the user's past memories. Some or all of the above-mentioned processing in the memory sharing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the memory sharing unit can input the user's past memories into the generation AI, and the generation AI can select the optimal sharing method.

[0114] The memory sharing unit can customize the memory sharing means based on the user's current living situation when sharing memories. For example, the memory sharing unit customizes the memory sharing means based on the user's current living situation when sharing memories. For example, the memory sharing unit can have the generation AI customize the memory sharing means according to the user's current living situation. The memory sharing unit can also have the generation AI select the optimal memory sharing method based on the user's living situation. The memory sharing unit can also have the generation AI adjust the memory sharing means taking the user's living situation into consideration. In this way, more appropriate memories can be shared by customizing the memory sharing means based on the user's current living situation. Some or all of the above-described processing in the memory sharing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the memory sharing unit can input the user's current living situation into the generation AI, and the generation AI can customize the memory sharing means.

[0115] The memory sharing unit can improve the memory sharing method by reflecting user feedback when sharing memories. The memory sharing unit, for example, improves the memory sharing method by reflecting user feedback when sharing memories. For example, the memory sharing unit can cause the generation AI to improve the memory sharing method based on user feedback. The memory sharing unit can also cause the generation AI to adjust the memory sharing method by reflecting the user's past feedback. The memory sharing unit can also analyze the user's feedback and cause the generation AI to select the optimal memory sharing method. In this way, the memory sharing method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the memory sharing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the memory sharing unit can input user feedback into the generation AI, and the generation AI can improve the memory sharing method.

[0116] The memory sharing unit can estimate the user's emotions and determine the priority of memory sharing based on the estimated user's emotions. The memory sharing unit, for example, estimates the user's emotions and determines the priority of memory sharing. For example, when the user is relaxed, the memory sharing unit can cause the generation AI to prioritize sharing of memories of high importance. Furthermore, when the user is stressed, the memory sharing unit can cause the generation AI to postpone memories of low importance. Furthermore, when the user is busy, the memory sharing unit can cause the generation AI to prioritize sharing of memories of high urgency. In this way, by determining the priority of memory sharing based on the user's emotions, important memories can be shared preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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 memory sharing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the memory sharing unit can input the user's emotional data into the generation AI, which can then estimate the emotions and determine the priority of memory sharing.

[0117] The memory sharing unit can select a memory sharing method taking into account the user's geographical location information when sharing memories. For example, the memory sharing unit selects the optimal memory sharing method taking into account the user's geographical location information when sharing memories. For example, if the user is in a specific area, the memory sharing unit can prioritize sharing memories related to that area. The memory sharing unit can also share highly relevant memories based on the user's current location. The memory sharing unit can also exclude unnecessary memories and share only necessary memories taking into account the user's geographical location information. In this way, the optimal memory sharing method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the memory sharing unit may be performed using or without the generation AI. For example, the memory sharing unit can input the user's geographical location information into the generation AI, which can then select the optimal memory sharing method.

[0118] The memory sharing unit can analyze the user's social media activity and suggest ways to share memories when sharing memories. For example, the memory sharing unit can analyze the user's social media activity and suggest ways to share memories when sharing memories. For example, the memory sharing unit can share related memories based on information the user has shared on social media. The memory sharing unit can also analyze the user's social media activity and prioritize sharing highly relevant memories. The memory sharing unit can also share related memories with reference to the activity of the user's friends on social media. In this way, highly relevant memories can be shared by analyzing the user's social media activity. Some or all of the above-described processing in the memory sharing unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the memory sharing unit can input the user's social media activity data into the generation AI, which can suggest related memories.

[0119] The memory sharing unit can adjust the memory sharing method by reflecting the user's past feedback when sharing memories. For example, the memory sharing unit can adjust the memory sharing method by reflecting the user's past feedback when sharing memories. For example, the memory sharing unit can have the generation AI customize the memory sharing method based on the user's past feedback. The memory sharing unit can also have the generation AI adjust the memory sharing method by reflecting the user's past feedback. The memory sharing unit can also have the generation AI select the optimal memory sharing method by referring to the user's past feedback. In this way, the memory sharing method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the memory sharing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the memory sharing unit can input the user's past feedback into the generation AI, and the generation AI can adjust the memory sharing method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, statistical processing unit, query generation unit, and memory sharing unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and automatically collects data from the user's smartphone or PC. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data in detail. The statistical processing unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and performs personal statistical processing based on the analysis results. The query generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes natural language commands to generate appropriate queries. The memory sharing unit is implemented, for example, by the control unit 46A of the smart device 14 and organizes and presents the user's memories based on the generated queries. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, statistical processing unit, query generation unit, and memory sharing 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 is realized by the control unit 46A of the smart glasses 214 and automatically collects data from the user's smartphone or PC. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data in detail. The statistical processing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs personal statistical processing based on the analysis results. The query generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes natural language commands to generate appropriate queries. The memory sharing unit is realized, for example, by the control unit 46A of the smart glasses 214 and organizes and presents the user's memories based on the generated queries. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, statistical processing unit, query generation unit, and memory sharing unit, described above, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the headset-type terminal 314 and automatically collects data from the user's smartphone or PC. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data in detail. The statistical processing unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and performs personal statistical processing based on the analysis results. The query generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes natural language commands to generate appropriate queries. The memory sharing unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and organizes and presents the user's memories based on the generated queries. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, statistical processing unit, query generation unit, and memory sharing 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 is realized by the control unit 46A of the robot 414 and automatically collects data from the user's smartphone or PC. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data in detail. The statistical processing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs personal statistical processing based on the analysis results. The query generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes natural language commands to generate appropriate queries. The memory sharing unit is realized, for example, by the control unit 46A of the robot 414 and organizes and presents the user's memories based on the generated queries.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] 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 relaxed, the collection unit can select the timing for the generation AI to collect the user's past data. Furthermore, if the user is feeling stressed, the collection unit can cause the generation AI to temporarily stop data collection and resume it when the user calms down. Furthermore, if the user is busy, the collection unit can cause the generation AI to postpone data collection and collect data when the user has more time. This allows data to be collected at a more appropriate time by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 collection unit may be performed using the generation AI, or may be performed without the generation AI. For example, the collection unit can input the user's emotion data into the generation AI, which can then estimate the emotion and adjust the timing of data collection.

[0122] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can cause the generation AI to provide detailed analysis results. Furthermore, if the user is stressed, the analysis unit can cause the generation AI to provide concise analysis results. Furthermore, if the user is busy, the analysis unit can cause the generation AI to provide analysis results that focus on the main points. By adjusting the presentation method of the analysis based on the user's emotions, more appropriate analysis results can be provided. 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 the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then estimate the emotion and adjust the presentation method of the analysis.

[0123] The statistical processing unit can estimate the user's emotions and adjust the statistical processing method based on the estimated user emotions. For example, if the user is relaxed, the statistical processing unit can cause the generation AI to perform detailed statistical processing. Furthermore, if the user is stressed, the statistical processing unit can cause the generation AI to perform concise statistical processing. Furthermore, if the user is busy, the statistical processing unit can cause the generation AI to perform statistical processing that focuses on the key points. This allows for more appropriate statistical processing by adjusting the statistical processing method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 statistical processing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the statistical processing unit can input the user's emotion data into the generation AI, which can then estimate the emotion and adjust the statistical processing method.

[0124] The query generation unit can estimate the user's emotions and adjust the query generation method based on the estimated user emotions. For example, if the user is relaxed, the query generation unit can cause the generation AI to generate a detailed query. Also, if the user is stressed, the query generation unit can cause the generation AI to generate a concise query. Also, if the user is busy, the query generation unit can cause the generation AI to generate a query that focuses on the main points. By adjusting the query generation method based on the user's emotions, a more appropriate query can be generated. 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 query generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the query generation unit can input the user's emotion data into the generation AI, which can estimate the emotion and adjust the query generation method.

[0125] The memory sharing unit can estimate the user's emotions and adjust the memory sharing method based on the estimated user emotions. For example, if the user is relaxed, the memory sharing unit can have the generation AI share detailed memories. Also, if the user is stressed, the memory sharing unit can have the generation AI share brief memories. Also, if the user is busy, the memory sharing unit can have the generation AI share memories that focus on the main points. By adjusting the memory sharing method based on the user's emotions, more appropriate memories can be shared. The emotion estimation is realized using an emotion estimation function, such as 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 memory sharing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the memory sharing unit can input the user's emotion data into the generation AI, which can estimate the emotion and adjust the memory sharing method.

[0126] The collection unit can analyze the user's past data collection history and select a collection method. For example, the collection unit can prioritize the selection of a data collection method that the user has frequently used in the past. The collection unit can also allow the generation AI to select the most efficient collection method from the user's past data collection history. The collection unit can also analyze the user's past data collection history and customize the collection method. For example, the collection unit adjusts the collection method based on the user's past data collection history. In this way, the optimal collection method can be selected by analyzing the user's past data collection history. Some or all of the above-described processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's past data collection history into the generation AI, which can then select the optimal collection method.

[0127] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, the collection unit can prioritize collecting data related to areas in which the user is currently interested. The collection unit can also filter and collect highly relevant data according to the user's living situation. The collection unit can also exclude unnecessary data and collect only necessary data based on the user's areas of interest. For example, the collection unit prioritizes collecting data related to the user's hobbies and topics of interest. This makes it possible to collect highly relevant data by filtering based on the user's current living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's current living situation and areas of interest into the generation AI, which then performs the filtering.

[0128] When collecting data, the collection unit can select a collection means according to the user's input method. For example, when the user uses voice input, the collection unit can cause the generation AI to prioritize collecting voice data. Also, when the user uses text input, the collection unit can cause the generation AI to prioritize collecting text data. Also, when the user uses image input, the collection unit can cause the generation AI to prioritize collecting image data. For example, the collection unit collects image data taken by the user using a smartphone camera. This allows efficient data collection by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's input method to the generation AI, which can select the optimal collection means.

[0129] 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 have the generation AI perform a detailed analysis of data with high importance. The analysis unit can also have the generation AI perform a concise analysis of data with low importance. The analysis unit can also have the generation AI adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the importance of the data to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0130] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can have the generation AI apply a specific analysis algorithm to household accounting data. The analysis unit can also have the generation AI apply a different analysis algorithm to diary data. The analysis unit can also have the generation AI select the optimal analysis algorithm depending on the data category. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the data category to the generation AI, and the generation AI can select the optimal analysis algorithm.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The collection unit collects the user's long-term historical data. The collection unit can collect personal data such as a household account book or diary. The collection unit can also use generative AI to efficiently collect the user's historical data. For example, the collection unit can automatically collect data from the user's smartphone or computer. The collection unit can also collect data from cloud storage with the user's permission. Step 2: The analysis unit analyzes the data collected by the collection unit to understand the user's behavioral patterns and trends. The analysis unit can use generative AI to analyze the data in detail. For example, the analysis unit can analyze the user's spending patterns and event participation history. The analysis unit can also analyze the user's behavioral patterns over time to understand long-term trends. Step 3: The statistical processing unit performs personalized statistical processing based on the analysis results obtained by the analysis unit. The statistical processing unit can efficiently perform statistical processing using generative AI. For example, the statistical processing unit can compile monthly spending trends or memories related to specific events. The statistical processing unit can also provide reference information for future planning based on the user's behavioral patterns. Step 4: The query generation unit analyzes the natural language command based on the results obtained by the statistical processing unit and generates an appropriate query. The query generation unit can analyze the natural language command using generative AI. For example, the query generation unit analyzes a command such as "Show me photos from last year's trip" and generates an appropriate query. The query generation unit can also retrieve information from multiple data sources based on the user's command. Step 5: The memory sharing unit organizes and presents the user's memories based on the query generated by the query generation unit. The memory sharing unit can efficiently organize the user's memories using the generation AI. For example, the memory sharing unit organizes photos of past trips and records of events and presents them to the user. The memory sharing unit can also organize the user's memories in chronological order and display them visually.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

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

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

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

[0140] The 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.

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

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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 long-term historical data of users; an analysis unit that analyzes the data collected by the collection unit and grasps user behavior patterns and tendencies; a statistical processing unit that performs personal statistical processing based on the analysis results obtained by the analysis unit; a query generation unit that analyzes a natural language command based on the result obtained by the statistical processing unit and generates a query; a memory sharing unit that organizes and presents the user's memories based on the query generated by the query generating unit. A system characterized by:

2. The collecting unit Collect personal data for a household ledger or diary 2. The system of claim 1.

3. The analysis unit Analyze collected data to understand user behavior patterns and trends 2. The system of claim 1.

4. The statistical processing unit Compile monthly spending trends or memories related to specific events 2. The system of claim 1.

5. The query generation unit Parsing natural language instructions and generating appropriate queries 2. The system of claim 1.

6. The memory sharing unit Organize and present past travel photos or event records to users 2. The system of claim 1.

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

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

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