System

Abstract

An object of a system according to an embodiment is to automatically estimate a subsidy system or a support system suitable for a user and to notify the user of an unsubmitted system.SOLUTION: A system according to an embodiment includes an input unit, an analysis unit, and a notification unit. An input part inputs the contents of a diary or a memo and a schedule of a user. The analysis unit analyzes the information input by the input unit and grasps the lifestyle and business situation of the user. The notification unit notifies the user of the subsidy system or the support system which is estimated by the analysis unit and has not been applied.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult for users to find suitable subsidy or support programs, and there is a risk that they will miss out and not apply for them.

[0005] The system according to the embodiment aims to automatically estimate subsidy and support programs suitable for the user and notify the user of those for which they have not yet applied. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, and a notification unit. The input unit inputs the contents of the user's diary, memos, and schedule. The analysis unit analyzes the information input by the input unit and grasps the user's lifestyle and business situation. The notification unit notifies the user of any subsidy or support programs estimated by the analysis unit that have not yet been applied for. [Effects of the Invention]

[0007] The system according to the embodiment can automatically estimate subsidy and support programs suitable for the user and notify the user of those for which they have not yet applied. [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 subsidy estimation system according to an embodiment of the present invention automatically analyzes the contents of a user's diary, memos, and schedule, estimates appropriate subsidy and support programs, and notifies the user of unapplied subsidy and support programs. In the subsidy estimation system, a user inputs the contents of their diary, memos, and schedule, and the system analyzes this information to estimate subsidy and support programs tailored to the user's lifestyle and business. For example, when a user starts a new project, the subsidy estimation system records the project's content and progress in a memo and inputs the memo into the system. The subsidy estimation system then analyzes the input information to understand the user's lifestyle and business situation. For example, when a user starts a new business, the subsidy estimation system analyzes the business content and progress and estimates appropriate subsidy and support programs. Furthermore, the subsidy estimation system notifies the user of any estimated subsidy and support programs for which the user has not yet applied. For example, when a user starts a new business, the subsidy estimation system estimates appropriate subsidy and support programs for the business, lists the unapplied subsidies and support programs, and notifies the user. This allows the subsidy estimation system to provide customized services tailored to the user's lifestyle and business. This allows the subsidy estimation system to easily find subsidy and support programs that suit the user, and to smoothly proceed with the application process. For example, a user who has started a new business can easily find subsidy and support programs that suit their business, and can smoothly proceed with the launch of their business.

[0029] A subsidy estimation system according to an embodiment includes an input unit, an analysis unit, and a notification unit. The input unit inputs the contents of a user's diary, memos, or schedule. The contents of the user's diary, memos, or schedule include, but are not limited to, text, voice memos, or calendar app data. The input unit allows the user to record daily events, plans, business progress, and other details. The analysis unit analyzes the information input by the input unit to understand the user's lifestyle and business situation. The analysis unit analyzes the contents of the user's diary, memos, or schedule using, for example, natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can analyze daily behavior patterns and business progress to understand the user's lifestyle and business situation. The notification unit notifies the user of any subsidy or support programs estimated by the analysis unit that have not yet been applied for. The notification unit can notify the user of unapplied subsidy or support programs via, for example, push notification or email. The notification unit can adjust the timing of notification, the method for setting the destination, etc. As a result, the subsidy estimation system according to the embodiment analyzes the contents of the user's diary, memo, and schedule, and notifies the user of suitable subsidy systems and support systems, allowing the user to easily find subsidy systems and support systems that suit them.

[0030] The analysis unit can analyze the contents of the user's diary, memos, and schedule using natural language processing. Natural language processing includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis, for example. The analysis unit analyzes the contents of the user's diary, memos, and schedule using, for example, morphological analysis. The analysis unit can also analyze the contents of the user's diary, memos, and schedule using grammatical analysis. The analysis unit can also analyze the contents of the user's diary, memos, and schedule using semantic analysis. This allows the use of natural language processing to more accurately analyze the contents of the user's diary, memos, and schedule. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs the contents of the user's diary, memos, and schedule and outputs analysis results.

[0031] The notification unit can inform the user of unapplied subsidy or support programs via push notification or email. The notification unit can, for example, inform the user of unapplied subsidy or support programs via push notification. The notification unit can also inform the user of unapplied subsidy or support programs via email. The notification unit can adjust the timing of the notification, the method for setting the recipient, etc. For example, the notification unit can send a push notification at a time period set by the user. The notification unit can also send a notification to an email address specified by the user. This allows the user to quickly receive information by notifying the user of unapplied subsidy or support programs via push notification or email. Some or all of the above-described processing by the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can perform notification using an AI model that inputs a list of unapplied subsidy or support programs and outputs the notification content.

[0032] The input unit can record the user's daily events or schedules, and business progress in detail. For example, the input unit can allow the user to record daily events, schedules, business progress, etc. in detail. Information to be recorded in detail includes, but is not limited to, daily events, schedule details, business progress, etc. For example, when the user starts a new project, the input unit records the project's content and progress in a memo. The input unit can also record the user's daily events in detail. For example, when the user goes on a trip, the input unit records the details of the trip in a memo. This allows for more accurate analysis by recording the user's daily events, schedules, business progress, etc. in detail. Some or all of the above-described processing in the input unit can be performed using, or without, AI. For example, the input unit can input the contents of the diary, memos, and schedule entered by the user into a generation AI and have the generation AI perform detailed recording.

[0033] The analysis unit can grasp the user's lifestyle and business situation. The analysis unit, for example, analyzes the contents of the user's diary, memos, and schedule to grasp the user's lifestyle and business situation. Examples of lifestyle and business situations include, but are not limited to, daily behavioral patterns and business progress. For example, if the user starts a new business, the analysis unit analyzes the details and progress of that business. The analysis unit can also analyze the user's daily behavioral patterns to grasp the user's lifestyle. For example, the analysis unit analyzes the user's daily activities and habits to grasp the user's lifestyle. By understanding the user's lifestyle and business situation, more appropriate subsidy and support systems can be estimated. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can perform analysis using an AI model that inputs the contents of the user's diary, memos, and schedule and outputs the user's lifestyle and business situation.

[0034] The notification unit can list the estimated subsidy programs and support programs that have not yet been applied for and notify the user. For example, the notification unit lists the estimated subsidy programs and support programs that have not yet been applied for and notifies the user. Examples of listing include, but are not limited to, creating a list of unapplied subsidy programs and support programs and notifying the user. For example, the notification unit creates a list of unapplied subsidy programs and support programs and notifies the user via push notification or email. The notification unit can also provide the user with detailed information about the listed subsidy programs and support programs. For example, the notification unit notifies the user of the application conditions and application methods for the subsidy programs and support programs. By listing and notifying the user of unapplied subsidy programs and support programs, the user can prevent missing applications. Some or all of the above-described processing by the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can perform notification using an AI model that inputs a list of unapplied subsidy programs and support programs and outputs notification content.

[0035] The input unit can analyze the user's past input history and select the optimal input method. The input unit, for example, analyzes the user's past input history and selects the optimal input method. The past input history includes, for example, input methods (voice, text, etc.) that the user has frequently used in the past, but is not limited to such examples. The input unit, for example, preferentially suggests input methods that the user has frequently used in the past. The input unit can also analyze patterns of content that the user has previously input and suggest an optimal input format. The input unit can also suggest an input method suitable for a specific time period based on the user's past input history. In this way, by analyzing the past input history, the optimal input method can be suggested to the user. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past input history data to a generation AI and cause the generation AI to select the optimal input method.

[0036] The input unit may filter the data based on the user's current projects and areas of interest when inputting a diary entry or memo. The input unit may filter the data based on the user's current projects and areas of interest when inputting a diary entry or memo. Examples of filtering include, but are not limited to, a method of preferentially inputting content related to a project currently being completed by the user. For example, the input unit may suggest that the user preferentially input content related to the project currently being completed by the user. The input unit may also automatically filter related topics based on the user's areas of interest to prompt the user to input the content. The input unit may also suggest that the user preferentially input information related to areas in which the user has previously shown interest. By filtering based on the user's current projects and areas of interest, highly relevant content can be preferentially input. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit may input data on the user's projects and areas of interest to a generation AI and have the generation AI perform the filtering.

[0037] The input unit can select an appropriate input means according to the user's input method when entering a diary or memo. The input unit can select an appropriate input means according to the user's input method when entering a diary or memo, for example. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the input unit can preferentially suggest voice input. Furthermore, if the user prefers text input, the input unit can preferentially suggest text input. Furthermore, if the user frequently uses images, the input unit can preferentially suggest image input. This improves input efficiency by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the input unit can be performed, for example, using AI or without AI. For example, the input unit can input the user's input method data to a generation AI and cause the generation AI to select the optimal input means.

[0038] The input unit can prioritize input of highly relevant content in consideration of the user's geographical location information when inputting a diary entry or memo. For example, the input unit prioritizes input of highly relevant content in consideration of the user's geographical location information when inputting a diary entry or memo. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, the input unit suggests that, when the user is in a specific location, the user should prioritize input of content related to that location. Furthermore, when the user is traveling, the input unit can also suggest that the user should prioritize input of content related to the travel destination. Furthermore, when the user is at home, the input unit can suggest that the user should prioritize input of content related to the home. In this way, highly relevant content can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's geographical location information data to the generation AI and cause the generation AI to prioritize input of highly relevant content.

[0039] The input unit can analyze the user's social media activity and input related content when entering a diary entry or a memo. For example, the input unit can analyze the user's social media activity and input related content when entering a diary entry or a memo. Social media activity includes, but is not limited to, analysis of posted content and activity frequency. For example, the input unit can prompt the user to enter related diary entries or memos based on content shared by the user on social media. The input unit can also analyze the user's social media activity and suggest entering related topics. The input unit can also suggest entering related content based on the activity of the user's friends on social media. This allows the user's social media activity to be analyzed, allowing related content to be entered preferentially. Some or all of the above-described processing by the input unit can be performed using, or without, AI. For example, the input unit can input the user's social media activity data into a generation AI and cause the generation AI to input related content.

[0040] The input unit can customize the input method by reflecting the user's past feedback when entering a diary entry or memo. The input unit customizes the input method by reflecting the user's past feedback, for example, when entering a diary entry or memo. Past feedback includes, but is not limited to, input methods that the user previously preferred or dissatisfied with. The input unit, for example, suggests an optimal input method based on the user's previously preferred input method. The input unit can also analyze the user's past feedback and customize the input method. The input unit can also suggest an optimal input method by avoiding input methods that the user previously dissatisfied with. In this way, the optimal input method can be suggested by reflecting the user's past feedback. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past feedback data to a generation AI and cause the generation AI to customize the input method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the diary or memo during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the diary or memo during analysis. Importance includes, for example, a calculation method for an importance score and an evaluation algorithm, but is not limited to these examples. The analysis unit, for example, performs a detailed analysis of important diary entries or memos. The analysis unit can also perform a concise analysis of less important diary entries or memos. The analysis unit can also adjust the level of detail of the analysis based on the importance specified by the user. By adjusting the level of detail of the analysis based on the importance of the diary or memo, important content can be analyzed in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the diary or memo to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the diary or memo during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the diary or memo during analysis. Categories include, but are not limited to, business-related and personal-related categories. For example, the analysis unit applies a business-oriented analysis algorithm to business-related diaries and memos. The analysis unit can also apply a personal-oriented analysis algorithm to personal diaries and memos. The analysis unit can also apply an appropriate analysis algorithm based on the category specified by the user. This allows for applying different analysis algorithms depending on the category of the diary or memo, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the diary or memo into a generation AI and cause the generation AI to apply an analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, but are not limited to, methods for saving analysis results and reference algorithms. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. By referring to the user's past analysis results, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority based on the submission date of the diary or memo during analysis. The analysis unit, for example, determines the analysis priority based on the submission date of the diary or memo during analysis. The submission date includes, but is not limited to, a method for calculating a submission deadline and a method for determining a priority. For example, the analysis unit prioritizes the analysis of recently submitted diaries or memos. The analysis unit can also determine the analysis priority based on a submission date specified by a user. The analysis unit can also postpone the analysis of older diaries or memos. By determining the analysis priority based on the submission date of the diary or memo, the latest information can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission date of the diary or memo into the generation AI and have the generation AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the diaries and notes during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the diaries and notes during analysis. Relevance includes, for example, a relevance score calculation method and an evaluation algorithm, but is not limited to these examples. The analysis unit, for example, prioritizes analysis of highly relevant diaries and notes. The analysis unit can also adjust the order of analysis based on the relevance specified by the user. The analysis unit can also postpone analysis of less relevant diaries and notes. In this way, by adjusting the order of analysis based on the relevance of the diaries and notes, highly relevant content can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the diaries and notes into the generation AI and cause the generation AI to adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. Examples of expertise levels include, but are not limited to, knowledge level classification methods and evaluation algorithms. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0047] The notification unit can adjust the level of detail of the notification based on the importance of the subsidy or support program at the time of notification. For example, the notification unit adjusts the level of detail of the notification based on the importance of the subsidy or support program at the time of notification. The importance includes, but is not limited to, for example, a calculation method for an importance score and an evaluation algorithm. For example, the notification unit provides detailed notification for important subsidy or support programs. The notification unit can also provide concise notification for less important subsidy or support programs. The notification unit can also adjust the level of detail of the notification based on the importance specified by the user. By adjusting the level of detail of the notification based on the importance of the subsidy or support program, important information can be notified in detail. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input importance data of the subsidy or support program into a generation AI and cause the generation AI to adjust the level of detail of the notification.

[0048] The notification unit can apply different notification algorithms depending on the category of the subsidy or support program when issuing a notification. For example, the notification unit can apply different notification algorithms depending on the category of the subsidy or support program when issuing a notification. Examples of categories include, but are not limited to, business-related and private-related. For example, the notification unit can apply a business-oriented notification algorithm to business-related subsidy or support programs. The notification unit can also apply a private-oriented notification algorithm to private subsidy or support programs. The notification unit can also apply an appropriate notification algorithm based on the category specified by the user. This allows for more appropriate notifications by applying different notification algorithms depending on the category of the subsidy or support program. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input category data of the subsidy or support program into a generation AI and cause the generation AI to apply a notification algorithm.

[0049] The notification unit can improve the accuracy of notifications by referring to the user's past notification results when providing notifications. For example, the notification unit can improve the accuracy of notifications by referring to the user's past notification results when providing notifications. Past notification results include, but are not limited to, notification result storage methods and reference algorithms. For example, the notification unit can adjust the notification algorithm based on the user's past notification results. The notification unit can also analyze the user's past notification results to improve the accuracy of notifications. The notification unit can also adjust the level of detail of notifications by referring to the user's past notification results. This improves the accuracy of notifications by referring to the user's past notification results. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input the user's past notification result data into a generation AI and cause the generation AI to improve the accuracy of notifications.

[0050] The notification unit can determine the priority of notifications based on the submission dates of the subsidy or support programs at the time of notification. For example, the notification unit determines the priority of notifications based on the submission dates of the subsidy or support programs at the time of notification. The submission dates include, but are not limited to, methods for calculating the submission deadline and methods for determining the priority. For example, the notification unit prioritizes notifications for subsidy or support programs with upcoming submission deadlines. The notification unit can also determine the priority of notifications based on the submission dates specified by the user. The notification unit can also postpone notifications for subsidy or support programs with distant submission deadlines. Thus, by determining the priority of notifications based on the submission dates of the subsidy or support programs, notifications for programs with upcoming submission deadlines can be prioritized. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can input submission date data for subsidy or support programs into a generation AI and have the generation AI determine the priority of notifications.

[0051] The notification unit can adjust the order of notifications based on the relevance of subsidy programs or support programs at the time of notification. For example, the notification unit adjusts the order of notifications based on the relevance of subsidy programs or support programs at the time of notification. Relevance includes, but is not limited to, a method for calculating a relevance score or an evaluation algorithm. For example, the notification unit prioritizes notification of highly relevant subsidy programs or support programs. The notification unit can also adjust the order of notifications based on the relevance specified by the user. The notification unit can also postpone notification of less relevant subsidy programs or support programs. By adjusting the order of notifications based on the relevance of subsidy programs or support programs, highly relevant programs can be prioritized. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input relevance data of subsidy programs or support programs into a generation AI and cause the generation AI to adjust the order of notifications.

[0052] The notification unit may adjust the use of technical terms in the notification according to the user's level of expertise. For example, the notification unit may adjust the use of technical terms in the notification according to the user's level of expertise. Examples of expertise levels include, but are not limited to, knowledge level classification methods and evaluation algorithms. For example, if the user has technical knowledge, the notification unit may provide a notification that uses a lot of technical terms. Furthermore, if the user does not have technical knowledge, the notification unit may provide a concise and easy-to-understand notification. Furthermore, the notification unit may adjust the use of technical terms in the notification according to the user's level of expertise. This allows for more understandable notifications by adjusting the use of technical terms in the notification according to the user's level of expertise. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit may input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The analysis unit can analyze a user's past behavioral patterns and predict future behavior. For example, if a user has behaved in a specific way at a specific time in the past, the analysis unit can predict future behavior based on that pattern. Also, if a user has participated in a specific event in the past, the analysis unit can predict behavior related to that event. Furthermore, if a user has started a specific project in the past, the analysis unit can predict behavior related to that project. This makes it possible to predict future behavior by analyzing a user's past behavioral patterns and provide more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's past behavioral data into a generation AI and have the generation AI perform behavior prediction.

[0055] The notification unit can analyze the user's past notification history and select the optimal notification method. For example, if the user has preferred push notifications in the past, the notification unit can preferentially suggest push notifications. Also, if the user has preferred email notifications in the past, the notification unit can preferentially suggest email notifications. Furthermore, if the user has preferred receiving notifications during a specific time period in the past, notifications can be sent during that time period. In this way, by analyzing the user's past notification history, the optimal notification method can be suggested. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input the user's past notification history data into a generation AI and have the generation AI select the optimal notification method.

[0056] The input unit can monitor the user's current activity status in real time and suggest the optimal input timing. For example, if the user is in a meeting, the input unit can prompt the user to input after the meeting ends. Also, if the user is exercising, the input unit can prompt the user to input after the exercise ends. Furthermore, if the user is taking a break, the input unit can prompt the user to input at that time. In this way, by monitoring the user's current activity status in real time, the optimal input timing can be suggested. Some or all of the above-mentioned processing in the input unit may be performed using AI, or may be performed without using AI. For example, the input unit can input the user's activity status data to the generation AI and have the generation AI suggest the optimal input timing.

[0057] The analysis unit can automatically adjust the analysis algorithm based on the user's past analysis results. For example, if the past analysis results were accurate, that algorithm can be used preferentially. Also, if the past analysis results were inaccurate, the algorithm can be modified. Furthermore, the analysis algorithm can be optimized based on user feedback. This allows the analysis accuracy to be improved by automatically adjusting the analysis algorithm based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI adjust the analysis algorithm.

[0058] The notification unit can select the optimal notification method by taking into account the user's geographical location information. For example, if the user is at home, push notifications can be suggested with priority. Also, if the user is out, email notifications can be suggested with priority. Furthermore, if the user is in a specific location, notifications containing information related to that location can be provided. In this way, the optimal notification method can be suggested by taking into account the user's geographical location information. Some or all of the above-described processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input the user's geographical location information data into the generation AI and have the generation AI select the optimal notification method.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The input unit inputs the contents of the user's diary, memo, or schedule. The contents of the user's diary, memo, or schedule may be in text format, voice memo, calendar app data, etc. The input unit allows the user to record daily events, plans, business progress, etc. in detail. Step 2: The analysis unit analyzes the information entered by the input unit and understands the user's lifestyle and business situation. The analysis unit uses natural language processing technology to analyze the contents of the user's diary, memos, and schedule. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can analyze daily behavior patterns and business progress. Step 3: The notification unit notifies the user of any subsidy or support programs estimated by the analysis unit that have not yet been applied for. The notification unit can inform the user of any subsidy or support programs that have not yet been applied for via push notification or email. The notification unit can adjust the timing of notifications, the method for setting the recipients, etc.

[0061] (Example 2) A subsidy estimation system according to an embodiment of the present invention automatically analyzes the contents of a user's diary, memos, and schedule, estimates appropriate subsidy and support programs, and notifies the user of unapplied subsidy and support programs. In the subsidy estimation system, a user inputs the contents of their diary, memos, and schedule, and the system analyzes this information to estimate subsidy and support programs tailored to the user's lifestyle and business. For example, when a user starts a new project, the subsidy estimation system records the project's content and progress in a memo and inputs the memo into the system. The subsidy estimation system then analyzes the input information to understand the user's lifestyle and business situation. For example, when a user starts a new business, the subsidy estimation system analyzes the business content and progress and estimates appropriate subsidy and support programs. Furthermore, the subsidy estimation system notifies the user of any estimated subsidy and support programs for which the user has not yet applied. For example, when a user starts a new business, the subsidy estimation system estimates appropriate subsidy and support programs for the business, lists the unapplied subsidies and support programs, and notifies the user. This allows the subsidy estimation system to provide customized services tailored to the user's lifestyle and business. This allows the subsidy estimation system to easily find subsidy and support programs that suit the user, and to smoothly proceed with the application process. For example, a user who has started a new business can easily find subsidy and support programs that suit their business, and can smoothly proceed with the launch of their business.

[0062] A subsidy estimation system according to an embodiment includes an input unit, an analysis unit, and a notification unit. The input unit inputs the contents of a user's diary, memos, or schedule. The contents of the user's diary, memos, or schedule include, but are not limited to, text, voice memos, or calendar app data. The input unit allows the user to record daily events, plans, business progress, and other details. The analysis unit analyzes the information input by the input unit to understand the user's lifestyle and business situation. The analysis unit analyzes the contents of the user's diary, memos, or schedule using, for example, natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can analyze daily behavior patterns and business progress to understand the user's lifestyle and business situation. The notification unit notifies the user of any subsidy or support programs estimated by the analysis unit that have not yet been applied for. The notification unit can notify the user of unapplied subsidy or support programs via, for example, push notification or email. The notification unit can adjust the timing of notification, the method for setting the destination, etc. As a result, the subsidy estimation system according to the embodiment analyzes the contents of the user's diary, memo, and schedule, and notifies the user of suitable subsidy systems and support systems, allowing the user to easily find subsidy systems and support systems that suit them.

[0063] The analysis unit can analyze the contents of the user's diary, memos, and schedule using natural language processing. Natural language processing includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis, for example. The analysis unit analyzes the contents of the user's diary, memos, and schedule using, for example, morphological analysis. The analysis unit can also analyze the contents of the user's diary, memos, and schedule using grammatical analysis. The analysis unit can also analyze the contents of the user's diary, memos, and schedule using semantic analysis. This allows the use of natural language processing to more accurately analyze the contents of the user's diary, memos, and schedule. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs the contents of the user's diary, memos, and schedule and outputs analysis results.

[0064] The notification unit can inform the user of unapplied subsidy or support programs via push notification or email. The notification unit can, for example, inform the user of unapplied subsidy or support programs via push notification. The notification unit can also inform the user of unapplied subsidy or support programs via email. The notification unit can adjust the timing of the notification, the method for setting the recipient, etc. For example, the notification unit can send a push notification at a time period set by the user. The notification unit can also send a notification to an email address specified by the user. This allows the user to quickly receive information by notifying the user of unapplied subsidy or support programs via push notification or email. Some or all of the above-described processing by the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can perform notification using an AI model that inputs a list of unapplied subsidy or support programs and outputs the notification content.

[0065] The input unit can record the user's daily events or schedules, and business progress in detail. For example, the input unit can allow the user to record daily events, schedules, business progress, etc. in detail. Information to be recorded in detail includes, but is not limited to, daily events, schedule details, business progress, etc. For example, when the user starts a new project, the input unit records the project's content and progress in a memo. The input unit can also record the user's daily events in detail. For example, when the user goes on a trip, the input unit records the details of the trip in a memo. This allows for more accurate analysis by recording the user's daily events, schedules, business progress, etc. in detail. Some or all of the above-described processing in the input unit can be performed using, or without, AI. For example, the input unit can input the contents of the diary, memos, and schedule entered by the user into a generation AI and have the generation AI perform detailed recording.

[0066] The analysis unit can grasp the user's lifestyle and business situation. The analysis unit, for example, analyzes the contents of the user's diary, memos, and schedule to grasp the user's lifestyle and business situation. Examples of lifestyle and business situations include, but are not limited to, daily behavioral patterns and business progress. For example, if the user starts a new business, the analysis unit analyzes the details and progress of that business. The analysis unit can also analyze the user's daily behavioral patterns to grasp the user's lifestyle. For example, the analysis unit analyzes the user's daily activities and habits to grasp the user's lifestyle. By understanding the user's lifestyle and business situation, more appropriate subsidy and support systems can be estimated. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can perform analysis using an AI model that inputs the contents of the user's diary, memos, and schedule and outputs the user's lifestyle and business situation.

[0067] The notification unit can list the estimated subsidy programs and support programs that have not yet been applied for and notify the user. For example, the notification unit lists the estimated subsidy programs and support programs that have not yet been applied for and notifies the user. Examples of listing include, but are not limited to, creating a list of unapplied subsidy programs and support programs and notifying the user. For example, the notification unit creates a list of unapplied subsidy programs and support programs and notifies the user via push notification or email. The notification unit can also provide the user with detailed information about the listed subsidy programs and support programs. For example, the notification unit notifies the user of the application conditions and application methods for the subsidy programs and support programs. By listing and notifying the user of unapplied subsidy programs and support programs, the user can prevent missing applications. Some or all of the above-described processing by the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can perform notification using an AI model that inputs a list of unapplied subsidy programs and support programs and outputs notification content.

[0068] The input unit can estimate the user's emotions and adjust the timing of diary entry or memo entry based on the estimated user emotions. The input unit, for example, estimates the user's emotions and adjusts the timing of diary entry or memo entry based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, methods using emotion recognition algorithms. For example, if the user is feeling stressed, the input unit can prompt the user to enter a diary entry or memo at a time when the user can relax. Furthermore, if the user is concentrating, the input unit can suggest a timing to enter detailed notes by taking advantage of the user's concentration. Furthermore, if the user is tired, the input unit can prompt the user to enter simple notes and postpone detailed entry. This allows the diary entry or memo entry to be more appropriately timed by adjusting the input timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 input unit may be performed using, for example, AI, or without AI. For example, the input unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the input timing based on the emotion.

[0069] The input unit can analyze the user's past input history and select the optimal input method. The input unit, for example, analyzes the user's past input history and selects the optimal input method. The past input history includes, for example, input methods (voice, text, etc.) that the user has frequently used in the past, but is not limited to such examples. The input unit, for example, preferentially suggests input methods that the user has frequently used in the past. The input unit can also analyze patterns of content that the user has previously input and suggest an optimal input format. The input unit can also suggest an input method suitable for a specific time period based on the user's past input history. In this way, by analyzing the past input history, the optimal input method can be suggested to the user. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past input history data to a generation AI and cause the generation AI to select the optimal input method.

[0070] The input unit may filter the data based on the user's current projects and areas of interest when inputting a diary entry or memo. The input unit may filter the data based on the user's current projects and areas of interest when inputting a diary entry or memo. Examples of filtering include, but are not limited to, a method of preferentially inputting content related to a project currently being completed by the user. For example, the input unit may suggest that the user preferentially input content related to the project currently being completed by the user. The input unit may also automatically filter related topics based on the user's areas of interest to prompt the user to input the content. The input unit may also suggest that the user preferentially input information related to areas in which the user has previously shown interest. By filtering based on the user's current projects and areas of interest, highly relevant content can be preferentially input. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit may input data on the user's projects and areas of interest to a generation AI and have the generation AI perform the filtering.

[0071] The input unit can select an appropriate input means according to the user's input method when entering a diary or memo. The input unit can select an appropriate input means according to the user's input method when entering a diary or memo, for example. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the input unit can preferentially suggest voice input. Furthermore, if the user prefers text input, the input unit can preferentially suggest text input. Furthermore, if the user frequently uses images, the input unit can preferentially suggest image input. This improves input efficiency by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the input unit can be performed, for example, using AI or without AI. For example, the input unit can input the user's input method data to a generation AI and cause the generation AI to select the optimal input means.

[0072] The input unit can estimate the user's emotions and determine the priority of the content to be input based on the estimated user emotions. The input unit, for example, estimates the user's emotions and determines the priority of the content to be input based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, a method using an emotion recognition algorithm. For example, the input unit suggests that the user prioritize input of important content when the user is stressed. The input unit can also suggest that the user prioritize input of detailed content when the user is relaxed. The input unit can also suggest that the user prioritize input of concise content when the user is in a hurry. In this way, the priority of the input content is determined according to the user's emotions, allowing important content to be input preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, 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 input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the input unit can input the user's emotional data into the generation AI and cause the generation AI to determine the priority of the input content based on the emotion.

[0073] The input unit can prioritize input of highly relevant content in consideration of the user's geographical location information when inputting a diary entry or memo. For example, the input unit prioritizes input of highly relevant content in consideration of the user's geographical location information when inputting a diary entry or memo. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, the input unit suggests that, when the user is in a specific location, the user should prioritize input of content related to that location. Furthermore, when the user is traveling, the input unit can also suggest that the user should prioritize input of content related to the travel destination. Furthermore, when the user is at home, the input unit can suggest that the user should prioritize input of content related to the home. In this way, highly relevant content can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's geographical location information data to the generation AI and cause the generation AI to prioritize input of highly relevant content.

[0074] The input unit can analyze the user's social media activity and input related content when entering a diary entry or a memo. For example, the input unit can analyze the user's social media activity and input related content when entering a diary entry or a memo. Social media activity includes, but is not limited to, analysis of posted content and activity frequency. For example, the input unit can prompt the user to enter related diary entries or memos based on content shared by the user on social media. The input unit can also analyze the user's social media activity and suggest entering related topics. The input unit can also suggest entering related content based on the activity of the user's friends on social media. This allows the user's social media activity to be analyzed, allowing related content to be entered preferentially. Some or all of the above-described processing by the input unit can be performed using, or without, AI. For example, the input unit can input the user's social media activity data into a generation AI and cause the generation AI to input related content.

[0075] The input unit can customize the input method by reflecting the user's past feedback when entering a diary entry or memo. The input unit customizes the input method by reflecting the user's past feedback, for example, when entering a diary entry or memo. Past feedback includes, but is not limited to, input methods that the user previously preferred or dissatisfied with. The input unit, for example, suggests an optimal input method based on the user's previously preferred input method. The input unit can also analyze the user's past feedback and customize the input method. The input unit can also suggest an optimal input method by avoiding input methods that the user previously dissatisfied with. In this way, the optimal input method can be suggested by reflecting the user's past feedback. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past feedback data to a generation AI and cause the generation AI to customize the input method.

[0076] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. For example, the analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. Methods for estimating emotion include, but are not limited to, methods using emotion recognition algorithms. For example, the analysis unit can provide detailed analysis results when the user is relaxed. For example, the analysis unit can provide concise analysis results when the user is in a hurry. For example, the analysis unit can provide visually stimulating analysis results when the user is excited. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed based on the emotion.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the diary or memo during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the diary or memo during analysis. Importance includes, for example, a calculation method for an importance score and an evaluation algorithm, but is not limited to these examples. The analysis unit, for example, performs a detailed analysis of important diary entries or memos. The analysis unit can also perform a concise analysis of less important diary entries or memos. The analysis unit can also adjust the level of detail of the analysis based on the importance specified by the user. By adjusting the level of detail of the analysis based on the importance of the diary or memo, important content can be analyzed in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the diary or memo to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the category of the diary or memo during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the diary or memo during analysis. Categories include, but are not limited to, business-related and personal-related categories. For example, the analysis unit applies a business-oriented analysis algorithm to business-related diaries and memos. The analysis unit can also apply a personal-oriented analysis algorithm to personal diaries and memos. The analysis unit can also apply an appropriate analysis algorithm based on the category specified by the user. This allows for applying different analysis algorithms depending on the category of the diary or memo, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the diary or memo into a generation AI and cause the generation AI to apply an analysis algorithm.

[0079] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, but are not limited to, methods for saving analysis results and reference algorithms. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. By referring to the user's past analysis results, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0080] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user emotion. For example, the analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user emotion. Methods for estimating emotion include, but are not limited to, methods using emotion recognition algorithms. For example, the analysis unit can provide a short and concise analysis result when the user is in a hurry. For example, the analysis unit can provide a detailed analysis result when the user is relaxed. For example, the analysis unit can provide a visually stimulating analysis result when the user is excited. This allows for adjusting the length of the analysis according to the user's emotion to provide a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the length of the analysis based on the emotion.

[0081] The analysis unit can determine the analysis priority based on the submission date of the diary or memo during analysis. The analysis unit, for example, determines the analysis priority based on the submission date of the diary or memo during analysis. The submission date includes, but is not limited to, a method for calculating a submission deadline and a method for determining a priority. For example, the analysis unit prioritizes the analysis of recently submitted diaries or memos. The analysis unit can also determine the analysis priority based on a submission date specified by a user. The analysis unit can also postpone the analysis of older diaries or memos. By determining the analysis priority based on the submission date of the diary or memo, the latest information can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission date of the diary or memo into the generation AI and have the generation AI determine the analysis priority.

[0082] The analysis unit can adjust the order of analysis based on the relevance of the diaries and notes during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the diaries and notes during analysis. Relevance includes, for example, a relevance score calculation method and an evaluation algorithm, but is not limited to these examples. The analysis unit, for example, prioritizes analysis of highly relevant diaries and notes. The analysis unit can also adjust the order of analysis based on the relevance specified by the user. The analysis unit can also postpone analysis of less relevant diaries and notes. In this way, by adjusting the order of analysis based on the relevance of the diaries and notes, highly relevant content can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the diaries and notes into the generation AI and cause the generation AI to adjust the order of analysis.

[0083] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. Examples of expertise levels include, but are not limited to, knowledge level classification methods and evaluation algorithms. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0084] The notification unit can estimate the user's emotion and adjust the notification method based on the estimated user's emotion. For example, the notification unit can estimate the user's emotion and adjust the notification method based on the estimated user's emotion. Methods for estimating emotion include, but are not limited to, methods using emotion recognition algorithms. For example, when the user is nervous, the notification unit can provide a simple, highly visible notification method. For example, when the user is relaxed, the notification unit can provide a notification method that includes detailed information. For example, when the user is in a hurry, the notification unit can provide a notification method that focuses on the main points. This allows for more appropriate notification by adjusting the notification method according to the user's emotion. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, for example, 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 notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI adjust the notification method based on the emotion.

[0085] The notification unit can adjust the level of detail of the notification based on the importance of the subsidy or support program at the time of notification. For example, the notification unit adjusts the level of detail of the notification based on the importance of the subsidy or support program at the time of notification. The importance includes, but is not limited to, for example, a calculation method for an importance score and an evaluation algorithm. For example, the notification unit provides detailed notification for important subsidy or support programs. The notification unit can also provide concise notification for less important subsidy or support programs. The notification unit can also adjust the level of detail of the notification based on the importance specified by the user. By adjusting the level of detail of the notification based on the importance of the subsidy or support program, important information can be notified in detail. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input importance data of the subsidy or support program into a generation AI and cause the generation AI to adjust the level of detail of the notification.

[0086] The notification unit can apply different notification algorithms depending on the category of the subsidy or support program when issuing a notification. For example, the notification unit can apply different notification algorithms depending on the category of the subsidy or support program when issuing a notification. Examples of categories include, but are not limited to, business-related and private-related. For example, the notification unit can apply a business-oriented notification algorithm to business-related subsidy or support programs. The notification unit can also apply a private-oriented notification algorithm to private subsidy or support programs. The notification unit can also apply an appropriate notification algorithm based on the category specified by the user. This allows for more appropriate notifications by applying different notification algorithms depending on the category of the subsidy or support program. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input category data of the subsidy or support program into a generation AI and cause the generation AI to apply a notification algorithm.

[0087] The notification unit can improve the accuracy of notifications by referring to the user's past notification results when providing notifications. For example, the notification unit can improve the accuracy of notifications by referring to the user's past notification results when providing notifications. Past notification results include, but are not limited to, notification result storage methods and reference algorithms. For example, the notification unit can adjust the notification algorithm based on the user's past notification results. The notification unit can also analyze the user's past notification results to improve the accuracy of notifications. The notification unit can also adjust the level of detail of notifications by referring to the user's past notification results. This improves the accuracy of notifications by referring to the user's past notification results. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input the user's past notification result data into a generation AI and cause the generation AI to improve the accuracy of notifications.

[0088] The notification unit can estimate the user's emotion and adjust the length of the notification based on the estimated user emotion. The notification unit, for example, estimates the user's emotion and adjusts the length of the notification based on the estimated user emotion. Methods for estimating emotion include, but are not limited to, methods using an emotion recognition algorithm. For example, if the user is nervous, the notification unit can provide a short, to-the-point notification. Furthermore, if the user is relaxed, the notification unit can provide a notification with detailed information. Furthermore, if the user is in a hurry, the notification unit can provide a concise, quick notification. This allows for more appropriate notification by adjusting the length of the notification based on the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 notification unit may be performed using, for example, an AI. For example, the notification unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the notification based on the emotion.

[0089] The notification unit can determine the priority of notifications based on the submission dates of the subsidy or support programs at the time of notification. For example, the notification unit determines the priority of notifications based on the submission dates of the subsidy or support programs at the time of notification. The submission dates include, but are not limited to, methods for calculating the submission deadline and methods for determining the priority. For example, the notification unit prioritizes notifications for subsidy or support programs with upcoming submission deadlines. The notification unit can also determine the priority of notifications based on the submission dates specified by the user. The notification unit can also postpone notifications for subsidy or support programs with distant submission deadlines. Thus, by determining the priority of notifications based on the submission dates of the subsidy or support programs, notifications for programs with upcoming submission deadlines can be prioritized. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can input submission date data for subsidy or support programs into a generation AI and have the generation AI determine the priority of notifications.

[0090] The notification unit can adjust the order of notifications based on the relevance of subsidy programs or support programs at the time of notification. For example, the notification unit adjusts the order of notifications based on the relevance of subsidy programs or support programs at the time of notification. Relevance includes, but is not limited to, a method for calculating a relevance score or an evaluation algorithm. For example, the notification unit prioritizes notification of highly relevant subsidy programs or support programs. The notification unit can also adjust the order of notifications based on the relevance specified by the user. The notification unit can also postpone notification of less relevant subsidy programs or support programs. By adjusting the order of notifications based on the relevance of subsidy programs or support programs, highly relevant programs can be prioritized. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input relevance data of subsidy programs or support programs into a generation AI and cause the generation AI to adjust the order of notifications.

[0091] The notification unit may adjust the use of technical terms in the notification according to the user's level of expertise. For example, the notification unit may adjust the use of technical terms in the notification according to the user's level of expertise. Examples of expertise levels include, but are not limited to, knowledge level classification methods and evaluation algorithms. For example, if the user has technical knowledge, the notification unit may provide a notification that uses a lot of technical terms. Furthermore, if the user does not have technical knowledge, the notification unit may provide a concise and easy-to-understand notification. Furthermore, the notification unit may adjust the use of technical terms in the notification according to the user's level of expertise. This allows for more understandable notifications by adjusting the use of technical terms in the notification according to the user's level of expertise. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit may input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, analysis unit, and notification unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the reception device 38 of the smart device 14, and inputs the contents of the user's diary, memos, and schedule. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using natural language processing technology. The notification unit is realized, for example, by the output device 40 of the smart device 14, and notifies the user of unapplied subsidy and support programs via push notification or email. === Hard Collateral 1-2 === Each of the multiple elements including the input unit, analysis unit, and notification 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 input unit is realized by the microphone 238 of the smart glasses 214, and inputs the contents of the user's diary, memos, and schedule by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using natural language processing technology. The notification unit is realized, for example, by the speaker 240 of the smart glasses 214, and notifies the user of unapplied subsidy and support programs through voice notification or display on the screen. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, and notification unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the headset terminal 314, and inputs the contents of the user's diary, memos, and schedule by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using natural language processing technology. The notification unit is realized, for example, by the display 343 of the headset terminal 314, and notifies the user of unapplied subsidy and support programs through visual notifications. === Hard Collateral 1-4 === Each of the multiple elements including the input unit, analysis unit, and notification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the robot 414, and inputs the contents of the user's diary, memos, and schedule by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using natural language processing technology. The notification unit is realized, for example, by the speaker 240 of the robot 414, and notifies the user of unapplied subsidy and support programs through voice notification or display on the screen.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analysis of information with high importance and provide results quickly. If the user is relaxed, the analysis unit can perform detailed analysis and provide comprehensive results. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. This allows for adjusting the analysis priority according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion recognition algorithm or a generation AI. The generation AI can be, but is not limited to, a text generation AI or a multimodal generation AI.

[0094] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated user emotions. For example, if the user is feeling stressed, the notification unit can send notifications at a time when the user is able to relax. Also, if the user is concentrating, the notification unit can take advantage of that concentration to send important notifications. Furthermore, if the user is tired, the notification can be postponed and sent after the user has refreshed. In this way, by adjusting the timing of notifications according to the user's emotions, the user can receive notifications at a more appropriate time. Emotion estimation is achieved using an emotion recognition algorithm or a generation AI. The generation AI can be, but is not limited to, a text generation AI or a multimodal generation AI.

[0095] The input unit can estimate the user's emotion and provide feedback on the input content based on the estimated user emotion. For example, if the user has a positive emotion, the input unit can provide feedback that reinforces that emotion. Also, if the user has a negative emotion, the input unit can provide feedback that alleviates that emotion. Furthermore, if the user has a neutral emotion, the input unit can provide feedback that maintains that emotion. This allows the quality of input to be improved by providing appropriate feedback according to the user's emotion. Emotion estimation is achieved using an emotion recognition algorithm or a generative AI. The generative AI can be, but is not limited to, a text generation AI or a multimodal generation AI.

[0096] The analysis unit can estimate the user's emotions and adjust the level of analysis detail based on the estimated user emotions. For example, if the user is relaxed, a detailed analysis result can be provided. If the user is in a hurry, a concise analysis result can be provided. Furthermore, if the user is excited, a visually stimulating analysis result can be provided. In this way, by adjusting the level of analysis detail according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion recognition algorithm or a generation AI. The generation AI can be, but is not limited to, a text generation AI or a multimodal generation AI.

[0097] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated user emotions. For example, if the user is feeling stressed, the notification unit can provide a concise and to-the-point notification. If the user is relaxed, the notification unit can provide a notification with detailed information. Furthermore, if the user is excited, the notification unit can provide a visually stimulating notification. This allows for more appropriate notifications by adjusting the content of the notification according to the user's emotions. Emotion estimation is achieved using an emotion recognition algorithm or a generation AI. The generation AI can be, but is not limited to, a text generation AI or a multimodal generation AI.

[0098] The analysis unit can analyze a user's past behavioral patterns and predict future behavior. For example, if a user has behaved in a specific way at a specific time in the past, the analysis unit can predict future behavior based on that pattern. Also, if a user has participated in a specific event in the past, the analysis unit can predict behavior related to that event. Furthermore, if a user has started a specific project in the past, the analysis unit can predict behavior related to that project. This makes it possible to predict future behavior by analyzing a user's past behavioral patterns and provide more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's past behavioral data into a generation AI and have the generation AI perform behavior prediction.

[0099] The notification unit can analyze the user's past notification history and select the optimal notification method. For example, if the user has preferred push notifications in the past, the notification unit can preferentially suggest push notifications. Also, if the user has preferred email notifications in the past, the notification unit can preferentially suggest email notifications. Furthermore, if the user has preferred receiving notifications during a specific time period in the past, notifications can be sent during that time period. In this way, by analyzing the user's past notification history, the optimal notification method can be suggested. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input the user's past notification history data into a generation AI and have the generation AI select the optimal notification method.

[0100] The input unit can monitor the user's current activity status in real time and suggest the optimal input timing. For example, if the user is in a meeting, the input unit can prompt the user to input after the meeting ends. Also, if the user is exercising, the input unit can prompt the user to input after the exercise ends. Furthermore, if the user is taking a break, the input unit can prompt the user to input at that time. In this way, by monitoring the user's current activity status in real time, the optimal input timing can be suggested. Some or all of the above-mentioned processing in the input unit may be performed using AI, or may be performed without using AI. For example, the input unit can input the user's activity status data to the generation AI and have the generation AI suggest the optimal input timing.

[0101] The analysis unit can automatically adjust the analysis algorithm based on the user's past analysis results. For example, if the past analysis results were accurate, that algorithm can be used preferentially. Also, if the past analysis results were inaccurate, the algorithm can be modified. Furthermore, the analysis algorithm can be optimized based on user feedback. This allows the analysis accuracy to be improved by automatically adjusting the analysis algorithm based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI adjust the analysis algorithm.

[0102] The notification unit can select the optimal notification method by taking into account the user's geographical location information. For example, if the user is at home, push notifications can be suggested with priority. Also, if the user is out, email notifications can be suggested with priority. Furthermore, if the user is in a specific location, notifications containing information related to that location can be provided. In this way, the optimal notification method can be suggested by taking into account the user's geographical location information. Some or all of the above-described processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input the user's geographical location information data into the generation AI and have the generation AI select the optimal notification method.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The input unit inputs the contents of the user's diary, memo, or schedule. The contents of the user's diary, memo, or schedule may be in text format, voice memo, calendar app data, etc. The input unit allows the user to record daily events, plans, business progress, etc. in detail. Step 2: The analysis unit analyzes the information entered by the input unit and understands the user's lifestyle and business situation. The analysis unit uses natural language processing technology to analyze the contents of the user's diary, memos, and schedule. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can analyze daily behavior patterns and business progress. Step 3: The notification unit notifies the user of any subsidy or support programs estimated by the analysis unit that have not yet been applied for. The notification unit can inform the user of any subsidy or support programs that have not yet been applied for via push notification or email. The notification unit can adjust the timing of notifications, the method for setting the recipients, etc.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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. an input section for inputting the contents of a user's diary, memo, or schedule; an analysis unit that analyzes the information input by the input unit and grasps the user's lifestyle and business situation; a notification unit that notifies the user of any subsidy programs or support programs estimated by the analysis unit that have not yet been applied for. A system characterized by:

2. The analysis unit Analyze the contents of a user's diary, memos, and schedule using natural language processing 2. The system of claim 1.

3. The notification unit Inform users about outstanding grants or assistance programs via push notifications or email 2. The system of claim 1.

4. The input unit Recording the user's daily events, schedules, and business progress in detail 2. The system of claim 1.

5. The analysis unit Understand your users' lifestyles and business situations 2. The system of claim 1.

6. The notification unit Lists estimated subsidy and support programs that have not yet been applied for and notifies the user 2. The system of claim 1.

7. The input unit Estimates the user's emotions and adjusts the timing of diary and memo entries based on the estimated user emotions.

2. The system of claim 1.

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

9. The input unit Filter your journal entries and notes based on your current projects and interests 2. The system of claim 1.

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