System

Abstract

A system according to an embodiment enables a user to easily select an appropriate charging method.SOLUTION: A system includes a device information acquisition unit, a charging standard determination unit, and a charging method suggestion unit. The device information acquisition unit acquires device information of a user. The charging standard determination unit determines an optimum charging standard based on the device information acquired by the device information acquisition unit. The charging method proposal unit proposes an appropriate charging method based on the charging standard determined by the charging standard determination unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the problem that there are a wide variety of charging standards for devices, making it difficult for unfamiliar users to select an appropriate charging method.

[0005] The system according to the embodiment aims to enable a user to easily select an appropriate charging method. [Means for solving the problem]

[0006] A system according to an embodiment includes a device information acquisition unit, a charging standard determination unit, and a charging method proposal unit. The device information acquisition unit acquires device information of a user. The charging standard determination unit determines an optimal charging standard based on the device information acquired by the device information acquisition unit. The charging method proposal unit proposes an appropriate charging method based on the charging standard determined by the charging standard determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable the user to easily select an appropriate charging method. [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 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 charging standard determination system according to an embodiment of the present invention is a system that automatically determines the optimal charging standard for a user's device and suggests an appropriate charging method, thereby enabling even non-technical users to correctly charge their devices.

[0029] A charging standard identification system according to an embodiment includes a device information acquisition unit, a charging standard identification unit, and a charging method suggestion unit. The device information acquisition unit acquires a user's device information. For example, the user inputs the device type and model. The device information acquisition unit can also acquire the device's serial number and manufacturing date. For example, by inputting the device's serial number, the manufacturing date and model information are acquired. The charging standard identification unit determines the optimal charging standard based on the device information acquired by the device information acquisition unit. For example, the generation AI determines the appropriate charging standard based on the device type and model. The generation AI can also determine the optimal charging standard taking into account the device's serial number and manufacturing date. For example, the generation AI suggests a charging method suitable for older models. The charging method suggestion unit suggests an appropriate charging method based on the charging standard identified by the charging standard identification unit. For example, the generation AI provides the user with specific instructions, such as "Use a Lightning cable to charge your iPhone 12." The generation AI also provides advice on how to choose a charger and precautions to take. For example, the generation AI suggests precautions to take when using a fast charger. As a result, the charging standard determination system according to the embodiment can automatically determine the optimal charging standard for the user's device and propose an appropriate charging method. For example, the user can easily understand the optimal charging method for their device and charge with peace of mind.

[0030] The charging standard determination unit can suggest the optimal charging standard based on the device's usage history and battery condition. For example, the generation AI analyzes the device's usage history and suggests the optimal charging standard by taking into account past charging patterns and battery deterioration. For example, for a device that is frequently charged, slow charging is recommended to extend battery life. The charging standard determination unit also analyzes the battery condition and suggests the optimal charging standard by taking into account the current charge level and degree of deterioration. For example, if the battery is degraded, it suggests a charging method that is gentle on the battery. This makes it possible to suggest the optimal charging standard by taking into account the device's usage history and battery condition.

[0031] The charging standard determination unit can refer to the user's past charging history and suggest the most efficient charging method. For example, the generation AI analyzes the user's past charging history and suggests the most efficient charging method. For example, for a user who has frequently used fast charging in the past, it may recommend slow charging to extend battery life. The charging standard determination unit also optimizes charging time and method based on the user's charging history. For example, it may suggest night mode charging for a user who charges at night. This makes it possible to refer to the user's past charging history and suggest the most efficient charging method.

[0032] The device information acquisition unit can determine the optimal charging standard based on the device's serial number and manufacturing date. For example, the generation AI analyzes the device's serial number and determines the optimal charging standard based on the device's manufacturing date and model information. For example, it suggests a charging method suitable for older models. The device information acquisition unit also estimates the device's lifespan based on the manufacturing date and suggests an appropriate charging method. For example, it suggests a charging method that is gentle on the battery for devices that are several years old. This makes it possible to determine the optimal charging standard taking into account the device's serial number and manufacturing date.

[0033] The device information acquisition unit can determine the optimal charging standard based on the device's usage environment. For example, the device information acquisition unit uses a generation AI to analyze the device's usage environment and propose the optimal charging standard based on the temperature and humidity. For example, in a high-temperature environment, it recommends a charging method that is gentle on the battery. The device information acquisition unit also proposes the optimal charging method for the device based on data from the usage environment. For example, in a high-humidity environment, it suggests a charger that is resistant to humidity. This makes it possible to determine the optimal charging standard taking into account the device's usage environment.

[0034] The device information acquisition unit can recognize the appearance of a device and determine the optimal charging standard. For example, the device information acquisition unit uses a generative AI to analyze the appearance of a device and determine the optimal charging standard based on its color and shape. For example, it can suggest a compatible charging cable for a device of a specific color or shape. The device information acquisition unit also suggests the optimal charging method for a device based on the appearance data. For example, it can suggest a charger that is suitable for a device of a specific shape. This makes it possible to recognize the appearance of a device and determine the optimal charging standard.

[0035] The device information acquisition unit can also accommodate user voice input, enabling device information to be entered via voice. For example, the device information acquisition unit uses a generation AI to accommodate user voice input, enabling device information to be entered via voice. For example, if a user simply says, "I want to charge my iPhone 12," the appropriate charging standard will be suggested. The device information acquisition unit also uses voice recognition technology to convert the user's voice into text data. For example, voice recognition software automatically analyzes the voice and saves it as text. This allows the device information acquisition unit to accommodate user voice input, enabling device information to be entered via voice.

[0036] The charging method suggestion unit can suggest the optimal charging method based on the user's charging habits. For example, the charging method suggestion unit uses a generation AI to analyze the user's charging habits and suggest the optimal charging method. For example, it recommends night mode charging for users who charge at night. The charging method suggestion unit also optimizes charging time and charging method based on the user's charging habits. For example, it suggests quick charging for users who charge during the day. This makes it possible to suggest the optimal charging method taking into account the user's charging habits.

[0037] The charging method suggestion unit can suggest a charging method to extend the battery life of the device. For example, the generation AI of the charging method suggestion unit suggests a charging method to extend the battery life of the device. For example, slow charging is recommended to prevent battery deterioration. The charging method suggestion unit also analyzes the degree of battery deterioration and suggests a charging method to extend the battery life. For example, a charging method that optimizes the charging cycle is suggested. This makes it possible to suggest a charging method to extend the battery life of the device.

[0038] The charging method suggestion unit can suggest the optimal charging method based on the user's lifestyle. For example, the generation AI analyzes the user's lifestyle and suggests the optimal charging method. For example, it recommends the use of a mobile battery for a user who travels a lot. The charging method suggestion unit also optimizes the charging method based on the user's lifestyle. For example, it suggests a home charger for a user who is often at home. This makes it possible to suggest the optimal charging method taking the user's lifestyle into consideration.

[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 charging standard identification system can also include a schedule linking unit that acquires the user's schedule information and suggests optimal charging timing based on the schedule. For example, if the user has a meeting or plans to go out, the schedule linking unit can suggest completing charging before that. The schedule linking unit can also link with the user's calendar app and automatically adjust the charging schedule. For example, charging can be done overnight so that the device can be used in the morning with a full charge. This makes it possible to suggest optimal charging timing that matches the user's schedule.

[0041] The charging standard identification system may further include a learning unit that learns a user's device usage patterns and suggests an optimal charging method based on the usage patterns. For example, if a user frequently uses a device during a specific time period, the learning unit may suggest charging at that time. The learning unit may also analyze the frequency and duration of a user's device use and suggest a charging method to extend battery life. For example, slow charging may be recommended for frequently used devices. This allows the system to suggest an optimal charging method based on the user's device usage patterns.

[0042] The charging standard identification system may further include a location information linking unit that acquires location information of the user's device and suggests the optimal charging method based on the location information. For example, when the user is at home, the location information linking unit may suggest using a home charger. When the user is out and about, the location information linking unit may also recommend using a mobile battery. Furthermore, the location information linking unit may guide the user to the location of a charging station when the user is in a specific location. This makes it possible to suggest the optimal charging method based on the user's location information.

[0043] The charging standard determination system may further include a usage purpose response unit that considers the user's device usage purpose and suggests the optimal charging method depending on the usage purpose. For example, if the user uses the device for gaming, the usage purpose response unit may recommend fast charging. Alternatively, if the user uses the device for long video calls, the usage purpose response unit may suggest slow charging to extend battery life. Furthermore, the usage purpose response unit may also provide advice on how to select a charger and precautions to take depending on the device usage purpose. This allows the system to suggest the optimal charging method depending on the user's device usage purpose.

[0044] The charging standard identification system may further include a battery replacement prediction unit that predicts when the battery in a user's device needs to be replaced and suggests replacing the battery at the appropriate time. For example, the battery replacement prediction unit may analyze the battery's deterioration status and predict when replacement is necessary. The battery replacement prediction unit may also notify the user when replacement is required and provide information on how and where to replace the battery. This allows the user to replace the battery at the appropriate time and maintain the performance of the device.

[0045] The charging standard identification system may further include an energy efficiency optimization unit that analyzes the energy consumption pattern of the user's device and proposes a charging method that optimizes energy efficiency. For example, the energy efficiency optimization unit may propose a charging method that minimizes energy consumption depending on the device's usage status. The energy efficiency optimization unit may also learn the energy consumption pattern of the user's device and propose an optimal charging schedule. This allows the user to use the device while optimizing its energy efficiency.

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

[0047] Step 1: The device information acquisition unit acquires the user's device information. For example, the user inputs the type and model of the device. The device information acquisition unit can also acquire the device's serial number and manufacturing date. For example, by inputting the device's serial number, the manufacturing date and model information are acquired. Step 2: The charging standard determination unit determines the optimal charging standard based on the device information acquired by the device information acquisition unit. For example, the generation AI determines the appropriate charging standard based on the device type and model. The generation AI can also determine the optimal charging standard by taking into account the device's serial number and manufacturing date. For example, it may suggest a suitable charging method for older models. Step 3: The charging method suggestion unit suggests an appropriate charging method based on the charging standard determined by the charging standard determination unit. For example, the generation AI provides the user with specific instructions such as, "To charge your iPhone 12, use a Lightning cable." The generation AI also provides advice on how to choose a charger and what to be careful of. For example, it suggests precautions to take when using a fast charger.

[0048] (Example 2) The charging standard determination system according to an embodiment of the present invention is a system that automatically determines the optimal charging standard for a user's device and suggests an appropriate charging method, thereby enabling even non-technical users to correctly charge their devices.

[0049] A charging standard identification system according to an embodiment includes a device information acquisition unit, a charging standard identification unit, and a charging method suggestion unit. The device information acquisition unit acquires a user's device information. For example, the user inputs the device type and model. The device information acquisition unit can also acquire the device's serial number and manufacturing date. For example, by inputting the device's serial number, the manufacturing date and model information are acquired. The charging standard identification unit determines the optimal charging standard based on the device information acquired by the device information acquisition unit. For example, the generation AI determines the appropriate charging standard based on the device type and model. The generation AI can also determine the optimal charging standard taking into account the device's serial number and manufacturing date. For example, the generation AI suggests a charging method suitable for older models. The charging method suggestion unit suggests an appropriate charging method based on the charging standard identified by the charging standard identification unit. For example, the generation AI provides the user with specific instructions, such as "Use a Lightning cable to charge your iPhone 12." The generation AI also provides advice on how to choose a charger and precautions to take. For example, the generation AI suggests precautions to take when using a fast charger. As a result, the charging standard determination system according to the embodiment can automatically determine the optimal charging standard for the user's device and propose an appropriate charging method. For example, the user can easily understand the optimal charging method for their device and charge with peace of mind.

[0050] The charging standard determination unit can suggest the optimal charging standard based on the device's usage history and battery condition. For example, the generation AI analyzes the device's usage history and suggests the optimal charging standard by taking into account past charging patterns and battery deterioration. For example, for a device that is frequently charged, slow charging is recommended to extend battery life. The charging standard determination unit also analyzes the battery condition and suggests the optimal charging standard by taking into account the current charge level and degree of deterioration. For example, if the battery is degraded, it suggests a charging method that is gentle on the battery. This makes it possible to suggest the optimal charging standard by taking into account the device's usage history and battery condition.

[0051] The charging standard determination unit can refer to the user's past charging history and suggest the most efficient charging method. For example, the generation AI analyzes the user's past charging history and suggests the most efficient charging method. For example, for a user who has frequently used fast charging in the past, it may recommend slow charging to extend battery life. The charging standard determination unit also optimizes charging time and method based on the user's charging history. For example, it may suggest night mode charging for a user who charges at night. This makes it possible to refer to the user's past charging history and suggest the most efficient charging method.

[0052] The charging standard determination unit can use the emotion estimation function to analyze the user's emotions regarding charging and suggest a charging method to reduce stress. The charging standard determination unit can, for example, use the emotion estimation function to analyze the user's emotions regarding charging in real time and suggest a charging method to reduce stress. For example, for a user who feels that charging is slow, the charging standard determination unit can recommend fast charging. The emotion estimation function also analyzes the user's facial expressions and voice and calculates an emotion score. For example, the emotion is analyzed based on changes in facial expressions and tone of voice. This makes it possible to analyze the user's emotions and suggest a charging method to reduce stress.

[0053] The device information acquisition unit can determine the optimal charging standard based on the device's serial number and manufacturing date. For example, the generation AI analyzes the device's serial number and determines the optimal charging standard based on the device's manufacturing date and model information. For example, it suggests a charging method suitable for older models. The device information acquisition unit also estimates the device's lifespan based on the manufacturing date and suggests an appropriate charging method. For example, it suggests a charging method that is gentle on the battery for devices that are several years old. This makes it possible to determine the optimal charging standard taking into account the device's serial number and manufacturing date.

[0054] The device information acquisition unit can determine the optimal charging standard based on the device's usage environment. For example, the device information acquisition unit uses a generation AI to analyze the device's usage environment and propose the optimal charging standard based on the temperature and humidity. For example, in a high-temperature environment, it recommends a charging method that is gentle on the battery. The device information acquisition unit also proposes the optimal charging method for the device based on data from the usage environment. For example, in a high-humidity environment, it suggests a charger that is resistant to humidity. This makes it possible to determine the optimal charging standard taking into account the device's usage environment.

[0055] The device information acquisition unit can recognize the appearance of a device and determine the optimal charging standard. For example, the device information acquisition unit uses a generative AI to analyze the appearance of a device and determine the optimal charging standard based on its color and shape. For example, it can suggest a compatible charging cable for a device of a specific color or shape. The device information acquisition unit also suggests the optimal charging method for a device based on the appearance data. For example, it can suggest a charger that is suitable for a device of a specific shape. This makes it possible to recognize the appearance of a device and determine the optimal charging standard.

[0056] The device information acquisition unit can also accommodate user voice input, enabling device information to be entered via voice. For example, the device information acquisition unit uses a generation AI to accommodate user voice input, enabling device information to be entered via voice. For example, if a user simply says, "I want to charge my iPhone 12," the appropriate charging standard will be suggested. The device information acquisition unit also uses voice recognition technology to convert the user's voice into text data. For example, voice recognition software automatically analyzes the voice and saves it as text. This allows the device information acquisition unit to accommodate user voice input, enabling device information to be entered via voice.

[0057] The charging method suggestion unit can suggest the optimal charging method based on the user's charging habits. For example, the charging method suggestion unit uses a generation AI to analyze the user's charging habits and suggest the optimal charging method. For example, it recommends night mode charging for users who charge at night. The charging method suggestion unit also optimizes charging time and charging method based on the user's charging habits. For example, it suggests quick charging for users who charge during the day. This makes it possible to suggest the optimal charging method taking into account the user's charging habits.

[0058] The charging method suggestion unit can suggest a charging method to extend the battery life of the device. For example, the generation AI of the charging method suggestion unit suggests a charging method to extend the battery life of the device. For example, slow charging is recommended to prevent battery deterioration. The charging method suggestion unit also analyzes the degree of battery deterioration and suggests a charging method to extend the battery life. For example, a charging method that optimizes the charging cycle is suggested. This makes it possible to suggest a charging method to extend the battery life of the device.

[0059] The charging method suggestion unit can use the emotion estimation function to analyze the user's emotions regarding charging methods and suggest charging methods to reduce stress. For example, the charging method suggestion unit can use the emotion estimation function to analyze the user's emotions regarding charging methods in real time and suggest charging methods to reduce stress. For example, for a user who feels that charging is slow, the unit can recommend fast charging. The emotion estimation function also analyzes the user's facial expressions and voice and calculates an emotion score. For example, the function analyzes emotions based on changes in facial expressions and tone of voice. This makes it possible to analyze the user's emotions and suggest charging methods to reduce stress.

[0060] The charging method suggestion unit can suggest the optimal charging method based on the user's lifestyle. For example, the generation AI analyzes the user's lifestyle and suggests the optimal charging method. For example, it recommends the use of a mobile battery for a user who travels a lot. The charging method suggestion unit also optimizes the charging method based on the user's lifestyle. For example, it suggests a home charger for a user who is often at home. This makes it possible to suggest the optimal charging method taking the user's lifestyle into consideration.

[0061] The charging method suggestion unit can use the emotion estimation function to monitor the user's emotions toward charging methods in real time and suggest charging methods that elicit positive emotions. For example, the charging method suggestion unit can use the emotion estimation function to monitor the user's emotions toward charging methods in real time and suggest charging methods that elicit positive emotions. For example, the charging method suggestion unit can play relaxing music while charging. The emotion estimation function can also analyze the user's facial expressions and voice to calculate an emotion score. For example, the emotion estimation function can analyze emotions based on changes in facial expressions and tone of voice. This makes it possible to monitor the user's emotions in real time and suggest charging methods that elicit positive emotions.

[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 charging standard identification system may further include a health monitoring unit that monitors the user's health and suggests a health-conscious charging method. For example, if the user uses the device for a long time, the health monitoring unit may send a notification encouraging the user to take a break and suggest charging the device during that time. The health monitoring unit may also monitor the user's heart rate and stress level to provide a relaxing charging environment. For example, if the user's heart rate is high, the system may suggest playing relaxing music while charging. This allows the system to suggest a charging method that takes the user's health into consideration.

[0064] The charging standard identification system can also include a schedule linking unit that acquires the user's schedule information and suggests optimal charging timing based on the schedule. For example, if the user has a meeting or plans to go out, the schedule linking unit can suggest completing charging before that. The schedule linking unit can also link with the user's calendar app and automatically adjust the charging schedule. For example, charging can be done overnight so that the device can be used in the morning with a full charge. This makes it possible to suggest optimal charging timing that matches the user's schedule.

[0065] The charging standard identification system may further include a learning unit that learns a user's device usage patterns and suggests an optimal charging method based on the usage patterns. For example, if a user frequently uses a device during a specific time period, the learning unit may suggest charging at that time. The learning unit may also analyze the frequency and duration of a user's device use and suggest a charging method to extend battery life. For example, slow charging may be recommended for frequently used devices. This allows the system to suggest an optimal charging method based on the user's device usage patterns.

[0066] The charging standard determination system may further include an emotion response unit that estimates the user's emotions and provides a charging environment based on the user's emotions. For example, if the user is feeling stressed, the emotion response unit may suggest activating an aroma diffuser while charging to provide a relaxing environment. The emotion response unit may also adjust the charging speed based on the user's emotions. For example, if the user is in a hurry, the emotion response unit may recommend fast charging, and if the user is relaxed, the emotion response unit may recommend slow charging. This allows the charging environment to be provided based on the user's emotions.

[0067] The charging standard identification system may further include a location information linking unit that acquires location information of the user's device and suggests the optimal charging method based on the location information. For example, when the user is at home, the location information linking unit may suggest using a home charger. When the user is out and about, the location information linking unit may also recommend using a mobile battery. Furthermore, the location information linking unit may guide the user to the location of a charging station when the user is in a specific location. This makes it possible to suggest the optimal charging method based on the user's location information.

[0068] The charging standard determination system may further include a usage purpose response unit that considers the user's device usage purpose and suggests the optimal charging method depending on the usage purpose. For example, if the user uses the device for gaming, the usage purpose response unit may recommend fast charging. Alternatively, if the user uses the device for long video calls, the usage purpose response unit may suggest slow charging to extend battery life. Furthermore, the usage purpose response unit may also provide advice on how to select a charger and precautions to take depending on the device usage purpose. This allows the system to suggest the optimal charging method depending on the user's device usage purpose.

[0069] The charging standard determination system may further include an emotion advice unit that estimates the user's emotion and provides charging advice based on the emotion. For example, if the user feels anxious about charging, the emotion advice unit provides advice to give the user a sense of security. The emotion advice unit may also adjust the charging method according to the user's emotion. For example, if the user feels that charging is slow, the emotion advice unit may recommend fast charging, and if the user feels anxious because charging is too fast, the emotion advice unit may recommend slow charging. In this way, charging advice based on the user's emotion can be provided.

[0070] The charging standard identification system may further include a battery replacement prediction unit that predicts when the battery in a user's device needs to be replaced and suggests replacing the battery at the appropriate time. For example, the battery replacement prediction unit may analyze the battery's deterioration status and predict when replacement is necessary. The battery replacement prediction unit may also notify the user when replacement is required and provide information on how and where to replace the battery. This allows the user to replace the battery at the appropriate time and maintain the performance of the device.

[0071] The charging standard determination system may further include an emotion reminder unit that estimates the user's emotion and provides a charging reminder based on the emotion. For example, if the user tends to forget to charge, the emotion reminder unit sends a reminder to encourage the user to charge. The emotion reminder unit may also adjust the content and timing of the reminder based on the user's emotion. For example, if the user is feeling stressed, the emotion reminder unit may send a reminder at a time when the user is likely to relax. In this way, charging reminders based on the user's emotion can be provided.

[0072] The charging standard identification system may further include an energy efficiency optimization unit that analyzes the energy consumption pattern of the user's device and proposes a charging method that optimizes energy efficiency. For example, the energy efficiency optimization unit may propose a charging method that minimizes energy consumption depending on the device's usage status. The energy efficiency optimization unit may also learn the energy consumption pattern of the user's device and propose an optimal charging schedule. This allows the user to use the device while optimizing its energy efficiency.

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

[0074] Step 1: The device information acquisition unit acquires the user's device information. For example, the user inputs the type and model of the device. The device information acquisition unit can also acquire the device's serial number and manufacturing date. For example, by inputting the device's serial number, the manufacturing date and model information are acquired. Step 2: The charging standard determination unit determines the optimal charging standard based on the device information acquired by the device information acquisition unit. For example, the generation AI determines the appropriate charging standard based on the device type and model. The generation AI can also determine the optimal charging standard by taking into account the device's serial number and manufacturing date. For example, it may suggest a suitable charging method for older models. Step 3: The charging method suggestion unit suggests an appropriate charging method based on the charging standard determined by the charging standard determination unit. For example, the generation AI provides the user with specific instructions such as, "To charge your iPhone 12, use a Lightning cable." The generation AI also provides advice on how to choose a charger and what to be careful of. For example, it suggests precautions to take when using a fast charger.

[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 a 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 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, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, 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 processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and 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 device information acquisition unit that acquires device information of a user; a charging standard determination unit that determines an optimal charging standard based on the device information acquired by the device information acquisition unit; a charging method suggestion unit that suggests an appropriate charging method based on the charging standard identified by the charging standard identification unit. A system characterized by:

2. The charging standard determination unit Recommends optimal charging standards based on device usage history and battery status 2. The system of claim 1.

3. The device information acquisition unit Determines the optimal charging standard based on the device's serial number and manufacturing date 2. The system of claim 1.

4. The charging method suggestion unit Recommends optimal charging methods based on the user's charging habits 2. The system of claim 1.

5. The charging standard determination unit Using the emotion estimation function, the emotions felt by the user regarding charging are analyzed and a charging method to reduce stress is proposed.

2. The system of claim 1.

6. The device information acquisition unit Determine the optimal charging standard based on the device's usage environment 2. The system of claim 1.

7. The charging method suggestion unit Suggest charging methods to extend your device's battery life 2. The system of claim 1.

8. The charging method suggestion unit Using the emotion estimation function, the emotions felt by the user regarding charging methods are monitored in real time, and charging methods that elicit positive emotions are proposed.

2. The system of claim 1.

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