System

Abstract

To provide a system which specifies necessary procedures and consultation destinations, on the basis of a user's history of life style and use services.SOLUTION: A system includes a collection part, a specification part, a determination part, and a proposal part. The collection part collects a user's history of life style and use services. The specification part specifies necessary procedures and consultation destinations on the basis of the data collected by the collection part. The determination part determines agencies or services on the basis of the specified procedures and consultation destinations. The proposal part proposes the determined agencies or services.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to identify the procedures and consultations required just before and after death, and to find appropriate businesses and services.

[0005] The system according to the embodiment aims to identify necessary procedures and consultation destinations based on the user's lifestyle and history of services used, and to suggest appropriate businesses and services. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an identification unit, a decision unit, and a suggestion unit. The collection unit collects a user's lifestyle and a history of services used. The identification unit identifies necessary procedures and consultation destinations based on the data collected by the collection unit. The decision unit decides on a provider or service based on the identified procedures and consultation destinations. The suggestion unit proposes the decided provider or service. Effect of the Invention

[0007] The system according to the embodiment can identify necessary procedures and consultations based on the user's lifestyle and history of services used, and can suggest appropriate businesses and services. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to the embodiment of the present invention is a system in which AI comprehensively determines and responds to the procedures and consultations required when a user dies or immediately before death. This system collects the user's lifestyle and the history of the services used, identifies the necessary procedures and consultations based on the collected data, and determines and proposes a company or service based on the identified procedures and consultations. For example, information on SNS, bank accounts, insurance contracts, and subscription services used by the user is collected and analyzed by AI. Next, based on the collected data, procedures such as writing a will, notifying government offices and banks, inheritance, sorting and terminating various contracts, disposing of SNS, and sorting belongings are identified. Furthermore, based on the identified procedures and consultations, a company or service such as a lawyer, administrative scrivener, tax accountant, contract management service, digital estate sorting service, estate sorting company, etc. are determined and proposed. This system is not a one-time thing, but is automatically reviewed every year or at a set frequency. For example, if the user's lifestyle or the history of the services used changes, AI automatically collects new data, re-identifies the necessary procedures and consultations, and re-determines and proposes a company or service. This allows the user to always proceed with procedures based on the latest information. This enables the system to comprehensively assess and respond to the procedures and consultations required at the time of a user's death or immediately before death.

[0029] The system according to the embodiment includes a collection unit, a specification unit, a decision unit, and a proposal unit. The collection unit collects the user's lifestyle and the history of the services used. For example, the collection unit can collect information on SNS, bank accounts, insurance contracts, and subscription services used by the user. The collection unit can also use AI to convert this information into a format that is easy to analyze. The specification unit specifies the necessary procedures and consultations based on the data collected by the collection unit. For example, the specification unit can specify procedures such as writing a will, notifying government offices and banks, inheritance, sorting and terminating various contracts, clearing SNS, and sorting belongings. The specification unit can also specify these procedures using AI. The determination unit determines a company or service based on the procedures and consultations specified by the specification unit. For example, the determination unit can determine a lawyer for writing a will, an administrative scrivener for notifying government offices and banks, a tax accountant for inheritance, a contract management service for sorting and terminating various contracts, a digital estate sorting service for clearing SNS, and an estate sorting company for sorting belongings. The determination unit may determine these businesses or services using AI. The suggestion unit proposes the businesses or services determined by the determination unit. For example, the suggestion unit enables the user to select the proposed business or service and proceed with the procedure. The suggestion unit may make these suggestions using AI. As a result, the system according to the embodiment can identify necessary procedures and consultation destinations based on the user's lifestyle and history of used services, and propose appropriate businesses and services.

[0030] The collection unit collects the user's lifestyle and the history of the services used. For example, the collection unit can collect information on the SNS, bank account, insurance contract, and subscription service used by the user. Specifically, the collection unit can understand the user's friendships and interests from the SNS usage history, and analyze the user's economic situation and spending patterns from the bank account transaction history. From the insurance contract information, the type of insurance the user has subscribed to and the compensation details can be confirmed, and from the subscription service information, the services the user regularly uses and the frequency of use can be understood. The collection unit centrally manages this information and converts it into a format that is easy to analyze using AI. For example, text data is converted into structured data using natural language processing technology, and image data is tagged using image recognition technology. This allows the collection unit to efficiently collect and manage various data of the user and use it for subsequent processing. Furthermore, the collection unit can appropriately collect necessary information while protecting the user's privacy by adjusting the frequency and accuracy of data collection. For example, data is collected based on a specific period and conditions after obtaining the user's consent. In addition, the collection unit takes security measures to prevent leakage and unauthorized access of collected data. This allows the collection unit to efficiently collect necessary data while gaining the trust of users, thereby improving the performance of the entire system.

[0031] The identification unit identifies the necessary procedures and consultation destinations based on the data collected by the collection unit. For example, the identification unit can identify procedures such as writing a will, notifying government offices and banks, inheritance, sorting and terminating various contracts, clearing SNS, and sorting belongings. Specifically, the identification unit analyzes the collected data and automatically extracts the user's life events and necessary procedures. For example, if the user is elderly or suffers from a specific illness, it determines that writing a will and inheritance procedures are necessary. In addition, if the user has multiple bank accounts, it determines that notifications to each bank are necessary. The identification unit can also use AI to identify these procedures. For example, it uses a machine learning algorithm to learn similar cases from past data and identify appropriate procedures for new cases. It also uses natural language processing technology to extract important information from the collected text data and determine the necessity of the procedures. This allows the identification unit to quickly and accurately analyze the collected data and identify the procedures required for the user. Furthermore, the identification unit can continuously improve the accuracy of identifying procedures based on user feedback. For example, it collects information on procedures and consultation destinations that users have actually performed, and improves the accuracy of the identification algorithm. In addition, in order to protect the privacy of users, the identification unit anonymizes the collected data and analyzes only the necessary information. This allows the identification unit to efficiently identify necessary procedures while gaining the trust of users and improving the performance of the entire system.

[0032] The determination unit determines a company or service based on the procedure and consultation destination identified by the identification unit. For example, the determination unit can determine a lawyer for writing a will, an administrative scrivener for filing with government offices and banks, a tax accountant for inheritance, a contract management service for sorting and terminating various contracts, a digital estate sorting service for clearing SNS, and an estate sorting company for sorting estates. Specifically, the determination unit selects the most suitable company or service for the specified procedure and proposes it to the user. The determination unit can also use AI to determine these companies or services. For example, a machine learning algorithm is used to learn the most suitable company or service from past data, and an appropriate company or service is selected for new cases. In addition, natural language processing technology is used to analyze the evaluations and reviews of companies and services from collected text data, and a reliable company or service is selected. This allows the determination unit to quickly and accurately analyze the collected data and determine the most suitable company or service for the user. Furthermore, the determination unit can continuously improve the selection accuracy of companies and services based on user feedback. For example, the evaluations of companies and services actually used by users are collected to improve the accuracy of the selection algorithm. In addition, in order to protect the privacy of users, the determination unit anonymizes the collected data and analyzes only the necessary information, thereby enabling the determination unit to efficiently determine optimal providers and services while gaining the trust of users and improving the performance of the entire system.

[0033] The suggestion unit proposes the trader or service determined by the decision unit. For example, the suggestion unit allows the user to select the proposed trader or service and proceed with the procedure. Specifically, the suggestion unit provides the user with detailed information on the selected trader or service, and guides the user on how to proceed with the procedure and contact information. The suggestion unit can also make these proposals using AI. For example, a natural language generation technology is used to automatically generate a proposal text that is easy for the user to understand, and send it via email or a message app. In addition, based on the user's past selection history and feedback, it is also possible to make individually customized proposals. As a result, the suggestion unit can quickly and accurately propose optimal traders and services to the user, and the procedure can proceed smoothly. Furthermore, the suggestion unit can continuously improve the accuracy and effectiveness of the proposal content based on the user's feedback. For example, the suggestion unit collects evaluations of traders and services that the user actually used, and improves the accuracy of the proposal algorithm. In addition, the suggestion unit can reliably transmit information using multiple communication means. For example, important information is reliably delivered by using not only email and message apps, but also voice calls and SMS. As a result, the suggestion unit can quickly and reliably provide proposals to the user, and the procedure can proceed smoothly.

[0034] The collection unit can collect information on SNS, bank accounts, insurance contracts, and subscription services used by the user. The collection unit can collect, for example, information on SNS used by the user. The SNS information includes the user's posted content, the number of followers, the number of likes, and the like. The collection unit can also collect information on bank accounts. The bank account information includes transaction history, balance information, and the like. The collection unit can also collect information on insurance contracts. The insurance contract information includes the contract content, the insurance amount, the contract period, and the like. The collection unit can also collect information on subscription services. The subscription service information includes the contract content, the usage history, the payment information, and the like. In this way, the collection unit can collect information on various services used by the user to more accurately identify the necessary procedures and consultation destinations. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's SNS information into AI, which can analyze and extract the necessary information.

[0035] The specification part can specify the procedures of creating a will, notifying a government office or bank, inheritance, sorting and terminating various contracts, deleting SNS, and sorting belongings of a deceased person. The specification part can specify, for example, creating a will. Creating a will includes legal requirements and necessary documents. The specification part can also specify notifying a government office or bank. Notifying a government office or bank includes necessary documents and a procedure flow. The specification part can specify inheritance. Inheritance includes calculation of inheritance tax and inheritance division consultation. The specification part can specify sorting and terminating various contracts. Sorting and terminating various contracts includes a method of contract termination and a necessary notice. The specification part can also specify deletion of SNS. Deletion of SNS includes a method of account deletion and data backup. The specification part can also specify sorting of belongings of a deceased person. Sorting belongings of a deceased person includes classification and disposal method of belongings of a deceased person. In this way, the specification part can clarify the procedures to be performed by the user by specifically specifying the necessary procedures. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input user data into AI, which analyzes the data and identifies the necessary procedures.

[0036] The collection unit can analyze the user's past data collection history and select the most appropriate collection method. The collection unit can, for example, preferentially collect data of services that the user has frequently used in the past. The collection unit can also analyze the pattern of data collected by the user in the past and determine the optimal collection timing. Furthermore, the collection unit can automatically collect data of a specific service from the user's past data collection history. As a result, the collection unit can select the optimal collection method by analyzing the past data collection history, making it possible to collect data efficiently. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past data collection history into AI, which can select the optimal collection method.

[0037] The collection unit can perform filtering based on the user's current living situation or field of interest when collecting data. The collection unit can, for example, preferentially collect data in fields in which the user is currently interested. The collection unit can also collect only necessary data according to the user's living situation. Furthermore, the collection unit can also filter and collect related data based on the user's field of interest. This allows the collection unit to collect only necessary data by filtering data based on the user's living situation or field of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's field of interest into AI, and the AI ​​can filter and collect related data.

[0038] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting data. The collection unit can, for example, preferentially collect data related to the location where the user is currently located. The collection unit can also filter and collect necessary data based on the geographical location information of the user. Furthermore, when the user is in a specific area, the collection unit can also preferentially collect data related to the area. As a result, the collection unit can efficiently collect necessary data by preferentially collecting highly relevant data based on the geographical location information of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI, and the AI ​​can preferentially collect highly relevant data.

[0039] The collection unit can analyze the user's social media activity and collect related data when collecting data. The collection unit can collect related data based on, for example, information shared by the user on social media. The collection unit can also preferentially collect data in areas of interest from the user's social media activity. Furthermore, the collection unit can collect related data based on information on accounts the user follows on social media. This allows the collection unit to efficiently collect related data by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI, which can collect related data.

[0040] The identification unit can select the most appropriate identification method by referring to the past procedure history when identifying a procedure. The identification unit can select the optimal identification method based on, for example, procedures performed by the user in the past. The identification unit can also select an efficient identification method from the user's past procedure history. Furthermore, the identification unit can preferentially identify a procedure method used by the user in the past. This allows the identification unit to select the optimal identification method by referring to the past procedure history, making it possible to identify an efficient procedure. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the user's past procedure history into AI, which can select the optimal identification method.

[0041] The identification unit can adjust the identification means based on the user's current living situation when identifying the procedure. The identification unit can customize the necessary procedure according to the user's current living situation, for example. The identification unit can also identify the optimal procedure based on the user's living situation. Furthermore, the identification unit can customize the procedure means according to the user's current situation. In this way, the identification unit can efficiently identify the necessary procedure by customizing the procedure based on the user's living situation. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the user's living situation data into AI, and the AI ​​can identify the optimal procedure.

[0042] When identifying a procedure, the identification unit can preferentially identify a procedure with high relevance in consideration of the geographical location information of the user. For example, the identification unit can preferentially identify a procedure related to the location where the user is currently located. The identification unit can also filter and identify necessary procedures based on the geographical location information of the user. Furthermore, when the user is in a specific area, the identification unit can preferentially identify procedures related to the area. As a result, the identification unit can efficiently identify necessary procedures by preferentially identifying procedures with high relevance based on the geographical location information of the user. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the geographical location information of the user to AI, and the AI ​​can preferentially identify procedures with high relevance.

[0043] The identification unit can analyze the user's social media activity and identify related procedures when identifying a procedure. The identification unit can identify related procedures based on, for example, information shared by the user on social media. The identification unit can also preferentially identify procedures in the user's field of interest from the user's social media activity. Furthermore, the identification unit can identify related procedures based on information on accounts the user follows on social media. In this way, the identification unit can efficiently identify related procedures by analyzing the user's social media activity. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the user's social media activity data into AI, which can identify related procedures.

[0044] The determination unit can select the most appropriate determination method by referring to the past decision history when determining a provider or service. The determination unit can determine the optimal provider based on, for example, a provider used by the user in the past. The determination unit can also select an efficient determination method from the user's past decision history. Furthermore, the determination unit can preferentially determine a service used by the user in the past. This allows the determination unit to select the optimal determination method by referring to the past decision history, making it possible to determine an efficient provider or service. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the user's past decision history into AI, which can select the optimal determination method.

[0045] The determination unit can adjust the means of determination based on the user's current living situation when determining a provider or service. The determination unit can customize a necessary provider according to, for example, the user's current living situation. The determination unit can also determine an optimal provider based on the user's living situation. Furthermore, the determination unit can customize a provider determination means according to the user's current situation. In this way, the determination unit can efficiently determine a necessary provider or service by customizing the provider or service based on the user's living situation. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the user's living situation data into AI, and the AI ​​can determine the optimal provider or service.

[0046] When determining a trader or service, the determination unit can prioritize a trader or service with high relevance in consideration of the user's geographical location information. The determination unit can, for example, prioritize a trader related to the user's current location. The determination unit can also filter and determine necessary traders based on the user's geographical location information. Furthermore, when the user is in a specific area, the determination unit can also prioritize a trader related to the area. This allows the determination unit to efficiently determine necessary traders and services by prioritizing traders and services with high relevance based on the user's geographical location information. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the user's geographical location information into AI, and the AI ​​can prioritize traders and services with high relevance.

[0047] The determination unit can analyze the user's social media activity and determine a relevant business or service when determining a business or service. The determination unit can determine a relevant business based on, for example, information shared by the user on social media. The determination unit can also preferentially determine businesses in the user's field of interest from the user's social media activity. Furthermore, the determination unit can determine a relevant business based on information on accounts the user follows on social media. This allows the determination unit to efficiently determine a relevant business or service by analyzing the user's social media activity. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's social media activity data into AI, which can determine a relevant business or service.

[0048] The suggestion unit can adjust the level of detail of the proposal based on the importance of the trader or service at the time of proposal. The suggestion unit can provide detailed information for traders or services with high importance, for example. The suggestion unit can also provide concise information for traders or services with low importance. Furthermore, the suggestion unit can adjust the level of detail of the proposal according to the importance of the trader or service. As a result, the suggestion unit can appropriately provide information necessary for the user by adjusting the level of detail of the proposal according to the importance of the trader or service. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the importance data of the trader or service to AI, and the AI ​​can adjust the level of detail of the proposal.

[0049] The suggestion unit can apply different suggestion algorithms depending on the category of the business or service when making a suggestion. For example, the suggestion unit can apply a specialized suggestion algorithm to a legal business. The suggestion unit can also apply a finance-specialized suggestion algorithm to a financial business. The suggestion unit can also apply a digital-specialized suggestion algorithm to a digital service-related business. This allows the suggestion unit to make more appropriate suggestions by applying a suggestion algorithm according to the category of the business or service. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input business or service category data to AI, and the AI ​​can apply different suggestion algorithms.

[0050] The suggestion unit can determine the priority of the proposal based on the submission time of the vendor or service at the time of proposal. For example, the suggestion unit can give priority to a vendor or service whose submission deadline is approaching. The suggestion unit can also postpone a vendor or service whose submission deadline is far away. Furthermore, the suggestion unit can adjust the priority of the proposal based on the submission time. In this way, the suggestion unit can prioritize proposals that are important to the user by determining the priority of the proposal based on the submission time of the vendor or service. Part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input submission time data of the vendor or service into AI, and the AI ​​can determine the priority of the proposal.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of the traders or services at the time of suggestion. For example, the suggestion unit can give priority to suggesting traders or services with high relevance. The suggestion unit can also postpone suggesting traders or services with low relevance. Furthermore, the suggestion unit can adjust the order of suggestions based on the relevance of the traders or services. As a result, the suggestion unit can give priority to suggestions with high relevance to the user by adjusting the order of suggestions based on the relevance of the traders or services. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input relevance data of traders and services to AI, and the AI ​​can adjust the order of suggestions.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The collection unit collects the user's health data, and the identification unit can identify a necessary procedure based on the collected health data. For example, the collection unit can collect the user's medical records, fitness tracker data, meal records, etc. Based on this data, the identification unit can identify contact with a medical institution, procedures for health insurance, suggestions for health management services, etc. This makes it possible to identify and suggest appropriate procedures according to the user's health condition.

[0054] The identification unit can suggest specific services related to post-mortem procedures based on the user's hobbies and interests. For example, if the user has a pet, pet care and adoption services can be identified. Also, if the user has a collection, collection organization and sale services can be identified. Furthermore, if the user follows a particular religion or culture, funeral services based on that religion or culture can be identified. This makes it possible to identify and suggest procedures according to the user's individual hobbies and interests.

[0055] The determination unit can analyze the user's past selection history and determine the most suitable provider or service. For example, it can prioritize highly reliable providers based on the evaluations of lawyers and administrative scriveners used by the user in the past. It can also analyze the satisfaction of services used by the user in the past and prioritize services with high satisfaction. Furthermore, it can refer to the history of providers and services selected by the user in the past and suggest that they be used again in a similar situation. In this way, it is possible to determine and suggest the most suitable provider or service based on the user's past selection history.

[0056] The collection unit can collect region-specific procedures and services taking into account the user's geographical location information. For example, if the user lives in a particular region, it can collect procedure information of government offices and banks in that region with priority. Also, if the user is traveling, it can collect procedure information related to the region to which the user is traveling. Furthermore, if the user is planning to move, it can collect procedure information for the new region in advance. This allows the collection unit to efficiently collect region-specific procedures and services based on the user's geographical location information.

[0057] The identification unit can adjust the priority of procedures based on the user's living situation. For example, if the user starts a new job, it can prioritize identifying procedures related to the job. Also, if the user has a family, it can prioritize identifying procedures related to the family. Furthermore, if the user is planning to move, it can prioritize identifying procedures related to the move. In this way, the identification unit can prioritize identifying procedures that are important to the user by adjusting the priority of procedures based on the user's living situation.

[0058] The process flow of the first embodiment will be briefly described below.

[0059] Step 1: The collection unit collects the user's lifestyle and service usage history. For example, the collection unit can collect information on the social media, bank accounts, insurance contracts, and subscription services used by the user. The collection unit can also use AI to convert this information into a format that is easy to analyze. Step 2: The Identification Department identifies the necessary procedures and consultations based on the data collected by the Collection Department. For example, the Identification Department can identify procedures such as writing a will, notifying government offices and banks, inheritance, sorting and terminating various contracts, clearing social media accounts, and sorting belongings. The Identification Department can also use AI to identify these procedures. Step 3: The determination unit determines a business or service based on the procedures and consultations identified by the determination unit. For example, the determination unit can determine a lawyer for writing a will, an administrative scrivener for filing with government offices and banks, a tax accountant for inheritance, a contract management service for sorting and terminating various contracts, a digital estate sorting service for clearing out SNS, and an estate sorting company for sorting estates. The determination unit can also use AI to determine these businesses or services. Step 4: The suggestion unit suggests the trader or service determined by the decision unit. For example, the suggestion unit allows the user to select the suggested trader or service and proceed with the procedure. The suggestion unit may also make these suggestions using AI.

[0060] (Example 2) The system according to the embodiment of the present invention is a system in which AI comprehensively determines and responds to the procedures and consultations required when a user dies or immediately before death. This system collects the user's lifestyle and the history of the services used, identifies the necessary procedures and consultations based on the collected data, and determines and proposes a company or service based on the identified procedures and consultations. For example, information on SNS, bank accounts, insurance contracts, and subscription services used by the user is collected and analyzed by AI. Next, based on the collected data, procedures such as writing a will, notifying government offices and banks, inheritance, sorting and terminating various contracts, disposing of SNS, and sorting belongings are identified. Furthermore, based on the identified procedures and consultations, a company or service such as a lawyer, administrative scrivener, tax accountant, contract management service, digital estate sorting service, estate sorting company, etc. are determined and proposed. This system is not a one-time thing, but is automatically reviewed every year or at a set frequency. For example, if the user's lifestyle or the history of the services used changes, AI automatically collects new data, re-identifies the necessary procedures and consultations, and re-determines and proposes a company or service. This allows the user to always proceed with procedures based on the latest information. This enables the system to comprehensively assess and respond to the procedures and consultations required at the time of a user's death or immediately before death.

[0061] The system according to the embodiment includes a collection unit, a specification unit, a decision unit, and a proposal unit. The collection unit collects the user's lifestyle and the history of the services used. For example, the collection unit can collect information on SNS, bank accounts, insurance contracts, and subscription services used by the user. The collection unit can also use AI to convert this information into a format that is easy to analyze. The specification unit specifies the necessary procedures and consultations based on the data collected by the collection unit. For example, the specification unit can specify procedures such as writing a will, notifying government offices and banks, inheritance, sorting and terminating various contracts, clearing SNS, and sorting belongings. The specification unit can also specify these procedures using AI. The determination unit determines a company or service based on the procedures and consultations specified by the specification unit. For example, the determination unit can determine a lawyer for writing a will, an administrative scrivener for notifying government offices and banks, a tax accountant for inheritance, a contract management service for sorting and terminating various contracts, a digital estate sorting service for clearing SNS, and an estate sorting company for sorting belongings. The determination unit may determine these businesses or services using AI. The suggestion unit proposes the businesses or services determined by the determination unit. For example, the suggestion unit enables the user to select the proposed business or service and proceed with the procedure. The suggestion unit may make these suggestions using AI. As a result, the system according to the embodiment can identify necessary procedures and consultation destinations based on the user's lifestyle and history of used services, and propose appropriate businesses and services.

[0062] The collection unit collects the user's lifestyle and the history of the services used. For example, the collection unit can collect information on the SNS, bank account, insurance contract, and subscription service used by the user. Specifically, the collection unit can understand the user's friendships and interests from the SNS usage history, and analyze the user's economic situation and spending patterns from the bank account transaction history. From the insurance contract information, the type of insurance the user has subscribed to and the compensation details can be confirmed, and from the subscription service information, the services the user regularly uses and the frequency of use can be understood. The collection unit centrally manages this information and converts it into a format that is easy to analyze using AI. For example, text data is converted into structured data using natural language processing technology, and image data is tagged using image recognition technology. This allows the collection unit to efficiently collect and manage various data of the user and use it for subsequent processing. Furthermore, the collection unit can appropriately collect necessary information while protecting the user's privacy by adjusting the frequency and accuracy of data collection. For example, data is collected based on a specific period and conditions after obtaining the user's consent. In addition, the collection unit takes security measures to prevent leakage and unauthorized access of collected data. This allows the collection unit to efficiently collect necessary data while gaining the trust of users, thereby improving the performance of the entire system.

[0063] The identification unit identifies the necessary procedures and consultation destinations based on the data collected by the collection unit. For example, the identification unit can identify procedures such as writing a will, notifying government offices and banks, inheritance, sorting and terminating various contracts, clearing SNS, and sorting belongings. Specifically, the identification unit analyzes the collected data and automatically extracts the user's life events and necessary procedures. For example, if the user is elderly or suffers from a specific illness, it determines that writing a will and inheritance procedures are necessary. In addition, if the user has multiple bank accounts, it determines that notifications to each bank are necessary. The identification unit can also use AI to identify these procedures. For example, it uses a machine learning algorithm to learn similar cases from past data and identify appropriate procedures for new cases. It also uses natural language processing technology to extract important information from the collected text data and determine the necessity of the procedures. This allows the identification unit to quickly and accurately analyze the collected data and identify the procedures required for the user. Furthermore, the identification unit can continuously improve the accuracy of identifying procedures based on user feedback. For example, it collects information on procedures and consultation destinations that users have actually performed, and improves the accuracy of the identification algorithm. In addition, in order to protect the privacy of users, the identification unit anonymizes the collected data and analyzes only the necessary information. This allows the identification unit to efficiently identify necessary procedures while gaining the trust of users and improving the performance of the entire system.

[0064] The determination unit determines a company or service based on the procedure and consultation destination identified by the identification unit. For example, the determination unit can determine a lawyer for writing a will, an administrative scrivener for filing with government offices and banks, a tax accountant for inheritance, a contract management service for sorting and terminating various contracts, a digital estate sorting service for clearing SNS, and an estate sorting company for sorting estates. Specifically, the determination unit selects the most suitable company or service for the specified procedure and proposes it to the user. The determination unit can also use AI to determine these companies or services. For example, a machine learning algorithm is used to learn the most suitable company or service from past data, and an appropriate company or service is selected for new cases. In addition, natural language processing technology is used to analyze the evaluations and reviews of companies and services from collected text data, and a reliable company or service is selected. This allows the determination unit to quickly and accurately analyze the collected data and determine the most suitable company or service for the user. Furthermore, the determination unit can continuously improve the selection accuracy of companies and services based on user feedback. For example, the evaluations of companies and services actually used by users are collected to improve the accuracy of the selection algorithm. In addition, in order to protect the privacy of users, the determination unit anonymizes the collected data and analyzes only the necessary information, thereby enabling the determination unit to efficiently determine optimal providers and services while gaining the trust of users and improving the performance of the entire system.

[0065] The suggestion unit proposes the trader or service determined by the decision unit. For example, the suggestion unit allows the user to select the proposed trader or service and proceed with the procedure. Specifically, the suggestion unit provides the user with detailed information on the selected trader or service, and guides the user on how to proceed with the procedure and contact information. The suggestion unit can also make these proposals using AI. For example, a natural language generation technology is used to automatically generate a proposal text that is easy for the user to understand, and send it via email or a message app. In addition, based on the user's past selection history and feedback, it is also possible to make individually customized proposals. As a result, the suggestion unit can quickly and accurately propose optimal traders and services to the user, and the procedure can proceed smoothly. Furthermore, the suggestion unit can continuously improve the accuracy and effectiveness of the proposal content based on the user's feedback. For example, the suggestion unit collects evaluations of traders and services that the user actually used, and improves the accuracy of the proposal algorithm. In addition, the suggestion unit can reliably transmit information using multiple communication means. For example, important information is reliably delivered by using not only email and message apps, but also voice calls and SMS. As a result, the suggestion unit can quickly and reliably provide proposals to the user, and the procedure can proceed smoothly.

[0066] The collection unit can collect information on SNS, bank accounts, insurance contracts, and subscription services used by the user. The collection unit can collect, for example, information on SNS used by the user. The SNS information includes the user's posted content, the number of followers, the number of likes, and the like. The collection unit can also collect information on bank accounts. The bank account information includes transaction history, balance information, and the like. The collection unit can also collect information on insurance contracts. The insurance contract information includes the contract content, the insurance amount, the contract period, and the like. The collection unit can also collect information on subscription services. The subscription service information includes the contract content, the usage history, the payment information, and the like. In this way, the collection unit can collect information on various services used by the user to more accurately identify the necessary procedures and consultation destinations. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's SNS information into AI, which can analyze and extract the necessary information.

[0067] The specification part can specify the procedures of creating a will, notifying a government office or bank, inheritance, sorting and terminating various contracts, deleting SNS, and sorting belongings of a deceased person. The specification part can specify, for example, creating a will. Creating a will includes legal requirements and necessary documents. The specification part can also specify notifying a government office or bank. Notifying a government office or bank includes necessary documents and a procedure flow. The specification part can specify inheritance. Inheritance includes calculation of inheritance tax and inheritance division consultation. The specification part can specify sorting and terminating various contracts. Sorting and terminating various contracts includes a method of contract termination and a necessary notice. The specification part can also specify deletion of SNS. Deletion of SNS includes a method of account deletion and data backup. The specification part can also specify sorting of belongings of a deceased person. Sorting belongings of a deceased person includes classification and disposal method of belongings of a deceased person. In this way, the specification part can clarify the procedures to be performed by the user by specifically specifying the necessary procedures. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input user data into AI, which analyzes the data and identifies the necessary procedures.

[0068] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated emotion. For example, when the user is stressed, the collection unit can reduce the frequency of data collection and collect data when the user is relaxed. Also, when the user is relaxed, the collection unit can increase the frequency of data collection and collect detailed information. Furthermore, when the user is busy, the collection unit can postpone data collection and collect data when the user is calm. In this way, the collection unit can reduce the burden on the user by adjusting the timing of data collection according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data to the generation AI, which can estimate the emotion, and adjust the timing of data collection based on the result.

[0069] The collection unit can analyze the user's past data collection history and select the most appropriate collection method. The collection unit can, for example, preferentially collect data of services that the user has frequently used in the past. The collection unit can also analyze the pattern of data collected by the user in the past and determine the optimal collection timing. Furthermore, the collection unit can automatically collect data of a specific service from the user's past data collection history. As a result, the collection unit can select the optimal collection method by analyzing the past data collection history, making it possible to collect data efficiently. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past data collection history into AI, which can select the optimal collection method.

[0070] The collection unit can perform filtering based on the user's current living situation or field of interest when collecting data. The collection unit can, for example, preferentially collect data in fields in which the user is currently interested. The collection unit can also collect only necessary data according to the user's living situation. Furthermore, the collection unit can also filter and collect related data based on the user's field of interest. This allows the collection unit to collect only necessary data by filtering data based on the user's living situation or field of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's field of interest into AI, and the AI ​​can filter and collect related data.

[0071] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated emotion. For example, when the user is stressed, the collection unit can postpone collection of data with low importance. In addition, when the user is relaxed, the collection unit can preferentially collect data with high importance. Furthermore, when the user is busy, the collection unit can preferentially collect data with high urgency. In this way, the collection unit can prioritize data collection by determining the priority of data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data to the generation AI, which can estimate the emotion, and determine the priority of the data based on the result.

[0072] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting data. The collection unit can, for example, preferentially collect data related to the location where the user is currently located. The collection unit can also filter and collect necessary data based on the geographical location information of the user. Furthermore, when the user is in a specific area, the collection unit can also preferentially collect data related to the area. As a result, the collection unit can efficiently collect necessary data by preferentially collecting highly relevant data based on the geographical location information of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI, and the AI ​​can preferentially collect highly relevant data.

[0073] The collection unit can analyze the user's social media activity and collect related data when collecting data. The collection unit can collect related data based on, for example, information shared by the user on social media. The collection unit can also preferentially collect data in areas of interest from the user's social media activity. Furthermore, the collection unit can collect related data based on information on accounts the user follows on social media. This allows the collection unit to efficiently collect related data by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI, which can collect related data.

[0074] The identification unit can estimate the user's emotion and adjust the procedure identification method based on the estimated emotion. For example, when the user is stressed, the identification unit can prioritize simple procedures. Also, when the user is relaxed, the identification unit can also prioritize detailed procedures. Furthermore, when the user is busy, the identification unit can prioritize procedures with high urgency. In this way, the identification unit can adjust the procedure identification method according to the user's emotion, thereby reducing the burden on the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input the user's facial expression data to the generation AI, which can estimate the emotion, and adjust the procedure identification method based on the result.

[0075] The identification unit can select the most appropriate identification method by referring to the past procedure history when identifying a procedure. The identification unit can select the optimal identification method based on, for example, procedures performed by the user in the past. The identification unit can also select an efficient identification method from the user's past procedure history. Furthermore, the identification unit can preferentially identify a procedure method used by the user in the past. This allows the identification unit to select the optimal identification method by referring to the past procedure history, making it possible to identify an efficient procedure. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the user's past procedure history into AI, which can select the optimal identification method.

[0076] The identification unit can adjust the identification means based on the user's current living situation when identifying the procedure. The identification unit can customize the necessary procedure according to the user's current living situation, for example. The identification unit can also identify the optimal procedure based on the user's living situation. Furthermore, the identification unit can customize the procedure means according to the user's current situation. In this way, the identification unit can efficiently identify the necessary procedure by customizing the procedure based on the user's living situation. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the user's living situation data into AI, and the AI ​​can identify the optimal procedure.

[0077] The identification unit can estimate the user's emotion and determine the priority of procedures to be identified based on the estimated emotion. For example, when the user is stressed, the identification unit can postpone procedures with low importance. In addition, when the user is relaxed, the identification unit can also preferentially identify procedures with high importance. Furthermore, when the user is busy, the identification unit can preferentially identify procedures with high urgency. In this way, the identification unit can preferentially identify important procedures by determining the priority of procedures according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input the user's facial expression data to the generation AI, which can estimate the emotion, and determine the priority of procedures based on the result.

[0078] When identifying a procedure, the identification unit can preferentially identify a procedure with high relevance in consideration of the geographical location information of the user. For example, the identification unit can preferentially identify a procedure related to the location where the user is currently located. The identification unit can also filter and identify necessary procedures based on the geographical location information of the user. Furthermore, when the user is in a specific area, the identification unit can preferentially identify procedures related to the area. As a result, the identification unit can efficiently identify necessary procedures by preferentially identifying procedures with high relevance based on the geographical location information of the user. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the geographical location information of the user to AI, and the AI ​​can preferentially identify procedures with high relevance.

[0079] The identification unit can analyze the user's social media activity and identify related procedures when identifying a procedure. The identification unit can identify related procedures based on, for example, information shared by the user on social media. The identification unit can also preferentially identify procedures in the user's field of interest from the user's social media activity. Furthermore, the identification unit can identify related procedures based on information on accounts the user follows on social media. In this way, the identification unit can efficiently identify related procedures by analyzing the user's social media activity. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the user's social media activity data into AI, which can identify related procedures.

[0080] The determination unit can estimate the user's emotion and adjust the method of determining a provider or service based on the estimated emotion. For example, when the user is stressed, the determination unit can preferentially determine a provider that provides simple procedures. In addition, when the user is relaxed, the determination unit can also preferentially determine a provider that can respond quickly when the user is busy. In this way, the determination unit can reduce the burden on the user by adjusting the method of determining a provider or service according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's facial expression data to the generation AI, which can estimate the emotion, and adjust the method of determining a provider or service based on the result.

[0081] The determination unit can select the most appropriate determination method by referring to the past decision history when determining a provider or service. The determination unit can determine the optimal provider based on, for example, a provider used by the user in the past. The determination unit can also select an efficient determination method from the user's past decision history. Furthermore, the determination unit can preferentially determine a service used by the user in the past. This allows the determination unit to select the optimal determination method by referring to the past decision history, making it possible to determine an efficient provider or service. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the user's past decision history into AI, which can select the optimal determination method.

[0082] The determination unit can adjust the means of determination based on the user's current living situation when determining a provider or service. The determination unit can customize a necessary provider according to, for example, the user's current living situation. The determination unit can also determine an optimal provider based on the user's living situation. Furthermore, the determination unit can customize a provider determination means according to the user's current situation. In this way, the determination unit can efficiently determine a necessary provider or service by customizing the provider or service based on the user's living situation. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the user's living situation data into AI, and the AI ​​can determine the optimal provider or service.

[0083] The determination unit can estimate the user's emotion and determine the priority of the provider or service to be determined based on the estimated emotion. For example, when the user is stressed, the determination unit can postpone the determination of a provider with a low importance. In addition, when the user is relaxed, the determination unit can also determine a provider with a high importance as a priority. Furthermore, when the user is busy, the determination unit can also determine a provider with a high urgency as a priority. In this way, the determination unit can determine the priority of providers and services according to the user's emotion, thereby determining important providers and services as a priority. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's facial expression data to the generation AI, which can estimate the emotion, and determine the priority of the provider or service based on the result.

[0084] When determining a trader or service, the determination unit can prioritize a trader or service with high relevance in consideration of the user's geographical location information. The determination unit can, for example, prioritize a trader related to the user's current location. The determination unit can also filter and determine necessary traders based on the user's geographical location information. Furthermore, when the user is in a specific area, the determination unit can also prioritize a trader related to the area. This allows the determination unit to efficiently determine necessary traders and services by prioritizing traders and services with high relevance based on the user's geographical location information. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the user's geographical location information into AI, and the AI ​​can prioritize traders and services with high relevance.

[0085] The determination unit can analyze the user's social media activity and determine a relevant business or service when determining a business or service. The determination unit can determine a relevant business based on, for example, information shared by the user on social media. The determination unit can also preferentially determine businesses in the user's field of interest from the user's social media activity. Furthermore, the determination unit can determine a relevant business based on information on accounts the user follows on social media. This allows the determination unit to efficiently determine a relevant business or service by analyzing the user's social media activity. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's social media activity data into AI, which can determine a relevant business or service.

[0086] The suggestion unit can estimate the user's emotion and adjust the expression method of the proposal based on the estimated emotion. For example, when the user is stressed, the suggestion unit can use a simple and easy-to-understand expression method. Furthermore, when the user is relaxed, the suggestion unit can also use an expression method including detailed information. Furthermore, when the user is busy, the suggestion unit can use a short expression method that focuses on the main points. In this way, the suggestion unit can adjust the expression method of the proposal according to the user's emotion, thereby making it possible to make a proposal that is easy for the user to understand. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data to the generation AI, which can estimate the emotion, and adjust the expression method of the proposal based on the result.

[0087] The suggestion unit can adjust the level of detail of the proposal based on the importance of the trader or service at the time of proposal. The suggestion unit can provide detailed information for traders or services with high importance, for example. The suggestion unit can also provide concise information for traders or services with low importance. Furthermore, the suggestion unit can adjust the level of detail of the proposal according to the importance of the trader or service. As a result, the suggestion unit can appropriately provide information necessary for the user by adjusting the level of detail of the proposal according to the importance of the trader or service. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the importance data of the trader or service to AI, and the AI ​​can adjust the level of detail of the proposal.

[0088] The suggestion unit can apply different suggestion algorithms depending on the category of the business or service when making a suggestion. For example, the suggestion unit can apply a specialized suggestion algorithm to a legal business. The suggestion unit can also apply a finance-specialized suggestion algorithm to a financial business. The suggestion unit can also apply a digital-specialized suggestion algorithm to a digital service-related business. This allows the suggestion unit to make more appropriate suggestions by applying a suggestion algorithm according to the category of the business or service. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input business or service category data to AI, and the AI ​​can apply different suggestion algorithms.

[0089] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated emotion. For example, when the user is stressed, the suggestion unit can make a short and to the point suggestion. Also, when the user is relaxed, the suggestion unit can make a longer suggestion including a detailed explanation. Furthermore, when the user is busy, the suggestion unit can make a quick and concise suggestion. This allows the suggestion unit to adjust the length of the suggestion according to the user's emotion, thereby making it possible to make a suggestion of an appropriate length for the user. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data to the generation AI, which can estimate the emotion, and adjust the length of the suggestion based on the result.

[0090] The suggestion unit can determine the priority of the proposal based on the submission time of the vendor or service at the time of proposal. For example, the suggestion unit can give priority to a vendor or service whose submission deadline is approaching. The suggestion unit can also postpone a vendor or service whose submission deadline is far away. Furthermore, the suggestion unit can adjust the priority of the proposal based on the submission time. In this way, the suggestion unit can prioritize proposals that are important to the user by determining the priority of the proposal based on the submission time of the vendor or service. Part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input submission time data of the vendor or service into AI, and the AI ​​can determine the priority of the proposal.

[0091] The suggestion unit can adjust the order of suggestions based on the relevance of the traders or services at the time of suggestion. For example, the suggestion unit can give priority to suggesting traders or services with high relevance. The suggestion unit can also postpone suggesting traders or services with low relevance. Furthermore, the suggestion unit can adjust the order of suggestions based on the relevance of the traders or services. As a result, the suggestion unit can give priority to suggestions with high relevance to the user by adjusting the order of suggestions based on the relevance of the traders or services. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input relevance data of traders and services to AI, and the AI ​​can adjust the order of suggestions.

[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 collection unit collects the user's health data, and the identification unit can identify a necessary procedure based on the collected health data. For example, the collection unit can collect the user's medical records, fitness tracker data, meal records, etc. Based on this data, the identification unit can identify contact with a medical institution, procedures for health insurance, suggestions for health management services, etc. This makes it possible to identify and suggest appropriate procedures according to the user's health condition.

[0094] The identification unit can suggest specific services related to post-mortem procedures based on the user's hobbies and interests. For example, if the user has a pet, pet care and adoption services can be identified. Also, if the user has a collection, collection organization and sale services can be identified. Furthermore, if the user follows a particular religion or culture, funeral services based on that religion or culture can be identified. This makes it possible to identify and suggest procedures according to the user's individual hobbies and interests.

[0095] The determination unit can analyze the user's past selection history and determine the most suitable provider or service. For example, it can prioritize highly reliable providers based on the evaluations of lawyers and administrative scriveners used by the user in the past. It can also analyze the satisfaction of services used by the user in the past and prioritize services with high satisfaction. Furthermore, it can refer to the history of providers and services selected by the user in the past and suggest that they be used again in a similar situation. In this way, it is possible to determine and suggest the most suitable provider or service based on the user's past selection history.

[0096] The suggestion unit can estimate the user's emotion and adjust the timing of the suggestion based on the estimated emotion. For example, if the user feels sad, the timing of the suggestion can be delayed and the suggestion can be made when the user is calm. Also, if the user is relaxed, the timing of the suggestion can be advanced and the procedure can be progressed quickly. Furthermore, if the user feels stressed, the timing of the suggestion can be adjusted to reduce the burden on the user. In this way, by adjusting the timing of the suggestion according to the user's emotion, the suggestion can be made at the optimal timing for the user.

[0097] The collection unit can estimate the user's emotions and adjust the method of data collection based on the estimated emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced and collection can be performed when the user is relaxed. Also, if the user is relaxed, the frequency of data collection can be increased to collect more detailed information. Furthermore, if the user is busy, data collection can be postponed and collected when the user is calm. In this way, the collection unit can reduce the burden on the user by adjusting the method of data collection according to the user's emotions.

[0098] The identification unit can estimate the user's emotions and determine the priority order of procedures based on the estimated emotions. For example, if the user is feeling stressed, procedures with low importance can be postponed. Also, if the user is relaxed, procedures with high importance can be preferentially identified. Furthermore, if the user is busy, procedures with high urgency can be preferentially identified. In this way, the identification unit can preferentially identify important procedures by determining the priority order of procedures according to the user's emotions.

[0099] The determination unit can estimate the user's emotions and adjust the selection criteria for a provider or service based on the estimated emotions. For example, when the user is stressed, a provider that can respond quickly can be preferentially selected. When the user is relaxed, a provider that provides detailed explanations can be selected. Furthermore, when the user is busy, a provider that provides simple and efficient services can be selected. In this way, the determination unit can select the most suitable provider or service for the user by adjusting the selection criteria for a provider or service according to the user's emotions.

[0100] The suggestion unit can estimate the user's emotion and adjust the content of the suggestion based on the estimated emotion. For example, if the user feels sad, the suggestion can be made in gentle language. If the user feels relaxed, the suggestion can be made including detailed information. If the user feels stressed, the suggestion can be made concise and easy to understand. In this way, the suggestion unit can make a suggestion that is easy for the user to understand by adjusting the content of the suggestion according to the user's emotion.

[0101] The collection unit can collect region-specific procedures and services taking into account the user's geographical location information. For example, if the user lives in a particular region, it can collect procedure information of government offices and banks in that region with priority. Also, if the user is traveling, it can collect procedure information related to the region to which the user is traveling. Furthermore, if the user is planning to move, it can collect procedure information for the new region in advance. This allows the collection unit to efficiently collect region-specific procedures and services based on the user's geographical location information.

[0102] The identification unit can adjust the priority of procedures based on the user's living situation. For example, if the user starts a new job, it can prioritize identifying procedures related to the job. Also, if the user has a family, it can prioritize identifying procedures related to the family. Furthermore, if the user is planning to move, it can prioritize identifying procedures related to the move. In this way, the identification unit can prioritize identifying procedures that are important to the user by adjusting the priority of procedures based on the user's living situation.

[0103] The process flow of the second embodiment will be briefly described below.

[0104] Step 1: The collection unit collects the user's lifestyle and service usage history. For example, the collection unit can collect information on the social media, bank accounts, insurance contracts, and subscription services used by the user. The collection unit can also use AI to convert this information into a format that is easy to analyze. Step 2: The Identification Department identifies the necessary procedures and consultations based on the data collected by the Collection Department. For example, the Identification Department can identify procedures such as writing a will, notifying government offices and banks, inheritance, sorting and terminating various contracts, clearing social media accounts, and sorting belongings. The Identification Department can also use AI to identify these procedures. Step 3: The determination unit determines a business or service based on the procedures and consultations identified by the determination unit. For example, the determination unit can determine a lawyer for writing a will, an administrative scrivener for filing with government offices and banks, a tax accountant for inheritance, a contract management service for sorting and terminating various contracts, a digital estate sorting service for clearing out SNS, and an estate sorting company for sorting estates. The determination unit can also use AI to determine these businesses or services. Step 4: The suggestion unit suggests the trader or service determined by the decision unit. For example, the suggestion unit allows the user to select the suggested trader or service and proceed with the procedure. The suggestion unit may also make these suggestions using AI.

[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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0107] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0108] For example, the collection unit can collect the user's lifestyle and the history of the services used by using the camera 42 and microphone 38B of the smart device 14. The collection unit can also be realized by the identification processing unit 290 of the data processing device 12, and can convert the collected data into a format that is easy to analyze. The identification unit can be realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the necessary procedures and consultation destinations based on the data collected by the collection unit. The determination unit can be realized, for example, by the identification processing unit 290 of the data processing device 12, and determines a provider or service based on the procedures and consultation destinations identified by the identification unit. The suggestion unit can be realized, for example, by the control unit 46A of the smart device 14, and suggests the provider or service determined by the determination unit to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes 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 the 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, the RAM 30, and the 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 the voice according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average 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 performed in a secure state.

[0116] Fig. 4 shows an example of 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 glasses 214 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 also 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0124] For example, the collection unit can collect the user's lifestyle and the history of the services used by using the camera 42 and microphone 238 of the smart glasses 214. The collection unit can also be realized by the identification processing unit 290 of the data processing device 12, and can convert the collected data into a format that is easy to analyze. The identification unit can be realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the necessary procedures and consultation destinations based on the data collected by the collection unit. The determination unit can be realized, for example, by the identification processing unit 290 of the data processing device 12, and determines a provider or service based on the procedures and consultation destinations identified by the identification unit. The suggestion unit can be realized, for example, by the control unit 46A of the smart glasses 214, and suggests the provider or service determined by the determination unit to the user. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes 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 type 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, the RAM 30, and the 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 the voice according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average 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 performed 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0135] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) 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 voice indicating a user input for 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.

[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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] For example, the collection unit can collect the user's lifestyle and the history of the services used by using the camera 42 and microphone 238 of the headset type terminal 314. The collection unit can also be realized by the identification processing unit 290 of the data processing device 12, and can convert the collected data into a format that is easy to analyze. The identification unit can be realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the necessary procedures and consultation destinations based on the data collected by the collection unit. The determination unit can be realized, for example, by the identification processing unit 290 of the data processing device 12, and determines a provider or service based on the procedures and consultation destinations identified by the identification unit. The suggestion unit can be realized, for example, by the control unit 46A of the headset type terminal 314, and suggests the provider or service determined by the determination unit to the user. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes 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, the RAM 30, and the 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 the voice according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of 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 performed in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0152] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) 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 a voice indicating a user input for 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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] For example, the collection unit can collect the user's lifestyle and the history of the services used by using the camera 42 and microphone 238 of the robot 414. The collection unit can also be realized by the identification processing unit 290 of the data processing device 12, and can convert the collected data into a format that is easy to analyze. The identification unit can be realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the necessary procedures and consultation destinations based on the data collected by the collection unit. The determination unit can be realized, for example, by the identification processing unit 290 of the data processing device 12, and determines a trader or service based on the procedures and consultation destinations identified by the identification unit. The suggestion unit can be realized, for example, by the control unit 46A of the robot 414, and suggests the trader or service determined by the determination unit to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0159] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in 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 of emotion map 400 and usually 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 inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0163] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," 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 Universal Serial Bus (USB) 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] In addition, 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 upon 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] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0170] The hardware resource that executes the specific process may be one of these various processors, or may be 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 configuration using one 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 configuration using a processor that realizes the functions of the 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, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The above specific processes are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0174] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0176] (Appendix 1) A collection unit that collects a user's lifestyle and a history of services used; An identification unit that identifies a necessary procedure and a consultation destination based on the data collected by the collection unit; A decision unit that decides on a trader or service based on the identified procedure and consultation destination; A proposal unit that proposes the determined trader or service. A system characterized by: (Appendix 2) The collecting unit includes: Collect information about users’ social media, bank accounts, insurance contracts, and subscription services 2. The system of claim 1, (Appendix 3) The identification unit is Identify procedures for writing a will, filing with government offices and banks, inheritance, sorting and terminating various contracts, deleting social media accounts, and sorting belongings. 2. The system of claim 1, (Appendix 4) The collecting unit includes: Infer user emotions and adjust the timing of data collection based on the inferred emotions 2. The system of claim 1, (Appendix 5) The collecting unit includes: Analyze the user's past data collection history and select the most appropriate collection method 2. The system of claim 1, (Appendix 6) The collecting unit includes: Filter data as it is collected based on the user's current life situation or interests 2. The system of claim 1, (Appendix 7) The collecting unit includes: Infer user emotions and prioritize data collection based on the inferred emotions 2. The system of claim 1, (Appendix 8) The collecting unit includes: When collecting data, prioritize collecting data that is more relevant by taking into account the user's geographic location. 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1, (Appendix 10) The identification unit is Infer user emotions and adjust procedure identification methods based on the inferred emotions 2. The system of claim 1, (Appendix 11) The identification unit is When identifying procedures, select the most appropriate identification method by referring to past procedure history. 2. The system of claim 1, (Appendix 12) The identification unit is During procedure identification, tailor specific measures based on the user's current life situation 2. The system of claim 1, (Appendix 13) The identification unit is Estimate the user's emotions and prioritize the steps to be performed based on the estimated emotions. 2. The system of claim 1, (Appendix 14) The identification unit is When identifying procedures, the geographic location of the user is taken into consideration to prioritize identifying procedures that are more relevant. 2. The system of claim 1, (Appendix 15) The identification unit is When identifying a procedure, analyze the user's social media activity to identify related procedures. 2. The system of claim 1, (Appendix 16) The determination unit is Inferring a user's emotions and adjusting a vendor or service decision method based on the inferred emotions 2. The system of claim 1, (Appendix 17) The determination unit is When deciding on a supplier or service, select the most appropriate decision method by referring to past decision history 2. The system of claim 1, (Appendix 18) The determination unit is When deciding on a vendor or service, adjust the decision making tool based on the user's current life situation 2. The system of claim 1, (Appendix 19) The determination unit is To estimate a user's emotion and prioritize a service provider or service based on the estimated emotion. 2. The system of claim 1, (Appendix 20) The determination unit is When determining a business or service, the geographic location information of the user is taken into consideration to prioritize the business or service that is more relevant. 2. The system of claim 1, (Appendix 21) The determination unit is When deciding on a business or service, analyze the user's social media activity to decide on a relevant business or service 2. The system of claim 1, (Appendix 22) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred emotions 2. The system of claim 1, (Appendix 23) The suggestion unit, When making a proposal, adjust the level of detail in the proposal based on the importance of the vendor or service 2. The system of claim 1, (Appendix 24) The suggestion unit, When making suggestions, apply different suggestion algorithms depending on the merchant or service category 2. The system of claim 1, (Appendix 25) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the inferred sentiment 2. The system of claim 1, (Appendix 26) The suggestion unit, When proposals are submitted, prioritize proposals based on when the vendor or service was submitted 2. The system of claim 1, (Appendix 27) The suggestion unit, When making suggestions, adjust the order of suggestions based on the relevance of the vendor or service 2. The system of claim 1, [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. A collection unit that collects a user's lifestyle and service usage history; An identification unit that identifies a necessary procedure and a consultation destination based on the data collected by the collection unit; a determination unit that determines a trader or service based on the procedure and the consultation destination identified by the identification unit; A proposal unit that proposes the trader or service determined by the determination unit. A system characterized by:

2. The collecting unit includes: Collect information about users’ social media, bank accounts, insurance contracts, and subscription services 2. The system of claim 1 .

3. The identification unit is Identify procedures for writing a will, filing with government offices and banks, inheritance, sorting and terminating various contracts, deleting social media accounts, and sorting out belongings.

2. The system of claim 1 .

4. The collecting unit includes: Infer user emotions and adjust the timing of data collection based on the inferred emotions 2. The system of claim 1 .

5. The collecting unit includes: Analyze the user's past data collection history and select the most appropriate collection method 2. The system of claim 1 .

6. The collecting unit includes: Filter data as it is collected based on the user's current life situation or interests 2. The system of claim 1 .

7. The collecting unit includes: Infer user emotions and prioritize data collection based on the inferred emotions 2. The system of claim 1 .

8. The collecting unit includes: When collecting data, prioritize collecting data that is more relevant by taking into account the user's geographic location.

2. The system of claim 1 .

9. The collecting unit includes: When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1 .

10. The identification unit is Infer user emotions and adjust procedure identification methods based on the inferred emotions 2. The system of claim 1 .

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