System

Abstract

An object of a system according to an embodiment is to provide a flexible fee plan based on a data usage amount of a user and the number of connected terminals.SOLUTION: A system according to an embodiment includes a data usage amount acquisition unit, a connection terminal number acquisition unit, and a fee plan generation unit. The data usage amount acquisition unit acquires a data usage amount of a user. The connection terminal number acquisition unit acquires the number of connection terminals of a user. The fee plan generation unit generates a fee plan based on the information acquired by the data usage amount acquisition unit and the connection terminal number acquisition 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] With conventional technology, there is a risk that operators' revenues will decrease as users reduce their data usage.

[0005] The system according to the embodiment aims to provide flexible pricing plans based on the amount of data used by the user and the number of connected terminals. [Means for solving the problem]

[0006] The system according to the embodiment includes a data usage acquisition unit, a connected terminal number acquisition unit, and a rate plan generation unit. The data usage acquisition unit acquires a user's data usage. The connected terminal number acquisition unit acquires the number of connected terminals of the user. The rate plan generation unit generates a rate plan based on the information acquired by the data usage acquisition unit and the connected terminal number acquisition unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide flexible pricing plans based on the amount of data used by the user and the number of connected terminals. [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 next-generation mobile phone system according to an embodiment of the present invention is a system that provides flexible pricing plans based on data usage and the number of connected devices, thereby enabling the next-generation mobile phone system to provide flexible pricing plans to maintain and increase ARPU.

[0029] A next-generation mobile phone system according to an embodiment includes a data usage acquisition unit, a connected terminal number acquisition unit, and a rate plan generation unit. The data usage acquisition unit acquires the user's data usage. For example, the data usage acquisition unit measures monthly data usage. The data usage acquisition unit can also measure real-time data usage. The data usage acquisition unit can also measure data usage for each specific application. For example, the data usage acquisition unit measures the amount of data used by the user over a month and records it as monthly data usage. The real-time data usage is measured and displayed in real time as the amount of data currently used by the user. The data usage for each specific application is measured and recorded individually as the amount of data used by each application. The connected terminal number acquisition unit acquires the number of connected terminals of the user. For example, the connected terminal number acquisition unit measures the number of simultaneously connected terminals. The connected terminal number acquisition unit can also measure the number of registered terminals. The connected terminal number acquisition unit can also measure the connection status for each specific device. For example, the connected terminal number acquisition unit measures the number of terminals simultaneously connected by the user and records it as the number of simultaneously connected terminals. The number of registered terminals is the number of all terminals registered by the user, and is recorded. The connection status of each specific device is measured and recorded individually, indicating whether each device is connected. The rate plan generation unit generates a rate plan based on the information acquired by the data usage acquisition unit and the connected terminal number acquisition unit. For example, the rate plan generation unit generates a rate plan based on data usage. The rate plan generation unit can also generate a rate plan based on the number of connected terminals. The rate plan generation unit can also generate a rate plan based on both data usage and the number of connected terminals. For example, the rate plan generation unit sets a rate plan based on the user's monthly data usage, and provides a discount the greater the data usage. A rate plan based on the number of connected terminals sets an additional fee the greater the number of connected terminals. A rate plan based on both data usage and the number of connected terminals generates an optimal rate plan by taking into account the balance between data usage and the number of connected terminals.This allows next-generation mobile phone systems to offer flexible pricing plans based on data usage and the number of connected devices. For example, if a user uses a smartphone and multiple IoT devices, offering pricing plans based on the number of connected devices can maintain or increase ARPU. In addition, offering pricing plans based on the number of connected devices to users with low data usage can prevent a decrease in revenue.

[0030] The rate plan generation unit can provide discounts according to the data usage acquired by the data usage acquisition unit. The rate plan generation unit, for example, provides discounts to users with large data usage. For example, a 10% discount is provided to users whose monthly data usage exceeds 10 GB. The rate plan generation unit can also provide discounts to users with small data usage. For example, a 5% discount is provided to users whose monthly data usage is less than 1 GB. The rate plan generation unit can also provide discounts according to the data usage of a specific application. For example, a discount for a specific streaming service is provided to users who use a large amount of data for that service. In this way, user satisfaction can be improved by providing discounts according to data usage.

[0031] The rate plan generation unit can set an additional fee according to the number of connected terminals acquired by the connected terminal number acquisition unit. The rate plan generation unit, for example, sets an additional fee for a user with a large number of connected terminals. For example, if the number of connected terminals exceeds three, an additional fee is set for each connected terminal. The rate plan generation unit can also set an additional fee for a specific device. For example, an additional fee is set for a smart home device. The rate plan generation unit can also set an additional fee according to the number of simultaneously connected terminals. For example, if more than five terminals are connected simultaneously, an additional fee is set for each connected terminal. In this way, by setting an additional fee according to the number of connected terminals, it is possible to prevent a decrease in income.

[0032] The rate plan generation unit includes a device usage history acquisition unit that proposes an optimal rate plan based on the user's device usage history. The device usage history acquisition unit, for example, records the user's device usage time. For example, the device usage history acquisition unit individually records the usage time of each device. The device usage history acquisition unit can also record applications used by the user. For example, the device usage history acquisition unit records the usage time of each application. The device usage history acquisition unit can also analyze the user's device usage pattern. For example, the device usage history acquisition unit records a pattern in which the user uses a specific device during a specific time period. The rate plan generation unit proposes an optimal rate plan based on the device usage history acquired by the device usage history acquisition unit. For example, if the user frequently uses a specific device, the rate plan generation unit can provide a discount for the device. Furthermore, if the user frequently uses a specific application, the rate plan generation unit can also provide a discount for the application. The rate plan generation unit can also propose an optimal rate plan based on the user's device usage pattern. For example, if the user frequently uses the device at night, a discount on nighttime data usage can be provided. This allows the system to propose the most suitable rate plan based on the device usage history, thereby improving user satisfaction.

[0033] The pricing plan generation unit includes a lifestyle data acquisition unit that provides a customizable pricing plan based on lifestyle data of the user. The lifestyle data acquisition unit, for example, records the user's lifestyle habits. For example, the lifestyle data acquisition unit records the user's wake-up time and bedtime. The lifestyle data acquisition unit can also record the user's hobbies and preferences. For example, the lifestyle data acquisition unit records the user's favorite music and movie genres. The lifestyle data acquisition unit can also record the user's consumption behavior. For example, the lifestyle data acquisition unit records a history of products purchased by the user. The pricing plan generation unit provides a customizable pricing plan based on the lifestyle data acquired by the lifestyle data acquisition unit. For example, the pricing plan generation unit adjusts data usage according to the user's wake-up time. The pricing plan generation unit can also provide discounts on specific services according to the user's hobbies and preferences. The pricing plan generation unit can also provide specific promotions based on the user's consumption behavior. For example, if a user frequently purchases products from a specific brand, a discount on that brand can be provided. In this way, by providing a customizable pricing plan based on lifestyle data, user satisfaction can be improved.

[0034] The rate plan generation unit includes a shared data plan acquisition unit that provides a shared data plan intended for use by a family or group. The shared data plan acquisition unit, for example, registers family members and group members. For example, the shared data plan acquisition unit registers family members and records the data usage of each member. The shared data plan acquisition unit can also register group members and record the data usage of each member. The shared data plan acquisition unit can also manage the data capacity of the shared data plan. For example, the shared data plan acquisition unit manages the data usage of the entire family and sets an additional fee if the data capacity is exceeded. The rate plan generation unit provides the shared data plan based on the information acquired by the shared data plan acquisition unit. For example, the rate plan generation unit sets a rate plan based on the data usage of the entire family. The rate plan generation unit can also set a rate plan based on the data usage of the entire group. The rate plan generation unit can also adjust the fee according to the data capacity of the shared data plan. For example, if the data usage of the entire family is high, an additional fee is set. In this way, by providing a shared data plan intended for use by a family or group, it is possible to efficiently manage data usage while reducing costs.

[0035] The rate plan generation unit includes an algorithm that automatically proposes an optimal rate plan based on the user's device usage history. The algorithm, for example, analyzes the user's device usage history and proposes an optimal rate plan. For example, the algorithm identifies devices and applications that the user frequently uses and proposes a rate plan based on the devices and applications. The algorithm can also analyze the user's device usage patterns and propose an optimal rate plan. For example, if the user frequently uses the device during a specific time period, the algorithm proposes a rate plan specific to that time period. The algorithm can also predict future device usage based on the user's device usage history and propose a rate plan based on the prediction. For example, the algorithm predicts device usage for the next month based on past device usage history and proposes an optimal rate plan based on the prediction. This allows the system to automatically propose an optimal rate plan based on the device usage history, thereby improving user satisfaction.

[0036] The pricing plan generation unit includes a lifestyle data acquisition unit that provides a customizable pricing plan according to the user's lifestyle. The lifestyle data acquisition unit, for example, records the user's lifestyle habits. For example, the lifestyle data acquisition unit records the user's wake-up time and bedtime. The lifestyle data acquisition unit can also record the user's hobbies and preferences. For example, the lifestyle data acquisition unit records the user's favorite music and movie genres. The lifestyle data acquisition unit can also record the user's consumption behavior. For example, the lifestyle data acquisition unit records the history of products purchased by the user. The pricing plan generation unit provides a customizable pricing plan based on the lifestyle data acquired by the lifestyle data acquisition unit. For example, the pricing plan generation unit adjusts data usage according to the user's wake-up time. The pricing plan generation unit can also provide discounts on specific services according to the user's hobbies and preferences. The pricing plan generation unit can also provide specific promotions based on the user's consumption behavior. For example, if a user frequently purchases products from a specific brand, a discount on that brand can be provided. This allows for providing customizable pricing plans according to lifestyles, thereby improving user satisfaction.

[0037] The rate plan generation unit may include a shared data plan acquisition unit that provides a shared data plan intended for use by a family or group. The shared data plan acquisition unit, for example, registers family and group members. For example, the shared data plan acquisition unit registers family members and records the data usage of each member. The shared data plan acquisition unit can also register group members and record the data usage of each member. The shared data plan acquisition unit can also manage the data capacity of the shared data plan. For example, the shared data plan acquisition unit manages the data usage of the entire family and sets an additional fee if the data capacity is exceeded. The rate plan generation unit provides the shared data plan based on the information acquired by the shared data plan acquisition unit. For example, the rate plan generation unit sets a rate plan based on the data usage of the entire family. The rate plan generation unit can also set a rate plan based on the data usage of the entire group. The rate plan generation unit can also adjust the fee according to the data capacity of the shared data plan. For example, if the data usage of the entire family is high, an additional fee is set. This allows for the provision of shared data plans designed for use by families or groups, reducing costs and efficiently managing data usage.

[0038] The rate plan generation unit may include an industry-specific plan acquisition unit that provides rate plans specialized for different industries. The industry-specific plan acquisition unit collects, for example, data related to a specific industry. For example, the industry-specific plan acquisition unit collects data related to the medical industry and provides a rate plan specialized for the medical industry. The industry-specific plan acquisition unit may also collect data related to the education industry and provide a rate plan specialized for the education industry. The industry-specific plan acquisition unit may also collect data related to the manufacturing industry and provide a rate plan specialized for the manufacturing industry. For example, the industry-specific plan acquisition unit collects data on data usage and the number of connected devices in the medical industry and provides special benefits and pricing for the medical industry. In the education industry, data related to online classes and remote learning is collected and special benefits and pricing for the education industry are provided. In the manufacturing industry, data usage from IoT devices and sensors in factories is collected and special benefits and pricing for the manufacturing industry are provided. This allows for providing rate plans specialized for different industries, making it possible to provide optimal services tailored to the needs of each industry.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The next-generation mobile phone system may further include a location information acquisition unit that adjusts a rate plan based on the user's location information. The location information acquisition unit may acquire the user's current location using, for example, GPS. For example, the location information acquisition unit may provide different rate plans depending on whether the user is in an urban area or a suburban area. The location information acquisition unit may also measure the amount of time the user spends in a specific area and provide a rate plan tailored to that area. For example, if the user stays in a tourist spot for a long time, a discount on data usage in that area may be provided. The location information acquisition unit may also analyze the user's movement patterns and provide a rate plan based on the frequency of movement. For example, if the user travels frequently, a discount on data usage during travel may be provided. This allows for adjusting a rate plan based on the user's location information, thereby improving user satisfaction.

[0041] The rate plan generation unit may further include a health data acquisition unit that adjusts the rate plan based on the user's health data. The health data acquisition unit, for example, records the user's number of steps and heart rate. For example, the health data acquisition unit may record the number of steps the user takes per day and offer discounts to users who live a healthy lifestyle. The health data acquisition unit may also record the user's sleep data and offer discounts to users who get good quality sleep. For example, if a user gets good quality sleep over a week, a discount on data usage for that week may be offered. The health data acquisition unit may also record the user's exercise data and offer discounts to users who exercise regularly. For example, if a user exercises three or more times a week, a discount on data usage for that month may be offered. In this way, adjusting the rate plan based on the user's health data may increase the user's health awareness.

[0042] The rate plan generation unit may further include a purchase history acquisition unit that adjusts the rate plan based on the user's purchase history. The purchase history acquisition unit, for example, records the history of products purchased by the user. For example, if the user frequently purchases products of a particular brand, the purchase history acquisition unit may offer a discount on that brand. Furthermore, if the user frequently purchases products of a particular category, the purchase history acquisition unit may offer a discount on that category. For example, if the user frequently purchases electronic devices, a discount on that category may be offered. Furthermore, the purchase history acquisition unit may analyze the user's purchasing patterns and offer discounts for specific periods. For example, if the user makes a lot of purchases at the end of the year, a discount for that period may be offered. In this way, adjusting the rate plan based on the user's purchase history can increase the user's purchasing motivation.

[0043] The rate plan generation unit may further include a battery level acquisition unit that adjusts the rate plan based on the remaining battery level of the user's device. The battery level acquisition unit, for example, acquires the remaining battery level of the user's device in real time. For example, the battery level acquisition unit may provide a discount on data usage when the battery of the user's device is low. The battery level acquisition unit may also provide a regular rate plan when the battery of the user's device is full. For example, when the battery of the user's device is low while the user is out and about, a discount on data usage at that time is provided. The battery level acquisition unit may also analyze the battery usage pattern of the user's device and provide a discount for cases where the battery is rapidly depleted. In this way, adjusting the rate plan based on the remaining battery level of the user's device can improve user satisfaction.

[0044] The rate plan generation unit may further include a storage usage acquisition unit that adjusts the rate plan based on the storage usage of the user's device. The storage usage acquisition unit, for example, acquires the storage usage of the user's device in real time. For example, the storage usage acquisition unit may provide a discount on data usage when the storage of the user's device is nearly full. The storage usage acquisition unit may also provide a regular rate plan when the storage of the user's device is sufficiently free. For example, if the user frequently uses the storage of the device, a discount on the data usage at that time may be provided. The storage usage acquisition unit may also analyze the storage usage pattern of the user's device and provide a discount for cases where storage is rapidly depleted. In this way, adjusting the rate plan based on the storage usage of the user's device can improve user satisfaction.

[0045] The rate plan generation unit may further include a temperature acquisition unit that adjusts the rate plan based on the temperature of the user's device. The temperature acquisition unit, for example, acquires the temperature of the user's device in real time. For example, the temperature acquisition unit may provide a discount on data usage when the temperature of the user's device is high. The temperature acquisition unit may also provide a regular rate plan when the temperature of the user's device is low. For example, when the user has used the device for a long time and the temperature is rising, a discount on data usage at that time is provided. The temperature acquisition unit may also analyze the temperature fluctuation pattern of the user's device and provide a discount when the temperature fluctuates drastically. In this way, adjusting the rate plan based on the temperature of the user's device can improve user satisfaction.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The data usage acquisition unit acquires the user's data usage, for example, by measuring and recording monthly data usage, real-time data usage, and data usage for each specific application. Step 2: The connected terminal number acquisition unit acquires the number of connected terminals of the user. For example, it measures and records the number of simultaneously connected terminals, the number of registered terminals, and the connection status of each specific device. Step 3: The rate plan generation unit generates a rate plan based on the information acquired by the data usage acquisition unit and the connected terminal number acquisition unit. For example, a rate plan based on data usage, a rate plan based on the number of connected terminals, or a rate plan based on both data usage and the number of connected terminals is generated.

[0048] (Example 2) The next-generation mobile phone system according to an embodiment of the present invention is a system that provides flexible pricing plans based on data usage and the number of connected devices, thereby enabling the next-generation mobile phone system to provide flexible pricing plans to maintain and increase ARPU.

[0049] A next-generation mobile phone system according to an embodiment includes a data usage acquisition unit, a connected terminal number acquisition unit, and a rate plan generation unit. The data usage acquisition unit acquires the user's data usage. For example, the data usage acquisition unit measures monthly data usage. The data usage acquisition unit can also measure real-time data usage. The data usage acquisition unit can also measure data usage for each specific application. For example, the data usage acquisition unit measures the amount of data used by the user over a month and records it as monthly data usage. The real-time data usage is measured and displayed in real time as the amount of data currently used by the user. The data usage for each specific application is measured and recorded individually as the amount of data used by each application. The connected terminal number acquisition unit acquires the number of connected terminals of the user. For example, the connected terminal number acquisition unit measures the number of simultaneously connected terminals. The connected terminal number acquisition unit can also measure the number of registered terminals. The connected terminal number acquisition unit can also measure the connection status for each specific device. For example, the connected terminal number acquisition unit measures the number of terminals simultaneously connected by the user and records it as the number of simultaneously connected terminals. The number of registered terminals is the number of all terminals registered by the user, and is recorded. The connection status of each specific device is measured and recorded individually, indicating whether each device is connected. The rate plan generation unit generates a rate plan based on the information acquired by the data usage acquisition unit and the connected terminal number acquisition unit. For example, the rate plan generation unit generates a rate plan based on data usage. The rate plan generation unit can also generate a rate plan based on the number of connected terminals. The rate plan generation unit can also generate a rate plan based on both data usage and the number of connected terminals. For example, the rate plan generation unit sets a rate plan based on the user's monthly data usage, and provides a discount the greater the data usage. A rate plan based on the number of connected terminals sets an additional fee the greater the number of connected terminals. A rate plan based on both data usage and the number of connected terminals generates an optimal rate plan by taking into account the balance between data usage and the number of connected terminals.This allows next-generation mobile phone systems to offer flexible pricing plans based on data usage and the number of connected devices. For example, if a user uses a smartphone and multiple IoT devices, offering pricing plans based on the number of connected devices can maintain or increase ARPU. In addition, offering pricing plans based on the number of connected devices to users with low data usage can prevent a decrease in revenue.

[0050] The rate plan generation unit can provide discounts according to the data usage acquired by the data usage acquisition unit. The rate plan generation unit, for example, provides discounts to users with large data usage. For example, a 10% discount is provided to users whose monthly data usage exceeds 10 GB. The rate plan generation unit can also provide discounts to users with small data usage. For example, a 5% discount is provided to users whose monthly data usage is less than 1 GB. The rate plan generation unit can also provide discounts according to the data usage of a specific application. For example, a discount for a specific streaming service is provided to users who use a large amount of data for that service. In this way, user satisfaction can be improved by providing discounts according to data usage.

[0051] The rate plan generation unit can set an additional fee according to the number of connected terminals acquired by the connected terminal number acquisition unit. The rate plan generation unit, for example, sets an additional fee for a user with a large number of connected terminals. For example, if the number of connected terminals exceeds three, an additional fee is set for each connected terminal. The rate plan generation unit can also set an additional fee for a specific device. For example, an additional fee is set for a smart home device. The rate plan generation unit can also set an additional fee according to the number of simultaneously connected terminals. For example, if more than five terminals are connected simultaneously, an additional fee is set for each connected terminal. In this way, by setting an additional fee according to the number of connected terminals, it is possible to prevent a decrease in income.

[0052] The pricing plan generation unit includes an emotion estimation unit that dynamically adjusts the pricing plan based on the user's emotions. The emotion estimation unit, for example, captures the user's facial expression with a camera and analyzes the emotion using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expression. The emotion estimation unit also records the user's voice and estimates the emotion using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates the emotion score. The emotion estimation unit also collects the user's biometric data (heart rate and electrodermal activity) with a sensor and analyzes the emotion using an emotion estimation algorithm. For example, it calculates the emotion score based on heart rate fluctuations. The pricing plan generation unit dynamically adjusts the pricing plan based on the emotion estimated by the emotion estimation unit. For example, if the user is feeling stressed, the fee can be discounted. Alternatively, if the user is relaxed, the fee can be set at the normal rate. Alternatively, if the user is in a specific emotional state, a special promotion can be offered. For example, if the user is happy, a special discount can be offered. In this way, by dynamically adjusting the pricing plan based on the user's emotions, user satisfaction can be improved.

[0053] The rate plan generation unit includes a device usage history acquisition unit that proposes an optimal rate plan based on the user's device usage history. The device usage history acquisition unit, for example, records the user's device usage time. For example, the device usage history acquisition unit individually records the usage time of each device. The device usage history acquisition unit can also record applications used by the user. For example, the device usage history acquisition unit records the usage time of each application. The device usage history acquisition unit can also analyze the user's device usage pattern. For example, the device usage history acquisition unit records a pattern in which the user uses a specific device during a specific time period. The rate plan generation unit proposes an optimal rate plan based on the device usage history acquired by the device usage history acquisition unit. For example, if the user frequently uses a specific device, the rate plan generation unit can provide a discount for the device. Furthermore, if the user frequently uses a specific application, the rate plan generation unit can also provide a discount for the application. The rate plan generation unit can also propose an optimal rate plan based on the user's device usage pattern. For example, if the user frequently uses the device at night, a discount on nighttime data usage can be provided. This allows the system to propose the most suitable rate plan based on the device usage history, thereby improving user satisfaction.

[0054] The pricing plan generation unit includes a lifestyle data acquisition unit that provides a customizable pricing plan based on lifestyle data of the user. The lifestyle data acquisition unit, for example, records the user's lifestyle habits. For example, the lifestyle data acquisition unit records the user's wake-up time and bedtime. The lifestyle data acquisition unit can also record the user's hobbies and preferences. For example, the lifestyle data acquisition unit records the user's favorite music and movie genres. The lifestyle data acquisition unit can also record the user's consumption behavior. For example, the lifestyle data acquisition unit records a history of products purchased by the user. The pricing plan generation unit provides a customizable pricing plan based on the lifestyle data acquired by the lifestyle data acquisition unit. For example, the pricing plan generation unit adjusts data usage according to the user's wake-up time. The pricing plan generation unit can also provide discounts on specific services according to the user's hobbies and preferences. The pricing plan generation unit can also provide specific promotions based on the user's consumption behavior. For example, if a user frequently purchases products from a specific brand, a discount on that brand can be provided. In this way, by providing a customizable pricing plan based on lifestyle data, user satisfaction can be improved.

[0055] The rate plan generation unit includes a shared data plan acquisition unit that provides a shared data plan intended for use by a family or group. The shared data plan acquisition unit, for example, registers family members and group members. For example, the shared data plan acquisition unit registers family members and records the data usage of each member. The shared data plan acquisition unit can also register group members and record the data usage of each member. The shared data plan acquisition unit can also manage the data capacity of the shared data plan. For example, the shared data plan acquisition unit manages the data usage of the entire family and sets an additional fee if the data capacity is exceeded. The rate plan generation unit provides the shared data plan based on the information acquired by the shared data plan acquisition unit. For example, the rate plan generation unit sets a rate plan based on the data usage of the entire family. The rate plan generation unit can also set a rate plan based on the data usage of the entire group. The rate plan generation unit can also adjust the fee according to the data capacity of the shared data plan. For example, if the data usage of the entire family is high, an additional fee is set. In this way, by providing a shared data plan intended for use by a family or group, it is possible to efficiently manage data usage while reducing costs.

[0056] The pricing plan generation unit includes an emotion estimation unit that provides promotions and discounts based on the user's emotions. The emotion estimation unit, for example, captures the user's facial expression with a camera and analyzes the emotion using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expression. The emotion estimation unit also records the user's voice and estimates the emotion using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates the emotion score. The emotion estimation unit also collects the user's biometric data (heart rate and electrodermal activity) with a sensor and analyzes the emotion using an emotion estimation algorithm. For example, it calculates the emotion score based on heart rate fluctuations. The pricing plan generation unit provides promotions and discounts based on the emotions estimated by the emotion estimation unit. For example, if the user is happy, a special discount can be provided. Also, if the user is feeling stressed, a special promotion can be provided. Also, if the user is in a specific emotional state, a service corresponding to the emotion can be provided. For example, if the user is relaxed, a service with a relaxing effect can be provided. In this way, by providing promotions and discounts based on the user's emotions, user satisfaction can be improved.

[0057] The rate plan generation unit includes an algorithm that automatically proposes an optimal rate plan based on the user's device usage history. The algorithm, for example, analyzes the user's device usage history and proposes an optimal rate plan. For example, the algorithm identifies devices and applications that the user frequently uses and proposes a rate plan based on the devices and applications. The algorithm can also analyze the user's device usage patterns and propose an optimal rate plan. For example, if the user frequently uses the device during a specific time period, the algorithm proposes a rate plan specific to that time period. The algorithm can also predict future device usage based on the user's device usage history and propose a rate plan based on the prediction. For example, the algorithm predicts device usage for the next month based on past device usage history and proposes an optimal rate plan based on the prediction. This allows the system to automatically propose an optimal rate plan based on the device usage history, thereby improving user satisfaction.

[0058] The pricing plan generation unit includes a lifestyle data acquisition unit that provides a customizable pricing plan according to the user's lifestyle. The lifestyle data acquisition unit, for example, records the user's lifestyle habits. For example, the lifestyle data acquisition unit records the user's wake-up time and bedtime. The lifestyle data acquisition unit can also record the user's hobbies and preferences. For example, the lifestyle data acquisition unit records the user's favorite music and movie genres. The lifestyle data acquisition unit can also record the user's consumption behavior. For example, the lifestyle data acquisition unit records the history of products purchased by the user. The pricing plan generation unit provides a customizable pricing plan based on the lifestyle data acquired by the lifestyle data acquisition unit. For example, the pricing plan generation unit adjusts data usage according to the user's wake-up time. The pricing plan generation unit can also provide discounts on specific services according to the user's hobbies and preferences. The pricing plan generation unit can also provide specific promotions based on the user's consumption behavior. For example, if a user frequently purchases products from a specific brand, a discount on that brand can be provided. This allows for providing customizable pricing plans according to lifestyles, thereby improving user satisfaction.

[0059] The rate plan generation unit may include a shared data plan acquisition unit that provides a shared data plan intended for use by a family or group. The shared data plan acquisition unit, for example, registers family and group members. For example, the shared data plan acquisition unit registers family members and records the data usage of each member. The shared data plan acquisition unit can also register group members and record the data usage of each member. The shared data plan acquisition unit can also manage the data capacity of the shared data plan. For example, the shared data plan acquisition unit manages the data usage of the entire family and sets an additional fee if the data capacity is exceeded. The rate plan generation unit provides the shared data plan based on the information acquired by the shared data plan acquisition unit. For example, the rate plan generation unit sets a rate plan based on the data usage of the entire family. The rate plan generation unit can also set a rate plan based on the data usage of the entire group. The rate plan generation unit can also adjust the fee according to the data capacity of the shared data plan. For example, if the data usage of the entire family is high, an additional fee is set. This allows for the provision of shared data plans designed for use by families or groups, reducing costs and efficiently managing data usage.

[0060] The rate plan generation unit may include an industry-specific plan acquisition unit that provides rate plans specialized for different industries. The industry-specific plan acquisition unit collects, for example, data related to a specific industry. For example, the industry-specific plan acquisition unit collects data related to the medical industry and provides a rate plan specialized for the medical industry. The industry-specific plan acquisition unit may also collect data related to the education industry and provide a rate plan specialized for the education industry. The industry-specific plan acquisition unit may also collect data related to the manufacturing industry and provide a rate plan specialized for the manufacturing industry. For example, the industry-specific plan acquisition unit collects data on data usage and the number of connected devices in the medical industry and provides special benefits and pricing for the medical industry. In the education industry, data related to online classes and remote learning is collected and special benefits and pricing for the education industry are provided. In the manufacturing industry, data usage from IoT devices and sensors in factories is collected and special benefits and pricing for the manufacturing industry are provided. This allows for providing rate plans specialized for different industries, making it possible to provide optimal services tailored to the needs of each industry.

[0061] The pricing plan generation unit may include an emotion estimation unit that provides promotions and discounts based on the user's emotions. The emotion estimation unit, for example, captures the user's facial expression with a camera and analyzes the emotion using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expression. The emotion estimation unit also records the user's voice and estimates the emotion using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates the emotion score. The emotion estimation unit also collects the user's biometric data (heart rate and electrodermal activity) with a sensor and analyzes the emotion using an emotion estimation algorithm. For example, it calculates the emotion score based on heart rate fluctuations. The pricing plan generation unit provides promotions and discounts based on the emotions estimated by the emotion estimation unit. For example, if the user is happy, a special discount may be provided. Also, if the user is feeling stressed, a special promotion may be provided. Also, if the user is in a specific emotional state, a service corresponding to the emotion may be provided. For example, if the user is relaxed, a service with a relaxing effect may be provided. In this way, by providing promotions and discounts based on the user's emotions, user satisfaction can be improved.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The next-generation mobile phone system may further include a location information acquisition unit that adjusts a rate plan based on the user's location information. The location information acquisition unit may acquire the user's current location using, for example, GPS. For example, the location information acquisition unit may provide different rate plans depending on whether the user is in an urban area or a suburban area. The location information acquisition unit may also measure the amount of time the user spends in a specific area and provide a rate plan tailored to that area. For example, if the user stays in a tourist spot for a long time, a discount on data usage in that area may be provided. The location information acquisition unit may also analyze the user's movement patterns and provide a rate plan based on the frequency of movement. For example, if the user travels frequently, a discount on data usage during travel may be provided. This allows for adjusting a rate plan based on the user's location information, thereby improving user satisfaction.

[0064] The rate plan generation unit may further include a health data acquisition unit that adjusts the rate plan based on the user's health data. The health data acquisition unit, for example, records the user's number of steps and heart rate. For example, the health data acquisition unit may record the number of steps the user takes per day and offer discounts to users who live a healthy lifestyle. The health data acquisition unit may also record the user's sleep data and offer discounts to users who get good quality sleep. For example, if a user gets good quality sleep over a week, a discount on data usage for that week may be offered. The health data acquisition unit may also record the user's exercise data and offer discounts to users who exercise regularly. For example, if a user exercises three or more times a week, a discount on data usage for that month may be offered. In this way, adjusting the rate plan based on the user's health data may increase the user's health awareness.

[0065] The rate plan generation unit may further include a purchase history acquisition unit that adjusts the rate plan based on the user's purchase history. The purchase history acquisition unit, for example, records the history of products purchased by the user. For example, if the user frequently purchases products of a particular brand, the purchase history acquisition unit may offer a discount on that brand. Furthermore, if the user frequently purchases products of a particular category, the purchase history acquisition unit may offer a discount on that category. For example, if the user frequently purchases electronic devices, a discount on that category may be offered. Furthermore, the purchase history acquisition unit may analyze the user's purchasing patterns and offer discounts for specific periods. For example, if the user makes a lot of purchases at the end of the year, a discount for that period may be offered. In this way, adjusting the rate plan based on the user's purchase history can increase the user's purchasing motivation.

[0066] The pricing plan generation unit can further provide entertainment content customized based on the user's emotions. For example, if the user is feeling stressed, relaxing music or videos can be provided. Also, if the user is happy, energetic music or videos can be provided. For example, the emotion estimation unit can capture the user's facial expressions with a camera and analyze the emotions using an emotion estimation algorithm. Furthermore, the emotion estimation unit can record the user's voice and estimate the emotions using voice analysis technology. For example, the emotion estimation unit can analyze the tone and speed of the voice and calculate an emotion score. The emotion estimation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and analyze the emotions using an emotion estimation algorithm. This makes it possible to provide entertainment content customized based on the user's emotions, thereby improving user satisfaction.

[0067] The pricing plan generation unit can further provide customized notifications based on the user's emotions. For example, if the user is feeling stressed, a notification with a relaxing effect can be sent. Also, if the user is happy, a notification containing a positive message can be sent. For example, the emotion estimation unit can capture the user's facial expression with a camera and analyze the emotion using an emotion estimation algorithm. Furthermore, the emotion estimation unit can record the user's voice and estimate the emotion using voice analysis technology. For example, the emotion estimation unit can analyze the tone and speed of the voice and calculate an emotion score. The emotion estimation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and analyze the emotion using an emotion estimation algorithm. This makes it possible to provide customized notifications based on the user's emotions, thereby improving user satisfaction.

[0068] The pricing plan generation unit can further provide advertisements customized based on the user's emotions. For example, if the user is feeling stressed, an advertisement with a relaxing effect can be displayed. Also, if the user is happy, an energetic advertisement can be displayed. For example, the emotion estimation unit can capture the user's facial expression with a camera and analyze the emotion using an emotion estimation algorithm. Furthermore, the emotion estimation unit can record the user's voice and estimate the emotion using voice analysis technology. For example, the emotion estimation unit can analyze the tone and speed of the voice and calculate an emotion score. The emotion estimation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and analyze the emotion using an emotion estimation algorithm. In this way, by providing advertisements customized based on the user's emotions, user satisfaction can be improved.

[0069] The pricing plan generation unit can further provide customized feedback based on the user's emotions. For example, if the user is feeling stressed, it can provide relaxing feedback. Also, if the user is happy, it can provide positive feedback. For example, the emotion estimation unit can capture the user's facial expression with a camera and analyze the emotion using an emotion estimation algorithm. Furthermore, the emotion estimation unit can record the user's voice and estimate the emotion using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The emotion estimation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and analyze the emotion using an emotion estimation algorithm. In this way, by providing customized feedback based on the user's emotions, it is possible to improve user satisfaction.

[0070] The rate plan generation unit may further include a battery level acquisition unit that adjusts the rate plan based on the remaining battery level of the user's device. The battery level acquisition unit, for example, acquires the remaining battery level of the user's device in real time. For example, the battery level acquisition unit may provide a discount on data usage when the battery of the user's device is low. The battery level acquisition unit may also provide a regular rate plan when the battery of the user's device is full. For example, when the battery of the user's device is low while the user is out and about, a discount on data usage at that time is provided. The battery level acquisition unit may also analyze the battery usage pattern of the user's device and provide a discount for cases where the battery is rapidly depleted. In this way, adjusting the rate plan based on the remaining battery level of the user's device can improve user satisfaction.

[0071] The rate plan generation unit may further include a storage usage acquisition unit that adjusts the rate plan based on the storage usage of the user's device. The storage usage acquisition unit, for example, acquires the storage usage of the user's device in real time. For example, the storage usage acquisition unit may provide a discount on data usage when the storage of the user's device is nearly full. The storage usage acquisition unit may also provide a regular rate plan when the storage of the user's device is sufficiently free. For example, if the user frequently uses the storage of the device, a discount on the data usage at that time may be provided. The storage usage acquisition unit may also analyze the storage usage pattern of the user's device and provide a discount for cases where storage is rapidly depleted. In this way, adjusting the rate plan based on the storage usage of the user's device can improve user satisfaction.

[0072] The rate plan generation unit may further include a temperature acquisition unit that adjusts the rate plan based on the temperature of the user's device. The temperature acquisition unit, for example, acquires the temperature of the user's device in real time. For example, the temperature acquisition unit may provide a discount on data usage when the temperature of the user's device is high. The temperature acquisition unit may also provide a regular rate plan when the temperature of the user's device is low. For example, when the user has used the device for a long time and the temperature is rising, a discount on data usage at that time is provided. The temperature acquisition unit may also analyze the temperature fluctuation pattern of the user's device and provide a discount when the temperature fluctuates drastically. In this way, adjusting the rate plan based on the temperature of the user's device can improve user satisfaction.

[0073] The processing flow of the second embodiment will be briefly explained below.

[0074] Step 1: The data usage acquisition unit acquires the user's data usage, for example, by measuring and recording monthly data usage, real-time data usage, and data usage for each specific application. Step 2: The connected terminal number acquisition unit acquires the number of connected terminals of the user. For example, it measures and records the number of simultaneously connected terminals, the number of registered terminals, and the connection status of each specific device. Step 3: The rate plan generation unit generates a rate plan based on the information acquired by the data usage acquisition unit and the connected terminal number acquisition unit. For example, a rate plan based on data usage, a rate plan based on the number of connected terminals, or a rate plan based on both data usage and the number of connected terminals is generated.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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).

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0119] 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.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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."

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0141] 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]

[0142] 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 data usage acquisition unit that acquires data usage of a user; a connected terminal number acquisition unit that acquires the number of connected terminals of a user; a rate plan generation unit that generates a rate plan based on the information acquired by the data usage amount acquisition unit and the number of connected terminals acquisition unit. A system characterized by:

2. The rate plan generation unit Setting an additional fee according to the number of connected terminals acquired by the connected terminal number acquisition unit 2. The system of claim 1.

3. The rate plan generation unit Equipped with a device usage history acquisition unit that proposes the optimal rate plan based on the user's device usage history 2. The system of claim 1.

4. The rate plan generation unit Equipped with a shared data plan acquisition unit that provides shared data plans for use by families or groups 2. The system of claim 1.

5. The rate plan generation unit Equipped with an emotion estimation unit that dynamically adjusts pricing plans based on user emotions 2. The system of claim 1.

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