system

Abstract

An object of the system according to the embodiment is to allow a freelance creator to receive multilingual transmission and legal / tax support.SOLUTION: A system according to an embodiment includes a translation unit, a posting unit, an advice collection unit, and a law support unit. The translation unit translates the content transmitted by the creator into multiple languages. The posting unit posts the outgoing content translated by the translation unit to an SNS or a blog. An advice collection part collects advice from fans for a work of a creator. The legal support department provides legal consultation and tax support to the creators.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology made it difficult for freelance creators to communicate in multiple languages ​​or receive legal and tax support, resulting in a high workload.

[0005] The system of the embodiment aims to enable freelance creators to communicate in multiple languages ​​and receive legal and tax support. [Means for solving the problem]

[0006] The system according to the embodiment includes a translation unit, a posting unit, an advice collection unit, and a legal support unit. The translation unit translates content posted by creators into multiple languages. The posting unit posts the content translated by the translation unit on social media or a blog. The advice collection unit collects advice from fans regarding the creators' works. The legal support unit provides legal consultations and tax support to creators. [Effects of the Invention]

[0007] The system according to the embodiment allows freelance creators to communicate in multiple languages ​​and receive legal and tax support. [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) The creator support AI according to an embodiment of the present invention is a system that translates creators' content into multiple languages, posts it on social media and blogs, collects advice from fans, and provides legal advice and tax support. As a result, the creator support AI allows creators to post content in multiple languages, collect advice from fans, and receive legal advice and tax support.

[0029] A creator support AI according to an embodiment includes a translation unit, a posting unit, an advice collection unit, and a legal support unit. The translation unit translates content posted by a creator into multiple languages. For example, the generation AI translates content that a creator wants to post into multiple languages ​​and posts it on a social media platform or blog. The generation AI translates a blog post written by a creator in Japanese into English, French, Chinese, etc., and posts it to the social media platform or blog in each language. The generation AI receives input from a prompt containing instructions on the content that the creator wants to post, and the generation AI generates multilingual posts based on the prompt. The posting unit posts the content translated by the translation unit to the social media platform or blog. For example, the generation AI translates content that a creator wants to post into multiple languages ​​and posts it on a social media platform or blog. The generation AI translates a blog post written by a creator in Japanese into English, French, Chinese, etc., and posts it to the social media platform or blog in each language. The generation AI receives input from a prompt containing instructions on the content that the creator wants to post, and the generation AI generates multilingual posts based on the prompt. The advice collection unit collects advice from fans regarding a creator's work. For example, when a creator posts a new illustration, the generation AI collects comments and feedback from fans, analyzes them, and provides them to the creator. The generation AI receives input from fans, which it analyzes to generate advice to provide to the creator. The legal support unit provides legal advice and tax support to creators. For example, when a creator wants to consult about the contents of a contract or wants to know how to file a tax return, the generation AI provides appropriate advice. The generation AI receives input from prompts containing instructions about the content the creator wants to consult about, and the generation AI generates appropriate advice based on the prompts. As a result, the creator support AI according to the embodiment enables creators to communicate in multiple languages, collect advice from fans, and receive legal advice and tax support.

[0030] The translation unit can automatically adjust expressions and tones appropriate for each language, taking into account the cultural background of the post content. For example, when the generation AI translates a post from Japanese into English, the translation unit adjusts the expressions and tone to be appropriate, taking into account the cultural background of English-speaking countries. For example, it converts honorific Japanese expressions into polite English expressions. When the generation AI translates into French, the translation unit appropriately adjusts expressions that include humor or satire, taking into account the cultural background of France. For example, it uses expressions that are in line with France's satirical culture. When the generation AI translates into Chinese, the translation unit takes into account the cultural background of China and uses expressions that take into account traditional values ​​and social topics. For example, it selects expressions that emphasize family and social ties. This makes it possible to communicate in multiple languages ​​using expressions and tones appropriate for each language.

[0031] The posting unit can analyze user reactions to posted content in real time and optimize the content of the next post. For example, the posting unit's generation AI can analyze user comments and the number of likes in real time after a post and reflect this in the content of the next post. For example, the posting unit can cover topics that received a lot of positive reactions next time. The posting unit's generation AI can also adjust the tone and theme of the post based on user reaction data. For example, if there are a lot of negative reactions, the tone can be softened. The posting unit's generation AI can also analyze user reactions and suggest the optimal time and frequency to post. For example, if there are a lot of reactions during a certain time period, the posting can be concentrated during that time period. This allows the content of the next post to be optimized based on user reactions.

[0032] The legal support department can analyze data from past legal consultations and tax support to provide optimal advice. For example, the generation AI in the legal support department analyzes past legal consultation data to provide optimal advice based on similar cases. For example, advice is provided based on past consultation cases regarding the contents of contracts. The legal support department also analyzes past tax support data to provide optimal tax advice. For example, it suggests the optimal method for filing tax returns based on past tax return cases. The legal support department also analyzes past legal consultation and tax support data in real time to provide optimal advice instantly. For example, it provides advice that responds to new laws and changes in the tax system. This allows the department to provide optimal advice based on past data.

[0033] The legal support department can automatically collect the latest legal and tax information and provide it to creators. For example, the generation AI in the legal support department automatically collects the latest legal and tax information and provides it to creators. For example, it notifies creators of information on new laws and changes to tax systems in real time. The legal support department also has the generation AI analyze the latest legal and tax information and provide appropriate advice to creators. For example, it suggests how to prepare contracts based on new laws. The legal support department also has the generation AI collect the latest legal and tax information and create a dashboard to provide to creators. For example, it lists the latest changes to laws and tax systems. This allows the latest legal and tax information to be automatically collected and provided to creators.

[0034] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0035] The creator support AI can further include a schedule management unit that manages the creator's schedule. The schedule management unit automatically organizes the creator's schedule and reminds them of important deadlines and events. For example, the generation AI registers deadlines and events set by the creator in a calendar and sends reminders. The schedule management unit can also make suggestions to optimize the creator's work time. For example, the generation AI analyzes the creator's work patterns and suggests optimal work times. Furthermore, the schedule management unit can appropriately allocate break times based on the creator's schedule. This allows the creator to work efficiently.

[0036] The creator support AI can further include a health management unit that manages the creator's health. The health management unit monitors the creator's health status and provides appropriate health advice. For example, the generation AI can analyze the creator's sleep data and provide advice on improving sleep quality. The health management unit can also analyze the creator's dietary data and suggest a nutritionally balanced meal plan. Furthermore, the health management unit can suggest an appropriate exercise program based on the creator's exercise data. This allows creators to maintain their health while engaging in creative activities.

[0037] Creator support AI can also be equipped with a copyright management unit that manages the copyright of creators' works. The copyright management unit automatically registers and protects the copyright of creators' works. For example, when a creator creates a new work, the generative AI automatically registers the copyright of that work. The copyright management unit can also monitor whether the creator's work is being used illegally and notify users if a violation occurs. Furthermore, the copyright management unit can manage the licenses of the creator's work and provide appropriate usage permissions. This allows creators to release their works with peace of mind.

[0038] The creator support AI can also be equipped with a marketing support section that supports creators' marketing activities. The marketing support section plans and executes promotion strategies for creators' works. For example, the generative AI suggests optimal promotion methods for creators' works and supports effective posting on social media and blogs. The marketing support section can also analyze creators' target audiences and provide optimal marketing strategies. Furthermore, the marketing support section can analyze sales data of creators' works and make proposals for sales promotion. This allows creators to effectively promote their works.

[0039] The creator support AI can further include a networking support unit that supports creators' networking activities. The networking support unit provides opportunities for creators to connect with other creators and industry professionals. For example, the generative AI can recommend appropriate events and communities based on the creator's interests and activities. The networking support unit can also provide advice on optimizing the creator's profile and appealing to other creators and industry professionals. Furthermore, the networking support unit can support the creator's activities at events and communities and make suggestions for achieving effective networking. This allows creators to carry out effective networking activities.

[0040] The processing flow of the first embodiment will be briefly explained below.

[0041] Step 1: The translation department translates the creator's content into multiple languages. For example, the generation AI translates a blog post written by a creator in Japanese into English, French, Chinese, etc., and posts it to social media or a blog in each language. The input to the generation AI is a prompt containing instructions on the content the creator wants to post, and the generation AI generates the post content in multiple languages ​​based on that prompt. Step 2: The posting unit posts the translated content to social media or a blog. For example, the generation AI could translate a blog post written by a creator in Japanese into English, French, Chinese, etc., and post it to social media or a blog in that language. Step 3: The advice collection unit collects advice from fans about the creator's work. For example, when a creator posts a new illustration, the generation AI collects comments and feedback from fans, analyzes them, and provides them to the creator. The input to the generation AI is comments and feedback from fans, which the generation AI analyzes to generate advice to provide to the creator. Step 4: The legal support department provides legal advice and tax support to creators. For example, if a creator wants to consult about the contents of a contract or how to file a tax return, the generation AI will provide appropriate advice. The input to the generation AI is a prompt containing instructions on what the creator wants to consult about, and the generation AI generates appropriate advice based on the prompt.

[0042] (Example 2) The creator support AI according to an embodiment of the present invention is a system that translates creators' content into multiple languages, posts it on social media and blogs, collects advice from fans, and provides legal advice and tax support. As a result, the creator support AI allows creators to post content in multiple languages, collect advice from fans, and receive legal advice and tax support.

[0043] A creator support AI according to an embodiment includes a translation unit, a posting unit, an advice collection unit, and a legal support unit. The translation unit translates content posted by a creator into multiple languages. For example, the generation AI translates content that a creator wants to post into multiple languages ​​and posts it on a social media platform or blog. The generation AI translates a blog post written by a creator in Japanese into English, French, Chinese, etc., and posts it to the social media platform or blog in each language. The generation AI receives input from a prompt containing instructions on the content that the creator wants to post, and the generation AI generates multilingual posts based on the prompt. The posting unit posts the content translated by the translation unit to the social media platform or blog. For example, the generation AI translates content that a creator wants to post into multiple languages ​​and posts it on a social media platform or blog. The generation AI translates a blog post written by a creator in Japanese into English, French, Chinese, etc., and posts it to the social media platform or blog in each language. The generation AI receives input from a prompt containing instructions on the content that the creator wants to post, and the generation AI generates multilingual posts based on the prompt. The advice collection unit collects advice from fans regarding a creator's work. For example, when a creator posts a new illustration, the generation AI collects comments and feedback from fans, analyzes them, and provides them to the creator. The generation AI receives input from fans, which it analyzes to generate advice to provide to the creator. The legal support unit provides legal advice and tax support to creators. For example, when a creator wants to consult about the contents of a contract or wants to know how to file a tax return, the generation AI provides appropriate advice. The generation AI receives input from prompts containing instructions about the content the creator wants to consult about, and the generation AI generates appropriate advice based on the prompts. As a result, the creator support AI according to the embodiment enables creators to communicate in multiple languages, collect advice from fans, and receive legal advice and tax support.

[0044] The translation unit can automatically adjust expressions and tones appropriate for each language, taking into account the cultural background of the post content. For example, when the generation AI translates a post from Japanese into English, the translation unit adjusts the expressions and tone to be appropriate, taking into account the cultural background of English-speaking countries. For example, it converts honorific Japanese expressions into polite English expressions. When the generation AI translates into French, the translation unit appropriately adjusts expressions that include humor or satire, taking into account the cultural background of France. For example, it uses expressions that are in line with France's satirical culture. When the generation AI translates into Chinese, the translation unit takes into account the cultural background of China and uses expressions that take into account traditional values ​​and social topics. For example, it selects expressions that emphasize family and social ties. This makes it possible to communicate in multiple languages ​​using expressions and tones appropriate for each language.

[0045] The posting unit can analyze user reactions to posted content in real time and optimize the content of the next post. For example, the posting unit's generation AI can analyze user comments and the number of likes in real time after a post and reflect this in the content of the next post. For example, the posting unit can cover topics that received a lot of positive reactions next time. The posting unit's generation AI can also adjust the tone and theme of the post based on user reaction data. For example, if there are a lot of negative reactions, the tone can be softened. The posting unit's generation AI can also analyze user reactions and suggest the optimal time and frequency to post. For example, if there are a lot of reactions during a certain time period, the posting can be concentrated during that time period. This allows the content of the next post to be optimized based on user reactions.

[0046] The advice collection unit can emotionally analyze advice from fans and provide creators with positive feedback as a priority. For example, the generation AI in the advice collection unit emotionally analyzes comments from fans and provides creators with positive feedback as a priority. For example, it highlights comments of praise and encouragement. The advice collection unit also extracts positive feedback based on fans' emotional data and provides it to creators. For example, it highlights points that fans particularly liked. The advice collection unit also emotionally analyzes fans' comments in real time and immediately notifies creators of positive feedback. For example, it immediately provides positive reactions immediately after posting. This prioritizes positive feedback, thereby improving creators' motivation.

[0047] The legal support department can analyze data from past legal consultations and tax support to provide optimal advice. For example, the generation AI in the legal support department analyzes past legal consultation data to provide optimal advice based on similar cases. For example, advice is provided based on past consultation cases regarding the contents of contracts. The legal support department also analyzes past tax support data to provide optimal tax advice. For example, it suggests the optimal method for filing tax returns based on past tax return cases. The legal support department also analyzes past legal consultation and tax support data in real time to provide optimal advice instantly. For example, it provides advice that responds to new laws and changes in the tax system. This allows the department to provide optimal advice based on past data.

[0048] The legal support department can automatically collect the latest legal and tax information and provide it to creators. For example, the generation AI in the legal support department automatically collects the latest legal and tax information and provides it to creators. For example, it notifies creators of information on new laws and changes to tax systems in real time. The legal support department also has the generation AI analyze the latest legal and tax information and provide appropriate advice to creators. For example, it suggests how to prepare contracts based on new laws. The legal support department also has the generation AI collect the latest legal and tax information and create a dashboard to provide to creators. For example, it lists the latest changes to laws and tax systems. This allows the latest legal and tax information to be automatically collected and provided to creators.

[0049] The legal support department can use the emotion estimation function to suggest stress reduction measures based on the creator's emotions. For example, the generation AI in the legal support department analyzes the creator's emotional data and suggests stress reduction measures based on the emotions. For example, if the creator is feeling stressed, it suggests ways to relax. The legal support department also uses the emotion estimation function to suggest stress reduction measures based on the creator's emotional data. For example, if the emotion score is low, it suggests ways to refresh. The legal support department also uses the generation AI to analyze the creator's emotional data in real time and instantly suggest stress reduction measures. For example, if the creator is tired, it encourages them to take a break. This makes it possible to suggest stress reduction measures based on the creator's emotions.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The creator support AI can further include a schedule management unit that manages the creator's schedule. The schedule management unit automatically organizes the creator's schedule and reminds them of important deadlines and events. For example, the generation AI registers deadlines and events set by the creator in a calendar and sends reminders. The schedule management unit can also make suggestions to optimize the creator's work time. For example, the generation AI analyzes the creator's work patterns and suggests optimal work times. Furthermore, the schedule management unit can appropriately allocate break times based on the creator's schedule. This allows the creator to work efficiently.

[0052] The creator support AI can further include a health management unit that manages the creator's health. The health management unit monitors the creator's health status and provides appropriate health advice. For example, the generation AI can analyze the creator's sleep data and provide advice on improving sleep quality. The health management unit can also analyze the creator's dietary data and suggest a nutritionally balanced meal plan. Furthermore, the health management unit can suggest an appropriate exercise program based on the creator's exercise data. This allows creators to maintain their health while engaging in creative activities.

[0053] Creator support AI can also be equipped with a copyright management unit that manages the copyright of creators' works. The copyright management unit automatically registers and protects the copyright of creators' works. For example, when a creator creates a new work, the generative AI automatically registers the copyright of that work. The copyright management unit can also monitor whether the creator's work is being used illegally and notify users if a violation occurs. Furthermore, the copyright management unit can manage the licenses of the creator's work and provide appropriate usage permissions. This allows creators to release their works with peace of mind.

[0054] The creator support AI can also be equipped with a marketing support section that supports creators' marketing activities. The marketing support section plans and executes promotion strategies for creators' works. For example, the generative AI suggests optimal promotion methods for creators' works and supports effective posting on social media and blogs. The marketing support section can also analyze creators' target audiences and provide optimal marketing strategies. Furthermore, the marketing support section can analyze sales data of creators' works and make proposals for sales promotion. This allows creators to effectively promote their works.

[0055] The creator support AI can further include a networking support unit that supports creators' networking activities. The networking support unit provides opportunities for creators to connect with other creators and industry professionals. For example, the generative AI can recommend appropriate events and communities based on the creator's interests and activities. The networking support unit can also provide advice on optimizing the creator's profile and appealing to other creators and industry professionals. Furthermore, the networking support unit can support the creator's activities at events and communities and make suggestions for achieving effective networking. This allows creators to carry out effective networking activities.

[0056] The creator support AI can further include an emotional support unit that estimates the creator's emotions and supports the creator's creative activities based on the estimated emotions. The emotional support unit analyzes the creator's emotional data and provides advice appropriate for creative activities. For example, if the generation AI is feeling stressed, it can suggest ways for the creator to relax. The emotional support unit can also adjust the pace of the creator's creative activities based on the creator's emotions. For example, it can encourage the creator to take a rest if they are tired. Furthermore, the emotional support unit can suggest an environment appropriate for creative activities based on the creator's emotional data. This allows the creator to engage in creative activities while receiving support based on their emotions.

[0057] The creator support AI can further include a motivation improvement unit that estimates the creator's emotions and improves the creator's motivation based on the estimated emotions. The motivation improvement unit analyzes the creator's emotional data and provides advice to improve motivation. For example, the generation AI can send an encouraging message if the creator is feeling down. The motivation improvement unit can also support goal setting to maintain motivation based on the creator's emotions. For example, it can set small goals that will give the creator a sense of accomplishment. Furthermore, the motivation improvement unit can suggest a reward system to improve motivation based on the creator's emotional data. This allows creators to receive support for improving their motivation based on their emotions.

[0058] The creator support AI can further include a communication support unit that estimates the creator's emotions and supports the creator's communication based on the estimated emotions. The communication support unit analyzes the creator's emotional data and suggests appropriate communication methods. For example, the generation AI can provide advice to help the creator relax if they are nervous. The communication support unit can also suggest effective communication strategies based on the creator's emotions. For example, it can select topics that the creator can speak about with confidence. Furthermore, the communication support unit can adjust the timing and method of communication based on the creator's emotional data. This allows creators to receive communication support based on their emotions.

[0059] The creator support AI can further include a learning support unit that estimates the creator's emotions and supports the creator's learning activities based on the estimated emotions. The learning support unit analyzes the creator's emotional data and provides advice appropriate for learning. For example, if the generation AI is lacking in concentration, it can suggest ways to improve the creator's concentration. The learning support unit can also manage the creator's learning progress based on the creator's emotions. For example, it can encourage the creator to take a break if they are tired. Furthermore, the learning support unit can suggest learning materials and resources appropriate for learning based on the creator's emotional data. This allows the creator to receive learning support based on their emotions.

[0060] The creator support AI can further include an emotional support unit that estimates the creator's emotions and supports the creator's creative activities based on the estimated emotions. The emotional support unit analyzes the creator's emotional data and provides advice appropriate for creative activities. For example, if the generation AI is feeling stressed, it can suggest ways for the creator to relax. The emotional support unit can also adjust the pace of the creator's creative activities based on the creator's emotions. For example, it can encourage the creator to take a rest if they are tired. Furthermore, the emotional support unit can suggest an environment appropriate for creative activities based on the creator's emotional data. This allows the creator to engage in creative activities while receiving support based on their emotions.

[0061] The processing flow of the second embodiment will be briefly explained below.

[0062] Step 1: The translation department translates the creator's content into multiple languages. For example, the generation AI translates a blog post written by a creator in Japanese into English, French, Chinese, etc., and posts it to social media or a blog in each language. The input to the generation AI is a prompt containing instructions on the content the creator wants to post, and the generation AI generates the post content in multiple languages ​​based on that prompt. Step 2: The posting unit posts the translated content to social media or a blog. For example, the generation AI could translate a blog post written by a creator in Japanese into English, French, Chinese, etc., and post it to social media or a blog in that language. Step 3: The advice collection unit collects advice from fans about the creator's work. For example, when a creator posts a new illustration, the generation AI collects comments and feedback from fans, analyzes them, and provides them to the creator. The input to the generation AI is comments and feedback from fans, which the generation AI analyzes to generate advice to provide to the creator. Step 4: The legal support department provides legal advice and tax support to creators. For example, if a creator wants to consult about the contents of a contract or how to file a tax return, the generation AI will provide appropriate advice. The input to the generation AI is a prompt containing instructions on what the creator wants to consult about, and the generation AI generates appropriate advice based on the prompt.

[0063] 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.

[0064] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0065] 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.

[0066] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0067] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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).

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0082] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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).

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0097] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] In the robot 414, 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 robot 414 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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."

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0130] 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 translation department that translates creators' content into multiple languages, a posting unit that posts the transmitted content translated by the translation unit to a social networking site or a blog; an advice collection unit that collects advice from fans regarding the creator's work; a legal support department that provides legal advice and tax support to the creators; A system characterized by:

2. The translation unit Considering the cultural context of the post, the system automatically adjusts the expression and tone to suit each language.

2. The system of claim 1.

3. The posting unit: Analyze user reactions to posts in real time and optimize the content of your next post.

2. The system of claim 1.

4. The advice collection unit Sentiment analysis is performed on the advice from the fans, and positive feedback is given to the creator preferentially.

2. The system of claim 1.

5. The legal support department: Analyzing data on past legal consultations and tax support to provide optimal advice 2. The system of claim 1.

Images