system

Abstract

The system according to the embodiment aims to improve motivation for achieving goals and challenges. [Solution] A system according to an embodiment includes a setting unit, a monitoring unit, and a providing unit. The setting unit sets a goal or challenge. The monitoring unit records the goal or challenge set by the setting unit and monitors progress. The providing unit provides a reward based on the progress monitored by the monitoring 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 technology has the problem of making it difficult to maintain motivation to achieve goals and challenges.

[0005] The system according to the embodiment aims to improve motivation for achieving goals and challenges. [Means for solving the problem]

[0006] A system according to an embodiment includes a setting unit, a monitoring unit, and a providing unit. The setting unit sets a goal or challenge. The monitoring unit records the goal or challenge set by the setting unit and monitors progress. The providing unit provides a reward based on the progress monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve motivation to achieve goals and challenges. [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) An electronic payment system according to an embodiment of the present invention allows users to set goals and challenges, monitor their progress, and provide rewards upon achievement. In this electronic payment system, users set their own goals and challenges, and the electronic payment system records the goals and challenges and monitors their progress. When the user achieves the goal or challenge, the electronic payment system provides a reward. This system can increase the user's motivation to achieve their goals. For example, a user may set a goal such as "lose 5 kilograms in one month" or "read for one hour every day." This information is entered into the electronic payment system. The electronic payment system then records the set goals and challenges and monitors the progress. For example, the user's daily weight record or reading time entry allows the system to grasp the progress. When the user achieves the goal or challenge, the electronic payment system provides a reward. For example, a user who achieves a goal may be awarded electronic money or points. The reward varies depending on the goal or challenge set by the user. This system can increase the user's motivation to achieve their goals. For example, a user who achieves a weight loss goal may receive electronic money as a reward, which motivates them to work harder toward their next goal. Furthermore, users who achieve their reading goals can receive points as a reward, which can be used to purchase the next book. This allows the electronic payment system to support users in achieving their goals and provide rewards.

[0029] An electronic payment system according to an embodiment includes a setting unit, a monitoring unit, and a providing unit. The setting unit allows a user to set goals and challenges. For example, a user can set goals such as "lose 5 kilograms in one month" or "read for one hour every day." The setting unit provides an interface for the user to input the goals and challenges. For example, methods such as text input, voice input, and multiple-choice input are available. The monitoring unit records the goals and challenges set by the setting unit and monitors progress. For example, the system grasps progress by having the user record their weight or input their reading time every day. The monitoring unit includes a calculation unit that calculates progress based on data input by the user. For example, progress can be calculated based on weight data and reading time data input by the user. The providing unit provides rewards based on the progress monitored by the monitoring unit. For example, users who achieve their goals are awarded electronic money or points. The providing unit includes a management unit that manages types of rewards. For example, reward types can be classified into electronic money, points, badges, etc. and managed. The providing unit provides different rewards depending on the content of the goals and challenges. For example, a user who achieves a weight loss goal can be provided with electronic money, and a user who achieves a reading goal can be provided with points, so that the electronic payment system according to the embodiment can support users in achieving their goals and provide rewards.

[0030] The monitoring unit may include a calculation unit that calculates progress based on data input by the user. The calculation unit calculates progress based on, for example, weight data and reading time data input by the user. For example, if the user records their weight every day, the calculation unit can calculate weight changes and understand progress toward achieving their goal. If the user inputs their reading time every day, the calculation unit can calculate the cumulative reading time and understand progress toward achieving their goal. Furthermore, the calculation unit can adjust the algorithm for calculating progress depending on the type of data input by the user. For example, if the data input by the user is numerical data, the progress can be calculated using linear regression. If the data input by the user is text data, the progress can be calculated using natural language processing technology. This allows for accurate calculation of progress based on the user's input data.

[0031] The providing unit may include a management unit that manages the types of rewards. The management unit, for example, classifies and manages the types of rewards into electronic money, points, badges, etc. For example, the management unit can set the type of reward to be provided when a user achieves a goal. For example, a user who achieves a weight loss goal can be provided with electronic money, and a user who achieves a reading goal can be provided with points. The management unit can also customize the types of rewards according to the user's preferences. For example, if a user desires a specific reward, that reward can be provided preferentially. Furthermore, the management unit can periodically update the types of rewards and provide new rewards to the user. In this way, by managing the types of rewards, appropriate rewards can be provided to the user.

[0032] The setting unit may include an input unit for the user to input a goal or challenge. The input unit provides, for example, an interface for the user to input the goal or challenge. Examples of input methods include text input, voice input, and multiple choice input. For example, if the user selects text input, the input unit provides an interface that allows the user to input the goal or challenge in text format. Also, if the user selects voice input, the input unit provides an interface that allows the user to input the goal or challenge in voice format. Furthermore, if the user selects multiple choice input, the input unit provides an interface that allows the user to select and input the goal or challenge from options. This allows the user to easily input the goal or challenge.

[0033] The providing unit can provide different rewards depending on the content of the goal or challenge. For example, the providing unit can provide different rewards depending on the content of the goal or challenge. For example, it can provide electronic money to a user who achieves a weight loss goal, and points to a user who achieves a reading goal. The providing unit can also adjust the type of reward depending on the difficulty of the goal or challenge. For example, it can provide a high-value reward to a user who achieves a high-difficulty goal, and a low-value reward to a user who achieves a low-difficulty goal. Furthermore, the providing unit can adjust the amount of reward depending on the level of achievement of the goal or challenge. For example, it can provide a large reward to a user who completely achieves a goal, and a small reward to a user who partially achieves a goal. In this way, by providing rewards depending on the content of the goal or challenge, it is possible to increase user motivation.

[0034] The setting unit can analyze the user's past goal achievement history and support optimal goal setting. The setting unit, for example, analyzes the user's past goal achievement history and supports optimal goal setting. For example, the setting unit can suggest the next goal based on goals the user has achieved in the past. It can also analyze goals that the user has failed to achieve in the past and suggest areas for improvement. Furthermore, it can suggest goals that are easy to achieve based on the user's past goal achievement history. This can increase the success rate of goal achievement by setting optimal goals based on the user's past goal achievement history.

[0035] The setting unit can propose customized goals based on the user's current living situation and areas of interest when setting a goal or challenge. For example, when setting a goal or challenge, the setting unit proposes customized goals based on the user's current living situation and areas of interest. For example, if the user is interested in health, the setting unit can propose health-related goals. Also, if the user is busy with work, it can propose goals that can be achieved in a short time. Furthermore, if the user is interested in a hobby, it can propose goals related to that hobby. This allows for more appropriate goal setting by proposing goals according to the user's living situation and areas of interest.

[0036] The setting unit can suggest highly relevant goals by taking into account the user's geographical location information when setting goals or challenges. For example, when setting goals or challenges, the setting unit can suggest highly relevant goals by taking into account the user's geographical location information. For example, if the user lives in a specific area, goals related to that area can be suggested. Also, if the user is traveling, goals related to the travel destination can be suggested. Furthermore, if the user frequently visits a specific place, goals related to that place can be suggested. This allows for more realistic goal setting by suggesting goals based on the user's geographical location information.

[0037] The setting unit can analyze the user's social media activity and suggest related goals when setting a goal or challenge. For example, the setting unit can analyze the user's social media activity and suggest related goals when setting a goal or challenge. For example, the setting unit can suggest goals related to topics that the user frequently mentions on social media. It can also suggest goals related to accounts that the user follows on social media. It can also suggest goals related to groups that the user participates in on social media. This allows for more interesting goal setting by suggesting goals based on the user's social media activity.

[0038] The monitoring unit can predict the progress status by referring to the user's past data during monitoring. The monitoring unit, for example, predicts the progress status by referring to the user's past data during monitoring. For example, the monitoring unit predicts the possibility of goal achievement based on the user's past data. Also, it can predict a delay in progress based on the user's past data. Furthermore, it can predict an acceleration of progress based on the user's past data. This allows for more accurate progress management by predicting the progress status based on past data.

[0039] The monitoring unit can customize the evaluation criteria for the progress status based on the user's current living situation during monitoring. The monitoring unit, for example, customizes the evaluation criteria for the progress status based on the user's current living situation during monitoring. For example, if the user is busy, the evaluation criteria for the progress status can be relaxed. Also, if the user has time to spare, the evaluation criteria for the progress status can be tightened. Furthermore, if the user is in a specific situation, evaluation criteria can be set according to that situation. This enables more realistic progress management by setting evaluation criteria according to the user's living situation.

[0040] The monitoring unit can evaluate the progress status taking into account the user's geographical location information during monitoring. For example, the monitoring unit evaluates the progress status taking into account the user's geographical location information during monitoring. For example, if the user is in a specific area, the progress status can be evaluated according to the area. Also, if the user is traveling, the progress status can be evaluated according to the travel destination. Furthermore, if the user frequently visits a specific place, the progress status can be evaluated according to the place. This enables more realistic progress management by evaluating the progress based on the user's geographical location information.

[0041] The monitoring unit can analyze the user's social media activities during monitoring and reflect them in the progress evaluation. For example, the monitoring unit can analyze the user's social media activities during monitoring and reflect them in the progress evaluation. For example, the monitoring unit can evaluate progress related to topics that the user frequently mentions on social media. It can also evaluate progress related to accounts that the user follows on social media. It can also evaluate progress related to groups that the user participates in on social media. This enables more interesting progress management by evaluating progress based on the user's social media activities.

[0042] The providing unit can select the optimal reward by referring to the user's past reward history when providing a reward. For example, the providing unit selects the optimal reward by referring to the user's past reward history when providing a reward. For example, the next reward is selected based on rewards the user has received in the past. The next reward can also be selected based on rewards that the user has enjoyed in the past. Furthermore, the optimal reward can be selected from the user's past reward history. This makes it possible to increase user satisfaction by selecting the optimal reward based on the user's past reward history.

[0043] The providing unit can customize the content of the reward based on the user's current living situation when providing the reward. For example, the providing unit customizes the content of the reward based on the user's current living situation when providing the reward. For example, if the user is busy, a reward that saves time can be provided. Also, if the user has time, a reward that the user can enjoy can be provided. Furthermore, if the user is in a specific situation, a reward that suits the situation can be provided. This makes it possible to provide more appropriate rewards by providing rewards that suit the user's living situation.

[0044] The providing unit can provide an optimal reward by taking into consideration the user's geographical location information when providing a reward. For example, when providing a reward, the providing unit can provide an optimal reward by taking into consideration the user's geographical location information. For example, if the user lives in a specific area, the providing unit can provide a reward related to that area. Also, if the user is traveling, the providing unit can provide a reward related to the travel destination. Furthermore, if the user frequently visits a specific place, the providing unit can provide a reward related to that place. This makes it possible to provide more realistic rewards by providing rewards based on the user's geographical location information.

[0045] The providing unit can analyze the user's social media activity and determine the content of the reward when providing the reward. For example, the providing unit can analyze the user's social media activity and determine the content of the reward when providing the reward. For example, the providing unit can provide a reward related to topics that the user frequently mentions on social media. It can also provide a reward related to accounts that the user follows on social media. It can also provide a reward related to groups that the user participates in on social media. This makes it possible to provide more interesting rewards by providing rewards based on the user's social media activity.

[0046] The calculation unit can improve the accuracy of progress calculation by referring to the user's past data during calculation. The calculation unit can improve the accuracy of progress calculation by referring to the user's past data during calculation. For example, the calculation unit can improve the accuracy of progress calculation based on the user's past data. Furthermore, the calculation unit can reduce errors in progress calculation based on the user's past data. Furthermore, the calculation unit can optimize the progress calculation algorithm based on the user's past data. This improves the accuracy of progress calculation based on past data, enabling more accurate progress management.

[0047] The calculation unit can perform progress calculations taking into account the user's geographical location information when performing calculations. For example, the calculation unit performs progress calculations taking into account the user's geographical location information when performing calculations. For example, if the user is in a specific area, progress calculations can be performed according to that area. Also, if the user is traveling, progress calculations can be performed according to the travel destination. Furthermore, if the user frequently visits a specific place, progress calculations can be performed according to that place. This enables more realistic progress management by performing progress calculations based on the user's geographical location information.

[0048] The management unit can optimize the type of reward by referring to the user's past reward history during management. The management unit, for example, can optimize the type of reward by referring to the user's past reward history during management. For example, the management unit selects the optimal type of reward based on the user's past reward history. The type of reward can also be customized based on the user's past reward history. Furthermore, the type of reward can be optimized based on the user's past reward history. In this way, by optimizing the type of reward based on the user's past reward history, user satisfaction can be increased.

[0049] The management unit can perform reward management taking into account the user's geographical location information during management. The management unit, for example, performs reward management taking into account the user's geographical location information during management. For example, if the user is in a specific area, reward management can be performed according to the area. Also, if the user is traveling, reward management can be performed according to the travel destination. Furthermore, if the user frequently visits a specific place, reward management can be performed according to the location. This enables more realistic reward management by performing reward management based on the user's geographical location information.

[0050] The input unit can suggest the optimal input method by referring to the user's past input history when inputting. For example, the input unit can suggest the optimal input method by referring to the user's past input history when inputting. For example, goals and challenges that the user has frequently input in the past can be automatically displayed as candidates. Also, it can preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest goals and challenges to be used in a specific time period from the user's past input history. This allows for more efficient input by suggesting the optimal input method based on the user's past input history.

[0051] The input unit can propose the optimal input method by taking into account the user's device information when inputting. For example, the input unit proposes the optimal input method by taking into account the user's device information when inputting. For example, if the user is using a smartphone, an input method tailored to the screen size can be provided. Also, if the user is using a tablet, an input method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a simple and highly visible input method can be provided. In this way, by proposing an input method based on the user's device information, a more user-friendly interface can be provided.

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

[0053] The setting unit can analyze the user's past goal achievement history and support optimal goal setting. For example, it can suggest the next goal based on goals the user has achieved in the past. It can also analyze goals the user has failed to achieve in the past and suggest areas for improvement. It can also suggest goals that are easier to achieve based on the user's past goal achievement history. This can increase the success rate of goal achievement by setting optimal goals based on the user's past goal achievement history.

[0054] When providing a reward, the providing unit can select the optimal reward by referring to the user's past reward history. For example, the next reward can be selected based on rewards the user has received in the past. The next reward can also be selected based on rewards that the user has enjoyed in the past. Furthermore, the optimal reward can be selected from the user's past reward history. This allows the user's satisfaction to be increased by selecting the optimal reward based on the user's past reward history.

[0055] During monitoring, the monitoring unit can predict progress by referring to the user's past data. For example, the possibility of goal achievement can be predicted based on the user's past data. Also, delays in progress can be predicted based on the user's past data. Furthermore, acceleration of progress can be predicted based on the user's past data. This allows for more accurate progress management by predicting progress based on past data.

[0056] When setting goals or challenges, the setting unit can suggest highly relevant goals by taking into account the user's geographical location information. For example, if the user lives in a specific area, goals related to that area can be suggested. Also, if the user is traveling, goals related to the travel destination can be suggested. Furthermore, if the user frequently visits a specific place, goals related to that place can be suggested. This allows for more realistic goal setting by suggesting goals based on the user's geographical location information.

[0057] When providing a reward, the providing unit can provide an optimal reward by taking into consideration the user's geographical location information. For example, if the user lives in a specific area, the providing unit can provide a reward related to that area. Also, if the user is traveling, the providing unit can provide a reward related to the travel destination. Furthermore, if the user frequently visits a specific place, the providing unit can provide a reward related to that place. This allows for more realistic reward provision by providing a reward based on the user's geographical location information.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The setting unit allows the user to set goals and challenges. For example, the user can set goals such as "lose 5 kg in one month" or "read for one hour every day." The setting unit provides an interface for the user to input the goals and challenges. For example, there are methods such as text input, voice input, and multiple choice input. Step 2: The monitoring unit records the goals and challenges set by the setting unit and monitors the progress. For example, the system grasps the progress by having the user record their weight every day or input their reading time. The monitoring unit includes a calculation unit that calculates the progress based on the data entered by the user. For example, the progress can be calculated based on the weight data and reading time data entered by the user. Step 3: The providing unit provides rewards based on the progress monitored by the monitoring unit. For example, a user who achieves a goal is given electronic money or points. The providing unit includes a management unit that manages the types of rewards. For example, the types of rewards can be categorized and managed into electronic money, points, badges, etc. The providing unit provides different rewards depending on the content of the goal or challenge. For example, a user who achieves a weight loss goal can be given electronic money, and a user who achieves a reading goal can be given points.

[0060] (Example 2) An electronic payment system according to an embodiment of the present invention allows users to set goals and challenges, monitor their progress, and provide rewards upon achievement. In this electronic payment system, users set their own goals and challenges, and the electronic payment system records the goals and challenges and monitors their progress. When the user achieves the goal or challenge, the electronic payment system provides a reward. This system can increase the user's motivation to achieve their goals. For example, a user may set a goal such as "lose 5 kilograms in one month" or "read for one hour every day." This information is entered into the electronic payment system. The electronic payment system then records the set goals and challenges and monitors the progress. For example, the user's daily weight record or reading time entry allows the system to grasp the progress. When the user achieves the goal or challenge, the electronic payment system provides a reward. For example, a user who achieves a goal may be awarded electronic money or points. The reward varies depending on the goal or challenge set by the user. This system can increase the user's motivation to achieve their goals. For example, a user who achieves a weight loss goal may receive electronic money as a reward, which motivates them to work harder toward their next goal. Furthermore, users who achieve their reading goals can receive points as a reward, which can be used to purchase the next book. This allows the electronic payment system to support users in achieving their goals and provide rewards.

[0061] An electronic payment system according to an embodiment includes a setting unit, a monitoring unit, and a providing unit. The setting unit allows a user to set goals and challenges. For example, a user can set goals such as "lose 5 kilograms in one month" or "read for one hour every day." The setting unit provides an interface for the user to input the goals and challenges. For example, methods such as text input, voice input, and multiple-choice input are available. The monitoring unit records the goals and challenges set by the setting unit and monitors progress. For example, the system grasps progress by having the user record their weight or input their reading time every day. The monitoring unit includes a calculation unit that calculates progress based on data input by the user. For example, progress can be calculated based on weight data and reading time data input by the user. The providing unit provides rewards based on the progress monitored by the monitoring unit. For example, users who achieve their goals are awarded electronic money or points. The providing unit includes a management unit that manages types of rewards. For example, reward types can be classified into electronic money, points, badges, etc. and managed. The providing unit provides different rewards depending on the content of the goals and challenges. For example, a user who achieves a weight loss goal can be provided with electronic money, and a user who achieves a reading goal can be provided with points, so that the electronic payment system according to the embodiment can support users in achieving their goals and provide rewards.

[0062] The monitoring unit may include a calculation unit that calculates progress based on data input by the user. The calculation unit calculates progress based on, for example, weight data and reading time data input by the user. For example, if the user records their weight every day, the calculation unit can calculate weight changes and understand progress toward achieving their goal. If the user inputs their reading time every day, the calculation unit can calculate the cumulative reading time and understand progress toward achieving their goal. Furthermore, the calculation unit can adjust the algorithm for calculating progress depending on the type of data input by the user. For example, if the data input by the user is numerical data, the progress can be calculated using linear regression. If the data input by the user is text data, the progress can be calculated using natural language processing technology. This allows for accurate calculation of progress based on the user's input data.

[0063] The providing unit may include a management unit that manages the types of rewards. The management unit, for example, classifies and manages the types of rewards into electronic money, points, badges, etc. For example, the management unit can set the type of reward to be provided when a user achieves a goal. For example, a user who achieves a weight loss goal can be provided with electronic money, and a user who achieves a reading goal can be provided with points. The management unit can also customize the types of rewards according to the user's preferences. For example, if a user desires a specific reward, that reward can be provided preferentially. Furthermore, the management unit can periodically update the types of rewards and provide new rewards to the user. In this way, by managing the types of rewards, appropriate rewards can be provided to the user.

[0064] The setting unit may include an input unit for the user to input a goal or challenge. The input unit provides, for example, an interface for the user to input the goal or challenge. Examples of input methods include text input, voice input, and multiple choice input. For example, if the user selects text input, the input unit provides an interface that allows the user to input the goal or challenge in text format. Also, if the user selects voice input, the input unit provides an interface that allows the user to input the goal or challenge in voice format. Furthermore, if the user selects multiple choice input, the input unit provides an interface that allows the user to select and input the goal or challenge from options. This allows the user to easily input the goal or challenge.

[0065] The providing unit can provide different rewards depending on the content of the goal or challenge. For example, the providing unit can provide different rewards depending on the content of the goal or challenge. For example, it can provide electronic money to a user who achieves a weight loss goal, and points to a user who achieves a reading goal. The providing unit can also adjust the type of reward depending on the difficulty of the goal or challenge. For example, it can provide a high-value reward to a user who achieves a high-difficulty goal, and a low-value reward to a user who achieves a low-difficulty goal. Furthermore, the providing unit can adjust the amount of reward depending on the level of achievement of the goal or challenge. For example, it can provide a large reward to a user who completely achieves a goal, and a small reward to a user who partially achieves a goal. In this way, by providing rewards depending on the content of the goal or challenge, it is possible to increase user motivation.

[0066] The setting unit can estimate the user's emotions and suggest goals and challenges based on the estimated user emotions. For example, the setting unit can estimate the user's emotions and suggest goals and challenges based on the estimated user emotions. For example, if the user is feeling stressed, the setting unit can suggest relaxing goals and challenges. Also, if the user is highly motivated, the setting unit can suggest difficult goals and challenges. Furthermore, if the user is tired, the setting unit can suggest goals and challenges that can be achieved in a short period of time. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows for more appropriate goal setting by suggesting goals and challenges according to the user's emotions.

[0067] The setting unit can analyze the user's past goal achievement history and support optimal goal setting. The setting unit, for example, analyzes the user's past goal achievement history and supports optimal goal setting. For example, the setting unit can suggest the next goal based on goals the user has achieved in the past. It can also analyze goals that the user has failed to achieve in the past and suggest areas for improvement. Furthermore, it can suggest goals that are easy to achieve based on the user's past goal achievement history. This can increase the success rate of goal achievement by setting optimal goals based on the user's past goal achievement history.

[0068] The setting unit can propose customized goals based on the user's current living situation and areas of interest when setting a goal or challenge. For example, when setting a goal or challenge, the setting unit proposes customized goals based on the user's current living situation and areas of interest. For example, if the user is interested in health, the setting unit can propose health-related goals. Also, if the user is busy with work, it can propose goals that can be achieved in a short time. Furthermore, if the user is interested in a hobby, it can propose goals related to that hobby. This allows for more appropriate goal setting by proposing goals according to the user's living situation and areas of interest.

[0069] The setting unit can estimate the user's emotions and adjust the difficulty of goals and challenges based on the estimated user emotions. The setting unit, for example, estimates the user's emotions and adjusts the difficulty of goals and challenges based on the estimated user emotions. For example, if the user is feeling stressed, a low-difficulty goal can be suggested. Also, if the user is highly motivated, a high-difficulty goal can be suggested. Furthermore, if the user is tired, a goal that can be achieved in a short period of time can be suggested. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This can increase the success rate of goal achievement by setting goals and challenges with difficulty levels that correspond to the user's emotions.

[0070] The setting unit can suggest highly relevant goals by taking into account the user's geographical location information when setting goals or challenges. For example, when setting goals or challenges, the setting unit can suggest highly relevant goals by taking into account the user's geographical location information. For example, if the user lives in a specific area, goals related to that area can be suggested. Also, if the user is traveling, goals related to the travel destination can be suggested. Furthermore, if the user frequently visits a specific place, goals related to that place can be suggested. This allows for more realistic goal setting by suggesting goals based on the user's geographical location information.

[0071] The setting unit can analyze the user's social media activity and suggest related goals when setting a goal or challenge. For example, the setting unit can analyze the user's social media activity and suggest related goals when setting a goal or challenge. For example, the setting unit can suggest goals related to topics that the user frequently mentions on social media. It can also suggest goals related to accounts that the user follows on social media. It can also suggest goals related to groups that the user participates in on social media. This allows for more interesting goal setting by suggesting goals based on the user's social media activity.

[0072] The monitoring unit can estimate the user's emotions and adjust the progress feedback method based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions and adjusts the progress feedback method based on the estimated user emotions. For example, if the user is feeling stressed, positive feedback can be provided. Also, if the user is highly motivated, detailed feedback can be provided. Furthermore, if the user is tired, concise feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This enables more effective progress management by providing feedback according to the user's emotions.

[0073] The monitoring unit can predict the progress status by referring to the user's past data during monitoring. The monitoring unit, for example, predicts the progress status by referring to the user's past data during monitoring. For example, the monitoring unit predicts the possibility of goal achievement based on the user's past data. Also, it can predict a delay in progress based on the user's past data. Furthermore, it can predict an acceleration of progress based on the user's past data. This allows for more accurate progress management by predicting the progress status based on past data.

[0074] The monitoring unit can customize the evaluation criteria for the progress status based on the user's current living situation during monitoring. The monitoring unit, for example, customizes the evaluation criteria for the progress status based on the user's current living situation during monitoring. For example, if the user is busy, the evaluation criteria for the progress status can be relaxed. Also, if the user has time to spare, the evaluation criteria for the progress status can be tightened. Furthermore, if the user is in a specific situation, evaluation criteria can be set according to that situation. This enables more realistic progress management by setting evaluation criteria according to the user's living situation.

[0075] The monitoring unit can estimate the user's emotions and adjust the progress notification frequency based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions and adjusts the progress notification frequency based on the estimated user emotions. For example, if the user is feeling stressed, the notification frequency can be reduced. Also, if the user is highly motivated, the notification frequency can be increased. Furthermore, if the user is tired, the notification frequency can be adjusted. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This enables more effective progress management by setting the notification frequency according to the user's emotions.

[0076] The monitoring unit can evaluate the progress status taking into account the user's geographical location information during monitoring. For example, the monitoring unit evaluates the progress status taking into account the user's geographical location information during monitoring. For example, if the user is in a specific area, the progress status can be evaluated according to the area. Also, if the user is traveling, the progress status can be evaluated according to the travel destination. Furthermore, if the user frequently visits a specific place, the progress status can be evaluated according to the place. This enables more realistic progress management by evaluating the progress based on the user's geographical location information.

[0077] The monitoring unit can analyze the user's social media activities during monitoring and reflect them in the progress evaluation. For example, the monitoring unit can analyze the user's social media activities during monitoring and reflect them in the progress evaluation. For example, the monitoring unit can evaluate progress related to topics that the user frequently mentions on social media. It can also evaluate progress related to accounts that the user follows on social media. It can also evaluate progress related to groups that the user participates in on social media. This enables more interesting progress management by evaluating progress based on the user's social media activities.

[0078] The providing unit can estimate the user's emotion and adjust the reward provision method based on the estimated user's emotion. For example, the providing unit can estimate the user's emotion and adjust the reward provision method based on the estimated user's emotion. For example, if the user is feeling stressed, a relaxing reward can be provided. Also, if the user is highly motivated, a challenging reward can be provided. Furthermore, if the user is tired, a refreshing reward can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This enables more effective reward provision by setting a reward provision method according to the user's emotion.

[0079] The providing unit can select the optimal reward by referring to the user's past reward history when providing a reward. For example, the providing unit selects the optimal reward by referring to the user's past reward history when providing a reward. For example, the next reward is selected based on rewards the user has received in the past. The next reward can also be selected based on rewards that the user has enjoyed in the past. Furthermore, the optimal reward can be selected from the user's past reward history. This makes it possible to increase user satisfaction by selecting the optimal reward based on the user's past reward history.

[0080] The providing unit can customize the content of the reward based on the user's current living situation when providing the reward. For example, the providing unit customizes the content of the reward based on the user's current living situation when providing the reward. For example, if the user is busy, a reward that saves time can be provided. Also, if the user has time, a reward that the user can enjoy can be provided. Furthermore, if the user is in a specific situation, a reward that suits the situation can be provided. This makes it possible to provide more appropriate rewards by providing rewards that suit the user's living situation.

[0081] The providing unit can estimate the user's emotion and adjust the type of reward based on the estimated user's emotion. For example, the providing unit can estimate the user's emotion and adjust the type of reward based on the estimated user's emotion. For example, if the user is feeling stressed, a relaxing reward can be provided. Also, if the user is highly motivated, a challenging reward can be provided. Furthermore, if the user is tired, a refreshing reward can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This enables more effective reward provision by setting the type of reward according to the user's emotion.

[0082] The providing unit can provide an optimal reward by taking into consideration the user's geographical location information when providing a reward. For example, when providing a reward, the providing unit can provide an optimal reward by taking into consideration the user's geographical location information. For example, if the user lives in a specific area, the providing unit can provide a reward related to that area. Also, if the user is traveling, the providing unit can provide a reward related to the travel destination. Furthermore, if the user frequently visits a specific place, the providing unit can provide a reward related to that place. This makes it possible to provide more realistic rewards by providing rewards based on the user's geographical location information.

[0083] The providing unit can analyze the user's social media activity and determine the content of the reward when providing the reward. For example, the providing unit can analyze the user's social media activity and determine the content of the reward when providing the reward. For example, the providing unit can provide a reward related to topics that the user frequently mentions on social media. It can also provide a reward related to accounts that the user follows on social media. It can also provide a reward related to groups that the user participates in on social media. This makes it possible to provide more interesting rewards by providing rewards based on the user's social media activity.

[0084] The calculation unit can estimate the user's emotions and adjust the progress calculation algorithm based on the estimated user emotions. The calculation unit, for example, estimates the user's emotions and adjusts the progress calculation algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the progress calculation algorithm can be relaxed. Also, if the user is highly motivated, the progress calculation algorithm can be tightened. Furthermore, if the user is tired, the progress calculation algorithm can be adjusted. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This enables more accurate progress management by setting the progress calculation algorithm according to the user's emotions.

[0085] The calculation unit can improve the accuracy of progress calculation by referring to the user's past data during calculation. The calculation unit can improve the accuracy of progress calculation by referring to the user's past data during calculation. For example, the calculation unit can improve the accuracy of progress calculation based on the user's past data. Furthermore, the calculation unit can reduce errors in progress calculation based on the user's past data. Furthermore, the calculation unit can optimize the progress calculation algorithm based on the user's past data. This improves the accuracy of progress calculation based on past data, enabling more accurate progress management.

[0086] The calculation unit can estimate the user's emotions and adjust the frequency of progress calculation based on the estimated user emotions. The calculation unit, for example, estimates the user's emotions and adjusts the frequency of progress calculation based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of progress calculation can be reduced. Also, if the user is highly motivated, the frequency of progress calculation can be increased. Furthermore, if the user is tired, the frequency of progress calculation can be adjusted. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This enables more effective progress management by setting the frequency of progress calculation according to the user's emotions.

[0087] The calculation unit can perform progress calculations taking into account the user's geographical location information when performing calculations. For example, the calculation unit performs progress calculations taking into account the user's geographical location information when performing calculations. For example, if the user is in a specific area, progress calculations can be performed according to that area. Also, if the user is traveling, progress calculations can be performed according to the travel destination. Furthermore, if the user frequently visits a specific place, progress calculations can be performed according to that place. This enables more realistic progress management by performing progress calculations based on the user's geographical location information.

[0088] The management unit can estimate the user's emotions and adjust the reward management method based on the estimated user emotions. For example, the management unit estimates the user's emotions and adjusts the reward management method based on the estimated user emotions. For example, if the user is feeling stressed, the reward management method can be relaxed. Also, if the user is highly motivated, the reward management method can be tightened. Furthermore, if the user is tired, the reward management method can be adjusted. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This enables more effective reward management by setting a reward management method according to the user's emotions.

[0089] The management unit can optimize the type of reward by referring to the user's past reward history during management. The management unit, for example, can optimize the type of reward by referring to the user's past reward history during management. For example, the management unit selects the optimal type of reward based on the user's past reward history. The type of reward can also be customized based on the user's past reward history. Furthermore, the type of reward can be optimized based on the user's past reward history. In this way, by optimizing the type of reward based on the user's past reward history, user satisfaction can be increased.

[0090] The management unit can estimate the user's emotions and adjust the frequency of reward management based on the estimated user emotions. The management unit, for example, estimates the user's emotions and adjusts the frequency of reward management based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of reward management can be reduced. Also, if the user is highly motivated, the frequency of reward management can be increased. Furthermore, if the user is tired, the frequency of reward management can be adjusted. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This enables more effective reward management by setting the frequency of reward management according to the user's emotions.

[0091] The management unit can perform reward management taking into account the user's geographical location information during management. The management unit, for example, performs reward management taking into account the user's geographical location information during management. For example, if the user is in a specific area, reward management can be performed according to the area. Also, if the user is traveling, reward management can be performed according to the travel destination. Furthermore, if the user frequently visits a specific place, reward management can be performed according to the location. This enables more realistic reward management by performing reward management based on the user's geographical location information.

[0092] The input unit can estimate the user's emotion and adjust the display method of the input interface based on the estimated user emotion. For example, the input unit can estimate the user's emotion and adjust the display method of the input interface based on the estimated user emotion. For example, if the user is stressed, a simple interface can be provided. Also, if the user is relaxed, detailed input options can be provided. Furthermore, if the user is in a hurry, voice input can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. As a result, a more user-friendly interface can be provided by setting the display method of the input interface according to the user's emotion.

[0093] The input unit can suggest the optimal input method by referring to the user's past input history when inputting. For example, the input unit can suggest the optimal input method by referring to the user's past input history when inputting. For example, goals and challenges that the user has frequently input in the past can be automatically displayed as candidates. Also, it can preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest goals and challenges to be used in a specific time period from the user's past input history. This allows for more efficient input by suggesting the optimal input method based on the user's past input history.

[0094] The input unit can estimate the user's emotion and adjust the operation procedure of the input interface based on the estimated user emotion. The input unit, for example, estimates the user's emotion and adjusts the operation procedure of the input interface based on the estimated user emotion. For example, if the user is feeling stressed, the operation procedure can be simplified. Also, if the user is relaxed, detailed operation procedures can be provided. Furthermore, if the user is in a hurry, operation procedures that can be quickly input can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. As a result, a more user-friendly interface can be provided by setting operation procedures according to the user's emotion.

[0095] The input unit can propose the optimal input method by taking into account the user's device information when inputting. For example, the input unit proposes the optimal input method by taking into account the user's device information when inputting. For example, if the user is using a smartphone, an input method tailored to the screen size can be provided. Also, if the user is using a tablet, an input method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a simple and highly visible input method can be provided. In this way, by proposing an input method based on the user's device information, a more user-friendly interface can be provided. === Hard Collateral 1-1 === Each of the multiple elements including the setting unit, monitoring unit, and providing 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 setting unit is realized by the control unit 46A of the smart device 14 and provides an interface for the user to input goals and challenges. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the set goals and challenges and monitors the progress. The providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides rewards based on the progress. The providing unit is also realized, for example, by the control unit 46A of the smart device 14 and includes a management unit that manages the types of rewards. === Hard Collateral 1-2 === Each of the multiple elements including the setting unit, monitoring unit, and providing 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 setting unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for the user to input goals and challenges. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the set goals and challenges and monitors the progress. The providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides rewards based on the progress. The providing unit is also realized, for example, by the control unit 46A of the smart glasses 214 and includes a management unit that manages the types of rewards. === Hard Collateral 1-3 === Each of the multiple elements including the setting unit, monitoring unit, and providing unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the headset type terminal 314 and provides an interface for the user to input goals and challenges. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the set goals and challenges and monitors the progress. The providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides rewards based on the progress. The providing unit is also realized, for example, by the control unit 46A of the headset type terminal 314 and includes a management unit that manages the types of rewards. === Hard Collateral 1-4 === Each of the multiple elements including the setting unit, monitoring unit, and providing unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the robot 414 and provides an interface for the user to input goals and challenges. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the set goals and challenges and monitors the progress. The providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides rewards based on the progress. The providing unit is also realized, for example, by the control unit 46A of the robot 414 and includes a management unit that manages the types of rewards.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The setting unit can analyze the user's past goal achievement history and support optimal goal setting. For example, it can suggest the next goal based on goals the user has achieved in the past. It can also analyze goals the user has failed to achieve in the past and suggest areas for improvement. It can also suggest goals that are easier to achieve based on the user's past goal achievement history. This can increase the success rate of goal achievement by setting optimal goals based on the user's past goal achievement history.

[0098] The monitoring unit can estimate the user's emotions and adjust the progress feedback method based on the estimated user emotions. For example, if the user is feeling stressed, positive feedback can be provided. If the user is highly motivated, detailed feedback can be provided. Furthermore, if the user is tired, brief feedback can be provided. This allows for more effective progress management by providing feedback according to the user's emotions.

[0099] When providing a reward, the providing unit can select the optimal reward by referring to the user's past reward history. For example, the next reward can be selected based on rewards the user has received in the past. The next reward can also be selected based on rewards that the user has enjoyed in the past. Furthermore, the optimal reward can be selected from the user's past reward history. This allows the user's satisfaction to be increased by selecting the optimal reward based on the user's past reward history.

[0100] The setting unit can estimate the user's emotions and adjust the difficulty of goals and challenges based on the estimated user's emotions. For example, if the user is feeling stressed, it can suggest goals with low difficulty. Also, if the user is highly motivated, it can suggest goals with high difficulty. Furthermore, if the user is tired, it can suggest goals that can be achieved in a short period of time. In this way, by setting goals and challenges with difficulty levels that correspond to the user's emotions, it is possible to increase the success rate of goal achievement.

[0101] During monitoring, the monitoring unit can predict progress by referring to the user's past data. For example, the possibility of goal achievement can be predicted based on the user's past data. Also, delays in progress can be predicted based on the user's past data. Furthermore, acceleration of progress can be predicted based on the user's past data. This allows for more accurate progress management by predicting progress based on past data.

[0102] The providing unit can estimate the user's emotions and adjust the reward provision method based on the estimated user's emotions. For example, if the user is feeling stressed, a relaxing reward can be provided. If the user is highly motivated, a challenging reward can be provided. Furthermore, if the user is tired, a refreshing reward can be provided. This allows for more effective reward provision by setting a reward provision method according to the user's emotions.

[0103] When setting goals or challenges, the setting unit can suggest highly relevant goals by taking into account the user's geographical location information. For example, if the user lives in a specific area, goals related to that area can be suggested. Also, if the user is traveling, goals related to the travel destination can be suggested. Furthermore, if the user frequently visits a specific place, goals related to that place can be suggested. This allows for more realistic goal setting by suggesting goals based on the user's geographical location information.

[0104] The monitoring unit can estimate the user's emotions and adjust the frequency of progress status notifications based on the estimated user emotions. For example, if the user is feeling stressed, the notification frequency can be reduced. Alternatively, if the user is highly motivated, the notification frequency can be increased. Furthermore, if the user is tired, the notification frequency can be adjusted. This allows for more effective progress management by setting the notification frequency according to the user's emotions.

[0105] When providing a reward, the providing unit can provide an optimal reward by taking into consideration the user's geographical location information. For example, if the user lives in a specific area, the providing unit can provide a reward related to that area. Also, if the user is traveling, the providing unit can provide a reward related to the travel destination. Furthermore, if the user frequently visits a specific place, the providing unit can provide a reward related to that place. This allows for more realistic reward provision by providing a reward based on the user's geographical location information.

[0106] The calculation unit can estimate the user's emotions and adjust the progress calculation algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the progress calculation algorithm can be relaxed. Alternatively, if the user is highly motivated, the progress calculation algorithm can be tightened. Furthermore, if the user is tired, the progress calculation algorithm can be adjusted. This allows for more accurate progress management by setting the progress calculation algorithm according to the user's emotions.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The setting unit allows the user to set goals and challenges. For example, the user can set goals such as "lose 5 kg in one month" or "read for one hour every day." The setting unit provides an interface for the user to input the goals and challenges. For example, there are methods such as text input, voice input, and multiple choice input. Step 2: The monitoring unit records the goals and challenges set by the setting unit and monitors the progress. For example, the system grasps the progress by having the user record their weight every day or input their reading time. The monitoring unit includes a calculation unit that calculates the progress based on the data entered by the user. For example, the progress can be calculated based on the weight data and reading time data entered by the user. Step 3: The providing unit provides rewards based on the progress monitored by the monitoring unit. For example, a user who achieves a goal is given electronic money or points. The providing unit includes a management unit that manages the types of rewards. For example, the types of rewards can be categorized and managed into electronic money, points, badges, etc. The providing unit provides different rewards depending on the content of the goal or challenge. For example, a user who achieves a weight loss goal can be given electronic money, and a user who achieves a reading goal can be given points.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 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 system comprising: a setting unit that sets a goal or challenge; a monitoring unit that records the goal or challenge set by the setting unit and monitors progress; and a providing unit that provides a reward based on the progress monitored by the monitoring unit.

2. The monitoring unit Equipped with a calculation unit that calculates progress based on data entered by the user 2. The system of claim 1.

3. The providing unit Have an administration department that manages the types of compensation 2. The system of claim 1.

4. The setting unit It has an input section for users to input goals and challenges.

2. The system of claim 1.

5. The system according to claim 1 , wherein the providing unit provides different rewards depending on the content of the goal or challenge.

6. The setting unit Estimate the user's emotions and suggest goals and challenges based on the estimated user emotions.

2. The system of claim 1.

7. The setting unit Analyze the user's past goal achievement history and support optimal goal setting 2. The system of claim 1.

8. The setting unit When setting goals or challenges, we suggest customized goals based on your current life situation and areas of interest.

2. The system of claim 1.

9. The setting unit Estimate the user's emotions and adjust the difficulty of goals and challenges based on the estimated user emotions.

2. The system of claim 1.

10. The system according to claim 1 , wherein the setting unit suggests highly relevant goals based on the user's geographical location information when the goal or challenge is set.

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