system

Abstract

The system according to the embodiment aims to enable creators to acquire fans, increase revenue, and focus on their creative work. [Solution] A system according to an embodiment includes a reception unit, a generation unit, an advice unit, and a consultation unit. The reception unit inputs keywords. The generation unit creates text for SNS or blogs in multiple languages ​​based on the keywords input by the reception unit. The advice unit provides advice on works based on the text generated by the generation unit. The consultation unit provides legal or tax consultation based on the advice provided by the advice 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] Conventional technology does not adequately support creators in gaining fans, increasing revenue, or focusing on their creative work, and there is room for improvement.

[0005] The system according to the embodiment aims to enable creators to acquire fans, increase revenue, and focus on their creative work. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, an advice unit, and a consultation unit. The reception unit inputs keywords. The generation unit creates text for SNS or blogs in multiple languages ​​based on the keywords input by the reception unit. The advice unit provides advice on works based on the text generated by the generation unit. The consultation unit provides legal or tax consultation based on the advice provided by the advice unit. [Effects of the Invention]

[0007] The system according to the embodiment allows creators to gain fans, increase revenue, and focus on their creative work. [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 system according to an embodiment of the present invention provides functions that allow creators to acquire fans, increase revenue, and focus on tasks other than their primary creative activities. This creator support system utilizes generative AI to build a unique AI model and provides the following functions. First, it provides a function that allows creators to effectively create posts for social media and blogs in multiple languages ​​simply by inputting keywords. For example, when a creator inputs "new product announcement," the generative AI automatically generates posts for social media and blogs in multiple languages ​​based on the keywords. This makes it easier for creators to disseminate information in multiple languages ​​and promotes fan acquisition. Second, it provides a function that allows creators to receive advice on their work. The generative AI analyzes the work uploaded by the creator and suggests improvements and new ideas. For example, when an illustrator uploads their own illustration, the generative AI provides advice on color usage and composition. This allows creators to improve the quality of their work. Furthermore, it provides a function that allows creators to receive legal and tax consultations. The generative AI provides appropriate legal and tax advice in response to questions input by the creator. For example, if a creator asks "how to register copyright," the generation AI will provide information on the procedures and necessary documents. This allows creators to quickly resolve legal and tax issues. In this way, the present invention provides advanced functions that allow creators to focus on gaining fans, increasing revenue, and other tasks outside of their primary creative activities. This allows the creator support system to allow creators to focus on gaining fans, increasing revenue, and other tasks outside of their primary creative activities.

[0029] The creator support system according to the embodiment includes a reception unit, a generation unit, an advice unit, and a consultation unit. The reception unit is a unit through which a creator inputs keywords. Keywords input by a creator include, but are not limited to, new product announcements, event announcements, and work descriptions. The generation unit uses a generation AI to create text for social media and blogs in multiple languages ​​based on the keywords input by the reception unit. For example, when a creator inputs "new product announcement," the generation AI automatically generates text for social media and blogs in multiple languages ​​based on the keywords. The generation AI is a text generation AI (e.g., GPT-4 (registered trademark)) or a multimodal generation AI, which makes it easier for creators to disseminate information in multiple languages. The advice unit provides advice on the creator's work based on the text generated by the generation unit. For example, the advice unit analyzes works uploaded by creators and suggests improvements and new ideas. The generation AI analyzes illustrations uploaded by creators and provides advice on color usage and composition. The consultation unit is a component that allows creators to receive legal and tax consultations based on the advice provided by the advice unit. For example, when a creator asks about "how to register copyright," the generation AI provides information on the procedures and necessary documents. This allows creators to quickly resolve legal and tax issues. As a result, the creator support system according to the embodiment allows creators to focus on acquiring fans, increasing revenue, and other tasks outside of their primary creative activities.

[0030] The generation unit can generate text in multiple languages ​​using a generative AI. For example, when a creator inputs "new release announcement," the generative AI automatically generates text for social media and blogs in multiple languages ​​based on the keyword. The generative AI is a text generation AI (e.g., GPT-4) or a multimodal generative AI, making it easier for creators to disseminate information in multiple languages. The generative AI can generate text in multiple languages, such as English, Japanese, and Spanish. For example, when a creator inputs "new release announcement," the generative AI generates text such as "New Release Announcement" in English, "New Release Announcement" in Japanese, and "Anuncio de Nuevo Lanzamiento" in Spanish. This makes it possible to generate text in multiple languages ​​using generative AI.

[0031] The advice unit can use the generative AI to provide specific advice on works. For example, the advice unit analyzes works uploaded by creators and suggests areas for improvement or new ideas. The generative AI can analyze illustrations uploaded by creators and provide advice on color usage and composition. For example, the generative AI can analyze musical works uploaded by creators and provide advice on melody and rhythm. For example, the generative AI can analyze text uploaded by creators and provide advice on story development and character settings. This makes it possible to use the generative AI to provide specific advice on works.

[0032] The consultation department can use the generation AI to respond to specific consultations on legal and tax matters. For example, when a creator asks about "how to register copyright," the generation AI can provide information on the procedures and necessary documents. For example, when a creator asks about "how to file income tax," the generation AI can also provide information on the procedures and necessary documents. For example, when a creator asks about "how to create a contract," the generation AI can also provide information on the procedures and points to note. This makes it possible to use the generation AI to provide specific consultations on legal and tax matters.

[0033] The reception unit can analyze the user's past keyword input history and select the optimal input method. For example, the reception unit can automatically display keywords that the user has frequently used in the past as candidates. For example, the reception unit can preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can predict and suggest keywords that will be used in a specific time period based on the user's past input history. In this way, the optimal input method can be selected by analyzing the past input history.

[0034] The reception unit can filter keywords based on the user's project or field of interest when the keywords are input. For example, the reception unit preferentially displays keywords related to a project the user is currently working on. For example, the reception unit can suggest highly relevant keywords based on the user's field of interest. For example, the reception unit can suggest optimal keywords by referring to the user's past project history. In this way, highly relevant keywords can be suggested by filtering based on the current project or field of interest.

[0035] When inputting keywords, the reception unit can prioritize inputting highly relevant keywords taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize displaying keywords related to that area. For example, when the user is traveling, the reception unit can suggest keywords related to the travel destination. For example, when the user is at home, the reception unit can prioritize displaying keywords related to information around the user's home. This allows highly relevant keywords to be input preferentially by taking into account the geographical location information.

[0036] When a keyword is input, the reception unit can analyze the user's social media activity and suggest related keywords. The reception unit can suggest related keywords based on, for example, content recently posted by the user. The reception unit can suggest related keywords by analyzing, for example, content posted by accounts the user follows. The reception unit can suggest keywords by taking into account, for example, the activity content of groups or communities in which the user participates. In this way, related keywords can be suggested by analyzing social media activity.

[0037] The generation unit can adjust the level of detail of the sentence based on the priority of the keyword during generation. For example, the generation unit generates a sentence including a detailed explanation for a keyword with high importance. For example, the generation unit can generate a sentence including a concise explanation for a keyword with low importance. For example, the generation unit can adjust the length and content of the sentence according to the importance of the keyword. In this way, by adjusting the level of detail of the sentence based on the importance of the keyword, more appropriate sentences can be generated.

[0038] The generation unit can apply different generation algorithms depending on the keyword category during generation. For example, the generation unit generates sentences using specialized expressions for technical keywords. For example, the generation unit can generate sentences using light-hearted expressions for entertainment keywords. For example, the generation unit can generate sentences using formal expressions for business keywords. In this way, by applying different generation algorithms depending on the keyword category, more appropriate sentences can be generated.

[0039] At the time of generation, the generation unit can determine the priority of the sentences to be generated based on the submission date and time of the keywords. For example, the generation unit generates sentences with priority for keywords that are submitted early. For example, the generation unit can generate sentences later for keywords that are submitted late. For example, the generation unit can adjust the order of the sentences to be generated depending on the submission date. In this way, by determining the priority of sentences based on the submission date and time of the keywords, more appropriate sentences can be generated.

[0040] The generation unit can adjust the order of sentences to be generated based on the relevance of keywords during generation. For example, the generation unit generates sentences with priority for highly relevant keywords. For example, the generation unit can generate sentences later for less relevant keywords. For example, the generation unit can adjust the order of sentences to be generated according to the relevance of keywords. In this way, by adjusting the order of sentences based on the relevance of keywords, more appropriate sentences can be generated.

[0041] When providing advice, the advice unit can adjust the level of detail of the advice based on the priority of the work. For example, the advice unit can provide detailed advice for a work with a high level of importance. For example, the advice unit can provide concise advice for a work with a low level of importance. For example, the advice unit can adjust the content and level of detail of the advice according to the importance of the work. In this way, by adjusting the level of detail of the advice based on the importance of the work, more appropriate advice can be provided.

[0042] When providing advice, the advice unit can apply different advice algorithms depending on the category of the work. For example, the advice unit can provide advice on color usage and composition for an illustration work. For example, the advice unit can provide advice on melody and rhythm for a musical work. For example, the advice unit can provide advice on story development and character settings for a literary work. In this way, by applying different advice algorithms depending on the category of the work, more appropriate advice can be provided.

[0043] When providing advice, the advice unit can determine the priority of advice based on the submission date and time of the work. For example, the advice unit can provide advice preferentially to works that are submitted early. For example, the advice unit can provide advice later to works that are submitted late. For example, the advice unit can adjust the order of advice depending on the submission date. In this way, by determining the priority of advice based on the submission date, more appropriate advice can be provided.

[0044] When providing advice, the advice unit can adjust the order of advice based on the relevance of the works. For example, the advice unit provides advice preferentially for works that are highly relevant. For example, the advice unit can provide advice later for works that are less relevant. For example, the advice unit can adjust the order of advice according to the relevance of the works. In this way, by adjusting the order of advice based on the relevance of the works, more appropriate advice can be provided.

[0045] When providing consultation, the consultation department can adjust the level of detail of the consultation based on the priority of the legal or tax issue. For example, the consultation department can provide detailed consultation for legal or tax issues of high importance. For example, the consultation department can provide brief consultation for legal or tax issues of low importance. For example, the consultation department can adjust the content and level of detail of the consultation depending on the importance of the legal or tax issue. In this way, by adjusting the level of detail of the consultation based on the importance of the legal or tax issue, more appropriate consultation can be provided.

[0046] When providing consultation, the consultation unit can apply different consultation algorithms depending on the legal or tax category. For example, for a consultation regarding copyright, the consultation unit can provide information on registration methods and necessary documents. For example, for a consultation regarding tax, the consultation unit can provide information on reporting methods and deductions. For example, for a consultation regarding contracts, the consultation unit can provide information on how to create contracts and points to note. In this way, by applying different consultation algorithms depending on the legal or tax category, more appropriate consultation can be provided.

[0047] When providing consultation, the consultation department can determine the priority of consultations based on the submission date and time of legal and tax matters. For example, the consultation department can provide consultations preferentially for legal and tax matters that are submitted early. For example, the consultation department can provide consultations later for legal and tax matters that are submitted later. For example, the consultation department can adjust the order of consultations depending on the submission date. In this way, by determining the priority of consultations based on the submission date, more appropriate consultations can be provided.

[0048] When providing consultation, the consultation unit can adjust the order of consultations based on the relevance of the law or taxation. For example, the consultation unit can provide consultations on highly relevant laws or taxation matters with priority. For example, the consultation unit can provide consultations on less relevant laws or taxation matters at a later date. For example, the consultation unit can adjust the order of consultations based on the relevance of the law or taxation. In this way, by adjusting the order of consultations based on the relevance of the law or taxation, more appropriate consultations can be provided.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The generation unit not only generates sentences in multiple languages ​​using generative AI, but also analyzes the user's past posts and provides a style guide to maintain consistency in posts. For example, it can suggest that the user prioritize the use of specific phrases or expressions that they have used in the past. It can also provide advice on maintaining a tone and style that matches the user's brand image. Furthermore, when a user posts on a specific topic, the consistency and quality of posts can be improved by allowing the user to refer to past posts related to that topic.

[0051] The advice module not only uses generative AI to provide specific advice on artwork, but also has the ability to evaluate progress by comparing it with the user's past works. For example, it can compare a new illustration uploaded by a user with past illustrations to specifically indicate areas for technical improvement and refinement. It can also provide feedback on new artwork based on advice the user has received in the past. It can also suggest practice methods and resources to help the user improve specific skills. This supports the user's growth and encourages continuous skill development.

[0052] The consultation department uses generative AI to not only provide specific legal and tax consultations, but also advice on the user's business strategy. For example, if a user is considering a new business model, it can provide information on the benefits and risks of that model. It can also make specific proposals based on target market and competitive analysis when the user is formulating a marketing strategy. Furthermore, if the user is considering fundraising, it can provide advice on appropriate fundraising methods and how to negotiate with investors. This helps support the user's business success.

[0053] The reception unit can analyze the user's past keyword input history and select the optimal input method, as well as learn the user's input patterns and provide predictive input. For example, if a user frequently uses a specific keyword, a function can be provided to automatically complete that keyword. Also, if a user frequently uses a specific phrase, that phrase can be displayed as a candidate. Furthermore, if a user tends to use a specific keyword during a specific time period, keywords appropriate for that time period can be suggested. This can improve the user's input efficiency.

[0054] When a keyword is entered, the reception unit not only filters the keywords based on the user's project or area of ​​interest, but also suggests keywords according to the user's project progress. For example, if the user is in the early stages of a project, basic keywords and basic advice can be provided. If the project is approaching the middle stage, more specific and detailed keywords can be suggested. If the project is nearing the final stage, keywords related to finishing and final confirmation can be provided. This makes it possible to suggest appropriate keywords according to the project progress.

[0055] When a keyword is input, the reception unit not only prioritizes inputting highly relevant keywords taking into consideration the user's geographical location information, but also provides information on trends and events specific to the region based on the user's location information. For example, if the user is in a particular city, keywords related to events and trends held in that city can be suggested. If the user is traveling, keywords related to tourist information about the travel destination and local culture can be suggested. Furthermore, if the user is at home, keywords related to local news and community information can be suggested. This makes it possible to provide highly relevant information based on the user's location information.

[0056] When a keyword is entered, the reception unit analyzes the user's social media activity and suggests related keywords, as well as evaluating the user's influence on social media and suggesting the optimal posting timing. For example, if a user receives a lot of responses during a specific time period, it can recommend posting during that time period. It can also analyze the activity patterns of the user's followers and suggest the most effective posting timing. Furthermore, when a user posts on a specific topic, it can suggest hashtags and keywords related to that topic. This can optimize the user's social media activity and increase their influence.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception area is where creators can enter keywords. Keywords entered by creators include new product announcements, event announcements, and work descriptions. Step 2: The generation unit uses the generation AI to create text for social media and blog posts in multiple languages ​​based on the keywords entered by the reception unit. For example, if a creator enters "new product announcement," the generation AI automatically generates text for social media and blog posts in multiple languages ​​based on the keywords. The generation AI may be a text generation AI (e.g., GPT-4) or a multimodal generation AI. Step 3: The advice section provides advice to the creator on their work based on the text generated by the generation section. For example, it analyzes the work uploaded by the creator and suggests areas for improvement or new ideas. The generation AI analyzes the illustrations uploaded by the creator and provides advice on color usage and composition. Step 4: The Consultation Department allows creators to receive legal and tax advice based on the advice provided by the Advice Department. For example, if a creator asks about "how to register copyright," the Generative AI will provide information on the procedures and necessary documents.

[0059] (Example 2) The creator support system according to an embodiment of the present invention provides functions that allow creators to acquire fans, increase revenue, and focus on tasks other than their primary creative activities. This creator support system utilizes generative AI to build a unique AI model and provides the following functions. First, it provides a function that allows creators to effectively create posts for social media and blogs in multiple languages ​​simply by inputting keywords. For example, when a creator inputs "new product announcement," the generative AI automatically generates posts for social media and blogs in multiple languages ​​based on the keywords. This makes it easier for creators to disseminate information in multiple languages ​​and promotes fan acquisition. Second, it provides a function that allows creators to receive advice on their work. The generative AI analyzes the work uploaded by the creator and suggests improvements and new ideas. For example, when an illustrator uploads their own illustration, the generative AI provides advice on color usage and composition. This allows creators to improve the quality of their work. Furthermore, it provides a function that allows creators to receive legal and tax consultations. The generative AI provides appropriate legal and tax advice in response to questions input by the creator. For example, if a creator asks "how to register copyright," the generation AI will provide information on the procedures and necessary documents. This allows creators to quickly resolve legal and tax issues. In this way, the present invention provides advanced functions that allow creators to focus on gaining fans, increasing revenue, and other tasks outside of their primary creative activities. This allows the creator support system to allow creators to focus on gaining fans, increasing revenue, and other tasks outside of their primary creative activities.

[0060] A creator support system according to an embodiment includes a reception unit, a generation unit, an advice unit, and a consultation unit. The reception unit is a unit through which a creator inputs keywords. Keywords input by a creator include, but are not limited to, new product announcements, event announcements, and work descriptions. The generation unit uses a generation AI to create text for social media and blogs in multiple languages ​​based on the keywords input by the reception unit. For example, when a creator inputs "new product announcement," the generation AI automatically generates text for social media and blogs in multiple languages ​​based on the keywords. The generation AI is a text generation AI (e.g., GPT-4) or a multimodal generation AI, which makes it easier for creators to disseminate information in multiple languages. The advice unit provides advice on the creator's work based on the text generated by the generation unit. For example, the advice unit analyzes the work uploaded by the creator and suggests improvements and new ideas. The generation AI analyzes the illustrations uploaded by the creator and provides advice on color usage and composition. The consultation unit is a component that allows creators to receive legal and tax consultations based on the advice provided by the advice unit. For example, when a creator asks about "how to register copyright," the generation AI provides information on the procedures and necessary documents. This allows creators to quickly resolve legal and tax issues. As a result, the creator support system according to the embodiment allows creators to focus on acquiring fans, increasing revenue, and other tasks outside of their primary creative activities.

[0061] The generation unit can generate text in multiple languages ​​using a generative AI. For example, when a creator inputs "new release announcement," the generative AI automatically generates text for social media and blogs in multiple languages ​​based on the keyword. The generative AI is a text generation AI (e.g., GPT-4) or a multimodal generative AI, making it easier for creators to disseminate information in multiple languages. The generative AI can generate text in multiple languages, such as English, Japanese, and Spanish. For example, when a creator inputs "new release announcement," the generative AI generates text such as "New Release Announcement" in English, "New Release Announcement" in Japanese, and "Anuncio de Nuevo Lanzamiento" in Spanish. This makes it possible to generate text in multiple languages ​​using generative AI.

[0062] The advice unit can use the generative AI to provide specific advice on works. For example, the advice unit analyzes works uploaded by creators and suggests areas for improvement or new ideas. The generative AI can analyze illustrations uploaded by creators and provide advice on color usage and composition. For example, the generative AI can analyze musical works uploaded by creators and provide advice on melody and rhythm. For example, the generative AI can analyze text uploaded by creators and provide advice on story development and character settings. This makes it possible to use the generative AI to provide specific advice on works.

[0063] The consultation department can use the generation AI to respond to specific consultations on legal and tax matters. For example, when a creator asks about "how to register copyright," the generation AI can provide information on the procedures and necessary documents. For example, when a creator asks about "how to file income tax," the generation AI can also provide information on the procedures and necessary documents. For example, when a creator asks about "how to create a contract," the generation AI can also provide information on the procedures and points to note. This makes it possible to use the generation AI to provide specific consultations on legal and tax matters.

[0064] The reception unit can estimate the user's emotions and adjust the timing of keyword input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can delay the input timing to allow the user to relax. For example, if the user is relaxed, the reception unit can advance the input timing to allow the user to work efficiently. For example, if the user is in a hurry, the reception unit can immediately accept input. This allows the keyword input timing to be adjusted according to the user's emotions, enabling input at a more appropriate timing. 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 such examples.

[0065] The reception unit can analyze the user's past keyword input history and select the optimal input method. For example, the reception unit can automatically display keywords that the user has frequently used in the past as candidates. For example, the reception unit can preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can predict and suggest keywords that will be used in a specific time period based on the user's past input history. In this way, the optimal input method can be selected by analyzing the past input history.

[0066] The reception unit can filter keywords based on the user's project or field of interest when the keywords are input. For example, the reception unit preferentially displays keywords related to a project the user is currently working on. For example, the reception unit can suggest highly relevant keywords based on the user's field of interest. For example, the reception unit can suggest optimal keywords by referring to the user's past project history. In this way, highly relevant keywords can be suggested by filtering based on the current project or field of interest.

[0067] The reception unit can estimate the user's emotions and determine the priority of input keywords based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit prioritizes processing of keywords with high importance. For example, when the user is relaxed, the reception unit can process keywords with low importance as well. For example, when the user is in a hurry, the reception unit can immediately process important keywords. In this way, by determining the priority of keywords according to the user's emotions, important keywords can be processed with priority. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0068] When inputting keywords, the reception unit can prioritize inputting highly relevant keywords taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize displaying keywords related to that area. For example, when the user is traveling, the reception unit can suggest keywords related to the travel destination. For example, when the user is at home, the reception unit can prioritize displaying keywords related to information around the user's home. This allows highly relevant keywords to be input preferentially by taking into account the geographical location information.

[0069] When a keyword is input, the reception unit can analyze the user's social media activity and suggest related keywords. The reception unit can suggest related keywords based on, for example, content recently posted by the user. The reception unit can suggest related keywords by analyzing, for example, content posted by accounts the user follows. The reception unit can suggest keywords by taking into account, for example, the activity content of groups or communities in which the user participates. In this way, related keywords can be suggested by analyzing social media activity.

[0070] The generation unit can estimate the user's emotions and adjust the expression style of the generated sentences based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate sentences using soft expressions. For example, if the user is in a hurry, the generation unit can generate sentences that are concise and to the point. For example, if the user is excited, the generation unit can generate sentences using energetic expressions. This allows for the generation of more appropriate sentences by adjusting the expression style of the sentences 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.

[0071] The generation unit can adjust the level of detail of the sentence based on the priority of the keyword during generation. For example, the generation unit generates a sentence including a detailed explanation for a keyword with high importance. For example, the generation unit can generate a sentence including a concise explanation for a keyword with low importance. For example, the generation unit can adjust the length and content of the sentence according to the importance of the keyword. In this way, by adjusting the level of detail of the sentence based on the importance of the keyword, more appropriate sentences can be generated.

[0072] The generation unit can apply different generation algorithms depending on the keyword category during generation. For example, the generation unit generates sentences using specialized expressions for technical keywords. For example, the generation unit can generate sentences using light-hearted expressions for entertainment keywords. For example, the generation unit can generate sentences using formal expressions for business keywords. In this way, by applying different generation algorithms depending on the keyword category, more appropriate sentences can be generated.

[0073] The generation unit can estimate the user's emotion and adjust the length of the generated sentence based on the estimated user's emotion. For example, if the user is relaxed, the generation unit can generate longer sentences. For example, if the user is in a hurry, the generation unit can generate shorter sentences. For example, if the user is excited, the generation unit can generate sentences of an appropriate length. This allows more appropriate sentences to be generated by adjusting the length of the sentences according to the user's emotion. 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.

[0074] At the time of generation, the generation unit can determine the priority of the sentences to be generated based on the submission date and time of the keywords. For example, the generation unit generates sentences with priority for keywords that are submitted early. For example, the generation unit can generate sentences later for keywords that are submitted late. For example, the generation unit can adjust the order of the sentences to be generated depending on the submission date. In this way, by determining the priority of sentences based on the submission date and time of the keywords, more appropriate sentences can be generated.

[0075] The generation unit can adjust the order of sentences to be generated based on the relevance of keywords during generation. For example, the generation unit generates sentences with priority for highly relevant keywords. For example, the generation unit can generate sentences later for less relevant keywords. For example, the generation unit can adjust the order of sentences to be generated according to the relevance of keywords. In this way, by adjusting the order of sentences based on the relevance of keywords, more appropriate sentences can be generated.

[0076] The advice unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. For example, if the user is relaxed, the advice unit can provide advice using soft expressions. For example, if the user is in a hurry, the advice unit can provide concise and to-the-point advice. For example, if the user is excited, the advice unit can provide advice using energetic expressions. This allows more appropriate advice to be provided by adjusting the way the advice is expressed depending on 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.

[0077] When providing advice, the advice unit can adjust the level of detail of the advice based on the priority of the work. For example, the advice unit can provide detailed advice for a work with a high level of importance. For example, the advice unit can provide concise advice for a work with a low level of importance. For example, the advice unit can adjust the content and level of detail of the advice according to the importance of the work. In this way, by adjusting the level of detail of the advice based on the importance of the work, more appropriate advice can be provided.

[0078] When providing advice, the advice unit can apply different advice algorithms depending on the category of the work. For example, the advice unit can provide advice on color usage and composition for an illustration work. For example, the advice unit can provide advice on melody and rhythm for a musical work. For example, the advice unit can provide advice on story development and character settings for a literary work. In this way, by applying different advice algorithms depending on the category of the work, more appropriate advice can be provided.

[0079] The advice unit can estimate the user's emotion and adjust the length of the advice based on the estimated user's emotion. For example, if the user is relaxed, the advice unit can provide longer advice. For example, if the user is in a hurry, the advice unit can provide shorter advice. For example, if the user is excited, the advice unit can provide advice of an appropriate length. This allows more appropriate advice to be provided by adjusting the length of the advice according to the user's emotion. 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 such examples.

[0080] When providing advice, the advice unit can determine the priority of advice based on the submission date and time of the work. For example, the advice unit can provide advice preferentially to works that are submitted early. For example, the advice unit can provide advice later to works that are submitted late. For example, the advice unit can adjust the order of advice depending on the submission date. In this way, by determining the priority of advice based on the submission date, more appropriate advice can be provided.

[0081] When providing advice, the advice unit can adjust the order of advice based on the relevance of the works. For example, the advice unit provides advice preferentially for works that are highly relevant. For example, the advice unit can provide advice later for works that are less relevant. For example, the advice unit can adjust the order of advice according to the relevance of the works. In this way, by adjusting the order of advice based on the relevance of the works, more appropriate advice can be provided.

[0082] The consultation unit can estimate the user's emotions and adjust the way the consultation is expressed based on the estimated user's emotions. For example, if the user is relaxed, the consultation unit can provide consultation using soft expressions. For example, if the user is in a hurry, the consultation unit can provide consultation that is concise and to the point. For example, if the user is excited, the consultation unit can provide consultation using energetic expressions. This allows for more appropriate consultation to be provided by adjusting the way the consultation is expressed depending on 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.

[0083] When providing consultation, the consultation department can adjust the level of detail of the consultation based on the priority of the legal or tax issue. For example, the consultation department can provide detailed consultation for legal or tax issues of high importance. For example, the consultation department can provide brief consultation for legal or tax issues of low importance. For example, the consultation department can adjust the content and level of detail of the consultation depending on the importance of the legal or tax issue. In this way, by adjusting the level of detail of the consultation based on the importance of the legal or tax issue, more appropriate consultation can be provided.

[0084] When providing consultation, the consultation unit can apply different consultation algorithms depending on the legal or tax category. For example, for a consultation regarding copyright, the consultation unit can provide information on registration methods and necessary documents. For example, for a consultation regarding tax, the consultation unit can provide information on reporting methods and deductions. For example, for a consultation regarding contracts, the consultation unit can provide information on how to create contracts and points to note. In this way, by applying different consultation algorithms depending on the legal or tax category, more appropriate consultation can be provided.

[0085] The consultation unit can estimate the user's emotions and adjust the length of the consultation based on the estimated user's emotions. For example, if the user is relaxed, the consultation unit can provide a longer consultation. For example, if the user is in a hurry, the consultation unit can provide a shorter consultation. For example, if the user is excited, the consultation unit can provide a consultation of an appropriate length. This allows for adjusting the length of the consultation according to the user's emotions, thereby providing a more appropriate consultation. The 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 such examples.

[0086] When providing consultation, the consultation department can determine the priority of consultations based on the submission date and time of legal and tax matters. For example, the consultation department can provide consultations preferentially for legal and tax matters that are submitted early. For example, the consultation department can provide consultations later for legal and tax matters that are submitted later. For example, the consultation department can adjust the order of consultations depending on the submission date. In this way, by determining the priority of consultations based on the submission date, more appropriate consultations can be provided.

[0087] When providing consultation, the consultation unit can adjust the order of consultations based on the relevance of the law or taxation. For example, the consultation unit can provide consultations on highly relevant laws or taxation matters with priority. For example, the consultation unit can provide consultations on less relevant laws or taxation matters at a later date. For example, the consultation unit can adjust the order of consultations based on the relevance of the law or taxation. In this way, by adjusting the order of consultations based on the relevance of the law or taxation, more appropriate consultations can be provided. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, advice unit, and consultation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives keyword input from a creator using a touch panel 38A or a microphone 38B of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates multilingual text for SNS and blogs using a generation AI. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice on the creator's work. The consultation unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice on legal and tax matters. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, advice unit, and consultation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives keyword input from a creator using the microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates multilingual text for SNS and blogs using a generation AI. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice on the creator's work. The consultation unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice on legal and tax matters. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, advice unit, and consultation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives keyword input from a creator using the microphone 238 of the headset-type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates text for multilingual SNS and blogs using a generation AI. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice on the creator's work. The consultation unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice on legal and tax matters. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, advice unit, and consultation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives keyword input from the creator using the microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates text for multilingual SNS and blogs using a generation AI. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice on the creator's work. The consultation unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice on legal and tax matters.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The creator support system can also estimate the user's emotions and provide feedback to improve the creator's motivation based on the estimated emotions. For example, if the user is feeling down, it can present encouraging messages and success stories. If the user is excited, it can suggest specific actions to utilize that energy. Also, if the user is feeling stressed, it can provide advice on relaxation methods and the importance of rest. In this way, by providing feedback according to the user's emotions, it is possible to maintain the creator's motivation and support their creative activities.

[0090] The generation unit not only generates sentences in multiple languages ​​using generative AI, but also analyzes the user's past posts and provides a style guide to maintain consistency in posts. For example, it can suggest that the user prioritize the use of specific phrases or expressions that they have used in the past. It can also provide advice on maintaining a tone and style that matches the user's brand image. Furthermore, when a user posts on a specific topic, the consistency and quality of posts can be improved by allowing the user to refer to past posts related to that topic.

[0091] The advice module not only uses generative AI to provide specific advice on artwork, but also has the ability to evaluate progress by comparing it with the user's past works. For example, it can compare a new illustration uploaded by a user with past illustrations to specifically indicate areas for technical improvement and refinement. It can also provide feedback on new artwork based on advice the user has received in the past. It can also suggest practice methods and resources to help the user improve specific skills. This supports the user's growth and encourages continuous skill development.

[0092] The consultation department uses generative AI to not only provide specific legal and tax consultations, but also advice on the user's business strategy. For example, if a user is considering a new business model, it can provide information on the benefits and risks of that model. It can also make specific proposals based on target market and competitive analysis when the user is formulating a marketing strategy. Furthermore, if the user is considering fundraising, it can provide advice on appropriate fundraising methods and how to negotiate with investors. This helps support the user's business success.

[0093] The reception unit can estimate the user's emotions and adjust the timing of keyword input based on the estimated user's emotions, as well as customize the interface according to the user's emotions. For example, if the user is feeling stressed, the color tone of the interface can be changed to a calmer tone, providing a relaxing environment. If the user is relaxed, the interface can be made brighter, improving work efficiency. Furthermore, if the user is in a hurry, important information can be highlighted to enable quick access. In this way, customizing the interface according to the user's emotions can provide a more comfortable usage environment.

[0094] The reception unit can analyze the user's past keyword input history and select the optimal input method, as well as learn the user's input patterns and provide predictive input. For example, if a user frequently uses a specific keyword, a function can be provided to automatically complete that keyword. Also, if a user frequently uses a specific phrase, that phrase can be displayed as a candidate. Furthermore, if a user tends to use a specific keyword during a specific time period, keywords appropriate for that time period can be suggested. This can improve the user's input efficiency.

[0095] When a keyword is entered, the reception unit not only filters the keywords based on the user's project or area of ​​interest, but also suggests keywords according to the user's project progress. For example, if the user is in the early stages of a project, basic keywords and basic advice can be provided. If the project is approaching the middle stage, more specific and detailed keywords can be suggested. If the project is nearing the final stage, keywords related to finishing and final confirmation can be provided. This makes it possible to suggest appropriate keywords according to the project progress.

[0096] The reception unit can estimate the user's emotions and prioritize the input keywords based on the estimated user's emotions, as well as suggest keywords according to the user's emotions. For example, if the user is feeling stressed, keywords related to relaxation and stress relief can be suggested. If the user is relaxed, keywords that stimulate creativity can be suggested. Furthermore, if the user is in a hurry, keywords that will help the user work efficiently can be suggested. In this way, by suggesting keywords according to the user's emotions, more effective support can be provided.

[0097] When a keyword is input, the reception unit not only prioritizes inputting highly relevant keywords taking into consideration the user's geographical location information, but also provides information on trends and events specific to the region based on the user's location information. For example, if the user is in a particular city, keywords related to events and trends held in that city can be suggested. If the user is traveling, keywords related to tourist information about the travel destination and local culture can be suggested. Furthermore, if the user is at home, keywords related to local news and community information can be suggested. This makes it possible to provide highly relevant information based on the user's location information.

[0098] When a keyword is entered, the reception unit analyzes the user's social media activity and suggests related keywords, as well as evaluating the user's influence on social media and suggesting the optimal posting timing. For example, if a user receives a lot of responses during a specific time period, it can recommend posting during that time period. It can also analyze the activity patterns of the user's followers and suggest the most effective posting timing. Furthermore, when a user posts on a specific topic, it can suggest hashtags and keywords related to that topic. This can optimize the user's social media activity and increase their influence.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The reception area is where creators can enter keywords. Keywords entered by creators include new product announcements, event announcements, and work descriptions. Step 2: The generation unit uses the generation AI to create text for social media and blog posts in multiple languages ​​based on the keywords entered by the reception unit. For example, if a creator enters "new product announcement," the generation AI automatically generates text for social media and blog posts in multiple languages ​​based on the keywords. The generation AI may be a text generation AI (e.g., GPT-4) or a multimodal generation AI. Step 3: The advice section provides advice to the creator on their work based on the text generated by the generation section. For example, it analyzes the work uploaded by the creator and suggests areas for improvement or new ideas. The generation AI analyzes the illustrations uploaded by the creator and provides advice on color usage and composition. Step 4: The Consultation Department allows creators to receive legal and tax advice based on the advice provided by the Advice Department. For example, if a creator asks about "how to register copyright," the Generative AI will provide information on the procedures and necessary documents.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section for inputting keywords; a generation unit that generates text for an SNS or blog in multiple languages ​​based on the keywords input by the reception unit; an advice unit that provides advice on the work based on the sentence generated by the generation unit; a consultation department that provides legal and tax consultations based on the advice provided by the advice department; Equipped with A system characterized by:

2. The generation unit Generate text in multiple languages ​​using generative AI 2. The system of claim 1.

3. The advice unit Using generative AI to provide specific advice on your work 2. The system of claim 1.

4. The consultation department: Using generative AI to provide specific legal and tax consultations 2. The system of claim 1.

5. The reception unit Using a method for estimating user emotions, the timing of keyword input is adjusted based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Analyze the user's past keyword input history and select the appropriate input method 2. The system of claim 1.

7. The reception unit Filter based on your project or area of ​​interest as you type in keywords 2. The system of claim 1.

8. The reception unit Using a method for estimating user emotions, the priority of input keywords is determined based on the estimated user emotions.

2. The system of claim 1.

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