system

Abstract

The system according to the embodiment aims to collect users' concerns and develop new businesses based on them. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a development unit, and a provision unit. The collection unit collects worries from users. The analysis unit analyzes the worry data collected by the collection unit. The development unit develops new businesses based on the analysis results obtained by the analysis unit. The provision unit provides compensation to users based on the worry data collected by the collection unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been effective in collecting users' concerns and developing new businesses based on them, and there is room for improvement.

[0005] The system according to the embodiment aims to collect users' concerns and develop new businesses based on them. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a development unit, and a provision unit. The collection unit collects worries from users. The analysis unit analyzes the worry data collected by the collection unit. The development unit develops new businesses based on the analysis results obtained by the analysis unit. The provision unit provides compensation to users based on the worry data collected by the collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can collect user concerns and develop new businesses based on them. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The problem-collecting system according to an embodiment of the present invention is a system that purchases personal problems, aggregates them by age, gender, and in real time, and develops new businesses that meet customer needs quickly. This problem-collecting system allows users to input their own problems and purchase them in exchange for payment. Next, the problem data is aggregated along with attribute information such as age and gender and analyzed in real time. This allows for the development of new services and products to solve those problems if a large number of problems are common to a particular age group or gender. For example, if a large number of young people report stress-related concerns, it is possible to develop stress-relief products and relaxation services. The goal is to make people happier by developing businesses that can solve even a small amount of problems, from minor to major. This allows the problem-collecting system to collect and analyze users' problems, develop new businesses, and provide payment to solve users' problems.

[0029] A problem-collection system according to an embodiment includes a collection unit, an analysis unit, a development unit, and a provision unit. The collection unit collects problems from users. The collection unit provides, for example, an interface through which users input their problems. Examples of the interface include, but are not limited to, a web form, a mobile app, a chatbot, and the like. For example, the collection unit enables users to input their problems using a web form. The collection unit can also enable users to input their problems anytime and anywhere using a mobile app. The collection unit can also enable users to input their problems in a natural, interactive format using a chatbot. The analysis unit analyzes the problem data collected by the collection unit. For example, the analysis unit analyzes the data using AI to find common patterns and trends. Examples of AI include, but are not limited to, natural language processing, image recognition, and machine learning algorithms. For example, the analysis unit analyzes text data using natural language processing to detect frequent patterns and time series patterns. The analysis unit can also analyze image data using image recognition to find common features. The analysis unit can also cluster data using machine learning algorithms to find trends. The development department develops new businesses based on the analysis results obtained by the analysis department. The development department, for example, develops new products and provides new services. For example, if the development department receives a large number of stress-related worries from young people, it can develop stress relief goods and relaxation services. Furthermore, if the development department receives a large number of health-related worries from elderly people, it can develop health management services and health foods. The provision department provides compensation to users based on the worry data collected by the collection department. The provision unit provides, for example, monetary rewards, points, coupons, etc. For example, the provision unit can award points each time a user inputs a worry and allow the points to be exchanged for products or services. Furthermore, the provision unit can issue a coupon each time a user inputs a worry and allow the user to receive discounts using the coupon.As a result, the problem collection system according to the embodiment can solve users' problems by collecting and analyzing the users' problems, developing new businesses, and providing compensation.

[0030] The collection unit can provide an interface through which a user inputs their concerns. Examples of the interface include, but are not limited to, a web form, a mobile app, a chatbot, and the like. The collection unit can, for example, enable a user to input their concerns using a web form. For example, the collection unit can provide a text box in the web form to allow the user to freely input their concerns. The collection unit can also enable a user to input their concerns anytime, anywhere using a mobile app. For example, the collection unit can provide a notification function in the mobile app to remind the user when to input their concerns. The collection unit can also enable a user to input their concerns in a natural, conversational format using a chatbot. For example, the collection unit can incorporate natural language processing technology into the chatbot to allow the user to input their concerns in a conversational format. This makes it possible to provide an interface that allows a user to easily input their concerns.

[0031] The analysis unit can analyze the collected data using AI to find common patterns or trends. Examples of AI include, but are not limited to, natural language processing, image recognition, and machine learning algorithms. For example, the analysis unit can analyze text data using natural language processing to detect frequent patterns and time series patterns. For example, the analysis unit can find frequent patterns by tokenizing the text data and calculating the frequency of occurrence of each token. The analysis unit can also analyze time series data to detect patterns over a specific period of time. For example, the analysis unit can smooth the time series data using a moving average to find trends. The analysis unit can also analyze image data using image recognition to find common features. For example, the analysis unit can analyze image data using a convolutional neural network (CNN) to extract common features. The analysis unit can also cluster the data using a machine learning algorithm to find trends. For example, the analysis unit can divide the data into clusters using K-means clustering and analyze the features of each cluster. This makes it possible to analyze the data using AI and find common patterns and trends.

[0032] The development department can develop new businesses based on the analysis results. Examples of new businesses include, but are not limited to, developing new products and providing new services. For example, if the development department receives a large number of complaints about stress relief from young people, the development department can develop stress relief products and relaxation services. For example, the development department can develop relaxing aroma oils and other stress relief products as stress relief products. The development department can also offer massage and yoga classes as relaxation services. Furthermore, if the development department receives a large number of complaints about health from elderly people, the development department can develop health management services and health foods. For example, the development department can provide online health consultations and fitness programs as health management services. The development department can also develop nutritionally balanced supplements and health foods as health foods. This allows the development of new businesses based on the analysis results.

[0033] The providing unit can provide a user with a reward. Examples of the reward include, but are not limited to, monetary rewards, points, coupons, and the like. For example, the providing unit can grant points to the user each time the user inputs a concern and allow the user to exchange the points for goods or services. For example, the providing unit can allow the user to exchange a certain number of points for specific goods or services when the user accumulates a certain number of points. The providing unit can also issue a coupon each time the user inputs a concern and allow the user to use the coupon to receive a discount. For example, the providing unit can allow the user to use the coupon to receive a discount at a specific store. Furthermore, the providing unit can also provide a monetary reward each time the user inputs a concern. For example, the providing unit can provide a certain amount of electronic money each time the user inputs a concern. In this way, the user can be provided with a reward.

[0034] The collection unit can analyze the user's past problem submission history and select an appropriate collection method. For example, the collection unit analyzes the pattern of problems that the user has frequently submitted in the past and proposes an optimal collection method. For example, the collection unit analyzes the content and frequency of problems that the user has submitted in the past and selects an optimal collection method. Furthermore, if the user tends to submit problems during a specific time period, the collection unit can prompt the user to collect problems during that time period. For example, if the collection unit analyzes that the user tends to submit problems during a specific time period, the collection unit can send a notification during that time period and prompt the user to enter a problem. Furthermore, if the user submits many problems in a specific category, the collection unit can provide a collection method specialized for that category. For example, if the collection unit analyzes that the user tends to submit many problems in a specific category, the collection unit can display questions related to that category and prompt the user to enter a problem. In this way, the user's past problem submission history can be analyzed and an optimal collection method can be selected.

[0035] When collecting worries, the collection unit can filter the worries based on the user's current living situation or area of ​​interest. For example, when the user inputs their current living situation, the collection unit prioritizes collecting worries related to that situation. For example, when the user inputs their current living situation, the collection unit displays questions related to that situation and prompts the user to input a worry. The collection unit can also filter and collect related worries based on the user's area of ​​interest. For example, when the user inputs an area of ​​interest, the collection unit displays questions related to that area and prompts the user to input a worry. Furthermore, the collection unit can prioritize collecting worries of a specific category based on the user's living situation or area of ​​interest. For example, when the collection unit analyzes that the user has a specific living situation or area of ​​interest, the collection unit displays questions related to that category and prompts the user to input a worry. This makes it possible to filter worries based on the user's current living situation or area of ​​interest.

[0036] When collecting worries, the collection unit can prioritize collecting highly relevant worries by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting worries related to that area. For example, if the collection unit analyzes that the user is in a specific area, it displays questions related to that area and prompts the user to input a worry. Furthermore, if the user is traveling, the collection unit can prioritize collecting worries related to the travel destination. For example, if the collection unit analyzes that the user is traveling, it displays questions related to the travel destination and prompts the user to input a worry. Furthermore, if the user is at home, the collection unit can prioritize collecting worries related to the area around the user's home. For example, if the collection unit analyzes that the user is at home, it displays questions related to the area around the user's home and prompts the user to input a worry. In this way, it is possible to prioritize collecting highly relevant worries by taking into account the user's geographical location information.

[0037] When collecting worries, the collection unit can analyze the user's social media activities and collect related worries. The collection unit, for example, analyzes content posted by the user on social media and collects related worries. For example, the collection unit analyzes content posted by the user on social media, displays questions related to the content, and prompts the user to enter a worry. The collection unit can also analyze content of accounts the user follows on social media and collect related worries. For example, the collection unit analyzes content posted by accounts the user follows, displays questions related to the content, and prompts the user to enter a worry. The collection unit can also analyze content of groups the user participates in on social media and collect related worries. For example, the collection unit analyzes content posted by groups the user participates in, displays questions related to the content, and prompts the user to enter a worry. In this way, the user's social media activities can be analyzed and related worries can be collected.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the concern. For example, the analysis unit performs a detailed analysis on concerns of high importance. For example, the analysis unit performs a detailed data analysis and statistical analysis on concerns of high importance, and provides a detailed analysis result. Furthermore, the analysis unit can perform an analysis with a moderate level of detail on concerns of medium importance. For example, the analysis unit performs a moderate data analysis and statistical analysis on concerns of medium importance, and provides an analysis result with a moderate level of detail. Furthermore, the analysis unit can perform a concise analysis on concerns of low importance. For example, the analysis unit performs a concise data analysis and statistical analysis on concerns of low importance, and provides a concise analysis result. This makes it possible to adjust the level of detail of the analysis based on the importance of the concern.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the worry. For example, the analysis unit applies an analysis algorithm based on medical data to health-related worries. For example, the analysis unit applies a clustering algorithm or regression analysis based on medical data to health-related worries, and provides detailed analysis results. The analysis unit can also apply an analysis algorithm based on work environment data to work-related worries. For example, the analysis unit applies a classification algorithm or regression analysis based on work environment data to work-related worries, and provides detailed analysis results. The analysis unit can also apply an analysis algorithm based on psychology data to interpersonal relationship worries. For example, the analysis unit applies a clustering algorithm or regression analysis based on psychology data to interpersonal relationship worries, and provides detailed analysis results. This makes it possible to apply different analysis algorithms depending on the category of the worry.

[0040] During analysis, the analysis unit can determine the priority of the analysis based on when the concerns were submitted. For example, the analysis unit prioritizes the analysis of concerns submitted recently. For example, the analysis unit prioritizes the analysis of recently submitted concerns and quickly provides analysis results. The analysis unit can also prioritize the analysis of concerns submitted in a specific period. For example, the analysis unit prioritizes the analysis of concerns submitted in a specific period to find trends related to that period. Furthermore, the analysis unit can also prioritize the analysis of concerns that have been submitted continuously for a long period of time. For example, the analysis unit prioritizes the analysis of concerns that have been submitted continuously for a long period of time to find the root cause of the concern. In this way, the priority of the analysis can be determined based on when the concerns were submitted.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the concerns. For example, the analysis unit prioritizes analysis of highly relevant concerns. For example, the analysis unit prioritizes analysis of highly relevant concerns and provides analysis results quickly. The analysis unit can also analyze concerns of medium relevance next. For example, the analysis unit analyzes concerns of medium relevance next and provides analysis results with a moderate level of detail. Furthermore, the analysis unit can analyze concerns of low relevance last. For example, the analysis unit analyzes concerns of low relevance last and provides concise analysis results. This makes it possible to adjust the order of analysis based on the relevance of the concerns.

[0042] When developing a business, the development department can analyze the user's past problem data and select an appropriate business development method. The development department, for example, selects the optimal business development method based on the user's past problem data. For example, the development department analyzes the user's past problem data and selects a business development method that meets specific needs. The development department can also select a business development method based on trends from the user's past problem data. For example, the development department analyzes the user's past problem data and develops products or services based on trends. Furthermore, the development department can analyze the user's past problem data and select a business development method specialized for a specific category. For example, the development department analyzes the user's past problem data and develops products or services specialized for a specific category. In this way, the optimal business development method can be selected by analyzing the user's past problem data.

[0043] During business development, the development department can customize business development tools based on the user's current living situation. For example, when the user inputs their current living situation, the development department provides business development tools that correspond to that situation. For example, when the user inputs their current living situation, the development department develops products and services that correspond to that situation. The development department can also provide business development tools that meet specific needs based on the user's living situation. For example, the development department analyzes the user's living situation and develops products and services that correspond to that situation. Furthermore, the development department can also provide customized business development tools based on the user's living situation. For example, the development department analyzes the user's living situation and develops customized products and services. This makes it possible to customize business development tools based on the user's current living situation.

[0044] During business development, the development department can select an appropriate business development method by taking into account the user's geographical location information. For example, if the user is in a specific region, the development department selects a business development method related to that region. For example, if the development department analyzes that the user is in a specific region, the development department develops products and services related to that region. Furthermore, if the user is traveling, the development department can select a business development method related to the travel destination. For example, if the development department analyzes that the user is traveling, the development department develops products and services related to the travel destination. Furthermore, if the user is at home, the development department can select a business development method related to the area around the user's home. For example, if the development department analyzes that the user is at home, the development department develops products and services related to the area around the user's home. In this way, an appropriate business development method can be selected by taking into account the user's geographical location information.

[0045] During business development, the development department can analyze a user's social media activity to propose business development measures. For example, the development department analyzes the content posted by the user on social media and proposes related business development measures. For example, the development department analyzes the content posted by the user on social media and develops products and services related to that content. The development department can also analyze the content of accounts the user follows on social media and propose related business development measures. For example, the development department analyzes the content posted by the accounts the user follows and develops products and services related to that content. Furthermore, the development department can analyze the content of groups the user participates in on social media and propose related business development measures. For example, the development department analyzes the content posted by groups the user participates in and develops products and services related to that content. In this way, it is possible to propose business development measures by analyzing the user's social media activity.

[0046] When providing compensation, the providing unit can select an appropriate compensation method by referring to the user's past problem submission history. The providing unit provides an optimal compensation based on, for example, the content of problems submitted by the user in the past. For example, the providing unit analyzes the content of problems submitted by the user in the past and provides an optimal monetary reward or points based on the content. The providing unit can also select a specific compensation method from the user's past problem submission history. For example, the providing unit analyzes the user's past problem submission history and provides an optimal coupon or benefit based on that history. Furthermore, the providing unit can analyze the user's past problem submission history and select the most effective compensation method. For example, the providing unit analyzes the user's past problem submission history and provides the most effective monetary reward or points based on that history. In this way, the optimal compensation method can be selected by referring to the user's past problem submission history.

[0047] The providing unit can customize the means of providing compensation based on the user's current living situation when providing compensation. For example, when the user inputs their current living situation, the providing unit provides a means of providing compensation that corresponds to that situation. For example, when the user inputs their current living situation, the providing unit provides a monetary reward or points that correspond to that situation. The providing unit can also provide a specific means of providing compensation based on the user's living situation. For example, the providing unit can analyze the user's living situation and provide coupons or benefits that correspond to that situation. Furthermore, the providing unit can also provide a customized means of providing compensation based on the user's living situation. For example, the providing unit can analyze the user's living situation and provide a customized monetary reward or points. This makes it possible to customize the means of providing compensation based on the user's current living situation.

[0048] The providing unit can select an appropriate compensation method by taking into consideration the user's geographical location information when providing compensation. For example, if the user is in a specific area, the providing unit selects a compensation method related to that area. For example, if the providing unit analyzes that the user is in a specific area, the providing unit provides a monetary reward or points related to that area. Furthermore, if the user is traveling, the providing unit can also select a compensation method related to the travel destination. For example, if the providing unit analyzes that the user is traveling, the providing unit provides a monetary reward or points related to the travel destination. Furthermore, if the user is at home, the providing unit can also select a compensation method related to the vicinity of the user's home. For example, if the providing unit analyzes that the user is at home, the providing unit provides a monetary reward or points related to the vicinity of the user's home. In this way, an appropriate compensation method can be selected by taking into consideration the user's geographical location information.

[0049] When providing compensation, the providing unit can analyze the user's social media activity and suggest a means of providing compensation. The providing unit, for example, analyzes content posted by the user on social media and suggests a related means of providing compensation. For example, the providing unit analyzes content posted by the user on social media and suggests a monetary reward or points related to the content. The providing unit can also analyze the content of accounts the user follows on social media and suggest a related means of providing compensation. For example, the providing unit analyzes the content posted by the accounts the user follows and suggests a monetary reward or points related to the content. The providing unit can also analyze the content of groups the user participates in on social media and suggest a related means of providing compensation. For example, the providing unit analyzes the content posted by groups the user participates in and suggests a monetary reward or points related to the content. In this way, it is possible to analyze the user's social media activity and suggest a means of providing compensation.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The analysis unit can take into account the user's social network information when analyzing the collected data. For example, the analysis unit collects information about accounts the user follows on social media and groups the user participates in, and uses that information for analysis. The analysis unit can also analyze the user's statements and behavioral patterns within the user's social network to understand the background of the user's concerns. For example, the analysis unit can analyze the content the user frequently posts on social media and their interactions with other users to identify the cause of the user's concerns. Furthermore, the analysis unit can evaluate the user's influence within the social network and reflect that information in the analysis results. For example, the analysis unit can evaluate the degree of influence the user has within the social network and determine the importance of the concern based on that influence. This makes it possible to analyze data while taking into account the user's social network information.

[0052] The development department can take into account the user's purchasing history when developing new businesses based on the analysis results. For example, the development department collects data on products and services purchased by the user in the past and develops new products and services based on that data. The development department can also analyze the user's purchasing history to identify the user's preferences and needs. For example, the development department can analyze the categories and price ranges of products purchased by the user in the past to identify what products and services the user prefers. Furthermore, the development department can use the user's purchasing history to narrow down the target market. For example, the development department can analyze the user's purchasing history to determine what products and services are in demand in specific regions or age groups. This makes it possible to develop new businesses by taking the user's purchasing history into account.

[0053] The providing unit can take the user's health condition into consideration when providing compensation to the user. For example, the providing unit provides an interface for the user to input their health condition and provides compensation based on that information. The providing unit can also provide specific health-related products or services depending on the user's health condition. For example, when the user inputs their health condition, the providing unit provides supplements or fitness programs according to that condition. Furthermore, the providing unit can monitor the user's health condition and provide additional compensation in response to improvements in the health condition. For example, if the user improves their health condition, the providing unit provides additional points or benefits in response to the improvement. In this way, compensation can be provided taking the user's health condition into consideration.

[0054] The collection unit can analyze the user's past history of submitting worries and suggest an appropriate collection timing. For example, the collection unit can analyze the time periods and frequency at which the user submitted worries in the past and suggest the optimal collection timing based on that data. In addition, if the user tends to submit worries during specific events or situations, the collection unit can encourage collection at that timing. Furthermore, the collection unit can customize the timing of collecting worries based on the user's daily rhythm. This makes it possible to analyze the user's past history of submitting worries and suggest the optimal collection timing.

[0055] When collecting worries, the collection unit can filter the worries based on the user's current living situation and areas of interest. For example, when the user inputs their current living situation, the collection unit preferentially collects worries related to that situation. The collection unit can also filter and collect related worries based on the user's areas of interest. Furthermore, the collection unit can also preferentially collect worries in a specific category based on the user's living situation and areas of interest. This makes it possible to filter worries based on the user's current living situation and areas of interest.

[0056] When collecting worries, the collection unit can prioritize collecting highly relevant worries by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting worries related to that area. Also, when the user is traveling, the collection unit can prioritize collecting worries related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting worries related to the area around the user's home. In this way, highly relevant worries can be prioritized by taking into account the user's geographical location information.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects worries from users. The collection unit, for example, provides an interface through which users input worries. The interface includes a web form, a mobile app, a chatbot, and the like. For example, the collection unit enables users to input worries using a web form. The collection unit can also enable users to input worries anytime, anywhere using a mobile app. Furthermore, the collection unit can also enable users to input worries in a natural conversational format using a chatbot. Step 2: The analysis unit analyzes the worry data collected by the collection unit. The analysis unit analyzes the data using, for example, AI to find common patterns and trends. AI includes natural language processing, image recognition, and machine learning algorithms. For example, the analysis unit analyzes text data using natural language processing to detect frequent patterns and time series patterns. The analysis unit can also analyze image data using image recognition to find common features. Furthermore, the analysis unit can cluster the data using machine learning algorithms to find trends. Step 3: The Development Department develops new businesses based on the analysis results obtained by the Analysis Department. The Development Department may, for example, develop new products or provide new services. For example, if the Development Department receives a lot of information about young people's concerns about stress relief, it may develop stress relief products or relaxation services. Similarly, if the Development Department receives a lot of information about the health of elderly people, it may develop health management services or health foods. Step 4: The providing unit provides compensation to the user based on the worry data collected by the collecting unit. The providing unit provides, for example, monetary rewards, points, coupons, etc. For example, the providing unit may award points each time the user inputs a worry, allowing the user to exchange the points for goods or services. The providing unit may also issue a coupon each time the user inputs a worry, allowing the user to receive a discount using the coupon.

[0059] (Example 2) The problem-collecting system according to an embodiment of the present invention is a system that purchases personal problems, aggregates them by age, gender, and in real time, and develops new businesses that meet customer needs quickly. This problem-collecting system allows users to input their own problems and purchase them in exchange for payment. Next, the problem data is aggregated along with attribute information such as age and gender and analyzed in real time. This allows for the development of new services and products to solve those problems if a large number of problems are common to a particular age group or gender. For example, if a large number of young people report stress-related concerns, it is possible to develop stress-relief products and relaxation services. The goal is to make people happier by developing businesses that can solve even a small amount of problems, from minor to major. This allows the problem-collecting system to collect and analyze users' problems, develop new businesses, and provide payment to solve users' problems.

[0060] A problem-collection system according to an embodiment includes a collection unit, an analysis unit, a development unit, and a provision unit. The collection unit collects problems from users. The collection unit provides, for example, an interface through which users input their problems. Examples of the interface include, but are not limited to, a web form, a mobile app, a chatbot, and the like. For example, the collection unit enables users to input their problems using a web form. The collection unit can also enable users to input their problems anytime and anywhere using a mobile app. The collection unit can also enable users to input their problems in a natural, interactive format using a chatbot. The analysis unit analyzes the problem data collected by the collection unit. For example, the analysis unit analyzes the data using AI to find common patterns and trends. Examples of AI include, but are not limited to, natural language processing, image recognition, and machine learning algorithms. For example, the analysis unit analyzes text data using natural language processing to detect frequent patterns and time series patterns. The analysis unit can also analyze image data using image recognition to find common features. The analysis unit can also cluster data using machine learning algorithms to find trends. The development department develops new businesses based on the analysis results obtained by the analysis department. The development department, for example, develops new products and provides new services. For example, if the development department receives a large number of stress-related worries from young people, it can develop stress relief goods and relaxation services. Furthermore, if the development department receives a large number of health-related worries from elderly people, it can develop health management services and health foods. The provision department provides compensation to users based on the worry data collected by the collection department. The provision unit provides, for example, monetary rewards, points, coupons, etc. For example, the provision unit can award points each time a user inputs a worry and allow the points to be exchanged for products or services. Furthermore, the provision unit can issue a coupon each time a user inputs a worry and allow the user to receive discounts using the coupon.As a result, the problem collection system according to the embodiment can solve users' problems by collecting and analyzing the users' problems, developing new businesses, and providing compensation.

[0061] The collection unit can provide an interface through which a user inputs their concerns. Examples of the interface include, but are not limited to, a web form, a mobile app, a chatbot, and the like. The collection unit can, for example, enable a user to input their concerns using a web form. For example, the collection unit can provide a text box in the web form to allow the user to freely input their concerns. The collection unit can also enable a user to input their concerns anytime, anywhere using a mobile app. For example, the collection unit can provide a notification function in the mobile app to remind the user when to input their concerns. The collection unit can also enable a user to input their concerns in a natural, conversational format using a chatbot. For example, the collection unit can incorporate natural language processing technology into the chatbot to allow the user to input their concerns in a conversational format. This makes it possible to provide an interface that allows a user to easily input their concerns.

[0062] The analysis unit can analyze the collected data using AI to find common patterns or trends. Examples of AI include, but are not limited to, natural language processing, image recognition, and machine learning algorithms. For example, the analysis unit can analyze text data using natural language processing to detect frequent patterns and time series patterns. For example, the analysis unit can find frequent patterns by tokenizing the text data and calculating the frequency of occurrence of each token. The analysis unit can also analyze time series data to detect patterns over a specific period of time. For example, the analysis unit can smooth the time series data using a moving average to find trends. The analysis unit can also analyze image data using image recognition to find common features. For example, the analysis unit can analyze image data using a convolutional neural network (CNN) to extract common features. The analysis unit can also cluster the data using a machine learning algorithm to find trends. For example, the analysis unit can divide the data into clusters using K-means clustering and analyze the features of each cluster. This makes it possible to analyze the data using AI and find common patterns and trends.

[0063] The development department can develop new businesses based on the analysis results. Examples of new businesses include, but are not limited to, developing new products and providing new services. For example, if the development department receives a large number of complaints about stress relief from young people, the development department can develop stress relief products and relaxation services. For example, the development department can develop relaxing aroma oils and other stress relief products as stress relief products. The development department can also offer massage and yoga classes as relaxation services. Furthermore, if the development department receives a large number of complaints about health from elderly people, the development department can develop health management services and health foods. For example, the development department can provide online health consultations and fitness programs as health management services. The development department can also develop nutritionally balanced supplements and health foods as health foods. This allows the development of new businesses based on the analysis results.

[0064] The providing unit can provide a user with a reward. Examples of the reward include, but are not limited to, monetary rewards, points, coupons, and the like. For example, the providing unit can grant points to the user each time the user inputs a concern and allow the user to exchange the points for goods or services. For example, the providing unit can allow the user to exchange a certain number of points for specific goods or services when the user accumulates a certain number of points. The providing unit can also issue a coupon each time the user inputs a concern and allow the user to use the coupon to receive a discount. For example, the providing unit can allow the user to use the coupon to receive a discount at a specific store. Furthermore, the providing unit can also provide a monetary reward each time the user inputs a concern. For example, the providing unit can provide a certain amount of electronic money each time the user inputs a concern. In this way, the user can be provided with a reward.

[0065] The collection unit can estimate the user's emotions and determine the timing of collecting worries based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can immediately display an interface for collecting worries. For example, if the collection unit estimates that the user is feeling stressed, the collection unit can display a pop-up window to prompt the user to enter their worries. The collection unit can also periodically send reminders to collect worries when the user is relaxed. For example, if the collection unit estimates that the user is relaxed, the collection unit can periodically send notifications to prompt the user to enter their worries. Furthermore, if the user is busy, the collection unit can set a reminder to allow the user to enter their worries later. For example, if the collection unit estimates that the user is busy, the collection unit can set a reminder to allow the user to enter their worries later and send a notification. This makes it possible to adjust the timing of collecting worries based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0066] The collection unit can analyze the user's past problem submission history and select an appropriate collection method. For example, the collection unit analyzes the pattern of problems that the user has frequently submitted in the past and proposes an optimal collection method. For example, the collection unit analyzes the content and frequency of problems that the user has submitted in the past and selects an optimal collection method. Furthermore, if the user tends to submit problems during a specific time period, the collection unit can prompt the user to collect problems during that time period. For example, if the collection unit analyzes that the user tends to submit problems during a specific time period, the collection unit can send a notification during that time period and prompt the user to enter a problem. Furthermore, if the user submits many problems in a specific category, the collection unit can provide a collection method specialized for that category. For example, if the collection unit analyzes that the user tends to submit many problems in a specific category, the collection unit can display questions related to that category and prompt the user to enter a problem. In this way, the user's past problem submission history can be analyzed and an optimal collection method can be selected.

[0067] When collecting worries, the collection unit can filter the worries based on the user's current living situation or area of ​​interest. For example, when the user inputs their current living situation, the collection unit prioritizes collecting worries related to that situation. For example, when the user inputs their current living situation, the collection unit displays questions related to that situation and prompts the user to input a worry. The collection unit can also filter and collect related worries based on the user's area of ​​interest. For example, when the user inputs an area of ​​interest, the collection unit displays questions related to that area and prompts the user to input a worry. Furthermore, the collection unit can prioritize collecting worries of a specific category based on the user's living situation or area of ​​interest. For example, when the collection unit analyzes that the user has a specific living situation or area of ​​interest, the collection unit displays questions related to that category and prompts the user to input a worry. This makes it possible to filter worries based on the user's current living situation or area of ​​interest.

[0068] The collection unit can estimate the user's emotions and set a priority order for the worries to be collected based on the estimated user emotions. For example, if the user is feeling strong stress, the collection unit collects the worries as a top priority. For example, if the collection unit estimates that the user is feeling strong stress, it displays an interface that collects the worries as a top priority. Furthermore, if the user is feeling mild anxiety, the collection unit can also collect the worries as a second priority. For example, if the collection unit estimates that the user is feeling mild anxiety, it displays an interface that collects the worries as a second priority. Furthermore, if the user is relaxed, the collection unit can collect worries with a normal priority. For example, if the collection unit estimates that the user is relaxed, it displays an interface that collects worries with a normal priority. This makes it possible to determine the priority order for the worries to be collected based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] When collecting worries, the collection unit can prioritize collecting highly relevant worries by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting worries related to that area. For example, if the collection unit analyzes that the user is in a specific area, it displays questions related to that area and prompts the user to input a worry. Furthermore, if the user is traveling, the collection unit can prioritize collecting worries related to the travel destination. For example, if the collection unit analyzes that the user is traveling, it displays questions related to the travel destination and prompts the user to input a worry. Furthermore, if the user is at home, the collection unit can prioritize collecting worries related to the area around the user's home. For example, if the collection unit analyzes that the user is at home, it displays questions related to the area around the user's home and prompts the user to input a worry. In this way, it is possible to prioritize collecting highly relevant worries by taking into account the user's geographical location information.

[0070] When collecting worries, the collection unit can analyze the user's social media activities and collect related worries. The collection unit, for example, analyzes content posted by the user on social media and collects related worries. For example, the collection unit analyzes content posted by the user on social media, displays questions related to the content, and prompts the user to enter a worry. The collection unit can also analyze content of accounts the user follows on social media and collect related worries. For example, the collection unit analyzes content posted by accounts the user follows, displays questions related to the content, and prompts the user to enter a worry. The collection unit can also analyze content of groups the user participates in on social media and collect related worries. For example, the collection unit analyzes content posted by groups the user participates in, displays questions related to the content, and prompts the user to enter a worry. In this way, the user's social media activities can be analyzed and related worries can be collected.

[0071] The analysis unit can estimate the user's emotions and set the presentation method of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit provides simple and easy-to-understand analysis results. For example, if the analysis unit estimates that the user is feeling stressed, the analysis unit provides simple and easy-to-understand analysis results using graphs and charts. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, if the analysis unit estimates that the user is relaxed, the analysis unit provides analysis results including detailed text and data. Furthermore, the analysis unit can also provide visually appealing analysis results if the user is excited. For example, if the analysis unit estimates that the user is excited, the analysis unit provides analysis results with visually stimulating effects. This makes it possible to adjust the presentation method of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0072] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the concern. For example, the analysis unit performs a detailed analysis on concerns of high importance. For example, the analysis unit performs a detailed data analysis and statistical analysis on concerns of high importance, and provides a detailed analysis result. Furthermore, the analysis unit can perform an analysis with a moderate level of detail on concerns of medium importance. For example, the analysis unit performs a moderate data analysis and statistical analysis on concerns of medium importance, and provides an analysis result with a moderate level of detail. Furthermore, the analysis unit can perform a concise analysis on concerns of low importance. For example, the analysis unit performs a concise data analysis and statistical analysis on concerns of low importance, and provides a concise analysis result. This makes it possible to adjust the level of detail of the analysis based on the importance of the concern.

[0073] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the worry. For example, the analysis unit applies an analysis algorithm based on medical data to health-related worries. For example, the analysis unit applies a clustering algorithm or regression analysis based on medical data to health-related worries, and provides detailed analysis results. The analysis unit can also apply an analysis algorithm based on work environment data to work-related worries. For example, the analysis unit applies a classification algorithm or regression analysis based on work environment data to work-related worries, and provides detailed analysis results. The analysis unit can also apply an analysis algorithm based on psychology data to interpersonal relationship worries. For example, the analysis unit applies a clustering algorithm or regression analysis based on psychology data to interpersonal relationship worries, and provides detailed analysis results. This makes it possible to apply different analysis algorithms depending on the category of the worry.

[0074] The analysis unit can estimate the user's emotions and set the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, if the analysis unit estimates that the user is in a hurry, the analysis unit provides a short and concise analysis result using graphs and charts. The analysis unit can also provide a longer analysis result with detailed explanations if the user is relaxed. For example, if the analysis unit estimates that the user is relaxed, the analysis unit provides a longer analysis result with detailed text and data. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. For example, if the analysis unit estimates that the user is excited, the analysis unit provides an analysis result with visually stimulating effects. This allows the length of the analysis to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] During analysis, the analysis unit can determine the priority of the analysis based on when the concerns were submitted. For example, the analysis unit prioritizes the analysis of concerns submitted recently. For example, the analysis unit prioritizes the analysis of recently submitted concerns and quickly provides analysis results. The analysis unit can also prioritize the analysis of concerns submitted in a specific period. For example, the analysis unit prioritizes the analysis of concerns submitted in a specific period to find trends related to that period. Furthermore, the analysis unit can also prioritize the analysis of concerns that have been submitted continuously for a long period of time. For example, the analysis unit prioritizes the analysis of concerns that have been submitted continuously for a long period of time to find the root cause of the concern. In this way, the priority of the analysis can be determined based on when the concerns were submitted.

[0076] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the concerns. For example, the analysis unit prioritizes analysis of highly relevant concerns. For example, the analysis unit prioritizes analysis of highly relevant concerns and provides analysis results quickly. The analysis unit can also analyze concerns of medium relevance next. For example, the analysis unit analyzes concerns of medium relevance next and provides analysis results with a moderate level of detail. Furthermore, the analysis unit can analyze concerns of low relevance last. For example, the analysis unit analyzes concerns of low relevance last and provides concise analysis results. This makes it possible to adjust the order of analysis based on the relevance of the concerns.

[0077] The development department can estimate the user's emotions and set business development methods based on the estimated user emotions. For example, if the user is feeling stressed, the development department can develop businesses specialized in stress relief. For example, if the development department estimates that the user is feeling stressed, the development department can develop stress relief products and relaxation services. Furthermore, if the user is relaxed, the development department can develop businesses specialized in relaxation. For example, if the development department estimates that the user is relaxed, the development department can develop relaxation products and relaxation services. Furthermore, if the user is excited, the development department can develop businesses specialized in entertainment. For example, if the development department estimates that the user is excited, the development department can develop entertainment products and entertainment services. This makes it possible to adjust business development methods based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] When developing a business, the development department can analyze the user's past problem data and select an appropriate business development method. The development department, for example, selects the optimal business development method based on the user's past problem data. For example, the development department analyzes the user's past problem data and selects a business development method that meets specific needs. The development department can also select a business development method based on trends from the user's past problem data. For example, the development department analyzes the user's past problem data and develops products or services based on trends. Furthermore, the development department can analyze the user's past problem data and select a business development method specialized for a specific category. For example, the development department analyzes the user's past problem data and develops products or services specialized for a specific category. In this way, the optimal business development method can be selected by analyzing the user's past problem data.

[0079] During business development, the development department can customize business development tools based on the user's current living situation. For example, when the user inputs their current living situation, the development department provides business development tools that correspond to that situation. For example, when the user inputs their current living situation, the development department develops products and services that correspond to that situation. The development department can also provide business development tools that meet specific needs based on the user's living situation. For example, the development department analyzes the user's living situation and develops products and services that correspond to that situation. Furthermore, the development department can also provide customized business development tools based on the user's living situation. For example, the development department analyzes the user's living situation and develops customized products and services. This makes it possible to customize business development tools based on the user's current living situation.

[0080] The development department can estimate the user's emotions and set business development priorities based on the estimated user emotions. For example, if a user is feeling highly stressed, the development department develops a business that solves that problem as a top priority. For example, if it is estimated that a user is feeling highly stressed, the development department develops a product or service that solves that problem as a top priority. Furthermore, if a user is feeling mild anxiety, the development department can develop a business that solves that problem as a second priority. For example, if it is estimated that a user is feeling mild anxiety, the development department develops a product or service that solves that problem as a second priority. Furthermore, if a user is relaxed, the development department can develop a business with a normal priority. For example, if it is estimated that a user is relaxed, the development department develops a product or service with a normal priority. This makes it possible to determine business development priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] During business development, the development department can select an appropriate business development method by taking into account the user's geographical location information. For example, if the user is in a specific region, the development department selects a business development method related to that region. For example, if the development department analyzes that the user is in a specific region, the development department develops products and services related to that region. Furthermore, if the user is traveling, the development department can select a business development method related to the travel destination. For example, if the development department analyzes that the user is traveling, the development department develops products and services related to the travel destination. Furthermore, if the user is at home, the development department can select a business development method related to the area around the user's home. For example, if the development department analyzes that the user is at home, the development department develops products and services related to the area around the user's home. In this way, an appropriate business development method can be selected by taking into account the user's geographical location information.

[0082] During business development, the development department can analyze a user's social media activity to propose business development measures. For example, the development department analyzes the content posted by the user on social media and proposes related business development measures. For example, the development department analyzes the content posted by the user on social media and develops products and services related to that content. The development department can also analyze the content of accounts the user follows on social media and propose related business development measures. For example, the development department analyzes the content posted by the accounts the user follows and develops products and services related to that content. Furthermore, the development department can analyze the content of groups the user participates in on social media and propose related business development measures. For example, the development department analyzes the content posted by groups the user participates in and develops products and services related to that content. In this way, it is possible to propose business development measures by analyzing the user's social media activity.

[0083] The providing unit can estimate the user's emotions and set a method of providing compensation based on the estimated user emotions. For example, the providing unit can immediately provide compensation when the user is feeling stressed. For example, the providing unit can immediately provide a monetary reward or points when it is estimated that the user is feeling stressed. The providing unit can also adjust the timing of providing compensation when the user is relaxed. For example, the providing unit can adjust the timing of providing compensation and provide compensation at an appropriate time when it is estimated that the user is relaxed. Furthermore, the providing unit can also provide a special compensation when the user is excited. For example, the providing unit can provide a special monetary reward or points when it is estimated that the user is excited. This makes it possible to adjust the method of providing compensation based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] When providing compensation, the providing unit can select an appropriate compensation method by referring to the user's past problem submission history. The providing unit provides an optimal compensation based on, for example, the content of problems submitted by the user in the past. For example, the providing unit analyzes the content of problems submitted by the user in the past and provides an optimal monetary reward or points based on the content. The providing unit can also select a specific compensation method from the user's past problem submission history. For example, the providing unit analyzes the user's past problem submission history and provides an optimal coupon or benefit based on that history. Furthermore, the providing unit can analyze the user's past problem submission history and select the most effective compensation method. For example, the providing unit analyzes the user's past problem submission history and provides the most effective monetary reward or points based on that history. In this way, the optimal compensation method can be selected by referring to the user's past problem submission history.

[0085] The providing unit can customize the means of providing compensation based on the user's current living situation when providing compensation. For example, when the user inputs their current living situation, the providing unit provides a means of providing compensation that corresponds to that situation. For example, when the user inputs their current living situation, the providing unit provides a monetary reward or points that correspond to that situation. The providing unit can also provide a specific means of providing compensation based on the user's living situation. For example, the providing unit can analyze the user's living situation and provide coupons or benefits that correspond to that situation. Furthermore, the providing unit can also provide a customized means of providing compensation based on the user's living situation. For example, the providing unit can analyze the user's living situation and provide a customized monetary reward or points. This makes it possible to customize the means of providing compensation based on the user's current living situation.

[0086] The providing unit can estimate the user's emotions and set a priority for providing compensation based on the estimated user emotions. For example, if the user is feeling highly stressed, the providing unit provides compensation for that worry as a top priority. For example, if the providing unit estimates that the user is feeling highly stressed, the providing unit provides a monetary reward or points for that worry as a top priority. Furthermore, if the user is feeling mild anxiety, the providing unit can provide compensation for that worry as a second priority. For example, if the providing unit estimates that the user is feeling mild anxiety, the providing unit provides a monetary reward or points for that worry as a second priority. Furthermore, if the user is relaxed, the providing unit can provide compensation with a normal priority. For example, if the providing unit estimates that the user is relaxed, the providing unit provides a monetary reward or points with a normal priority. This makes it possible to determine the priority for providing compensation based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The providing unit can select an appropriate compensation method by taking into consideration the user's geographical location information when providing compensation. For example, if the user is in a specific area, the providing unit selects a compensation method related to that area. For example, if the providing unit analyzes that the user is in a specific area, the providing unit provides a monetary reward or points related to that area. Furthermore, if the user is traveling, the providing unit can also select a compensation method related to the travel destination. For example, if the providing unit analyzes that the user is traveling, the providing unit provides a monetary reward or points related to the travel destination. Furthermore, if the user is at home, the providing unit can also select a compensation method related to the vicinity of the user's home. For example, if the providing unit analyzes that the user is at home, the providing unit provides a monetary reward or points related to the vicinity of the user's home. In this way, an appropriate compensation method can be selected by taking into consideration the user's geographical location information.

[0088] When providing compensation, the providing unit can analyze the user's social media activity and suggest a means of providing compensation. The providing unit, for example, analyzes content posted by the user on social media and suggests a related means of providing compensation. For example, the providing unit analyzes content posted by the user on social media and suggests a monetary reward or points related to the content. The providing unit can also analyze the content of accounts the user follows on social media and suggest a related means of providing compensation. For example, the providing unit analyzes the content posted by the accounts the user follows and suggests a monetary reward or points related to the content. The providing unit can also analyze the content of groups the user participates in on social media and suggest a related means of providing compensation. For example, the providing unit analyzes the content posted by groups the user participates in and suggests a monetary reward or points related to the content. In this way, it is possible to analyze the user's social media activity and suggest a means of providing compensation. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, development unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit allows a user to input their concerns using the reception device 38 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI. The development unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and develops a new business based on the analysis results. The provision unit provides compensation to the user using, for example, the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, development unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit allows a user to input their concerns using the microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI. The development unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and develops new businesses based on the analysis results. The provision unit provides compensation to the user using, for example, the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, development unit, and provision unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit allows the user to input their concerns using the microphone 238 of the headset terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI. The development unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and develops new businesses based on the analysis results. The provision unit provides compensation to the user using, for example, the speaker 240 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, development unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit allows the user to input their concerns using the microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI. The development unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and develops a new business based on the analysis results. The provision unit provides compensation to the user using, for example, the speaker 240 of the robot 414.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] When collecting the user's worries, the collection unit can analyze the user's voice input and automatically convert the worries into text. For example, the collection unit allows the user to input the worries by voice and converts the voice data into text data using voice recognition technology. The collection unit can also perform emotion analysis during the voice input to estimate the user's emotional state. For example, the collection unit can analyze the tone and speed of the voice to estimate whether the user is stressed or relaxed. Furthermore, the collection unit can analyze background sounds during the voice input and collect information about the user's environment. For example, the collection unit can determine whether the user is in a noisy or quiet environment from the background sounds and store that information together with the worry data. In this way, the user's voice input can be analyzed to automatically convert the worries into text and collect emotional state and environmental information.

[0091] The collection unit can acquire biometric information of the user when the user inputs a worry and store the information together with the worry data. For example, the collection unit acquires biometric information such as heart rate and electrodermal activity when the user inputs a worry. The collection unit can also analyze the user's biometric information to estimate the user's stress level. For example, the collection unit can analyze fluctuations in heart rate and electrodermal activity to estimate whether the user is feeling stressed or relaxed. The collection unit can also evaluate the severity of the worry using the user's biometric information. For example, the collection unit can analyze data on the user's heart rate and electrodermal activity, and quantify and store the severity of the worry. In this way, the user's biometric information can be acquired and stored together with the worry data.

[0092] The analysis unit can take into account the user's social network information when analyzing the collected data. For example, the analysis unit collects information about accounts the user follows on social media and groups the user participates in, and uses that information for analysis. The analysis unit can also analyze the user's statements and behavioral patterns within the user's social network to understand the background of the user's concerns. For example, the analysis unit can analyze the content the user frequently posts on social media and their interactions with other users to identify the cause of the user's concerns. Furthermore, the analysis unit can evaluate the user's influence within the social network and reflect that information in the analysis results. For example, the analysis unit can evaluate the degree of influence the user has within the social network and determine the importance of the concern based on that influence. This makes it possible to analyze data while taking into account the user's social network information.

[0093] The development department can take into account the user's purchasing history when developing new businesses based on the analysis results. For example, the development department collects data on products and services purchased by the user in the past and develops new products and services based on that data. The development department can also analyze the user's purchasing history to identify the user's preferences and needs. For example, the development department can analyze the categories and price ranges of products purchased by the user in the past to identify what products and services the user prefers. Furthermore, the development department can use the user's purchasing history to narrow down the target market. For example, the development department can analyze the user's purchasing history to determine what products and services are in demand in specific regions or age groups. This makes it possible to develop new businesses by taking the user's purchasing history into account.

[0094] The providing unit can take the user's health condition into consideration when providing compensation to the user. For example, the providing unit provides an interface for the user to input their health condition and provides compensation based on that information. The providing unit can also provide specific health-related products or services depending on the user's health condition. For example, when the user inputs their health condition, the providing unit provides supplements or fitness programs according to that condition. Furthermore, the providing unit can monitor the user's health condition and provide additional compensation in response to improvements in the health condition. For example, if the user improves their health condition, the providing unit provides additional points or benefits in response to the improvement. In this way, compensation can be provided taking the user's health condition into consideration.

[0095] The collection unit can estimate the user's emotions and customize the worry collection method based on the estimated user emotions. For example, the collection unit can provide a simple and intuitive interface when the user is feeling stressed. Alternatively, the collection unit can provide an interface including detailed questions when the user is relaxed. Furthermore, the collection unit can provide a visually appealing interface when the user is excited. This makes it possible to customize the worry collection method based on the user's emotions.

[0096] The collection unit can analyze the user's past history of submitting worries and suggest an appropriate collection timing. For example, the collection unit can analyze the time periods and frequency at which the user submitted worries in the past and suggest the optimal collection timing based on that data. In addition, if the user tends to submit worries during specific events or situations, the collection unit can encourage collection at that timing. Furthermore, the collection unit can customize the timing of collecting worries based on the user's daily rhythm. This makes it possible to analyze the user's past history of submitting worries and suggest the optimal collection timing.

[0097] When collecting worries, the collection unit can filter the worries based on the user's current living situation and areas of interest. For example, when the user inputs their current living situation, the collection unit preferentially collects worries related to that situation. The collection unit can also filter and collect related worries based on the user's areas of interest. Furthermore, the collection unit can also preferentially collect worries in a specific category based on the user's living situation and areas of interest. This makes it possible to filter worries based on the user's current living situation and areas of interest.

[0098] The collection unit can estimate the user's emotions and set the priority of the worries to be collected based on the estimated user's emotions. For example, if the user is feeling strong stress, the collection unit can collect the worries as the top priority. Also, if the user is feeling mild anxiety, the collection unit can collect the worries as the second priority. Furthermore, if the user is relaxed, the collection unit can collect the worries with normal priority. In this way, the priority of the worries to be collected can be determined based on the user's emotions.

[0099] When collecting worries, the collection unit can prioritize collecting highly relevant worries by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting worries related to that area. Also, when the user is traveling, the collection unit can prioritize collecting worries related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting worries related to the area around the user's home. In this way, highly relevant worries can be prioritized by taking into account the user's geographical location information.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The collection unit collects worries from users. The collection unit, for example, provides an interface through which users input worries. The interface includes a web form, a mobile app, a chatbot, and the like. For example, the collection unit enables users to input worries using a web form. The collection unit can also enable users to input worries anytime, anywhere using a mobile app. Furthermore, the collection unit can also enable users to input worries in a natural conversational format using a chatbot. Step 2: The analysis unit analyzes the worry data collected by the collection unit. The analysis unit analyzes the data using, for example, AI to find common patterns and trends. AI includes natural language processing, image recognition, and machine learning algorithms. For example, the analysis unit analyzes text data using natural language processing to detect frequent patterns and time series patterns. The analysis unit can also analyze image data using image recognition to find common features. Furthermore, the analysis unit can cluster the data using machine learning algorithms to find trends. Step 3: The Development Department develops new businesses based on the analysis results obtained by the Analysis Department. The Development Department may, for example, develop new products or provide new services. For example, if the Development Department receives a lot of information about young people's concerns about stress relief, it may develop stress relief products or relaxation services. Similarly, if the Development Department receives a lot of information about the health of elderly people, it may develop health management services or health foods. Step 4: The providing unit provides compensation to the user based on the worry data collected by the collecting unit. The providing unit provides, for example, monetary rewards, points, coupons, etc. For example, the providing unit may award points each time the user inputs a worry, allowing the user to exchange the points for goods or services. The providing unit may also issue a coupon each time the user inputs a worry, allowing the user to receive a discount using the coupon.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] 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.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The 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.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0157] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0158] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0162] 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.

[0163] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0164] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0165] 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.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0167] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0168] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0170] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0171] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0172] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0173] [Explanation of symbols]

[0174] 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 worries from users; an analysis unit that analyzes the worry data collected by the collection unit; a development department that develops new businesses based on the analysis results obtained by the analysis department; a providing unit that provides a reward to a user based on the worry data collected by the collecting unit; A system characterized by:

2. The collecting unit Provide an interface for users to input their concerns The system of claim 1 .

3. The analysis unit Analyze the collected data using AI to find common patterns or trends. The system of claim 1 .

4. The development department: Develop new businesses based on the analysis results The system of claim 1 .

5. The providing unit Providing compensation to users The system of claim 1 .

6. The collecting unit Estimate the user's emotions and determine the timing of collecting worries based on the estimated user emotions. The system of claim 1 .

7. The collecting unit Analyze users' past problem submission history and select the appropriate collection method The system of claim 1 .

8. The collecting unit When collecting worries, filter them based on the user's current life situation and areas of interest. The system of claim 1 .

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