System

Abstract

An object of a system according to an embodiment is to easily apply a discount to a subscription fee for a generated AI.SOLUTION: A system includes a discount application unit, a contract information confirmation unit, and a notification unit. The discount application unit discounts a subscription fee of the generated AI. The contract information confirmation unit confirms a condition under which the discount is applied by the discount application unit. The notification unit notifies a result of the discount application confirmed by the contract information confirmation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that the application of discounts to subscription fees by the generation AI is complicated, making it difficult for users to confirm the conditions for applying the discount.

[0005] The system according to the embodiment aims to easily apply discounts to the subscription fee for the generated AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a discount application unit, a contract information confirmation unit, and a notification unit. The discount application unit discounts the subscription fee for the generated AI. The contract information confirmation unit confirms the conditions under which the discount is applied by the discount application unit. The notification unit notifies the result of the discount application confirmed by the contract information confirmation unit. [Effects of the Invention]

[0007] The system according to the embodiment can easily apply a discount to the subscription fee for the generated AI. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The discount application system according to an embodiment of the present invention is a system that applies a discount by contracting with a specific telecommunications carrier for the subscription fee of the generation AI. As a result, the discount application system can provide a discount to the user by contracting with a specific telecommunications carrier for the subscription fee of the generation AI.

[0029] The discount application system according to the embodiment includes a discount application unit, a contract information confirmation unit, and a notification unit. The discount application unit discounts the subscription fee for the generation AI. For example, when a subscriber of telecommunications carrier A subscribes to the generation AI's service, the discount application unit discounts a certain percentage from the regular subscription fee. The discount application unit can also apply the discount based on conditions pre-agreed between telecommunications carrier A and the service provider of the generation AI. The discount application unit can also apply the discount by, for example, inputting the subscriber ID or contract number of telecommunications carrier A. The contract information confirmation unit confirms the conditions under which the discount is applied by the discount application unit. For example, the contract information confirmation unit allows the service provider of the generation AI to compare the contract information of telecommunications carrier A with a database to confirm that the subscriber is a subscriber. The contract information confirmation unit can also confirm the contract information of telecommunications carrier A and determine whether a discount is applied. The contract information confirmation unit can also allow the generation AI to automatically confirm the contract information. The notification unit notifies the result of the discount application confirmed by the contract information confirmation unit. For example, the notification unit may cause the service provider of the generation AI to send an email or message to the user to notify them that a discount has been applied. The notification unit may also notify the user when a discount has been applied. The notification unit may also be configured, for example, for the generation AI to automatically notify them. As a result, the discount application system according to the embodiment can reduce user costs by discounting the subscription fee for the generation AI. For example, users can use the generation AI's services at a lower cost. Furthermore, the service provider of the generation AI may acquire more users by partnering with telecommunications carrier A.

[0030] The discount application unit can analyze a user's past usage history and dynamically propose an optimal discount rate based on the usage history. For example, the generation AI analyzes a user's past usage history and proposes an optimal discount rate based on usage frequency and contract period. For example, a higher discount rate is provided to users who have used the service for a long time. The discount application unit also dynamically adjusts the discount rate for a service if the frequency of use of a particular service is high based on the user's usage history. For example, a higher discount rate for a service is provided to users who frequently use data analysis services. The discount application unit also analyzes a user's past payment history and gives preferential treatment to users who have not made late payments. For example, an additional discount is provided to users with a good payment history. This makes it possible to propose an optimal discount rate based on the user's usage history.

[0031] The discount application unit can vary the discount rate in stages depending on the user's contract period and frequency of use. The discount application unit varies the discount rate in stages depending on the user's contract period, for example. For example, a 5% discount is applied for a one-year contract, and a 10% discount is applied for a two-year contract. The discount application unit also adjusts the discount rate based on the user's frequency of use. For example, an additional discount is provided for users who use the service more than 10 times a month. The discount application unit also sets the discount rate in a composite manner, taking into account both the contract period and frequency of use. For example, the maximum discount rate is applied to users with long-term contracts and frequent use. This makes it possible to adjust the discount rate depending on the user's contract period and frequency of use.

[0032] The discount application unit can also apply discounts to other services of the generation AI. For example, the discount application unit also applies discounts to the generation AI's data analysis service. For example, a special discount is provided to subscribers of telecommunications carrier A when using the data analysis service. The discount application unit also applies discounts to customer support services, providing discounts to subscribers of telecommunications carrier A when they receive support. For example, a discount is provided on the fee for support tickets. The discount application unit also extends discounts to other value-added services of the generation AI (for example, cloud storage and security services). For example, a discount is provided on the fee for cloud storage. This allows discounts to be applied to other services of the generation AI.

[0033] The discount application unit may also partner with multiple telecommunications carriers and provide different discount plans for each carrier. For example, the discount application unit may partner with telecommunications carrier B and telecommunications carrier C and provide different discount plans for each carrier. For example, a 10% discount may be applied to subscribers of telecommunications carrier B, and a 15% discount may be applied to subscribers of telecommunications carrier C. The discount application unit may also set different discount plans based on the content of the partnership with each telecommunications carrier. For example, a discount on data analysis services may be provided to subscribers of telecommunications carrier A, and a discount on customer support may be provided to subscribers of telecommunications carrier B. The discount application unit may also partner with multiple telecommunications carriers to increase the number of discount plans that users can choose from. For example, the discount application unit may allow users to select the discount plan that best suits them. This allows the unit to partner with multiple telecommunications carriers and provide different discount plans.

[0034] The discount application unit can customize the conditions for discount application according to the user's contract details and usage status. The discount application unit customizes the conditions for discount application based on, for example, the user's contract details. For example, special discount conditions are provided to subscribers of premium plans. The discount application unit also dynamically changes the conditions for discount application taking into account the user's usage status. For example, discount conditions are relaxed for users who use the service frequently. The discount application unit also sets the conditions for discount application in a composite manner, taking into account both the contract details and usage status. For example, special discount conditions are provided to users who have long-term contracts and use the service frequently. This makes it possible to customize the conditions for discount application according to the user's contract details and usage status.

[0035] The discount application unit can dynamically change the conditions for discount application based on the user's lifestyle and behavioral patterns. The discount application unit dynamically changes the conditions for discount application based on, for example, the user's lifestyle. For example, special discount conditions are provided to users who work remotely. The discount application unit also analyzes the user's behavioral patterns and adjusts the conditions for discount application. For example, a nighttime discount is provided to users who often use the service at night. The discount application unit also takes into account both the lifestyle and behavioral patterns and sets the conditions for discount application in a composite manner. For example, a weekend discount is provided to users who often use the service on weekends. This makes it possible to dynamically change the conditions for discount application based on the user's lifestyle and behavioral patterns.

[0036] The discount application unit can also apply the discount application conditions when the user uses other services. The discount application unit extends the discount application conditions to, for example, when the user uses a streaming service. For example, a discount is provided when a subscriber of telecommunications carrier A uses a streaming service. The discount application unit also extends the discount application conditions to a cloud storage service, and provides a discount when a subscriber of telecommunications carrier A uses cloud storage. For example, a discount is provided on the usage fee for cloud storage. The discount application unit also extends the discount application conditions to other value-added services (for example, online education or game services). For example, a discount is provided on the tuition fees for online education. This allows the discount application conditions to be applied when the user uses other services.

[0037] The discount application unit can relax the conditions for discount application when a user participates in a specific event or campaign. For example, the discount application unit relaxes the conditions for discount application when a user participates in a specific event. For example, special discount conditions are provided to a user who participated in an event hosted by telecommunications carrier A. The discount application unit also relaxes the conditions for discount application when a user participates in a campaign. For example, discount conditions are relaxed for a user who participated in a campaign hosted by telecommunications carrier A. The discount application unit also dynamically adjusts the conditions for discount application based on the participation history of events and campaigns. For example, an additional discount is provided to a user who participated in multiple events. In this way, the conditions for discount application can be relaxed when a user participates in a specific event or campaign.

[0038] The generation AI can compare the contract information of telecommunications carrier A in real time and instantly determine whether or not a discount can be applied. For example, the generation AI compares the contract information of telecommunications carrier A with a database in real time and instantly determines whether or not a discount can be applied. For example, the generation AI makes an instant determination by inputting the subscriber ID or contract number. Furthermore, when a user subscribes to the generation AI's service, the generation AI checks the contract information of telecommunications carrier A in real time and immediately notifies the user whether or not a discount can be applied. For example, it displays whether or not a discount can be applied during the subscription process. Furthermore, the generation AI periodically updates the contract information of telecommunications carrier A and determines whether or not a discount can be applied in real time based on the latest contract information. For example, if the contract information is changed, it is updated immediately. This allows the generation AI to compare the contract information in real time and instantly determine whether or not a discount can be applied.

[0039] The discount application unit can optimize the discount application confirmation process by taking into account the user's past contract history and usage status. The discount application unit, for example, optimizes the discount application confirmation process based on the user's past contract history. For example, the confirmation process is simplified for users who have a history of receiving discounts in the past. The discount application unit also dynamically adjusts the discount application confirmation process by taking into account the user's usage status. For example, a quick confirmation process is provided for users who use the service frequently. The discount application unit also optimizes the discount application confirmation process in a comprehensive manner by taking into account both the contract history and usage status. For example, the confirmation process is given priority for users with long-term contracts and frequent usage. This makes it possible to optimize the confirmation process by taking into account the user's past contract history and usage status.

[0040] The discount application unit can unify the confirmation process for discount application in cooperation with other telecommunications carriers and service providers. The discount application unit, for example, can unify the confirmation process for discount application in cooperation with other telecommunications carriers and service providers. For example, the discount application unit can centrally manage contract information for multiple telecommunications carriers and simplify the confirmation process. The discount application unit also sets a unified confirmation process based on the details of cooperation with each telecommunications carrier and service provider. For example, it can use a common contract information format. The discount application unit also works with multiple telecommunications carriers and service providers to increase the number of confirmation processes that users can select. For example, it can allow users to select the confirmation process that is most suitable for them. This makes it possible to unify the confirmation process in cooperation with other telecommunications carriers and service providers.

[0041] The discount application unit can display the confirmation result of discount application on the user's dashboard or app in real time, thereby increasing transparency. The discount application unit, for example, displays the confirmation result of discount application on the user's dashboard in real time. For example, the confirmation result is reflected immediately. The discount application unit can also display the confirmation result of discount application on the user's app in real time, thereby increasing transparency. For example, the confirmation result is notified within the app. The discount application unit can also optimize the interface of the dashboard or app so that the user can easily check the confirmation result. For example, the confirmation result is displayed visually. This allows the confirmation result to be displayed in real time, thereby increasing transparency.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The discount application unit can adjust the discount rate based on the user's health data. For example, by analyzing data obtained from a fitness tracker or a smart watch, the discount application unit can provide an additional discount to a user who is in good health. The discount application unit can also provide a preferential discount rate based on exercise data if the user exercises regularly. For example, a special discount can be provided to a user who achieves a certain amount of exercise per week. The discount application unit can also further increase the discount rate by having the user participate in a health promotion program based on the user's health data. For example, an additional discount can be provided to a user who has undergone a health checkup. In this way, the discount rate can be adjusted based on the user's health data.

[0044] The discount application unit can adjust the discount rate based on the user's purchasing history. For example, an additional discount can be provided to a user who has purchased expensive items in the past. The discount application unit can also give a preferential discount rate for items in a specific category to a user who frequently purchases items in that category. For example, a discount on electronic devices can be provided to a user who frequently purchases electronic devices. The discount application unit can also provide a special discount to a user who has made purchases of more than a certain amount within a specific period based on the user's purchasing history. For example, an additional discount can be provided to a user who has made purchases of more than a certain amount within one month. In this way, the discount rate can be adjusted based on the user's purchasing history.

[0045] The discount application unit can adjust the discount rate based on the user's location information. For example, a user who lives in a specific area can be offered a local discount. The discount application unit can also give a preferential discount rate based on the user's visit history when the user visits a specific store or facility. For example, a user who frequently visits a specific store can be offered a discount at that store. The discount application unit can also further increase the discount rate by participating in a specific event or campaign based on the user's location information. For example, an additional discount can be offered to a user who participates in a specific event. In this way, the discount rate can be adjusted based on the user's location information.

[0046] The discount application unit can adjust the discount rate based on the user's social media activity. For example, an additional discount can be provided to a user who posts using a specific hashtag. The discount application unit can also provide a preferential discount rate based on the review history of a user who has posted positive reviews about a specific brand or service. For example, a special discount can be provided to a user who has posted many positive reviews. The discount application unit can also further increase the discount rate by having the user participate in a specific campaign based on the user's social media activity. For example, an additional discount can be provided to a user who participates in a campaign. In this way, the discount rate can be adjusted based on the user's social media activity.

[0047] The discount application unit can adjust the discount rate based on the energy consumption data of the user. For example, an additional discount is provided to a user who consumes less energy. The discount application unit can also provide a preferential discount rate based on the usage data of an energy-efficient home appliance if the user uses such appliance. For example, a special discount is provided to a user who uses an energy-efficient refrigerator. The discount application unit can also further increase the discount rate by taking action to reduce energy consumption based on the energy consumption data of the user. For example, an additional discount is provided to a user who reduces energy consumption within a certain period of time. In this way, the discount rate can be adjusted based on the energy consumption data of the user.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The discount application unit discounts the subscription fee for the generation AI. For example, when a subscriber of telecommunications carrier A subscribes to the generation AI service, a certain percentage is discounted from the regular subscription fee. The discount can also be applied based on conditions agreed upon in advance between telecommunications carrier A and the service provider of the generation AI. Furthermore, the discount can also be applied by entering the subscriber ID or contract number of telecommunications carrier A. Step 2: The contract information confirmation unit checks the conditions under which the discount will be applied by the discount application unit. For example, the service provider of the generation AI checks the contract information of telecommunications carrier A against a database to confirm that the person is a subscriber. It can also check the contract information of telecommunications carrier A to determine whether the discount is applicable. Furthermore, the generation AI can automatically check the contract information. Step 3: The notification unit notifies the user of the result of the discount application confirmed by the contract information confirmation unit. For example, the service provider of the generation AI sends an email or message to the user to notify them that the discount has been applied. The user can also be notified when the discount has been applied. Furthermore, the generation AI can automatically notify them.

[0050] (Example 2) The discount application system according to an embodiment of the present invention is a system that applies a discount by contracting with a specific telecommunications carrier for the subscription fee of the generation AI. As a result, the discount application system can provide a discount to the user by contracting with a specific telecommunications carrier for the subscription fee of the generation AI.

[0051] The discount application system according to the embodiment includes a discount application unit, a contract information confirmation unit, and a notification unit. The discount application unit discounts the subscription fee for the generation AI. For example, when a subscriber of telecommunications carrier A subscribes to the generation AI's service, the discount application unit discounts a certain percentage from the regular subscription fee. The discount application unit can also apply the discount based on conditions pre-agreed between telecommunications carrier A and the service provider of the generation AI. The discount application unit can also apply the discount by, for example, inputting the subscriber ID or contract number of telecommunications carrier A. The contract information confirmation unit confirms the conditions under which the discount is applied by the discount application unit. For example, the contract information confirmation unit allows the service provider of the generation AI to compare the contract information of telecommunications carrier A with a database to confirm that the subscriber is a subscriber. The contract information confirmation unit can also confirm the contract information of telecommunications carrier A and determine whether a discount is applied. The contract information confirmation unit can also allow the generation AI to automatically confirm the contract information. The notification unit notifies the result of the discount application confirmed by the contract information confirmation unit. For example, the notification unit may cause the service provider of the generation AI to send an email or message to the user to notify them that a discount has been applied. The notification unit may also notify the user when a discount has been applied. The notification unit may also be configured, for example, for the generation AI to automatically notify them. As a result, the discount application system according to the embodiment can reduce user costs by discounting the subscription fee for the generation AI. For example, users can use the generation AI's services at a lower cost. Furthermore, the service provider of the generation AI may acquire more users by partnering with telecommunications carrier A.

[0052] The discount application unit can analyze a user's past usage history and dynamically propose an optimal discount rate based on the usage history. For example, the generation AI analyzes a user's past usage history and proposes an optimal discount rate based on usage frequency and contract period. For example, a higher discount rate is provided to users who have used the service for a long time. The discount application unit also dynamically adjusts the discount rate for a service if the frequency of use of a particular service is high based on the user's usage history. For example, a higher discount rate for a service is provided to users who frequently use data analysis services. The discount application unit also analyzes a user's past payment history and gives preferential treatment to users who have not made late payments. For example, an additional discount is provided to users with a good payment history. This makes it possible to propose an optimal discount rate based on the user's usage history.

[0053] The discount application unit can vary the discount rate in stages depending on the user's contract period and frequency of use. The discount application unit varies the discount rate in stages depending on the user's contract period, for example. For example, a 5% discount is applied for a one-year contract, and a 10% discount is applied for a two-year contract. The discount application unit also adjusts the discount rate based on the user's frequency of use. For example, an additional discount is provided for users who use the service more than 10 times a month. The discount application unit also sets the discount rate in a composite manner, taking into account both the contract period and frequency of use. For example, the maximum discount rate is applied to users with long-term contracts and frequent use. This makes it possible to adjust the discount rate depending on the user's contract period and frequency of use.

[0054] The discount application unit can use the emotion estimation function to evaluate how satisfied the user is with the discount and adjust the discount rate based on the evaluation result. The discount application unit, for example, uses the emotion estimation function to evaluate how satisfied the user is with the discount in real time. For example, it analyzes the user's facial expressions and voice to calculate a satisfaction score. The discount application unit also dynamically adjusts the discount rate based on the user's satisfaction score. For example, it provides an additional discount to a user with high satisfaction. The discount application unit also makes discount suggestions to improve the user's satisfaction based on the emotion estimation data. For example, it provides a special discount offer to a user with low satisfaction. This makes it possible to adjust the discount rate based on the user's satisfaction.

[0055] The discount application unit can also apply discounts to other services of the generation AI. For example, the discount application unit also applies discounts to the generation AI's data analysis service. For example, a special discount is provided to subscribers of telecommunications carrier A when using the data analysis service. The discount application unit also applies discounts to customer support services, providing discounts to subscribers of telecommunications carrier A when they receive support. For example, a discount is provided on the fee for support tickets. The discount application unit also extends discounts to other value-added services of the generation AI (for example, cloud storage and security services). For example, a discount is provided on the fee for cloud storage. This allows discounts to be applied to other services of the generation AI.

[0056] The discount application unit may also partner with multiple telecommunications carriers and provide different discount plans for each carrier. For example, the discount application unit may partner with telecommunications carrier B and telecommunications carrier C and provide different discount plans for each carrier. For example, a 10% discount may be applied to subscribers of telecommunications carrier B, and a 15% discount may be applied to subscribers of telecommunications carrier C. The discount application unit may also set different discount plans based on the content of the partnership with each telecommunications carrier. For example, a discount on data analysis services may be provided to subscribers of telecommunications carrier A, and a discount on customer support may be provided to subscribers of telecommunications carrier B. The discount application unit may also partner with multiple telecommunications carriers to increase the number of discount plans that users can choose from. For example, the discount application unit may allow users to select the discount plan that best suits them. This allows the unit to partner with multiple telecommunications carriers and provide different discount plans.

[0057] The discount application unit uses the emotion estimation function to propose the discount plan that the user is most interested in, thereby increasing the number of options available. The discount application unit, for example, uses the emotion estimation function to propose the discount plan that the user is most interested in. For example, it analyzes the user's facial expression and voice to calculate an interest score. The discount application unit also proposes the most suitable discount plan based on the user's interest score. For example, it preferentially displays plans with a high level of interest. The discount application unit also increases the number of options for discount plans that the user can select based on the emotion estimation data. For example, it proposes multiple discount plans so that the user can select the plan that is most suitable for them. This makes it possible to propose the most suitable discount plan based on the user's interests.

[0058] The discount application unit can customize the conditions for discount application according to the user's contract details and usage status. The discount application unit customizes the conditions for discount application based on, for example, the user's contract details. For example, special discount conditions are provided to subscribers of premium plans. The discount application unit also dynamically changes the conditions for discount application taking into account the user's usage status. For example, discount conditions are relaxed for users who use the service frequently. The discount application unit also sets the conditions for discount application in a composite manner, taking into account both the contract details and usage status. For example, special discount conditions are provided to users who have long-term contracts and use the service frequently. This makes it possible to customize the conditions for discount application according to the user's contract details and usage status.

[0059] The discount application unit can dynamically change the conditions for discount application based on the user's lifestyle and behavioral patterns. The discount application unit dynamically changes the conditions for discount application based on, for example, the user's lifestyle. For example, special discount conditions are provided to users who work remotely. The discount application unit also analyzes the user's behavioral patterns and adjusts the conditions for discount application. For example, a nighttime discount is provided to users who often use the service at night. The discount application unit also takes into account both the lifestyle and behavioral patterns and sets the conditions for discount application in a composite manner. For example, a weekend discount is provided to users who often use the service on weekends. This makes it possible to dynamically change the conditions for discount application based on the user's lifestyle and behavioral patterns.

[0060] The discount application unit can use the emotion estimation function to evaluate the degree to which the user is satisfied with the conditions for discount application and adjust the conditions based on the result. The discount application unit, for example, uses the emotion estimation function to evaluate in real time the degree to which the user is satisfied with the conditions for discount application. For example, it analyzes the user's facial expression and voice to calculate a satisfaction score. The discount application unit also dynamically adjusts the conditions for discount application based on the user's satisfaction score. For example, it relaxes the conditions when the satisfaction level is low. The discount application unit also proposes discount conditions to improve the user's satisfaction level based on the emotion estimation data. For example, it provides special discount conditions to users with low satisfaction levels. This makes it possible to adjust the conditions for discount application based on the user's satisfaction level.

[0061] The discount application unit can also apply the discount application conditions when the user uses other services. The discount application unit extends the discount application conditions to, for example, when the user uses a streaming service. For example, a discount is provided when a subscriber of telecommunications carrier A uses a streaming service. The discount application unit also extends the discount application conditions to a cloud storage service, and provides a discount when a subscriber of telecommunications carrier A uses cloud storage. For example, a discount is provided on the usage fee for cloud storage. The discount application unit also extends the discount application conditions to other value-added services (for example, online education or game services). For example, a discount is provided on the tuition fees for online education. This allows the discount application conditions to be applied when the user uses other services.

[0062] The discount application unit can relax the conditions for discount application when a user participates in a specific event or campaign. For example, the discount application unit relaxes the conditions for discount application when a user participates in a specific event. For example, special discount conditions are provided to a user who participated in an event hosted by telecommunications carrier A. The discount application unit also relaxes the conditions for discount application when a user participates in a campaign. For example, discount conditions are relaxed for a user who participated in a campaign hosted by telecommunications carrier A. The discount application unit also dynamically adjusts the conditions for discount application based on the participation history of events and campaigns. For example, an additional discount is provided to a user who participated in multiple events. In this way, the conditions for discount application can be relaxed when a user participates in a specific event or campaign.

[0063] The discount application unit uses the emotion estimation function to propose conditions that are most acceptable to the user, thereby increasing the flexibility of the conditions. The discount application unit, for example, uses the emotion estimation function to propose conditions for discount application that are most acceptable to the user. For example, it analyzes the user's facial expression and voice to calculate an acceptability score. The discount application unit also dynamically adjusts the conditions for discount application based on the user's acceptability score. For example, it prioritizes proposing conditions that are highly acceptable. The discount application unit also proposes conditions that are most acceptable to the user based on the emotion estimation data, thereby increasing the flexibility of the conditions. For example, it proposes multiple conditions and allows the user to select. This allows it to propose conditions that are most acceptable to the user, thereby increasing the flexibility of the conditions.

[0064] The generation AI can compare the contract information of telecommunications carrier A in real time and instantly determine whether or not a discount can be applied. For example, the generation AI compares the contract information of telecommunications carrier A with a database in real time and instantly determines whether or not a discount can be applied. For example, the generation AI makes an instant determination by inputting the subscriber ID or contract number. Furthermore, when a user subscribes to the generation AI's service, the generation AI checks the contract information of telecommunications carrier A in real time and immediately notifies the user whether or not a discount can be applied. For example, it displays whether or not a discount can be applied during the subscription process. Furthermore, the generation AI periodically updates the contract information of telecommunications carrier A and determines whether or not a discount can be applied in real time based on the latest contract information. For example, if the contract information is changed, it is updated immediately. This allows the generation AI to compare the contract information in real time and instantly determine whether or not a discount can be applied.

[0065] The discount application unit can optimize the discount application confirmation process by taking into account the user's past contract history and usage status. The discount application unit, for example, optimizes the discount application confirmation process based on the user's past contract history. For example, the confirmation process is simplified for users who have a history of receiving discounts in the past. The discount application unit also dynamically adjusts the discount application confirmation process by taking into account the user's usage status. For example, a quick confirmation process is provided for users who use the service frequently. The discount application unit also optimizes the discount application confirmation process in a comprehensive manner by taking into account both the contract history and usage status. For example, the confirmation process is given priority for users with long-term contracts and frequent usage. This makes it possible to optimize the confirmation process by taking into account the user's past contract history and usage status.

[0066] The discount application unit uses the emotion estimation function to evaluate the degree of satisfaction of the user with the discount application confirmation process, and can improve the confirmation process based on the result. The discount application unit, for example, uses the emotion estimation function to evaluate in real time the degree of satisfaction of the user with the discount application confirmation process. For example, it analyzes the user's facial expressions and voice to calculate a satisfaction score. The discount application unit also dynamically improves the confirmation process based on the user's satisfaction score. For example, it simplifies the process if satisfaction is low. The discount application unit also suggests a confirmation process to improve user satisfaction based on the emotion estimation data. For example, it provides special support to users with low satisfaction. This makes it possible to improve the confirmation process based on the user's satisfaction.

[0067] The discount application unit can unify the confirmation process for discount application in cooperation with other telecommunications carriers and service providers. The discount application unit, for example, can unify the confirmation process for discount application in cooperation with other telecommunications carriers and service providers. For example, the discount application unit can centrally manage contract information for multiple telecommunications carriers and simplify the confirmation process. The discount application unit also sets a unified confirmation process based on the details of cooperation with each telecommunications carrier and service provider. For example, it can use a common contract information format. The discount application unit also works with multiple telecommunications carriers and service providers to increase the number of confirmation processes that users can select. For example, it can allow users to select the confirmation process that is most suitable for them. This makes it possible to unify the confirmation process in cooperation with other telecommunications carriers and service providers.

[0068] The discount application unit can display the confirmation result of discount application on the user's dashboard or app in real time, thereby increasing transparency. The discount application unit, for example, displays the confirmation result of discount application on the user's dashboard in real time. For example, the confirmation result is reflected immediately. The discount application unit can also display the confirmation result of discount application on the user's app in real time, thereby increasing transparency. For example, the confirmation result is notified within the app. The discount application unit can also optimize the interface of the dashboard or app so that the user can easily check the confirmation result. For example, the confirmation result is displayed visually. This allows the confirmation result to be displayed in real time, thereby increasing transparency.

[0069] The discount application unit uses the emotion estimation function to propose a confirmation method that is easiest for the user to understand, thereby improving the usability of the confirmation process. The discount application unit, for example, uses the emotion estimation function to propose a confirmation method that is easiest for the user to understand. For example, it analyzes the user's facial expressions and voice to calculate a comprehension score. The discount application unit also dynamically adjusts the confirmation process based on the user's comprehension score. For example, it simplifies the process if the comprehension level is low. The discount application unit also proposes a confirmation method that is easiest for the user to understand based on the emotion estimation data, thereby improving the usability of the confirmation process. For example, it proposes multiple confirmation methods and allows the user to select one. This makes it possible to propose a confirmation method that is easiest for the user to understand, thereby improving the usability of the confirmation process.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The discount application unit can adjust the discount rate based on the user's health data. For example, by analyzing data obtained from a fitness tracker or a smart watch, the discount application unit can provide an additional discount to a user who is in good health. The discount application unit can also provide a preferential discount rate based on exercise data if the user exercises regularly. For example, a special discount can be provided to a user who achieves a certain amount of exercise per week. The discount application unit can also further increase the discount rate by having the user participate in a health promotion program based on the user's health data. For example, an additional discount can be provided to a user who has undergone a health checkup. In this way, the discount rate can be adjusted based on the user's health data.

[0072] The discount application unit can use the user's emotion estimation function to evaluate the user's level of interest in a particular discount plan and suggest a discount plan based on the result. For example, the discount application unit can analyze the user's facial expression and voice to calculate an interest score. The discount application unit can also suggest an optimal discount plan based on the user's interest score. For example, it can preferentially display plans with a high level of interest. The discount application unit can also increase the number of discount plan options available to the user based on the emotion estimation data. For example, it can suggest multiple discount plans so that the user can select the plan that best suits them. This makes it possible to suggest an optimal discount plan based on the user's interests.

[0073] The discount application unit can adjust the discount rate based on the user's purchasing history. For example, an additional discount can be provided to a user who has purchased expensive items in the past. The discount application unit can also give a preferential discount rate for items in a specific category to a user who frequently purchases items in that category. For example, a discount on electronic devices can be provided to a user who frequently purchases electronic devices. The discount application unit can also provide a special discount to a user who has made purchases of more than a certain amount within a specific period based on the user's purchasing history. For example, an additional discount can be provided to a user who has made purchases of more than a certain amount within one month. In this way, the discount rate can be adjusted based on the user's purchasing history.

[0074] The discount application unit can use the emotion estimation function to evaluate the degree to which a user is satisfied with the discount application conditions and adjust the conditions based on the results. For example, it can analyze the user's facial expressions and voice to calculate a satisfaction score. The discount application unit also dynamically adjusts the discount application conditions based on the user's satisfaction score. For example, it can relax the conditions if the satisfaction level is low. The discount application unit can also suggest discount conditions to improve the user's satisfaction level based on the emotion estimation data. For example, it can provide special discount conditions to users with low satisfaction levels. This makes it possible to adjust the discount application conditions based on the user's satisfaction level.

[0075] The discount application unit can adjust the discount rate based on the user's location information. For example, a user who lives in a specific area can be offered a local discount. The discount application unit can also give a preferential discount rate based on the user's visit history when the user visits a specific store or facility. For example, a user who frequently visits a specific store can be offered a discount at that store. The discount application unit can also further increase the discount rate by participating in a specific event or campaign based on the user's location information. For example, an additional discount can be offered to a user who participates in a specific event. In this way, the discount rate can be adjusted based on the user's location information.

[0076] The discount application unit can use the emotion estimation function to evaluate the user's satisfaction with the discount application confirmation process and improve the confirmation process based on the results. For example, it can analyze the user's facial expressions and voice to calculate a satisfaction score. The discount application unit also dynamically improves the confirmation process based on the user's satisfaction score. For example, it can simplify the process if satisfaction is low. The discount application unit can also suggest a confirmation process to improve user satisfaction based on the emotion estimation data. For example, it can provide special support to users with low satisfaction. This makes it possible to improve the confirmation process based on the user's satisfaction.

[0077] The discount application unit can adjust the discount rate based on the user's social media activity. For example, an additional discount can be provided to a user who posts using a specific hashtag. The discount application unit can also provide a preferential discount rate based on the review history of a user who has posted positive reviews about a specific brand or service. For example, a special discount can be provided to a user who has posted many positive reviews. The discount application unit can also further increase the discount rate by having the user participate in a specific campaign based on the user's social media activity. For example, an additional discount can be provided to a user who participates in a campaign. In this way, the discount rate can be adjusted based on the user's social media activity.

[0078] The discount application unit uses the emotion estimation function to propose conditions that are most acceptable to the user, thereby increasing the flexibility of the conditions. For example, it analyzes the user's facial expressions and voice to calculate an acceptability score. The discount application unit also dynamically adjusts the conditions for discount application based on the user's acceptability score. For example, it prioritizes proposing conditions that are highly acceptable. The discount application unit also proposes conditions that are most acceptable to the user based on the emotion estimation data, thereby increasing the flexibility of the conditions. For example, it proposes multiple conditions and allows the user to select from them. This allows it to propose conditions that are most acceptable to the user, thereby increasing the flexibility of the conditions.

[0079] The discount application unit can adjust the discount rate based on the energy consumption data of the user. For example, an additional discount is provided to a user who consumes less energy. The discount application unit can also provide a preferential discount rate based on the usage data of an energy-efficient home appliance if the user uses such appliance. For example, a special discount is provided to a user who uses an energy-efficient refrigerator. The discount application unit can also further increase the discount rate by taking action to reduce energy consumption based on the energy consumption data of the user. For example, an additional discount is provided to a user who reduces energy consumption within a certain period of time. In this way, the discount rate can be adjusted based on the energy consumption data of the user.

[0080] The discount application unit can use the emotion estimation function to evaluate the degree to which a user is satisfied with the discount application conditions and adjust the conditions based on the results. For example, it can analyze the user's facial expressions and voice to calculate a satisfaction score. The discount application unit also dynamically adjusts the discount application conditions based on the user's satisfaction score. For example, it can relax the conditions if the satisfaction level is low. The discount application unit can also suggest discount conditions to improve the user's satisfaction level based on the emotion estimation data. For example, it can provide special discount conditions to users with low satisfaction levels. This makes it possible to adjust the discount application conditions based on the user's satisfaction level.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The discount application unit discounts the subscription fee for the generation AI. For example, when a subscriber of telecommunications carrier A subscribes to the generation AI service, a certain percentage is discounted from the regular subscription fee. The discount can also be applied based on conditions agreed upon in advance between telecommunications carrier A and the service provider of the generation AI. Furthermore, the discount can also be applied by entering the subscriber ID or contract number of telecommunications carrier A. Step 2: The contract information confirmation unit checks the conditions under which the discount will be applied by the discount application unit. For example, the service provider of the generation AI checks the contract information of telecommunications carrier A against a database to confirm that the person is a subscriber. It can also check the contract information of telecommunications carrier A to determine whether the discount is applicable. Furthermore, the generation AI can automatically check the contract information. Step 3: The notification unit notifies the user of the result of the discount application confirmed by the contract information confirmation unit. For example, the service provider of the generation AI sends an email or message to the user to notify them that the discount has been applied. The user can also be notified when the discount has been applied. Furthermore, the generation AI can automatically notify them.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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, in order to avoid confusion and to 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.

[0149] 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. [Explanation of symbols]

[0150] 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 discount application unit that discounts the subscription fee of the generation AI; a contract information confirmation unit that confirms the conditions under which the discount is applied by the discount application unit; a notification unit that notifies the result of the discount application confirmed by the contract information confirmation unit. A system characterized by:

2. The discount application unit Analyzes the user's past usage history and dynamically proposes the optimal discount rate based on said usage history.

2. The system of claim 1.

3. The discount application unit Discounts also apply to other Generative AI services 2. The system of claim 1.

4. The discount application unit Customize discount application conditions based on user contract details and usage status 2. The system of claim 1.

5. The generated AI is The contract information of telecommunications carrier A is collated in real time, and the applicability of the discount is immediately determined.

2. The system of claim 1.

6. The discount application unit Evaluate how satisfied users are with the discount and adjust the discount percentage accordingly 2. The system of claim 1.

7. The discount application unit Evaluate the degree to which the user is satisfied with the discount conditions and adjust the conditions based on the results.

2. The system of claim 1.

8. The discount application unit Propose the most understandable confirmation method for users and improve the usability of the confirmation process 2. The system of claim 1.

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