system

Abstract

The system of the embodiment aims to differentiate itself from other carriers by providing courses that teach how to use chat generation AI. [Solution] The system according to the embodiment includes a classroom unit, a revenue unit, and a differentiation unit. The classroom unit offers courses that teach how to use the chat generation AI. The revenue unit charges for the courses offered by the classroom unit. The differentiation unit has specific means for differentiating itself from other carriers based on the revenue earned by the revenue 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, there were no courses offered to teach how to use chat generation AI, which meant that there was a problem of not being able to sufficiently differentiate from other carriers.

[0005] The system of the embodiment aims to differentiate itself from other carriers by providing courses that teach how to use chat generation AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a classroom unit, a revenue unit, and a differentiation unit. The classroom unit provides courses that teach how to use the chat generation AI. The revenue unit charges for the courses provided by the classroom unit. The differentiation unit has specific means for differentiating itself from other carriers based on the revenue earned by the revenue unit. [Effects of the Invention]

[0007] The system according to the embodiment provides courses on how to use chat generation AI, which differentiates the system from other carriers. [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 system according to an embodiment of the present invention aims to increase store revenue by adding a course on how to use chat generation AI to the curriculum of a smartphone class. The system includes a classroom section that offers courses on how to use the chat generation AI, a revenue section that charges for the courses offered by the classroom section, and a differentiation section that differentiates the store from other carriers based on the revenue earned by the revenue section. For example, the system adds a course on how to use the chat generation AI to the curriculum of a smartphone class. For example, the system teaches how to use the chat generation AI to answer everyday questions and generate sentences. This allows students to learn the basics of how to use the chat generation AI. Next, the system charges for the chat generation AI course. For example, the system offers a special course on how to use the chat generation AI and offers the course for a fee. This allows the store to obtain a new source of revenue. Furthermore, the system enhances the curriculum of the chat generation AI to differentiate itself from other carriers. For example, the system offers a course on how to use an advanced chat generation AI that is not offered by other carriers. This allows students to gain knowledge that is not available from other carriers, increasing the appeal of the smartphone class. This allows the system to improve store profits by adding chat generation AI to the smartphone class curriculum. Also, by differentiating itself from other carriers, it can provide a new advantage in place of its price advantage. This allows the system to improve store profits by adding chat generation AI to the smartphone class curriculum. Also, by differentiating itself from other carriers, it can provide a new advantage in place of its price advantage.

[0029] The revenue improvement system according to the embodiment includes a classroom unit, a revenue unit, and a differentiation unit. The classroom unit offers courses that teach how to use the chat generation AI. For example, the classroom unit teaches how to use the chat generation AI to answer everyday questions. The classroom unit can also teach how to use the chat generation AI to generate sentences. The classroom unit can also offer special courses for learning how to use the chat generation AI. The revenue unit charges a fee for the courses offered by the classroom unit. For example, the revenue unit offers special courses for learning how to use the chat generation AI for a fee. The revenue unit can also offer a subscription model for learning how to use the chat generation AI. The revenue unit can also offer a one-time fee model for learning how to use the chat generation AI. The differentiation unit differentiates itself from other carriers based on the revenue earned by the revenue unit. For example, the differentiation unit offers courses that teach how to use advanced chat generation AI that are not offered by other carriers. The differentiation unit can also provide a unique curriculum. The differentiation unit can also invite special instructors to hold courses. As a result, the revenue improvement system according to the embodiment can charge for courses that teach how to use the chat generation AI, thereby increasing revenue and differentiating itself from other carriers.

[0030] The classroom department can teach students how to use chat generation AI to answer everyday questions. For example, the classroom department can teach students how to use chat generation AI to answer questions about the weather. The classroom department can also teach students how to use chat generation AI to answer questions about traffic information. The classroom department can also teach students how to use chat generation AI to answer questions about cooking recipes. This allows students to learn how to answer everyday questions.

[0031] The classroom can teach how to use chat generation AI to generate text. The classroom can teach, for example, how to use chat generation AI to generate business documents. The classroom can also teach how to use chat generation AI to generate emails. The classroom can also teach how to use chat generation AI to generate blog articles. This allows students to learn how to generate text.

[0032] The revenue department can provide special courses for learning how to use the chat generation AI for a fee. For example, the revenue department can provide special courses for learning how to use the chat generation AI for a fee. The revenue department can also provide a subscription model for learning how to use the chat generation AI. The revenue department can also provide a one-time fee model for learning how to use the chat generation AI. In this way, the revenue can be increased by providing special courses for a fee.

[0033] The differentiation department can provide courses that teach how to use advanced chat generation AI, including specific technical content that is not offered by other carriers. For example, the differentiation department can provide courses that teach how to use advanced chat generation AI that is not offered by other carriers. The differentiation department can also teach how to train custom models that are not offered by other carriers. The differentiation department can also teach how to use APIs that are not offered by other carriers. This allows the company to differentiate itself from other carriers.

[0034] The classroom department can analyze the student's past learning history and provide the most suitable curriculum for the student. For example, the classroom department can provide a curriculum that emphasizes the parts that need to be reviewed based on the content that the student has learned in the past. The classroom department can also provide a curriculum that includes more advanced content based on the student's areas of strength. The classroom department can also provide a curriculum that carefully explains the basics based on the student's areas of weakness. In this way, the effectiveness of learning is improved by providing the most suitable curriculum based on the student's learning history.

[0035] When teaching students how to use the chat generation AI, the classroom department can provide customized scenarios based on the students' interests. For example, if a student is interested in business, the classroom department can teach them how to use the chat generation AI in business situations. If a student is interested in hobbies, the classroom department can also teach them how to use the chat generation AI using scenarios related to their hobbies. If a student is interested in education, the classroom department can also teach them how to use the chat generation AI in educational settings. This increases students' motivation to learn by providing them with scenarios that match their interests.

[0036] The classroom department can collect feedback from students in real time and instantly improve the course content. For example, the classroom department can collect in real time any parts that students find difficult to understand and instantly add explanations. The classroom department can also emphasize parts that students found interesting and instantly adjust the course content. The classroom department can also improve the content of the next course based on student feedback. In this way, by collecting feedback in real time and instantly improving the course content, student satisfaction is increased.

[0037] The classroom division can provide courses tailored to the specific needs of each region by taking into account the geographic location information of the participants. For example, the classroom division can teach participants in urban areas how to use chat generation AI in business situations. The classroom division can also teach participants in rural areas how to use chat generation AI using agricultural scenarios. The classroom division can also teach participants in tourist areas how to use chat generation AI in the tourism industry. This increases participant satisfaction by providing courses tailored to the specific needs of each region.

[0038] The classroom department can analyze students' social media activities and incorporate related topics into the course. For example, the classroom department can incorporate topics that students often talk about on social media into the course. The classroom department can also analyze students' social media posts and provide related scenarios. The classroom department can also incorporate related topics into the course based on the activities of students' friends on social media. In this way, incorporating topics based on students' social media activities into the course makes it easier to attract students' interest.

[0039] The classroom department can customize the content and format of the course by reflecting past feedback from students. For example, the classroom department can adjust the content of the course by reflecting points for improvement that students have pointed out in the past. The classroom department can also change the format of the course based on past feedback from students. The classroom department can also add new course content by referring to past feedback from students. In this way, by reflecting past feedback, the content and format of the course can be made more appropriate for students.

[0040] The revenue department can analyze the student's payment history and propose the most suitable pricing plan. The revenue department can propose the most suitable pricing plan based on, for example, the amount the student has paid in the past. The revenue department can also propose a discount plan based on the student's payment history. The revenue department can also analyze the student's payment history and propose a premium plan. In this way, by proposing the most suitable pricing plan based on the payment history, student satisfaction is improved.

[0041] The revenue department can suggest additional paid services based on the purchase history of the student. For example, the revenue department can suggest related paid services based on services that the student has previously purchased. The revenue department can also suggest special offers based on the student's purchase history. The revenue department can also analyze the student's purchase history and suggest premium services. This allows revenue to be increased by suggesting additional paid services based on the purchase history.

[0042] The revenue department can improve the pricing plans based on the feedback from the students. For example, if the students are dissatisfied with the fees, the revenue department can suggest a discount plan. The revenue department can also adjust the pricing plans based on the students' feedback. The revenue department can also add new pricing plans based on the students' feedback. In this way, improving the pricing plans based on the feedback increases student satisfaction.

[0043] The revenue department can provide region-specific pricing plans by taking into account the geographic location information of the students. For example, the revenue department can provide a premium plan for business to students in urban areas. The revenue department can also provide discount plans to students in rural areas. The revenue department can also provide a special plan for the tourism industry to students in tourist areas. In this way, providing region-specific pricing plans increases student satisfaction.

[0044] The revenue generating unit can analyze the student's social media activity and suggest related paid services. For example, the revenue generating unit can suggest paid services related to topics that the student frequently discusses on social media. The revenue generating unit can also analyze the content of the student's social media posts and suggest related paid services. The revenue generating unit can also suggest related paid services based on the activity of the student's friends on social media. In this way, revenue can be generated by suggesting paid services based on social media activity.

[0045] The revenue department can customize the pricing plans by reflecting the students' past feedback. For example, the revenue department can adjust the pricing plans by reflecting improvements that the students have pointed out in the past. The revenue department can also change the pricing plans based on the students' past feedback. The revenue department can also add new pricing plans by referring to the students' past feedback. In this way, by reflecting the students' past feedback, the pricing plans become more appropriate for the students.

[0046] The differentiation unit can analyze the services of other carriers and identify the most effective points for differentiating itself from other carriers. For example, the differentiation unit can analyze the service contents of other carriers and provide unique services. The differentiation unit can also analyze the rate plans of other carriers and provide competitive rate plans. The differentiation unit can also analyze the customer satisfaction of other carriers and identify points of differentiation. In this way, unique points of differentiation can be identified by analyzing the services of other carriers.

[0047] The differentiation unit can add new differentiation elements based on the needs of the students. For example, the differentiation unit can add new functions that students request. The differentiation unit can also provide special services in accordance with the needs of the students. The differentiation unit can also add new differentiation elements based on student feedback. In this way, adding differentiation elements that meet the needs of the students improves student satisfaction.

[0048] The differentiation unit can improve the differentiation strategy based on the feedback from the students. For example, the differentiation unit adjusts the differentiation strategy by reflecting the points for improvement pointed out by the students. The differentiation unit can also change the differentiation strategy based on the feedback from the students. The differentiation unit can also add a new differentiation strategy by referring to the feedback from the students. In this way, the differentiation strategy can be improved based on the feedback, thereby increasing the satisfaction of the students.

[0049] The differentiation department can provide a differentiation strategy specific to a region, taking into account the geographical distribution of other carriers. For example, in urban areas, the differentiation department can provide premium services for business. In rural areas, the differentiation department can also provide services tailored to the specific needs of the region. In tourist areas, the differentiation department can also provide special services for the tourism industry. In this way, by providing a differentiation strategy specific to a region, customer satisfaction can be improved.

[0050] The differentiation department can analyze the social media activities of other carriers and propose related differentiation factors. For example, the differentiation department can analyze the content of posts on social media by other carriers and propose unique services. The differentiation department can also analyze customer responses on social media by other carriers and propose differentiation factors. The differentiation department can also analyze social media campaigns by other carriers and propose competitive campaigns. In this way, by analyzing the social media activities of other carriers, it is possible to propose unique differentiation factors.

[0051] The differentiation department can customize the differentiation strategy by reflecting past feedback from other carriers. The differentiation department can provide unique services based on, for example, feedback from customers of other carriers. The differentiation department can also analyze past feedback from other carriers and adjust the differentiation strategy. The differentiation department can also add new differentiation elements by reflecting the needs of customers of other carriers. In this way, a unique differentiation strategy can be provided by reflecting past feedback from other carriers.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The classroom department can analyze the learning style of the student and suggest the most suitable learning method. For example, if the student prefers visual learning, the classroom department can provide a course that makes heavy use of visual aids. If the student prefers auditory learning, the classroom department can also provide a course that makes heavy use of audio guides. If the student prefers practical learning, the classroom department can also provide a course that includes many hands-on sessions. This improves learning effectiveness by providing the most suitable learning method according to the student's learning style.

[0054] The revenue department can analyze the purchase history of the student and suggest related paid services. For example, the revenue department can suggest related paid services based on the services the student has previously purchased. The revenue department can also suggest special offers based on the student's purchase history. The revenue department can also analyze the student's purchase history and suggest premium services. This allows the revenue to be increased by suggesting additional paid services based on the purchase history.

[0055] The differentiation department can analyze the services of other carriers and identify the most effective points for differentiating itself from other carriers. For example, the differentiation department can analyze the service contents of other carriers and provide unique services. The differentiation department can also analyze the pricing plans of other carriers and provide competitive pricing plans. The differentiation department can also analyze the customer satisfaction of other carriers and identify points of differentiation. In this way, unique points of differentiation can be identified by analyzing the services of other carriers.

[0056] The classroom division can provide courses tailored to the specific needs of each region by taking into account the students' geographical location information. For example, the classroom division can teach students in urban areas how to use chat generation AI in business situations. The classroom division can also teach students in rural areas how to use chat generation AI using agricultural scenarios. The classroom division can also teach students in tourist areas how to use chat generation AI in the tourism industry. This increases student satisfaction by providing courses tailored to the specific needs of each region.

[0057] The classroom department can analyze students' social media activities and incorporate related topics into the course. For example, the classroom department can incorporate topics that students often talk about on social media into the course. The classroom department can also analyze students' social media posts and provide related scenarios. The classroom department can also incorporate related topics into the course based on the activities of students' friends on social media. In this way, incorporating topics based on students' social media activities into the course makes it easier to attract students' interest.

[0058] The classroom department can customize the content and format of the course by reflecting past feedback from students. For example, the classroom department can adjust the content of the course by reflecting points for improvement that students have pointed out in the past. The classroom department can also change the format of the course based on past feedback from students. The classroom department can also add new course content by referring to past feedback from students. In this way, by reflecting past feedback, the content and format of the course can be made more appropriate for students.

[0059] The Differentiation Department can provide region-specific differentiation strategies by taking into account the geographical distribution of other carriers. For example, the Differentiation Department can provide premium services for business in urban areas. The Differentiation Department can also provide services tailored to region-specific needs in rural areas. The Differentiation Department can also provide special services for the tourism industry in tourist destinations. In this way, providing region-specific differentiation strategies can improve customer satisfaction.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The classroom department will hold a course to teach how to use the chat generation AI. For example, the classroom department will teach how to use the chat generation AI to answer everyday questions and generate sentences. Special courses can also be held. Step 2: The revenue department charges a fee for the courses offered by the classroom department. For example, they can offer special courses for a fee, or offer a subscription model or a one-time fee model. Step 3: The Differentiation Department differentiates itself from other carriers based on the revenue generated by the Revenue Department. For example, it can offer advanced courses not offered by other carriers, provide its own curriculum, or invite special lecturers to conduct courses.

[0062] (Example 2) A system according to an embodiment of the present invention aims to increase store revenue by adding a course on how to use chat generation AI to the curriculum of a smartphone class. The system includes a classroom section that offers courses on how to use the chat generation AI, a revenue section that charges for the courses offered by the classroom section, and a differentiation section that differentiates the store from other carriers based on the revenue earned by the revenue section. For example, the system adds a course on how to use the chat generation AI to the curriculum of a smartphone class. For example, the system teaches how to use the chat generation AI to answer everyday questions and generate sentences. This allows students to learn the basics of how to use the chat generation AI. Next, the system charges for the chat generation AI course. For example, the system offers a special course on how to use the chat generation AI and offers the course for a fee. This allows the store to obtain a new source of revenue. Furthermore, the system enhances the curriculum of the chat generation AI to differentiate itself from other carriers. For example, the system offers a course on how to use an advanced chat generation AI that is not offered by other carriers. This allows students to gain knowledge that is not available from other carriers, increasing the appeal of the smartphone class. This allows the system to improve store profits by adding chat generation AI to the smartphone class curriculum. Also, by differentiating itself from other carriers, it can provide a new advantage in place of its price advantage. This allows the system to improve store profits by adding chat generation AI to the smartphone class curriculum. Also, by differentiating itself from other carriers, it can provide a new advantage in place of its price advantage.

[0063] The revenue improvement system according to the embodiment includes a classroom unit, a revenue unit, and a differentiation unit. The classroom unit offers courses that teach how to use the chat generation AI. For example, the classroom unit teaches how to use the chat generation AI to answer everyday questions. The classroom unit can also teach how to use the chat generation AI to generate sentences. The classroom unit can also offer special courses for learning how to use the chat generation AI. The revenue unit charges a fee for the courses offered by the classroom unit. For example, the revenue unit offers special courses for learning how to use the chat generation AI for a fee. The revenue unit can also offer a subscription model for learning how to use the chat generation AI. The revenue unit can also offer a one-time fee model for learning how to use the chat generation AI. The differentiation unit differentiates itself from other carriers based on the revenue earned by the revenue unit. For example, the differentiation unit offers courses that teach how to use advanced chat generation AI that are not offered by other carriers. The differentiation unit can also provide a unique curriculum. The differentiation unit can also invite special instructors to hold courses. As a result, the revenue improvement system according to the embodiment can charge for courses that teach how to use the chat generation AI, thereby increasing revenue and differentiating itself from other carriers.

[0064] The classroom department can teach students how to use chat generation AI to answer everyday questions. For example, the classroom department can teach students how to use chat generation AI to answer questions about the weather. The classroom department can also teach students how to use chat generation AI to answer questions about traffic information. The classroom department can also teach students how to use chat generation AI to answer questions about cooking recipes. This allows students to learn how to answer everyday questions.

[0065] The classroom can teach how to use chat generation AI to generate text. The classroom can teach, for example, how to use chat generation AI to generate business documents. The classroom can also teach how to use chat generation AI to generate emails. The classroom can also teach how to use chat generation AI to generate blog articles. This allows students to learn how to generate text.

[0066] The revenue department can provide special courses for learning how to use the chat generation AI for a fee. For example, the revenue department can provide special courses for learning how to use the chat generation AI for a fee. The revenue department can also provide a subscription model for learning how to use the chat generation AI. The revenue department can also provide a one-time fee model for learning how to use the chat generation AI. In this way, the revenue can be increased by providing special courses for a fee.

[0067] The differentiation department can provide courses that teach how to use advanced chat generation AI, including specific technical content that is not offered by other carriers. For example, the differentiation department can provide courses that teach how to use advanced chat generation AI that is not offered by other carriers. The differentiation department can also teach how to train custom models that are not offered by other carriers. The differentiation department can also teach how to use APIs that are not offered by other carriers. This allows the company to differentiate itself from other carriers.

[0068] The classroom unit can estimate the user's emotions and adjust the speed of the course based on the estimated user emotions. For example, if the user is excited, the classroom unit can speed up the course to advance at a good pace. Also, if the user is feeling anxious, the classroom unit can slow down the course to provide detailed explanations. Also, if the user is relaxed, the classroom unit can advance the course at a normal speed. This allows for more effective learning by adjusting the course's speed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0069] The classroom department can analyze the student's past learning history and provide the most suitable curriculum for the student. For example, the classroom department can provide a curriculum that emphasizes the parts that need to be reviewed based on the content that the student has learned in the past. The classroom department can also provide a curriculum that includes more advanced content based on the student's areas of strength. The classroom department can also provide a curriculum that carefully explains the basics based on the student's areas of weakness. In this way, the effectiveness of learning is improved by providing the most suitable curriculum based on the student's learning history.

[0070] When teaching students how to use the chat generation AI, the classroom department can provide customized scenarios based on the students' interests. For example, if a student is interested in business, the classroom department can teach them how to use the chat generation AI in business situations. If a student is interested in hobbies, the classroom department can also teach them how to use the chat generation AI using scenarios related to their hobbies. If a student is interested in education, the classroom department can also teach them how to use the chat generation AI in educational settings. This increases students' motivation to learn by providing them with scenarios that match their interests.

[0071] The classroom department can collect feedback from students in real time and instantly improve the course content. For example, the classroom department can collect in real time any parts that students find difficult to understand and instantly add explanations. The classroom department can also emphasize parts that students found interesting and instantly adjust the course content. The classroom department can also improve the content of the next course based on student feedback. In this way, by collecting feedback in real time and instantly improving the course content, student satisfaction is increased.

[0072] The classroom unit can estimate the user's emotions and adjust the content of the course based on the estimated user's emotions. For example, if the user is excited, the classroom unit can add more difficult content. Also, if the user is feeling anxious, the classroom unit can emphasize basic content. Also, if the user is relaxed, the classroom unit can provide normal content. This allows for more effective learning by adjusting the content of the course according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The classroom division can provide courses tailored to the specific needs of each region by taking into account the geographic location information of the participants. For example, the classroom division can teach participants in urban areas how to use chat generation AI in business situations. The classroom division can also teach participants in rural areas how to use chat generation AI using agricultural scenarios. The classroom division can also teach participants in tourist areas how to use chat generation AI in the tourism industry. This increases participant satisfaction by providing courses tailored to the specific needs of each region.

[0074] The classroom department can analyze students' social media activities and incorporate related topics into the course. For example, the classroom department can incorporate topics that students often talk about on social media into the course. The classroom department can also analyze students' social media posts and provide related scenarios. The classroom department can also incorporate related topics into the course based on the activities of students' friends on social media. In this way, incorporating topics based on students' social media activities into the course makes it easier to attract students' interest.

[0075] The classroom department can customize the content and format of the course by reflecting past feedback from students. For example, the classroom department can adjust the content of the course by reflecting points for improvement that students have pointed out in the past. The classroom department can also change the format of the course based on past feedback from students. The classroom department can also add new course content by referring to past feedback from students. In this way, by reflecting past feedback, the content and format of the course can be made more appropriate for students.

[0076] The revenue unit can estimate the user's emotions and adjust the pricing plan based on the estimated user emotions. For example, if the user is satisfied, the revenue unit can suggest a premium plan. If the user is dissatisfied, the revenue unit can also suggest a discount plan. If the user is excited, the revenue unit can also suggest a special offer. This allows the pricing plan to be adjusted according to the user's emotions, thereby providing a more appropriate pricing plan. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] The revenue department can analyze the student's payment history and propose the most suitable pricing plan. The revenue department can propose the most suitable pricing plan based on, for example, the amount the student has paid in the past. The revenue department can also propose a discount plan based on the student's payment history. The revenue department can also analyze the student's payment history and propose a premium plan. In this way, by proposing the most suitable pricing plan based on the payment history, student satisfaction is improved.

[0078] The revenue department can suggest additional paid services based on the purchase history of the student. For example, the revenue department can suggest related paid services based on services that the student has previously purchased. The revenue department can also suggest special offers based on the student's purchase history. The revenue department can also analyze the student's purchase history and suggest premium services. This allows revenue to be increased by suggesting additional paid services based on the purchase history.

[0079] The revenue department can improve the pricing plans based on the feedback from the students. For example, if the students are dissatisfied with the fees, the revenue department can suggest a discount plan. The revenue department can also adjust the pricing plans based on the students' feedback. The revenue department can also add new pricing plans based on the students' feedback. In this way, improving the pricing plans based on the feedback increases student satisfaction.

[0080] The revenue unit can estimate the user's emotions and prioritize pricing plans based on the estimated user emotions. For example, if the user is satisfied, the revenue unit can prioritize premium plans. Also, if the user is dissatisfied, the revenue unit can prioritize discount plans. Also, if the user is excited, the revenue unit can prioritize special offers. This allows for more appropriate pricing plans to be provided by prioritizing pricing plans according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The revenue department can provide region-specific pricing plans by taking into account the geographic location information of the students. For example, the revenue department can provide a premium plan for business to students in urban areas. The revenue department can also provide discount plans to students in rural areas. The revenue department can also provide a special plan for the tourism industry to students in tourist areas. In this way, providing region-specific pricing plans increases student satisfaction.

[0082] The revenue generating unit can analyze the student's social media activity and suggest related paid services. For example, the revenue generating unit can suggest paid services related to topics that the student frequently discusses on social media. The revenue generating unit can also analyze the content of the student's social media posts and suggest related paid services. The revenue generating unit can also suggest related paid services based on the activity of the student's friends on social media. In this way, revenue can be generated by suggesting paid services based on social media activity.

[0083] The revenue department can customize the pricing plans by reflecting the students' past feedback. For example, the revenue department can adjust the pricing plans by reflecting improvements that the students have pointed out in the past. The revenue department can also change the pricing plans based on the students' past feedback. The revenue department can also add new pricing plans by referring to the students' past feedback. In this way, by reflecting the students' past feedback, the pricing plans become more appropriate for the students.

[0084] The differentiation unit can estimate the user's emotions and adjust the differentiation strategy based on the estimated user emotions. For example, if the user is satisfied, the differentiation unit can emphasize premium services. Also, if the user is dissatisfied, the differentiation unit can emphasize discount services. Also, if the user is excited, the differentiation unit can emphasize special offers. This enables more effective differentiation by adjusting the differentiation strategy according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] The differentiation unit can analyze the services of other carriers and identify the most effective points for differentiating itself from other carriers. For example, the differentiation unit can analyze the service contents of other carriers and provide unique services. The differentiation unit can also analyze the rate plans of other carriers and provide competitive rate plans. The differentiation unit can also analyze the customer satisfaction of other carriers and identify points of differentiation. In this way, unique points of differentiation can be identified by analyzing the services of other carriers.

[0086] The differentiation unit can add new differentiation elements based on the needs of the students. For example, the differentiation unit can add new functions that students request. The differentiation unit can also provide special services in accordance with the needs of the students. The differentiation unit can also add new differentiation elements based on student feedback. In this way, adding differentiation elements that meet the needs of the students improves student satisfaction.

[0087] The differentiation unit can improve the differentiation strategy based on the feedback from the students. For example, the differentiation unit adjusts the differentiation strategy by reflecting the points for improvement pointed out by the students. The differentiation unit can also change the differentiation strategy based on the feedback from the students. The differentiation unit can also add a new differentiation strategy by referring to the feedback from the students. In this way, the differentiation strategy can be improved based on the feedback, thereby increasing the satisfaction of the students.

[0088] The differentiation unit can estimate the user's emotions and determine the priority of differentiation factors based on the estimated user emotions. For example, if the user is satisfied, the differentiation unit can prioritize providing premium services. Also, if the user is dissatisfied, the differentiation unit can prioritize providing discount services. Also, if the user is excited, the differentiation unit can prioritize providing special offers. This enables more effective differentiation by determining the priority of differentiation factors according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] The differentiation department can provide a differentiation strategy specific to a region, taking into account the geographical distribution of other carriers. For example, in urban areas, the differentiation department can provide premium services for business. In rural areas, the differentiation department can also provide services tailored to the specific needs of the region. In tourist areas, the differentiation department can also provide special services for the tourism industry. In this way, by providing a differentiation strategy specific to a region, customer satisfaction can be improved.

[0090] The differentiation department can analyze the social media activities of other carriers and propose related differentiation factors. For example, the differentiation department can analyze the content of posts on social media by other carriers and propose unique services. The differentiation department can also analyze customer responses on social media by other carriers and propose differentiation factors. The differentiation department can also analyze social media campaigns by other carriers and propose competitive campaigns. In this way, by analyzing the social media activities of other carriers, it is possible to propose unique differentiation factors.

[0091] The differentiation department can customize the differentiation strategy by reflecting past feedback from other carriers. The differentiation department can provide unique services based on, for example, feedback from customers of other carriers. The differentiation department can also analyze past feedback from other carriers and adjust the differentiation strategy. The differentiation department can also add new differentiation elements by reflecting the needs of customers of other carriers. In this way, a unique differentiation strategy can be provided by reflecting past feedback from other carriers. === Hard Collateral 1-1 === Each of the multiple elements including the classroom unit, revenue unit, and differentiation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the classroom unit is realized by the control unit 46A of the smart device 14 and provides a course that teaches how to use the chat generation AI. The revenue unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and charges for the course. The differentiation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and differentiates the service from other carriers. === Hard Collateral 1-2 === Each of the multiple elements including the classroom unit, revenue unit, and differentiation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the classroom unit is realized by the control unit 46A of the smart glasses 214 and provides a course that teaches how to use the chat generation AI. The revenue unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and charges for the course. The differentiation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and differentiates the service from other carriers. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned classroom unit, revenue unit, and differentiation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the classroom unit is realized by the control unit 46A of the headset type terminal 314 and provides a course that teaches how to use the chat generation AI. The revenue unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and charges for the course. The differentiation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and differentiates the service from other carriers. === Hard Collateral 1-4 === Each of the multiple elements including the classroom unit, revenue unit, and differentiation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the classroom unit is realized by the control unit 46A of the robot 414 and provides a course that teaches how to use the chat generation AI. The revenue unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and charges for the course. The differentiation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and differentiates the service from other carriers.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The classroom department can analyze the learning style of the student and suggest the most suitable learning method. For example, if the student prefers visual learning, the classroom department can provide a course that makes heavy use of visual aids. If the student prefers auditory learning, the classroom department can also provide a course that makes heavy use of audio guides. If the student prefers practical learning, the classroom department can also provide a course that includes many hands-on sessions. This improves learning effectiveness by providing the most suitable learning method according to the student's learning style.

[0094] The classroom unit can estimate the emotions of the student and personalize the learning content based on the estimated emotions. For example, if the student is excited, the classroom unit can provide challenging tasks. If the student is anxious, the classroom unit can emphasize basic content. If the student is relaxed, the classroom unit can provide normal content. This allows for more effective learning by personalizing the learning content according to the student's emotions.

[0095] The revenue department can analyze the purchase history of the student and suggest related paid services. For example, the revenue department can suggest related paid services based on the services the student has previously purchased. The revenue department can also suggest special offers based on the student's purchase history. The revenue department can also analyze the student's purchase history and suggest premium services. This allows the revenue to be increased by suggesting additional paid services based on the purchase history.

[0096] The revenue unit can estimate the user's emotions and adjust the pricing plan based on the estimated emotions. For example, the revenue unit can suggest a premium plan if the user is satisfied. Alternatively, the revenue unit can suggest a discount plan if the user is dissatisfied. Alternatively, the revenue unit can suggest a special offer if the user is excited. In this way, by adjusting the pricing plan according to the user's emotions, a more appropriate pricing plan can be provided.

[0097] The differentiation department can analyze the services of other carriers and identify the most effective points for differentiating itself from other carriers. For example, the differentiation department can analyze the service contents of other carriers and provide unique services. The differentiation department can also analyze the pricing plans of other carriers and provide competitive pricing plans. The differentiation department can also analyze the customer satisfaction of other carriers and identify points of differentiation. In this way, unique points of differentiation can be identified by analyzing the services of other carriers.

[0098] The classroom division can provide courses tailored to the specific needs of each region by taking into account the students' geographical location information. For example, the classroom division can teach students in urban areas how to use chat generation AI in business situations. The classroom division can also teach students in rural areas how to use chat generation AI using agricultural scenarios. The classroom division can also teach students in tourist areas how to use chat generation AI in the tourism industry. This increases student satisfaction by providing courses tailored to the specific needs of each region.

[0099] The classroom department can analyze students' social media activities and incorporate related topics into the course. For example, the classroom department can incorporate topics that students often talk about on social media into the course. The classroom department can also analyze students' social media posts and provide related scenarios. The classroom department can also incorporate related topics into the course based on the activities of students' friends on social media. In this way, incorporating topics based on students' social media activities into the course makes it easier to attract students' interest.

[0100] The classroom department can customize the content and format of the course by reflecting past feedback from students. For example, the classroom department can adjust the content of the course by reflecting points for improvement that students have pointed out in the past. The classroom department can also change the format of the course based on past feedback from students. The classroom department can also add new course content by referring to past feedback from students. In this way, by reflecting past feedback, the content and format of the course can be made more appropriate for students.

[0101] The differentiation unit can estimate the user's emotions and adjust the differentiation strategy based on the estimated emotions. For example, the differentiation unit can emphasize premium services when the user is satisfied. Also, the differentiation unit can emphasize discount services when the user is dissatisfied. Also, the differentiation unit can emphasize special offers when the user is excited. This allows for more effective differentiation by adjusting the differentiation strategy according to the user's emotions.

[0102] The Differentiation Department can provide region-specific differentiation strategies by taking into account the geographical distribution of other carriers. For example, the Differentiation Department can provide premium services for business in urban areas. The Differentiation Department can also provide services tailored to region-specific needs in rural areas. The Differentiation Department can also provide special services for the tourism industry in tourist destinations. In this way, providing region-specific differentiation strategies can improve customer satisfaction.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The classroom department will hold a course to teach how to use the chat generation AI. For example, the classroom department will teach how to use the chat generation AI to answer everyday questions and generate sentences. Special courses can also be held. Step 2: The revenue department charges a fee for the courses offered by the classroom department. For example, they can offer special courses for a fee, or offer a subscription model or a one-time fee model. Step 3: The Differentiation Department differentiates itself from other carriers based on the revenue generated by the Revenue Department. For example, it can offer advanced courses not offered by other carriers, provide its own curriculum, or invite special lecturers to conduct courses.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

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

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

[0119] 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.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 classroom section that provides courses on how to use chat generation AI, a revenue-generating department that charges for the courses provided by the classroom department; a differentiation unit having specific means for differentiating the carrier from other carriers based on the profits obtained by the profit unit; Equipped with A system characterized by:

2. The classroom section: Teaching how to use chat generation AI to answer everyday questions 2. The system of claim 1.

3. The classroom section: Teaching how to generate text using chat generation AI 2. The system of claim 1.

4. The revenue department Offering special paid courses to learn how to use chat generation AI 2. The system of claim 1.

5. The differentiation unit Establish a course to teach how to use advanced chat generation AI, including specific technical content not offered by other carriers.

2. The system of claim 1.

6. The classroom section: Estimate the user's emotions and adjust the pace of the course based on the estimated user emotions.

2. The system of claim 1.

7. The classroom section: Analyze students' past learning history and provide the most suitable curriculum for them 2. The system of claim 1.

8. The classroom section: When teaching how to use chat generation AI, provide customized scenarios based on the learner's interests.

2. The system of claim 1.

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