system

Abstract

The system of the embodiment aims to enable the use of generative AI even in environments where PCs and smartphones cannot be used. [Solution] A system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives instructions via voice input or touch operation. The generation unit uses a generation AI to perform processing based on the instructions received by the reception unit. The provision unit provides the results generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to utilize generative AI in environments where PCs and smartphones could not be used.

[0005] The system of the embodiment aims to enable the use of generative AI even in environments where PCs and smartphones cannot be used. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives instructions via voice input or touch operation. The generation unit uses a generation AI to perform processing based on the instructions received by the reception unit. The provision unit provides the results generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can utilize generative AI even in environments where PCs or smartphones cannot be used. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A portable generative AI tool according to an embodiment of the present invention is a device that can be used even in environments where PCs or smartphones are unavailable. This portable generative AI tool aims to bring the new possibilities of generative AI to everyone. Specifically, users can issue instructions to the generative AI through voice input or simple touch operations. For example, they can request various tasks from the generative AI, such as generating text, generating images, and analyzing data. The generative AI then performs appropriate processing based on the user's instructions and provides the results. Next, this portable tool is designed to be usable even without an internet connection. For example, it is equipped with a local database and cache function that allows the generative AI to operate in offline environments. This allows the generative AI to be utilized in locations where internet connection is difficult, such as mountainous regions or underground. Furthermore, this tool is battery-powered and can be used for long periods of time. For example, it can be used safely even in environments where power is unavailable. Its lightweight and portable design allows users to easily use the generative AI anywhere. This portable tool, which allows users to utilize generative AI anywhere, is a device that can be used even in environments where PCs or smartphones are unavailable, bringing the new possibilities of generative AI to everyone. This allows the portable generative AI tool to provide an environment where users can utilize generative AI anywhere.

[0029] A portable generative AI tool according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives instructions via voice input or touch operation. For example, the reception unit can receive voice input through a microphone. The reception unit can also receive touch operation using a touchscreen. The generation unit causes the generation AI to perform processing based on the instructions received by the reception unit. For example, the generation unit can request various tasks, such as generating text, generating images, and analyzing data, from the generation AI. The generation unit uses the generation AI to perform appropriate processing based on user instructions and provide the results. The provision unit provides the results generated by the generation unit. For example, the provision unit displays the generated text, images, and analysis results to the user. The provision unit can use the generation AI to provide the generated results in an appropriate format. This allows the portable generative AI tool according to an embodiment to receive instructions via voice input or touch operation, have the generation AI perform processing, and provide the results.

[0030] The generation unit can be equipped with a local database or a cache function. The generation unit can use, for example, a local database to store data for the generation AI to operate even in an offline environment. The local database stores, for example, data and models required for generation. The generation unit can also use a cache function to temporarily store data required for generation. The cache function is intended, for example, to enable quick access to data required for generation. This allows the generation unit to store and quickly access data for the generation AI to operate even in an offline environment.

[0031] The providing unit can also operate in an offline environment. For example, the providing unit can provide the generated results even in an offline environment by using a local database or a cache function. The providing unit stores the generated results in a local database so that they can be accessed even in an offline environment. The providing unit can also temporarily store the generated results using a cache function so that they can be quickly provided even in an offline environment. This allows the providing unit to provide the results of the generation AI even in an offline environment.

[0032] The reception unit can receive voice input through a microphone. The reception unit can receive voice input using, for example, a directional microphone. A directional microphone can preferentially pick up voice from a specific direction, thereby reducing ambient noise. The reception unit can also receive voice input using a microphone with a noise-canceling function. The noise-canceling function removes ambient noise and achieves clear voice input. Furthermore, the reception unit can also receive voice input using multiple microphones. Using multiple microphones can identify the direction of the voice and achieve more accurate voice input. As a result, the reception unit can receive voice input through the microphone, enabling voice instructions.

[0033] The reception unit can receive touch operations using a touch screen. The reception unit can receive touch operations using, for example, a capacitive touch screen. A capacitive touch screen recognizes touch operations by detecting the capacitance of a finger. The reception unit can also receive touch operations using a resistive touch screen. A resistive touch screen recognizes touch operations by detecting the pressure of a finger or a stylus pen. The reception unit can also receive multiple touch operations simultaneously using a multi-touch compatible touch screen. A multi-touch compatible touch screen recognizes operations using multiple fingers and realizes gesture operations, etc. This allows the reception unit to receive touch operations using the touch screen, enabling intuitive operation.

[0034] The providing unit can be operated for a certain period of time by battery power. The providing unit can be operated using, for example, a lithium-ion battery. Lithium-ion batteries have high energy density and can be used for long periods of time. The providing unit can also monitor the remaining battery level in real time and switch to an energy-efficient operating mode depending on the remaining battery level. For example, when the battery level is low, it switches to a low-power consumption mode to extend the operating time. Furthermore, the providing unit has a charge management function for optimizing the battery's charge state. For example, it has a built-in protection circuit for preventing overcharging and over-discharging. This allows the providing unit to be operated for long periods of time by battery power, making it usable even in environments where a power source is not available.

[0035] The providing unit can have a lightweight and easily portable design. For example, the housing of the providing unit can be made of a lightweight material. Examples of lightweight materials include aluminum alloy and carbon fiber. The providing unit is also designed to be compact and easy to carry. For example, the providing unit weighs 500 grams or less and fits in the palm of your hand. Furthermore, the providing unit is designed with portability in mind. For example, it is designed with a non-slip surface and an easy-to-hold shape. As a result, the providing unit has a lightweight and easily portable design, allowing it to be easily used anywhere.

[0036] The reception unit can analyze the user's past operation history and suggest the optimal input method. The reception unit can, for example, collect and analyze the user's past operation log. The operation log includes a history of voice inputs and touch operations performed by the user in the past. The reception unit can analyze the collected operation log and identify the user's operation pattern. For example, if the user has frequently used voice input in the past, the reception unit can preferentially suggest voice input. Also, if the user has frequently used touch operations in the past, the reception unit can preferentially suggest touch operations. Furthermore, the reception unit can also suggest an input method suitable for a specific time period. For example, if the user has frequently used voice input during a specific time period, the reception unit can suggest voice input during that time period. In this way, the reception unit can suggest the optimal input method by analyzing the user's past operation history.

[0037] The reception unit can filter ambient sounds to remove noise when inputting voice. The reception unit can filter ambient sounds using, for example, noise canceling technology. Noise canceling technology detects ambient noise and generates anti-phase sound waves to cancel it. The reception unit can also use a filter to remove noise in a specific frequency band. For example, a low-pass filter can be used to remove low-frequency noise, or a high-pass filter can be used to remove high-frequency noise. Furthermore, the reception unit can analyze ambient sounds in real time and perform appropriate filtering. For example, if the surroundings are noisy, the noise canceling function can be enabled to provide clear voice input. If the surroundings are quiet, the noise canceling function can be turned off to allow natural voice input. In this way, the reception unit can filter ambient sounds and remove noise, enabling clear voice input.

[0038] The reception unit can automatically select an appropriate language or dialect based on the user's geographical location information when inputting voice. The reception unit can acquire the user's geographical location information using, for example, GPS data or a location information service. Based on the geographical location information, the reception unit can automatically select an appropriate language or dialect. For example, if the user is in Japan, Japanese can be automatically selected. Also, if the user is in the Kansai region, Kansai dialect can be automatically selected. Furthermore, if the user is overseas, the language of that country can be automatically selected. In this way, the reception unit can automatically select an appropriate language or dialect taking into account the user's geographical location information, enabling more natural voice input.

[0039] The reception unit can adjust the size of the interface based on the size of the user's hand and the thickness of the user's fingers during a touch operation. The reception unit can use, for example, a sensor that measures the size of the user's hand and the thickness of the user's fingers. The reception unit can adjust the size of the interface by measuring the size of the hand and the thickness of the fingers. For example, if the user's hand is large, the size of the interface buttons can be increased. Also, if the user's fingers are thin, the size of the interface buttons can be reduced. Furthermore, the reception unit can detect the size of the user's hand and the thickness of the fingers in real time and dynamically adjust the size of the interface. In this way, the reception unit can provide a more user-friendly interface by adjusting the size of the interface based on the size of the user's hand and the thickness of the fingers.

[0040] The generation unit can update the data in the local database in real time during generation to reflect the latest information. The generation unit can update the data in the local database in real time using, for example, data synchronization technology. Data synchronization technology is used to keep the data in the local database up to date during generation. For example, the generation unit can update the news data in the local database in real time during generation to reflect the latest news. The generation unit can also update the weather data in the local database in real time during generation to reflect the latest weather information. Furthermore, the generation unit can update the stock price data in the local database in real time during generation to reflect the latest stock price information. In this way, the generation unit can generate content that reflects the latest information by updating the data in the local database in real time.

[0041] The generation unit can provide more personalized results by referring to the user's past generation history at the time of generation. The generation unit can use, for example, a database that stores the user's past generation history. The generation history includes texts and images generated by the user in the past, data analysis results, etc. The generation unit can refer to this generation history at the time of generation to understand the user's preferences and tendencies. For example, the generation unit can refer to the style of texts generated by the user in the past and generate new texts in a similar style. Also, the generation unit can refer to the theme of images generated by the user in the past and generate new images with a similar theme. Furthermore, the generation unit can refer to the patterns of data analysis generated by the user in the past and provide new analysis results with a similar pattern. In this way, the generation unit can provide more personalized results by referring to the user's past generation history.

[0042] The generation unit can enhance a cache function that can be used even in an offline environment during generation, thereby maintaining data consistency. The generation unit can, for example, introduce an algorithm for enhancing the cache function. The cache function temporarily stores data required for generation, allowing quick access even in an offline environment. The generation unit can enhance the cache function during generation, thereby temporarily storing generated content. For example, content generated in an offline environment is automatically synchronized when the system returns online. The generation unit can also introduce an algorithm for maintaining data consistency, thereby ensuring data consistency even in an offline environment. As a result, the generation unit can maintain data consistency by enhancing the cache function that can be used even in an offline environment.

[0043] The generation unit can reflect region-specific information by taking into account the user's geographical location information during generation. The generation unit can acquire the user's geographical location information using, for example, GPS data or a location information service. Based on the geographical location information, the generation unit can generate content that reflects region-specific information. For example, when the user is in a specific region, content that reflects news from that region can be generated. Also, when the user is in a specific region, content that reflects weather information from that region can be generated. Furthermore, when the user is in a specific region, content that reflects the culture and customs of that region can be generated. In this way, the generation unit can provide more appropriate content by reflecting region-specific information by taking into account the user's geographical location information.

[0044] The providing unit can select an energy-efficient display method based on the remaining battery level. The providing unit can, for example, monitor the remaining battery level in real time. When the remaining battery level is low, the providing unit can select a low-power display method. For example, the providing unit can switch to a low-power mode and adjust the screen brightness. When the remaining battery level is sufficient, the providing unit can provide a normal display method. Furthermore, the providing unit can automatically adjust the display brightness and resolution according to the remaining battery level. As a result, the providing unit can optimize battery life by selecting an energy-efficient display method according to the remaining battery level.

[0045] The providing unit can suggest an optimal display format by referring to the user's past usage history. The providing unit can use, for example, a database that stores the user's past usage history. The usage history includes display formats used by the user in the past and frequency of use. The providing unit can analyze the usage history to understand the user's preferences and tendencies. For example, if the user has frequently used list view in the past, the providing unit can preferentially suggest list view. Also, if the user has frequently used card view in the past, the providing unit can preferentially suggest card view. Furthermore, the providing unit can also suggest a display format suitable for a specific time period. For example, if the user has frequently used list view during a specific time period, the providing unit can suggest list view during that time period. In this way, the providing unit can suggest an optimal display format by referring to the user's past usage history.

[0046] The providing unit can display the results compactly based on the portability of the device. The providing unit can adjust the display content, for example, taking into account the size and weight of the device. If the device is small, the providing unit can display the information compactly. For example, the information can be displayed in summary form. If the device is large, the providing unit can display the information in detail. Furthermore, the providing unit can automatically adjust the display content, taking into account the portability of the device. As a result, the providing unit can display the results compactly, taking into account the portability of the device, allowing the user to efficiently check the information.

[0047] The providing unit can prioritize displaying region-specific information by taking into account the user's geographical location information. The providing unit can acquire the user's geographical location information by using, for example, GPS data or a location information service. Based on the geographical location information, the providing unit can prioritize displaying region-specific information. For example, when the user is in a specific region, news for that region can be prioritized. Also, when the user is in a specific region, weather information for that region can be prioritized. Furthermore, when the user is in a specific region, information related to the culture and customs of that region can be prioritized. This allows the providing unit to prioritize displaying region-specific information by taking into account the user's geographical location information, thereby enabling more appropriate information to be provided.

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

[0049] The generation unit can refer to the user's past generation history to improve the accuracy of the generated content. For example, the generation unit can refer to the style and theme of sentences the user has previously generated and generate new sentences with a similar style and theme. The generation unit can also refer to the color and composition of images the user has previously generated and generate new images with similar color and composition. Furthermore, the generation unit can refer to the user's past data analysis results and provide new analysis results with a similar pattern. This allows the generation unit to utilize the user's past generation history to provide more personalized content.

[0050] The reception unit can automatically select an appropriate language or dialect based on the user's geographical location information. For example, if the user is in Japan, Japanese can be automatically selected. If the user is in the Kansai region, Kansai dialect can be automatically selected. Furthermore, if the user is overseas, the language of that country can be automatically selected. This allows the reception unit to realize more natural voice input by taking the user's geographical location information into consideration.

[0051] The providing unit can select an energy-efficient display method based on the remaining battery level. For example, when the remaining battery level is low, the providing unit can select a low-power display method. For example, the providing unit can switch to a low-power mode and adjust the screen brightness. Also, when the remaining battery level is sufficient, the providing unit can provide a normal display method. Furthermore, the providing unit can automatically adjust the display brightness and resolution according to the remaining battery level. In this way, the providing unit can optimize battery life by selecting an energy-efficient display method according to the remaining battery level.

[0052] The reception unit can analyze the user's past operation history and suggest the optimal input method. For example, if the user has frequently used voice input in the past, the reception unit can preferentially suggest voice input. Also, if the user has frequently used touch operations in the past, the reception unit can preferentially suggest touch operations. Furthermore, the reception unit can also suggest an input method suitable for a specific time period. For example, if the user has frequently used voice input during a specific time period, the reception unit can suggest voice input during that time period. In this way, the reception unit can suggest the optimal input method by analyzing the user's past operation history.

[0053] The generation unit can update the data in the local database in real time at the time of generation to reflect the latest information. For example, the generation unit can update the news data in the local database in real time at the time of generation to reflect the latest news. The generation unit can also update the weather data in the local database in real time at the time of generation to reflect the latest weather information. Furthermore, the generation unit can update the stock price data in the local database in real time at the time of generation to reflect the latest stock price information. In this way, the generation unit can generate content that reflects the latest information by updating the data in the local database in real time.

[0054] The reception unit can filter surrounding environmental sounds to remove noise when inputting voice. For example, the reception unit can use noise canceling technology to filter surrounding environmental sounds. Noise canceling technology detects surrounding noise and generates anti-phase sound waves to cancel the noise. The reception unit can also use a filter to remove noise in a specific frequency band. For example, a low-pass filter is used to remove noise in a low-frequency band, and a high-pass filter is used to remove noise in a high-frequency band. Furthermore, the reception unit can analyze the environmental sounds in real time and perform appropriate filtering. As a result, the reception unit can filter surrounding environmental sounds and remove noise, enabling clear voice input.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The reception unit receives instructions by voice input or touch operation. For example, the reception unit can receive voice input through a microphone, and can also receive touch operation using a touch screen. Step 2: The generation unit uses the generation AI to perform processing based on the instructions received by the reception unit. For example, the generation unit can request various tasks from the generation AI, such as generating text, generating images, or analyzing data. The generation unit uses the generation AI to perform appropriate processing based on the user's instructions and provide the results. Step 3: The providing unit provides the results generated by the generating unit. For example, the providing unit displays the generated text, images, and analysis results to the user. The providing unit can use the generating AI to provide the generated results in an appropriate format.

[0057] (Example 2) A portable generative AI tool according to an embodiment of the present invention is a device that can be used even in environments where PCs or smartphones are unavailable. This portable generative AI tool aims to bring the new possibilities of generative AI to everyone. Specifically, users can issue instructions to the generative AI through voice input or simple touch operations. For example, they can request various tasks from the generative AI, such as generating text, generating images, and analyzing data. The generative AI then performs appropriate processing based on the user's instructions and provides the results. Next, this portable tool is designed to be usable even without an internet connection. For example, it is equipped with a local database and cache function that allows the generative AI to operate in offline environments. This allows the generative AI to be utilized in locations where internet connection is difficult, such as mountainous regions or underground. Furthermore, this tool is battery-powered and can be used for long periods of time. For example, it can be used safely even in environments where power is unavailable. Its lightweight and portable design allows users to easily use the generative AI anywhere. This portable tool, which allows users to utilize generative AI anywhere, is a device that can be used even in environments where PCs or smartphones are unavailable, bringing the new possibilities of generative AI to everyone. This allows the portable generative AI tool to provide an environment where users can utilize generative AI anywhere.

[0058] A portable generative AI tool according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives instructions via voice input or touch operation. For example, the reception unit can receive voice input through a microphone. The reception unit can also receive touch operation using a touchscreen. The generation unit causes the generation AI to perform processing based on the instructions received by the reception unit. For example, the generation unit can request various tasks, such as generating text, generating images, and analyzing data, from the generation AI. The generation unit uses the generation AI to perform appropriate processing based on user instructions and provide the results. The provision unit provides the results generated by the generation unit. For example, the provision unit displays the generated text, images, and analysis results to the user. The provision unit can use the generation AI to provide the generated results in an appropriate format. This allows the portable generative AI tool according to an embodiment to receive instructions via voice input or touch operation, have the generation AI perform processing, and provide the results.

[0059] The generation unit can be equipped with a local database or a cache function. The generation unit can use, for example, a local database to store data for the generation AI to operate even in an offline environment. The local database stores, for example, data and models required for generation. The generation unit can also use a cache function to temporarily store data required for generation. The cache function is intended, for example, to enable quick access to data required for generation. This allows the generation unit to store and quickly access data for the generation AI to operate even in an offline environment.

[0060] The providing unit can also operate in an offline environment. For example, the providing unit can provide the generated results even in an offline environment by using a local database or a cache function. The providing unit stores the generated results in a local database so that they can be accessed even in an offline environment. The providing unit can also temporarily store the generated results using a cache function so that they can be quickly provided even in an offline environment. This allows the providing unit to provide the results of the generation AI even in an offline environment.

[0061] The reception unit can receive voice input through a microphone. The reception unit can receive voice input using, for example, a directional microphone. A directional microphone can preferentially pick up voice from a specific direction, thereby reducing ambient noise. The reception unit can also receive voice input using a microphone with a noise-canceling function. The noise-canceling function removes ambient noise and achieves clear voice input. Furthermore, the reception unit can also receive voice input using multiple microphones. Using multiple microphones can identify the direction of the voice and achieve more accurate voice input. As a result, the reception unit can receive voice input through the microphone, enabling voice instructions.

[0062] The reception unit can receive touch operations using a touch screen. The reception unit can receive touch operations using, for example, a capacitive touch screen. A capacitive touch screen recognizes touch operations by detecting the capacitance of a finger. The reception unit can also receive touch operations using a resistive touch screen. A resistive touch screen recognizes touch operations by detecting the pressure of a finger or a stylus pen. The reception unit can also receive multiple touch operations simultaneously using a multi-touch compatible touch screen. A multi-touch compatible touch screen recognizes operations using multiple fingers and realizes gesture operations, etc. This allows the reception unit to receive touch operations using the touch screen, enabling intuitive operation.

[0063] The providing unit can be operated for a certain period of time by battery power. The providing unit can be operated using, for example, a lithium-ion battery. Lithium-ion batteries have high energy density and can be used for long periods of time. The providing unit can also monitor the remaining battery level in real time and switch to an energy-efficient operating mode depending on the remaining battery level. For example, when the battery level is low, it switches to a low-power consumption mode to extend the operating time. Furthermore, the providing unit has a charge management function for optimizing the battery's charge state. For example, it has a built-in protection circuit for preventing overcharging and over-discharging. This allows the providing unit to be operated for long periods of time by battery power, making it usable even in environments where a power source is not available.

[0064] The providing unit can have a lightweight and easily portable design. For example, the housing of the providing unit can be made of a lightweight material. Examples of lightweight materials include aluminum alloy and carbon fiber. The providing unit is also designed to be compact and easy to carry. For example, the providing unit weighs 500 grams or less and fits in the palm of your hand. Furthermore, the providing unit is designed with portability in mind. For example, it is designed with a non-slip surface and an easy-to-hold shape. As a result, the providing unit has a lightweight and easily portable design, allowing it to be easily used anywhere.

[0065] The reception unit can estimate the user's emotion and adjust the sensitivity of the voice input based on the estimated user's emotion. The reception unit can estimate the user's emotion using, for example, voice analysis technology. The voice analysis technology analyzes the tone, speed, volume, etc. of the user's voice to estimate the emotion. The reception unit can also estimate the user's emotion using facial expression recognition technology. The facial expression recognition technology analyzes the user's facial expression captured by a camera to estimate the emotion. Furthermore, the reception unit can adjust the sensitivity of the voice input based on the estimated user's emotion. For example, if the user is nervous, the sensitivity of the voice input can be increased to recognize even quiet voices. If the user is relaxed, the sensitivity of the voice input can be set to normal to recognize natural conversation. If the user is excited, the sensitivity of the voice input can be lowered to prevent erroneous recognition. In this way, the reception unit can adjust the sensitivity of the voice input according to the user's emotion, enabling more appropriate voice input.

[0066] The reception unit can analyze the user's past operation history and suggest the optimal input method. The reception unit can, for example, collect and analyze the user's past operation log. The operation log includes a history of voice inputs and touch operations performed by the user in the past. The reception unit can analyze the collected operation log and identify the user's operation pattern. For example, if the user has frequently used voice input in the past, the reception unit can preferentially suggest voice input. Also, if the user has frequently used touch operations in the past, the reception unit can preferentially suggest touch operations. Furthermore, the reception unit can also suggest an input method suitable for a specific time period. For example, if the user has frequently used voice input during a specific time period, the reception unit can suggest voice input during that time period. In this way, the reception unit can suggest the optimal input method by analyzing the user's past operation history.

[0067] The reception unit can filter ambient sounds to remove noise when inputting voice. The reception unit can filter ambient sounds using, for example, noise canceling technology. Noise canceling technology detects ambient noise and generates anti-phase sound waves to cancel it. The reception unit can also use a filter to remove noise in a specific frequency band. For example, a low-pass filter can be used to remove low-frequency noise, or a high-pass filter can be used to remove high-frequency noise. Furthermore, the reception unit can analyze ambient sounds in real time and perform appropriate filtering. For example, if the surroundings are noisy, the noise canceling function can be enabled to provide clear voice input. If the surroundings are quiet, the noise canceling function can be turned off to allow natural voice input. In this way, the reception unit can filter ambient sounds and remove noise, enabling clear voice input.

[0068] The reception unit can estimate the user's emotions and adjust the sensitivity of touch operations based on the estimated user emotions. The reception unit can estimate the user's emotions using, for example, voice analysis technology or facial expression recognition technology. Voice analysis technology analyzes the tone, speed, volume, etc. of the user's voice to estimate emotions. Facial expression recognition technology analyzes the user's facial expressions captured by a camera to estimate emotions. Furthermore, the reception unit can adjust the sensitivity of touch operations based on the estimated user emotions. For example, if the user is nervous, the sensitivity of touch operations can be increased to respond to even light touches. If the user is relaxed, the sensitivity of touch operations can be set to normal to provide a natural operating feel. Furthermore, if the user is excited, the sensitivity of touch operations can be reduced to prevent erroneous operations. In this way, the reception unit can adjust the sensitivity of touch operations according to the user's emotions, enabling more appropriate touch operations.

[0069] The reception unit can automatically select an appropriate language or dialect based on the user's geographical location information when inputting voice. The reception unit can acquire the user's geographical location information using, for example, GPS data or a location information service. Based on the geographical location information, the reception unit can automatically select an appropriate language or dialect. For example, if the user is in Japan, Japanese can be automatically selected. Also, if the user is in the Kansai region, Kansai dialect can be automatically selected. Furthermore, if the user is overseas, the language of that country can be automatically selected. In this way, the reception unit can automatically select an appropriate language or dialect taking into account the user's geographical location information, enabling more natural voice input.

[0070] The reception unit can adjust the size of the interface based on the size of the user's hand and the thickness of the user's fingers during a touch operation. The reception unit can use, for example, a sensor that measures the size of the user's hand and the thickness of the user's fingers. The reception unit can adjust the size of the interface by measuring the size of the hand and the thickness of the fingers. For example, if the user's hand is large, the size of the interface buttons can be increased. Also, if the user's fingers are thin, the size of the interface buttons can be reduced. Furthermore, the reception unit can detect the size of the user's hand and the thickness of the fingers in real time and dynamically adjust the size of the interface. In this way, the reception unit can provide a more user-friendly interface by adjusting the size of the interface based on the size of the user's hand and the thickness of the fingers.

[0071] The generation unit can estimate the user's emotion and adjust the tone of the generated content based on the estimated user's emotion. The generation unit can estimate the user's emotion using, for example, voice analysis technology or facial expression recognition technology. Voice analysis technology analyzes the tone, speed, volume, etc. of the user's voice to estimate the emotion. Facial expression recognition technology analyzes the user's facial expression captured by a camera to estimate the emotion. Furthermore, the generation unit can adjust the tone of the generated content based on the estimated user's emotion. For example, if the user is relaxed, a sentence with a calm tone can be generated. Also, if the user is excited, a sentence with a lively tone can be generated. Furthermore, if the user is sad, a sentence with a comforting tone can be generated. In this way, the generation unit can provide more appropriate content by adjusting the tone of the generated content according to the user's emotion.

[0072] The generation unit can update the data in the local database in real time during generation to reflect the latest information. The generation unit can update the data in the local database in real time using, for example, data synchronization technology. Data synchronization technology is used to keep the data in the local database up to date during generation. For example, the generation unit can update the news data in the local database in real time during generation to reflect the latest news. The generation unit can also update the weather data in the local database in real time during generation to reflect the latest weather information. Furthermore, the generation unit can update the stock price data in the local database in real time during generation to reflect the latest stock price information. In this way, the generation unit can generate content that reflects the latest information by updating the data in the local database in real time.

[0073] The generation unit can provide more personalized results by referring to the user's past generation history at the time of generation. The generation unit can use, for example, a database that stores the user's past generation history. The generation history includes texts and images generated by the user in the past, data analysis results, etc. The generation unit can refer to this generation history at the time of generation to understand the user's preferences and tendencies. For example, the generation unit can refer to the style of texts generated by the user in the past and generate new texts in a similar style. Also, the generation unit can refer to the theme of images generated by the user in the past and generate new images with a similar theme. Furthermore, the generation unit can refer to the patterns of data analysis generated by the user in the past and provide new analysis results with a similar pattern. In this way, the generation unit can provide more personalized results by referring to the user's past generation history.

[0074] The generation unit can estimate the user's emotion and adjust the level of detail of the generated content based on the estimated user's emotion. The generation unit can estimate the user's emotion using, for example, voice analysis technology or facial expression recognition technology. Voice analysis technology analyzes the tone, speed, volume, etc. of the user's voice to estimate the emotion. Facial expression recognition technology analyzes the user's facial expression captured by a camera to estimate the emotion. Furthermore, the generation unit can adjust the level of detail of the generated content based on the estimated user's emotion. For example, if the user is in a hurry, the generation unit can generate content with concise content. On the other hand, if the user is relaxed, the generation unit can generate content with detailed content. Furthermore, if the user is excited, the generation unit can generate content with visually stimulating effects. In this way, the generation unit can provide more appropriate content by adjusting the level of detail of the generated content according to the user's emotion.

[0075] The generation unit can enhance a cache function that can be used even in an offline environment during generation, thereby maintaining data consistency. The generation unit can, for example, introduce an algorithm for enhancing the cache function. The cache function temporarily stores data required for generation, allowing quick access even in an offline environment. The generation unit can enhance the cache function during generation, thereby temporarily storing generated content. For example, content generated in an offline environment is automatically synchronized when the system returns online. The generation unit can also introduce an algorithm for maintaining data consistency, thereby ensuring data consistency even in an offline environment. As a result, the generation unit can maintain data consistency by enhancing the cache function that can be used even in an offline environment.

[0076] The generation unit can reflect region-specific information by taking into account the user's geographical location information during generation. The generation unit can acquire the user's geographical location information using, for example, GPS data or a location information service. Based on the geographical location information, the generation unit can generate content that reflects region-specific information. For example, when the user is in a specific region, content that reflects news from that region can be generated. Also, when the user is in a specific region, content that reflects weather information from that region can be generated. Furthermore, when the user is in a specific region, content that reflects the culture and customs of that region can be generated. In this way, the generation unit can provide more appropriate content by reflecting region-specific information by taking into account the user's geographical location information.

[0077] The providing unit can estimate the user's emotions and adjust the display method of the results based on the estimated user's emotions. The providing unit can estimate the user's emotions using, for example, voice analysis technology and facial expression recognition technology. Voice analysis technology analyzes the tone, speed, volume, etc. of the user's voice to estimate emotions. Facial expression recognition technology analyzes the user's facial expressions captured by a camera to estimate emotions. Furthermore, the providing unit can adjust the display method of the results based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. Furthermore, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows the providing unit to adjust the display method of the results according to the user's emotions, enabling more appropriate display.

[0078] The providing unit can select an energy-efficient display method based on the remaining battery level. The providing unit can, for example, monitor the remaining battery level in real time. When the remaining battery level is low, the providing unit can select a low-power display method. For example, the providing unit can switch to a low-power mode and adjust the screen brightness. When the remaining battery level is sufficient, the providing unit can provide a normal display method. Furthermore, the providing unit can automatically adjust the display brightness and resolution according to the remaining battery level. As a result, the providing unit can optimize battery life by selecting an energy-efficient display method according to the remaining battery level.

[0079] The providing unit can suggest an optimal display format by referring to the user's past usage history. The providing unit can use, for example, a database that stores the user's past usage history. The usage history includes display formats used by the user in the past and frequency of use. The providing unit can analyze the usage history to understand the user's preferences and tendencies. For example, if the user has frequently used list view in the past, the providing unit can preferentially suggest list view. Also, if the user has frequently used card view in the past, the providing unit can preferentially suggest card view. Furthermore, the providing unit can also suggest a display format suitable for a specific time period. For example, if the user has frequently used list view during a specific time period, the providing unit can suggest list view during that time period. In this way, the providing unit can suggest an optimal display format by referring to the user's past usage history.

[0080] The providing unit can estimate the user's emotions and adjust the display order of the results based on the estimated user's emotions. The providing unit can estimate the user's emotions using, for example, voice analysis technology or facial expression recognition technology. Voice analysis technology analyzes the tone, speed, volume, etc. of the user's voice to estimate emotions. Facial expression recognition technology analyzes the user's facial expressions captured by a camera to estimate emotions. Furthermore, the providing unit can adjust the display order of the results based on the estimated user's emotions. For example, if the user is nervous, important information can be displayed first. Alternatively, if the user is relaxed, detailed information can be displayed sequentially. Furthermore, if the user is in a hurry, the main points can be displayed first. In this way, the providing unit can adjust the display order of the results according to the user's emotions, thereby providing more appropriate information.

[0081] The providing unit can display the results compactly based on the portability of the device. The providing unit can adjust the display content, for example, taking into account the size and weight of the device. If the device is small, the providing unit can display the information compactly. For example, the information can be displayed in summary form. If the device is large, the providing unit can display the information in detail. Furthermore, the providing unit can automatically adjust the display content, taking into account the portability of the device. As a result, the providing unit can display the results compactly, taking into account the portability of the device, allowing the user to efficiently check the information.

[0082] The providing unit can prioritize displaying region-specific information by taking into account the user's geographical location information. The providing unit can acquire the user's geographical location information by using, for example, GPS data or a location information service. Based on the geographical location information, the providing unit can prioritize displaying region-specific information. For example, when the user is in a specific region, news for that region can be prioritized. Also, when the user is in a specific region, weather information for that region can be prioritized. Furthermore, when the user is in a specific region, information related to the culture and customs of that region can be prioritized. This allows the providing unit to prioritize displaying region-specific information by taking into account the user's geographical location information, thereby enabling more appropriate information to be provided. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives voice input or touch operation using the microphone 38B or touch panel 38A of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and performs processes such as generating text and images and analyzing data using a generation AI. The provision unit provides the generated results to the user using the display 40A or speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives voice input or touch operation using the microphone 238 or touch panel of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and performs processes such as sentence generation, image generation, and data analysis using a generation AI. The provision unit provides the generated results to the user using the display or speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives voice input and touch operations using the microphone 238 or touch panel of the headset type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and performs processes such as sentence generation, image generation, and data analysis using a generation AI. The provision unit provides the generated results to the user using the display 343 or speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives voice input and touch operations using the microphone 238 or touch panel of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and performs processes such as sentence generation, image generation, and data analysis using a generation AI. The provision unit provides the generated results to the user using the display or speaker 240 of the robot 414.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The reception unit can analyze the tone, speed, and volume of the user's voice to estimate the user's emotions. For example, if the user is excited, the reception unit detects that emotion and sends that information to the generation unit. The generation unit can adjust the tone and style of the generated content based on the user's emotions. For example, if the user is relaxed, the generation unit can generate sentences with a calm tone. On the other hand, if the user is nervous, the generation unit can generate sentences with an encouraging tone. Furthermore, the provision unit can display the generated content according to the user's emotions. For example, if the user is sad, the provision unit can select a comforting display method. This enables the portable generative AI tool to generate and display content according to the user's emotions.

[0085] The generation unit can refer to the user's past generation history to improve the accuracy of the generated content. For example, the generation unit can refer to the style and theme of sentences the user has previously generated and generate new sentences with a similar style and theme. The generation unit can also refer to the color and composition of images the user has previously generated and generate new images with similar color and composition. Furthermore, the generation unit can refer to the user's past data analysis results and provide new analysis results with a similar pattern. This allows the generation unit to utilize the user's past generation history to provide more personalized content.

[0086] The providing unit can estimate the user's emotions and adjust the display method of the results based on the estimated emotions. For example, if the user is nervous, the providing unit can select a simple, highly visible display method. If the user is relaxed, the providing unit can select a display method that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can select a display method that focuses on the main points. This allows the providing unit to provide the optimal display method according to the user's emotions.

[0087] The reception unit can automatically select an appropriate language or dialect based on the user's geographical location information. For example, if the user is in Japan, Japanese can be automatically selected. If the user is in the Kansai region, Kansai dialect can be automatically selected. Furthermore, if the user is overseas, the language of that country can be automatically selected. This allows the reception unit to realize more natural voice input by taking the user's geographical location information into consideration.

[0088] The generation unit can estimate the user's emotion and adjust the level of detail of the generated content based on the estimated emotion. For example, if the user is in a hurry, the generation unit can generate content with concise content. If the user is relaxed, the generation unit can generate content with detailed content. Furthermore, if the user is excited, the generation unit can generate content with visually stimulating effects. This allows the generation unit to provide optimal content according to the user's emotion.

[0089] The providing unit can select an energy-efficient display method based on the remaining battery level. For example, when the remaining battery level is low, the providing unit can select a low-power display method. For example, the providing unit can switch to a low-power mode and adjust the screen brightness. Also, when the remaining battery level is sufficient, the providing unit can provide a normal display method. Furthermore, the providing unit can automatically adjust the display brightness and resolution according to the remaining battery level. In this way, the providing unit can optimize battery life by selecting an energy-efficient display method according to the remaining battery level.

[0090] The reception unit can analyze the user's past operation history and suggest the optimal input method. For example, if the user has frequently used voice input in the past, the reception unit can preferentially suggest voice input. Also, if the user has frequently used touch operations in the past, the reception unit can preferentially suggest touch operations. Furthermore, the reception unit can also suggest an input method suitable for a specific time period. For example, if the user has frequently used voice input during a specific time period, the reception unit can suggest voice input during that time period. In this way, the reception unit can suggest the optimal input method by analyzing the user's past operation history.

[0091] The generation unit can update the data in the local database in real time at the time of generation to reflect the latest information. For example, the generation unit can update the news data in the local database in real time at the time of generation to reflect the latest news. The generation unit can also update the weather data in the local database in real time at the time of generation to reflect the latest weather information. Furthermore, the generation unit can update the stock price data in the local database in real time at the time of generation to reflect the latest stock price information. In this way, the generation unit can generate content that reflects the latest information by updating the data in the local database in real time.

[0092] The providing unit can estimate the user's emotions and adjust the display order of the results based on the estimated emotions. For example, if the user is nervous, the providing unit can display important information first. If the user is relaxed, detailed information can be displayed sequentially. Furthermore, if the user is in a hurry, the main points can be displayed first. In this way, the providing unit can provide an optimal display order according to the user's emotions.

[0093] The reception unit can filter surrounding environmental sounds to remove noise when inputting voice. For example, the reception unit can use noise canceling technology to filter surrounding environmental sounds. Noise canceling technology detects surrounding noise and generates anti-phase sound waves to cancel the noise. The reception unit can also use a filter to remove noise in a specific frequency band. For example, a low-pass filter is used to remove noise in a low-frequency band, and a high-pass filter is used to remove noise in a high-frequency band. Furthermore, the reception unit can analyze the environmental sounds in real time and perform appropriate filtering. As a result, the reception unit can filter surrounding environmental sounds and remove noise, enabling clear voice input.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The reception unit receives instructions by voice input or touch operation. For example, the reception unit can receive voice input through a microphone, and can also receive touch operation using a touch screen. Step 2: The generation unit uses the generation AI to perform processing based on the instructions received by the reception unit. For example, the generation unit can request various tasks from the generation AI, such as generating text, generating images, or analyzing data. The generation unit uses the generation AI to perform appropriate processing based on the user's instructions and provide the results. Step 3: The providing unit provides the results generated by the generating unit. For example, the providing unit displays the generated text, images, and analysis results to the user. The providing unit can use the generating AI to provide the generated results in an appropriate format.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0098] 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.

[0099] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0111] 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.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0114] 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.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The 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.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives instructions by voice input or touch operation; a generation unit in which a generation AI performs processing based on the instruction received by the reception unit; a providing unit that provides the result generated by the generating unit. A system characterized by:

2. The generation unit Has a local database or cache functionality 2. The system of claim 1.

3. The providing unit Works offline too 2. The system of claim 1.

4. The reception unit Accepts audio input through a microphone 2. The system of claim 1.

5. The reception unit Accepts touch operations using a touch screen 2. The system of claim 1.

6. The providing unit Battery-powered for a certain period of time 2. The system of claim 1.

7. The providing unit Lightweight and easy to carry design 2. The system of claim 1.

8. The reception unit Estimate the user's emotion and adjust the sensitivity of the voice input based on the estimated user's emotion.

2. The system of claim 1.

9. The reception unit Analyzes the user's past operation history and suggests the optimal input method 2. The system of claim 1.

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