system

Abstract

The system according to the embodiment aims to appropriately take over existing free options when changing mobile models and to manage the discount application period. [Solution] A system according to an embodiment includes a reception unit, a transfer unit, a monitoring unit, an analysis unit, and a notification unit. The reception unit receives a request from a user to change their mobile phone model. The transfer unit transfers existing free options to the new model based on the request received by the reception unit. The monitoring unit monitors the discount application period for the options transferred by the transfer unit. The analysis unit analyzes the timing at which the discount application period monitored by the monitoring unit will end. The notification unit notifies the user based on the timing analyzed by the analysis 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 does not adequately manage the transfer of existing free options or the period of discount application when changing mobile models, which can lead to billing issues.

[0005] The system according to the embodiment aims to appropriately take over existing free options when changing mobile models and to manage the discount application period. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a transfer unit, a monitoring unit, an analysis unit, and a notification unit. The reception unit receives a request from a user to change their mobile phone model. The transfer unit transfers existing free options to the new model based on the request received by the reception unit. The monitoring unit monitors the discount application period for the options transferred by the transfer unit. The analysis unit analyzes the timing at which the discount application period monitored by the monitoring unit will end. The notification unit notifies the user based on the timing analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately take over existing free options when changing mobile models and can manage the discount application period. [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 alarm system according to an embodiment of the present invention automatically transfers optional services when a user changes their mobile phone model and prevents them from becoming charged after the expiration of an existing discount period. When a user changes their mobile phone model, the alarm system automatically transfers existing free options to the new model. The system then recognizes that the existing discount will remain in effect for six months after the model change and uses AI to trigger an alarm to prevent the existing discount from automatically becoming charged after six months. For example, when a user changes their mobile phone model, the alarm system transfers existing free options to the new model. For example, when a user changes their mobile phone model, the existing free options are automatically applied to the new smartphone. The alarm system then recognizes that the existing discount will remain in effect for six months after the model change. The system monitors the user's contract information and determines when the discount period will end. For example, if the discount will end six months after the user changes their mobile phone model, the system automatically detects this timing. Furthermore, the alarm system uses AI to trigger an alarm before the discount period ends. The AI ​​analyzes the user's contract information and predicts when the discount period will end. For example, the AI ​​triggers an alarm to notify the user one week before the discount period ends. This allows the user to take appropriate action before the discount period ends. This allows the alarm system to automatically transfer optional services when changing mobile models and prevent charges from being incurred after the existing discount period ends. For example, if the user changes to a new discount plan before the discount period ends, charges can be prevented. It also prevents users from being charged without knowing that the discount period has ended.

[0029] An alarm system according to an embodiment includes a reception unit, a transfer unit, a monitoring unit, an analysis unit, and a notification unit. The reception unit receives a request for a mobile phone model change from a user. The user's request for a mobile phone model change includes, but is not limited to, an online application or an in-store application. The reception unit, for example, provides an online application form and receives the application by the user entering necessary information. The reception unit can also construct a system for receiving in-store applications. The transfer unit transfers existing free options to a new model based on the application received by the reception unit. The transfer unit, for example, references the user's contract information and automatically applies the existing free options to the new model. The transfer unit can also transfer options without the user having to perform any special procedures. The monitoring unit monitors the discount application period for the options transferred by the transfer unit. For example, the monitoring unit periodically checks the user's contract information to determine when the discount application period will end. The monitoring unit can also monitor the user's contract information in real time to immediately determine when the discount application period will end. The analysis unit analyzes the timing at which the discount application period monitored by the monitoring unit will end. The analysis unit predicts the timing at which the discount application period will end, for example, using an AI algorithm. The analysis unit can also analyze the user's contract information to accurately determine the timing at which the discount application period will end. The notification unit notifies the user based on the timing analyzed by the analysis unit. The notification unit notifies the user by a method such as email or SMS. The notification unit can also notify the user by an appropriate method depending on the notification method desired by the user. As a result, the alarm system according to the embodiment can automatically transfer optional services when the user changes their mobile model and prevent them from becoming charged after the discount application period ends.

[0030] The reception unit can analyze the user's past application history and select an appropriate application method. For example, the reception unit can prioritize and suggest application methods that the user has frequently used in the past. The reception unit can also suggest the optimal application method for a specific time period based on the user's past application history. The reception unit can also automatically display options that the user has used in the past as candidates. This improves user convenience by selecting the optimal application method based on the user's past application history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past application history data into a generation AI and have the generation AI select the optimal application method.

[0031] The reception unit may perform filtering based on the user's current contract status and areas of interest when receiving an application. The reception unit may, for example, automatically filter applicable options based on the user's current contract status. The reception unit may also preferentially display relevant options based on the user's areas of interest. The reception unit may also combine the user's contract status and areas of interest to propose optimal options. This allows appropriate options to be proposed by filtering based on the user's contract status and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's contract status data and area of ​​interest data into a generation AI and have the generation AI perform filtering.

[0032] When accepting an application, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, if the user selects voice input, the acceptance unit accepts the application using voice recognition technology. Furthermore, if the user selects text input, the acceptance unit can also accept the application by providing a simple form. Furthermore, if the user selects image input, the acceptance unit can automatically extract the application content using image analysis technology. This improves user convenience by selecting the optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input voice data into a generation AI and have the generation AI perform voice recognition.

[0033] At the time of transfer, the transfer unit can analyze the user's past option usage history and select an appropriate transfer method. For example, the transfer unit prioritizes the transfer of options that the user has frequently used in the past. The transfer unit can also suggest the optimal transfer method for a specific time period based on the user's past option usage history. The transfer unit can also automatically display options that the user has used in the past as candidates. This improves user convenience by selecting the optimal transfer method based on the user's past option usage history. Some or all of the above-described processing in the transfer unit may be performed using, for example, AI, or may be performed without using AI. For example, the transfer unit can input the user's past option usage history data into a generation AI and cause the generation AI to select the optimal transfer method.

[0034] During the transfer, the transfer unit can perform filtering based on the user's current contract status and areas of interest. The transfer unit, for example, automatically filters applicable options based on the user's current contract status. The transfer unit can also preferentially display relevant options based on the user's areas of interest. The transfer unit can also combine the user's contract status and areas of interest to propose optimal options. This makes it possible to propose appropriate options by filtering based on the user's contract status and areas of interest. Some or all of the above-described processing in the transfer unit can be performed using AI, for example, or without AI. For example, the transfer unit can input the user's contract status data and area of ​​interest data into a generation AI and have the generation AI perform filtering.

[0035] The handover unit can select an appropriate handover method depending on the user's input method during handover. For example, if the user selects voice input, the handover unit can use voice recognition technology to hand over the options. Furthermore, if the user selects text input, the handover unit can also provide a simple form to hand over the options. Furthermore, if the user selects image input, the handover unit can automatically extract the option content using image analysis technology. This improves user convenience by selecting the optimal handover method depending on the user's input method. Some or all of the above-described processing in the handover unit can be performed using AI, for example, or without AI. For example, the handover unit can input voice data to a generation AI and have the generation AI perform voice recognition.

[0036] During monitoring, the monitoring unit can analyze the user's past contract history and select an appropriate monitoring method. The monitoring unit can, for example, suggest an optimal monitoring method based on the user's past contract history. The monitoring unit can also suggest an optimal monitoring method for a specific time period based on the user's past contract history. The monitoring unit can also analyze the user's past contract history and suggest the most efficient monitoring method. This improves user convenience by selecting an optimal monitoring method based on the user's past contract history. Some or all of the above-described processing in the monitoring unit can be performed, for example, using AI or without AI. For example, the monitoring unit can input the user's past contract history data into a generation AI and have the generation AI select an optimal monitoring method.

[0037] During monitoring, the monitoring unit can perform filtering based on the user's current contract status and areas of interest. The monitoring unit, for example, automatically filters monitoring targets based on the user's current contract status. The monitoring unit can also prioritize monitoring of related contract information based on the user's areas of interest. The monitoring unit can also combine the user's contract status and areas of interest to suggest optimal monitoring targets. This makes it possible to suggest appropriate monitoring targets by filtering based on the user's contract status and areas of interest. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's contract status data and area of ​​interest data into a generation AI and have the generation AI perform filtering.

[0038] During monitoring, the monitoring unit can select an appropriate monitoring means depending on the user's input method. For example, if the user selects voice input, the monitoring unit performs monitoring using voice recognition technology. Furthermore, if the user selects text input, the monitoring unit can also perform monitoring by providing a simple form. Furthermore, if the user selects image input, the monitoring unit can automatically extract monitoring content using image analysis technology. This improves user convenience by selecting the optimal monitoring means depending on the user's input method. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input voice data into a generation AI and have the generation AI perform voice recognition.

[0039] During analysis, the analysis unit can analyze the user's past contract history and select an appropriate analysis method. The analysis unit can, for example, suggest an optimal analysis method based on the user's past contract history. The analysis unit can also suggest an optimal analysis method for a specific time period based on the user's past contract history. The analysis unit can also analyze the user's past contract history and suggest the most efficient analysis method. This improves user convenience by selecting an optimal analysis method based on the user's past contract history. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past contract history data into the generation AI and have the generation AI select the optimal analysis method.

[0040] During analysis, the analysis unit can perform filtering based on the user's current contract status and areas of interest. The analysis unit, for example, automatically filters analysis targets based on the user's current contract status. The analysis unit can also prioritize analysis of related contract information based on the user's areas of interest. The analysis unit can also combine the user's contract status and areas of interest to propose optimal analysis targets. This makes it possible to propose appropriate analysis targets by filtering based on the user's contract status and areas of interest. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's contract status data and area of ​​interest data into a generation AI and have the generation AI perform filtering.

[0041] During analysis, the analysis unit can select an appropriate analysis method depending on the user's input method. For example, if the user selects voice input, the analysis unit performs analysis using voice recognition technology. Furthermore, if the user selects text input, the analysis unit can also perform analysis by providing a simple form. Furthermore, if the user selects image input, the analysis unit can automatically extract analysis content using image analysis technology. This improves user convenience by selecting the optimal analysis method depending on the user's input method. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input voice data into a generation AI and have the generation AI perform voice recognition.

[0042] The notification unit can analyze the user's past notification history and select an appropriate notification method when notifying the user. For example, the notification unit can prioritize and suggest notification methods that the user has frequently used in the past. The notification unit can also suggest the optimal notification method for a specific time period based on the user's past notification history. The notification unit can also automatically display notification methods that the user has used in the past as candidates. This improves user convenience by selecting the optimal notification method based on the user's past notification history. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's past notification history data into a generation AI and have the generation AI select the optimal notification method.

[0043] The notification unit can filter notifications based on the user's current contract status and areas of interest. The notification unit can automatically filter notification targets based on the user's current contract status, for example. The notification unit can also prioritize related notifications based on the user's areas of interest. The notification unit can also combine the user's contract status and areas of interest to suggest optimal notification targets. This allows for appropriate notifications to be provided by filtering based on the user's contract status and areas of interest. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's contract status data and area of ​​interest data into a generation AI and have the generation AI perform the filtering.

[0044] The notification unit can select an appropriate notification means depending on the user's input method when notifying. For example, if the user selects voice input, the notification unit can use voice recognition technology to provide the notification. Furthermore, if the user selects text input, the notification unit can also provide a simple form to provide the notification. Furthermore, if the user selects image input, the notification unit can automatically extract the notification content using image analysis technology. This improves user convenience by selecting the optimal notification means depending on the user's input method. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input voice data to a generation AI and have the generation AI perform voice recognition.

[0045] When providing a notification, the notification unit can prioritize highly relevant notifications by taking into account the user's geographical location information. For example, if the user is in a specific area, the notification unit can prioritize notifications related to that area. The notification unit can also suggest optimal notification content based on the user's location information. If the user is moving, the notification unit can also prioritize displaying notification content closest to the user's current location. This improves user convenience by providing highly relevant notifications based on the user's geographical location information. Some or all of the above-described processing in the notification unit may be performed using, or without, AI. For example, the notification unit can input the user's location information data into a generation AI and cause the generation AI to select highly relevant notification content.

[0046] The notification unit can analyze the user's social media activity and provide relevant notifications at the time of notification. The notification unit can prioritize relevant notifications based on, for example, the content mentioned by the user on social media. The notification unit can also analyze the user's social media activity history and suggest optimal notification content. The notification unit can also prioritize relevant notifications based on the activity of the user's friends on social media. This improves user convenience by providing relevant notifications based on the user's social media activity. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's social media data into a generation AI and have the generation AI select relevant notification content.

[0047] The notification unit can customize the notification method by reflecting the user's past feedback when providing a notification. The notification unit can, for example, suggest an optimal notification method based on feedback provided by the user in the past. The notification unit can also analyze the user's past feedback and simplify the notification procedure. The notification unit can also provide a more intuitive notification interface by reflecting the user's feedback. This improves user convenience by customizing the notification method based on the user's past feedback. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input user feedback data into a generation AI and have the generation AI customize the notification method.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The reception unit can propose the optimal application reception method by taking into consideration the user's geographical location information. For example, if the user is in a specific area, application methods related to that area are preferentially proposed. The reception unit can also propose the optimal application reception time based on the user's location information. Furthermore, if the user is traveling, the reception unit can also preferentially display the application method at the store closest to the user's current location. This improves user convenience by proposing the optimal application reception method based on the user's geographical location information.

[0050] The reception unit can analyze the user's social media activity and suggest a related application reception method. For example, the reception unit can prioritize suggesting a related application method based on the content mentioned by the user on social media. The reception unit can also analyze the user's social media activity history and suggest the optimal application reception method. Furthermore, the reception unit can prioritize suggesting a related application method based on the activity of the user's friends on social media. This improves user convenience by suggesting the optimal application reception method based on the user's social media activity.

[0051] The handover unit can propose an optimal option handover method in consideration of the user's geographical location information. For example, if the user is in a specific area, options related to that area are given priority in being transferred. The handover unit can also propose an optimal handover time based on the user's location information. Furthermore, if the user is traveling, options closest to the user's current location can be given priority in being transferred. This improves user convenience by proposing an optimal option handover method based on the user's geographical location information.

[0052] The transfer unit can analyze the user's social media activity and suggest a related option transfer method. For example, related options are preferentially transferred based on the content the user mentioned on social media. The transfer unit can also analyze the user's social media activity history and suggest an optimal option transfer method. Furthermore, related options can be preferentially transferred based on the activity of the user's friends on social media. This improves user convenience by suggesting an optimal option transfer method based on the user's social media activity.

[0053] The monitoring unit can propose an optimal monitoring method by taking into account the user's geographical location information. For example, if the user is in a specific area, the monitoring unit will prioritize proposing a monitoring method related to that area. The monitoring unit can also propose an optimal monitoring time based on the user's location information. Furthermore, if the user is moving, the monitoring unit can prioritize proposing a monitoring method closest to the user's current location. This improves user convenience by proposing an optimal monitoring method based on the user's geographical location information.

[0054] The monitoring unit can analyze the user's social media activity and suggest relevant monitoring methods. For example, it can prioritize suggesting relevant monitoring methods based on the content the user has mentioned on social media. The monitoring unit can also analyze the user's social media activity history and suggest the most appropriate monitoring method. Furthermore, it can also prioritize suggesting relevant monitoring methods based on the activities of the user's friends on social media. This improves user convenience by suggesting the most appropriate monitoring method based on the user's social media activity.

[0055] The analysis unit can propose the optimal analysis method by taking into account the user's geographical location information. For example, if the user is in a specific area, it will preferentially propose an analysis method related to that area. The analysis unit can also propose the optimal analysis time based on the user's location information. Furthermore, if the user is moving, it can preferentially propose the analysis method closest to the user's current location. This improves user convenience by suggesting the optimal analysis method based on the user's geographical location information.

[0056] The analysis unit can analyze the user's social media activity and suggest relevant analysis methods. For example, it can prioritize suggest relevant analysis methods based on the content the user mentioned on social media. The analysis unit can also analyze the user's social media activity history and suggest the most appropriate analysis method. Furthermore, it can also prioritize suggesting relevant analysis methods based on the activity of the user's friends on social media. This improves user convenience by suggesting the most appropriate analysis method based on the user's social media activity.

[0057] The notification unit can propose the optimal notification method by taking into account the user's geographical location information. For example, if the user is in a specific area, the notification unit can prioritize the proposal of a notification method related to that area. The notification unit can also propose the optimal notification time based on the user's location information. Furthermore, if the user is moving, the notification unit can prioritize the proposal of a notification method closest to the user's current location. This improves user convenience by proposing the optimal notification method based on the user's geographical location information.

[0058] The notification unit can analyze the user's social media activity and suggest relevant notification methods. For example, it can prioritize suggesting relevant notification methods based on the content the user mentioned on social media. The notification unit can also analyze the user's social media activity history and suggest the most appropriate notification method. Furthermore, it can also prioritize suggesting relevant notification methods based on the activity of the user's friends on social media. This improves user convenience by suggesting the most appropriate notification method based on the user's social media activity.

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

[0060] Step 1: The reception unit accepts applications from users to upgrade their mobile phone models. Applications from users include online applications and applications at stores. The reception unit provides an online application form and accepts applications by allowing users to enter the required information. It is also possible to build a system for accepting applications at stores. Step 2: The transfer unit transfers the existing free options to the new model based on the application received by the reception unit. The transfer unit references the user's contract information and automatically applies the existing free options to the new model. It is also possible for the user to transfer the options without performing any special procedures. Step 3: The monitoring unit monitors the discount application period for the option taken over by the takeover unit. The monitoring unit periodically checks the user's contract information and determines when the discount application period will end. The monitoring unit can also monitor the user's contract information in real time and immediately determine when the discount application period will end. Step 4: The analysis unit analyzes the timing of the end of the discount application period monitored by the monitoring unit. The analysis unit uses an AI algorithm to predict when the discount application period will end. It can also analyze the user's contract information to accurately determine when the discount application period will end. Step 5: The notification unit notifies the user based on the timing analyzed by the analysis unit. The notification unit notifies the user by email, SMS, or other methods. The notification unit can also notify the user in an appropriate manner depending on the user's desired notification method.

[0061] (Example 2) An alarm system according to an embodiment of the present invention automatically transfers optional services when a user changes their mobile phone model and prevents them from becoming charged after the expiration of an existing discount period. When a user changes their mobile phone model, the alarm system automatically transfers existing free options to the new model. The system then recognizes that the existing discount will remain in effect for six months after the model change and uses AI to trigger an alarm to prevent the existing discount from automatically becoming charged after six months. For example, when a user changes their mobile phone model, the alarm system transfers existing free options to the new model. For example, when a user changes their mobile phone model, the existing free options are automatically applied to the new smartphone. The alarm system then recognizes that the existing discount will remain in effect for six months after the model change. The system monitors the user's contract information and determines when the discount period will end. For example, if the discount will end six months after the user changes their mobile phone model, the system automatically detects this timing. Furthermore, the alarm system uses AI to trigger an alarm before the discount period ends. The AI ​​analyzes the user's contract information and predicts when the discount period will end. For example, the AI ​​triggers an alarm to notify the user one week before the discount period ends. This allows the user to take appropriate action before the discount period ends. This allows the alarm system to automatically transfer optional services when changing mobile models and prevent charges from being incurred after the existing discount period ends. For example, if the user changes to a new discount plan before the discount period ends, charges can be prevented. It also prevents users from being charged without knowing that the discount period has ended.

[0062] An alarm system according to an embodiment includes a reception unit, a transfer unit, a monitoring unit, an analysis unit, and a notification unit. The reception unit receives a request for a mobile phone model change from a user. The user's request for a mobile phone model change includes, but is not limited to, an online application or an in-store application. The reception unit, for example, provides an online application form and receives the application by the user entering necessary information. The reception unit can also construct a system for receiving in-store applications. The transfer unit transfers existing free options to a new model based on the application received by the reception unit. The transfer unit, for example, references the user's contract information and automatically applies the existing free options to the new model. The transfer unit can also transfer options without the user having to perform any special procedures. The monitoring unit monitors the discount application period for the options transferred by the transfer unit. For example, the monitoring unit periodically checks the user's contract information to determine when the discount application period will end. The monitoring unit can also monitor the user's contract information in real time to immediately determine when the discount application period will end. The analysis unit analyzes the timing at which the discount application period monitored by the monitoring unit will end. The analysis unit predicts the timing at which the discount application period will end, for example, using an AI algorithm. The analysis unit can also analyze the user's contract information to accurately determine the timing at which the discount application period will end. The notification unit notifies the user based on the timing analyzed by the analysis unit. The notification unit notifies the user by a method such as email or SMS. The notification unit can also notify the user by an appropriate method depending on the notification method desired by the user. As a result, the alarm system according to the embodiment can automatically transfer optional services when the user changes their mobile model and prevent them from becoming charged after the discount application period ends.

[0063] The reception unit can estimate the user's emotions and adjust the timing of application acceptance based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can quickly accept the application and simplify the procedure. Furthermore, if the user is relaxed, the reception unit can provide detailed explanations of options to allow the user to accept the application with confidence. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to allow the user to complete the application quickly. This improves user convenience by adjusting the timing of application acceptance according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0064] The reception unit can analyze the user's past application history and select an appropriate application method. For example, the reception unit can prioritize and suggest application methods that the user has frequently used in the past. The reception unit can also suggest the optimal application method for a specific time period based on the user's past application history. The reception unit can also automatically display options that the user has used in the past as candidates. This improves user convenience by selecting the optimal application method based on the user's past application history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past application history data into a generation AI and have the generation AI select the optimal application method.

[0065] The reception unit may perform filtering based on the user's current contract status and areas of interest when receiving an application. The reception unit may, for example, automatically filter applicable options based on the user's current contract status. The reception unit may also preferentially display relevant options based on the user's areas of interest. The reception unit may also combine the user's contract status and areas of interest to propose optimal options. This allows appropriate options to be proposed by filtering based on the user's contract status and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's contract status data and area of ​​interest data into a generation AI and have the generation AI perform filtering.

[0066] When accepting an application, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, if the user selects voice input, the acceptance unit accepts the application using voice recognition technology. Furthermore, if the user selects text input, the acceptance unit can also accept the application by providing a simple form. Furthermore, if the user selects image input, the acceptance unit can automatically extract the application content using image analysis technology. This improves user convenience by selecting the optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input voice data into a generation AI and have the generation AI perform voice recognition.

[0067] The handover unit can estimate the user's emotions and adjust the timing of option transfer based on the estimated user emotions. For example, if the user is stressed, the handover unit can quickly transfer the options. If the user is relaxed, the handover unit can also perform option transfer while providing detailed explanations. If the user is in a hurry, the handover unit can prioritize voice input and quickly complete option transfer. This improves user convenience by adjusting the timing of option transfer according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the handover unit can be performed using an AI, for example, or without an AI. For example, the handover unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0068] At the time of transfer, the transfer unit can analyze the user's past option usage history and select an appropriate transfer method. For example, the transfer unit prioritizes the transfer of options that the user has frequently used in the past. The transfer unit can also suggest the optimal transfer method for a specific time period based on the user's past option usage history. The transfer unit can also automatically display options that the user has used in the past as candidates. This improves user convenience by selecting the optimal transfer method based on the user's past option usage history. Some or all of the above-described processing in the transfer unit may be performed using, for example, AI, or may be performed without using AI. For example, the transfer unit can input the user's past option usage history data into a generation AI and cause the generation AI to select the optimal transfer method.

[0069] During the transfer, the transfer unit can perform filtering based on the user's current contract status and areas of interest. The transfer unit, for example, automatically filters applicable options based on the user's current contract status. The transfer unit can also preferentially display relevant options based on the user's areas of interest. The transfer unit can also combine the user's contract status and areas of interest to propose optimal options. This makes it possible to propose appropriate options by filtering based on the user's contract status and areas of interest. Some or all of the above-described processing in the transfer unit can be performed using AI, for example, or without AI. For example, the transfer unit can input the user's contract status data and area of ​​interest data into a generation AI and have the generation AI perform filtering.

[0070] The handover unit can select an appropriate handover method depending on the user's input method during handover. For example, if the user selects voice input, the handover unit can use voice recognition technology to hand over the options. Furthermore, if the user selects text input, the handover unit can also provide a simple form to hand over the options. Furthermore, if the user selects image input, the handover unit can automatically extract the option content using image analysis technology. This improves user convenience by selecting the optimal handover method depending on the user's input method. Some or all of the above-described processing in the handover unit can be performed using AI, for example, or without AI. For example, the handover unit can input voice data to a generation AI and have the generation AI perform voice recognition.

[0071] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can reduce the monitoring frequency and minimize notifications. Furthermore, if the user is relaxed, the monitoring unit can monitor at a normal monitoring frequency. Furthermore, if the user is in a hurry, the monitoring unit can increase the monitoring frequency and quickly notify. This improves user convenience by adjusting the monitoring frequency according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0072] During monitoring, the monitoring unit can analyze the user's past contract history and select an appropriate monitoring method. The monitoring unit can, for example, suggest an optimal monitoring method based on the user's past contract history. The monitoring unit can also suggest an optimal monitoring method for a specific time period based on the user's past contract history. The monitoring unit can also analyze the user's past contract history and suggest the most efficient monitoring method. This improves user convenience by selecting an optimal monitoring method based on the user's past contract history. Some or all of the above-described processing in the monitoring unit can be performed, for example, using AI or without AI. For example, the monitoring unit can input the user's past contract history data into a generation AI and have the generation AI select an optimal monitoring method.

[0073] During monitoring, the monitoring unit can perform filtering based on the user's current contract status and areas of interest. The monitoring unit, for example, automatically filters monitoring targets based on the user's current contract status. The monitoring unit can also prioritize monitoring of related contract information based on the user's areas of interest. The monitoring unit can also combine the user's contract status and areas of interest to suggest optimal monitoring targets. This makes it possible to suggest appropriate monitoring targets by filtering based on the user's contract status and areas of interest. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's contract status data and area of ​​interest data into a generation AI and have the generation AI perform filtering.

[0074] During monitoring, the monitoring unit can select an appropriate monitoring means depending on the user's input method. For example, if the user selects voice input, the monitoring unit performs monitoring using voice recognition technology. Furthermore, if the user selects text input, the monitoring unit can also perform monitoring by providing a simple form. Furthermore, if the user selects image input, the monitoring unit can automatically extract monitoring content using image analysis technology. This improves user convenience by selecting the optimal monitoring means depending on the user's input method. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input voice data into a generation AI and have the generation AI perform voice recognition.

[0075] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can quickly perform an analysis and provide the results. Furthermore, if the user is relaxed, the analysis unit can also perform a detailed analysis and provide the results. Furthermore, if the user is in a hurry, the analysis unit can also perform a concise analysis and provide the results quickly. This improves user convenience by adjusting the timing of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0076] During analysis, the analysis unit can analyze the user's past contract history and select an appropriate analysis method. The analysis unit can, for example, suggest an optimal analysis method based on the user's past contract history. The analysis unit can also suggest an optimal analysis method for a specific time period based on the user's past contract history. The analysis unit can also analyze the user's past contract history and suggest the most efficient analysis method. This improves user convenience by selecting an optimal analysis method based on the user's past contract history. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past contract history data into the generation AI and have the generation AI select the optimal analysis method.

[0077] During analysis, the analysis unit can perform filtering based on the user's current contract status and areas of interest. The analysis unit, for example, automatically filters analysis targets based on the user's current contract status. The analysis unit can also prioritize analysis of related contract information based on the user's areas of interest. The analysis unit can also combine the user's contract status and areas of interest to propose optimal analysis targets. This makes it possible to propose appropriate analysis targets by filtering based on the user's contract status and areas of interest. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's contract status data and area of ​​interest data into a generation AI and have the generation AI perform filtering.

[0078] During analysis, the analysis unit can select an appropriate analysis method depending on the user's input method. For example, if the user selects voice input, the analysis unit performs analysis using voice recognition technology. Furthermore, if the user selects text input, the analysis unit can also perform analysis by providing a simple form. Furthermore, if the user selects image input, the analysis unit can automatically extract analysis content using image analysis technology. This improves user convenience by selecting the optimal analysis method depending on the user's input method. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input voice data into a generation AI and have the generation AI perform voice recognition.

[0079] The notification unit can estimate the user's emotions and adjust the timing of the notification based on the estimated user emotions. For example, if the user is feeling stressed, the notification unit can quickly notify the user and simplify the procedure. Furthermore, if the user is relaxed, the notification unit can provide a detailed explanation while notifying the user. Furthermore, if the user is in a hurry, the notification unit can prioritize voice notification and quickly complete the notification. This improves user convenience by adjusting the timing of the notification according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the notification unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0080] The notification unit can analyze the user's past notification history and select an appropriate notification method when notifying the user. For example, the notification unit can prioritize and suggest notification methods that the user has frequently used in the past. The notification unit can also suggest the optimal notification method for a specific time period based on the user's past notification history. The notification unit can also automatically display notification methods that the user has used in the past as candidates. This improves user convenience by selecting the optimal notification method based on the user's past notification history. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's past notification history data into a generation AI and have the generation AI select the optimal notification method.

[0081] The notification unit can filter notifications based on the user's current contract status and areas of interest. The notification unit can automatically filter notification targets based on the user's current contract status, for example. The notification unit can also prioritize related notifications based on the user's areas of interest. The notification unit can also combine the user's contract status and areas of interest to suggest optimal notification targets. This allows for appropriate notifications to be provided by filtering based on the user's contract status and areas of interest. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's contract status data and area of ​​interest data into a generation AI and have the generation AI perform the filtering.

[0082] The notification unit can select an appropriate notification means depending on the user's input method when notifying. For example, if the user selects voice input, the notification unit can use voice recognition technology to provide the notification. Furthermore, if the user selects text input, the notification unit can also provide a simple form to provide the notification. Furthermore, if the user selects image input, the notification unit can automatically extract the notification content using image analysis technology. This improves user convenience by selecting the optimal notification means depending on the user's input method. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input voice data to a generation AI and have the generation AI perform voice recognition.

[0083] The notification unit can estimate the user's emotions and determine the priority of notification content based on the estimated user emotions. For example, if the user is nervous, the notification unit can prioritize important notifications. Furthermore, if the user is relaxed, the notification unit can also prioritize notifications with normal priority. Furthermore, if the user is in a hurry, the notification unit can also prioritize urgent notifications. Thus, by determining the priority of notification content according to the user's emotions, important notifications can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit can be performed using, for example, an AI, or without an AI. For example, the notification unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0084] When providing a notification, the notification unit can prioritize highly relevant notifications by taking into account the user's geographical location information. For example, if the user is in a specific area, the notification unit can prioritize notifications related to that area. The notification unit can also suggest optimal notification content based on the user's location information. If the user is moving, the notification unit can also prioritize displaying notification content closest to the user's current location. This improves user convenience by providing highly relevant notifications based on the user's geographical location information. Some or all of the above-described processing in the notification unit may be performed using, or without, AI. For example, the notification unit can input the user's location information data into a generation AI and cause the generation AI to select highly relevant notification content.

[0085] The notification unit can analyze the user's social media activity and provide relevant notifications at the time of notification. The notification unit can prioritize relevant notifications based on, for example, the content mentioned by the user on social media. The notification unit can also analyze the user's social media activity history and suggest optimal notification content. The notification unit can also prioritize relevant notifications based on the activity of the user's friends on social media. This improves user convenience by providing relevant notifications based on the user's social media activity. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's social media data into a generation AI and have the generation AI select relevant notification content.

[0086] The notification unit can customize the notification method by reflecting the user's past feedback when providing a notification. The notification unit can, for example, suggest an optimal notification method based on feedback provided by the user in the past. The notification unit can also analyze the user's past feedback and simplify the notification procedure. The notification unit can also provide a more intuitive notification interface by reflecting the user's feedback. This improves user convenience by customizing the notification method based on the user's past feedback. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input user feedback data into a generation AI and have the generation AI customize the notification method. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, transfer unit, monitoring unit, analysis unit, and notification unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an online application form. The transfer unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically applies existing free options to a new model by referencing the user's contract information. The monitoring unit is implemented by the specific processing unit 290 of the data processing device 12 and periodically checks the user's contract information. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and predicts the end of the discount application period using an AI algorithm. The notification unit is implemented by the control unit 46A of the smart device 14 and notifies the user by email or SMS. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, handover unit, monitoring unit, analysis unit, and notification unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and provides an online application form. The handover unit is realized by the specific processing unit 290 of the data processing device 12 and automatically applies existing free options to a new model by referencing the user's contract information. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and periodically checks the user's contract information. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the timing when the discount application period will end using an AI algorithm. The notification unit is realized by the control unit 46A of the smart glasses 214 and notifies the user by email or SMS. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, handover unit, monitoring unit, analysis unit, and notification unit, is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and provides an online application form. The handover unit is realized by the specific processing unit 290 of the data processing device 12 and automatically applies existing free options to a new model by referring to the user's contract information. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and periodically checks the user's contract information. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the end of the discount application period using an AI algorithm. The notification unit is realized by the control unit 46A of the headset type terminal 314 and notifies the user by email or SMS. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, handover unit, monitoring unit, analysis unit, and notification unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and provides an online application form. The handover unit is realized by the specific processing unit 290 of the data processing device 12 and automatically applies existing free options to a new model by referring to the user's contract information. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and periodically checks the user's contract information. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the end of the discount application period using an AI algorithm. The notification unit is realized by the control unit 46A of the robot 414 and notifies the user by email or SMS.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The reception unit can propose the optimal application reception method by taking into consideration the user's geographical location information. For example, if the user is in a specific area, application methods related to that area are preferentially proposed. The reception unit can also propose the optimal application reception time based on the user's location information. Furthermore, if the user is traveling, the reception unit can also preferentially display the application method at the store closest to the user's current location. This improves user convenience by proposing the optimal application reception method based on the user's geographical location information.

[0089] The reception unit can analyze the user's social media activity and suggest a related application reception method. For example, the reception unit can prioritize suggesting a related application method based on the content mentioned by the user on social media. The reception unit can also analyze the user's social media activity history and suggest the optimal application reception method. Furthermore, the reception unit can prioritize suggesting a related application method based on the activity of the user's friends on social media. This improves user convenience by suggesting the optimal application reception method based on the user's social media activity.

[0090] The handover unit can propose an optimal option handover method in consideration of the user's geographical location information. For example, if the user is in a specific area, options related to that area are given priority in being transferred. The handover unit can also propose an optimal handover time based on the user's location information. Furthermore, if the user is traveling, options closest to the user's current location can be given priority in being transferred. This improves user convenience by proposing an optimal option handover method based on the user's geographical location information.

[0091] The transfer unit can analyze the user's social media activity and suggest a related option transfer method. For example, related options are preferentially transferred based on the content the user mentioned on social media. The transfer unit can also analyze the user's social media activity history and suggest an optimal option transfer method. Furthermore, related options can be preferentially transferred based on the activity of the user's friends on social media. This improves user convenience by suggesting an optimal option transfer method based on the user's social media activity.

[0092] The monitoring unit can propose an optimal monitoring method by taking into account the user's geographical location information. For example, if the user is in a specific area, the monitoring unit will prioritize proposing a monitoring method related to that area. The monitoring unit can also propose an optimal monitoring time based on the user's location information. Furthermore, if the user is moving, the monitoring unit can prioritize proposing a monitoring method closest to the user's current location. This improves user convenience by proposing an optimal monitoring method based on the user's geographical location information.

[0093] The monitoring unit can analyze the user's social media activity and suggest relevant monitoring methods. For example, it can prioritize suggesting relevant monitoring methods based on the content the user has mentioned on social media. The monitoring unit can also analyze the user's social media activity history and suggest the most appropriate monitoring method. Furthermore, it can also prioritize suggesting relevant monitoring methods based on the activities of the user's friends on social media. This improves user convenience by suggesting the most appropriate monitoring method based on the user's social media activity.

[0094] The analysis unit can propose the optimal analysis method by taking into account the user's geographical location information. For example, if the user is in a specific area, it will preferentially propose an analysis method related to that area. The analysis unit can also propose the optimal analysis time based on the user's location information. Furthermore, if the user is moving, it can preferentially propose the analysis method closest to the user's current location. This improves user convenience by suggesting the optimal analysis method based on the user's geographical location information.

[0095] The analysis unit can analyze the user's social media activity and suggest relevant analysis methods. For example, it can prioritize suggest relevant analysis methods based on the content the user mentioned on social media. The analysis unit can also analyze the user's social media activity history and suggest the most appropriate analysis method. Furthermore, it can also prioritize suggesting relevant analysis methods based on the activity of the user's friends on social media. This improves user convenience by suggesting the most appropriate analysis method based on the user's social media activity.

[0096] The notification unit can propose the optimal notification method by taking into account the user's geographical location information. For example, if the user is in a specific area, the notification unit can prioritize the proposal of a notification method related to that area. The notification unit can also propose the optimal notification time based on the user's location information. Furthermore, if the user is moving, the notification unit can prioritize the proposal of a notification method closest to the user's current location. This improves user convenience by proposing the optimal notification method based on the user's geographical location information.

[0097] The notification unit can analyze the user's social media activity and suggest relevant notification methods. For example, it can prioritize suggesting relevant notification methods based on the content the user mentioned on social media. The notification unit can also analyze the user's social media activity history and suggest the most appropriate notification method. Furthermore, it can also prioritize suggesting relevant notification methods based on the activity of the user's friends on social media. This improves user convenience by suggesting the most appropriate notification method based on the user's social media activity.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The reception unit accepts applications from users to upgrade their mobile phone models. Applications from users include online applications and applications at stores. The reception unit provides an online application form and accepts applications by allowing users to enter the required information. It is also possible to build a system for accepting applications at stores. Step 2: The transfer unit transfers the existing free options to the new model based on the application received by the reception unit. The transfer unit references the user's contract information and automatically applies the existing free options to the new model. It is also possible for the user to transfer the options without performing any special procedures. Step 3: The monitoring unit monitors the discount application period for the option taken over by the takeover unit. The monitoring unit periodically checks the user's contract information and determines when the discount application period will end. The monitoring unit can also monitor the user's contract information in real time and immediately determine when the discount application period will end. Step 4: The analysis unit analyzes the timing of the end of the discount application period monitored by the monitoring unit. The analysis unit uses an AI algorithm to predict when the discount application period will end. It can also analyze the user's contract information to accurately determine when the discount application period will end. Step 5: The notification unit notifies the user based on the timing analyzed by the analysis unit. The notification unit notifies the user by email, SMS, or other methods. The notification unit can also notify the user in an appropriate manner depending on the user's desired notification method.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the 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.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0151] The data processing system 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.

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

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] [Explanation of symbols]

[0172] 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 reception unit that receives a request for a mobile model change from a user; a transfer unit that transfers the existing free options to a new model based on the application received by the reception unit; a monitoring unit that monitors the discount application period of the option taken over by the taking over unit; an analysis unit that analyzes the timing at which the discount application period monitored by the monitoring unit ends; a notification unit that notifies the user based on the timing analyzed by the analysis unit. A system characterized by:

2. The reception unit Estimate user emotions and adjust application acceptance timing based on the estimated user emotions 2. The system of claim 1.

3. The reception unit Analyze the user's past application history and select the appropriate reception method 2. The system of claim 1.

4. The reception unit When accepting applications, filter based on the user's current contract status and areas of interest.

2. The system of claim 1.

5. The reception unit When accepting an application, select the appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

6. The transfer section is Estimate the user's emotions and adjust the timing of option handover based on the estimated user emotions.

2. The system of claim 1.

7. The transfer section is When transferring data, analyze the user's past option usage history and select the appropriate transfer method.

2. The system of claim 1.

8. The transfer section is During handover, filtering is performed based on the user's current contract status and interests.

2. The system of claim 1.

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