system

Abstract

The system according to the embodiment aims to provide incentives to contributors who use correct Japanese. [Solution] A system according to an embodiment includes a reception unit, an evaluation unit, a ranking unit, and a payment unit. The reception unit allows posters to post sentences containing correct Japanese. The evaluation unit evaluates the sentences received by the reception unit. The ranking unit performs rankings based on the results of the evaluation by the evaluation unit. The payment unit pays compensation based on the rankings created by the ranking 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 technologies lack incentives to use correct Japanese, and there is room for improvement.

[0005] The system according to the embodiment aims to provide incentives to contributors who use correct Japanese. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an evaluation unit, a ranking unit, and a payment unit. The reception unit allows posters to post sentences containing correct Japanese. The evaluation unit evaluates the sentences received by the reception unit. The ranking unit performs rankings based on the results of the evaluation by the evaluation unit. The payment unit pays compensation based on the rankings created by the ranking unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide incentives to contributors who use correct Japanese. [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 Japanese language evaluation system according to an embodiment of the present invention evaluates and ranks correct Japanese (e.g., humble language and polite language), and rewards posters for their submissions. In this Japanese language evaluation system, posters submit sentences containing correct Japanese, and the system evaluates and ranks them. For evaluation, AI is used to determine the accuracy and appropriateness of the sentences. Finally, the posters are paid based on the rankings. For example, a poster submits sentences containing correct Japanese. Next, the system evaluates and ranks the sentences. For evaluation, AI is used to determine the accuracy and appropriateness of the sentences. Possible evaluation criteria include grammatical accuracy, appropriateness of honorific language, and sentence flow. It is also necessary to clarify how the AI ​​evaluates these criteria. Finally, the posters are paid based on the rankings. It is important to clearly state how the reward will be paid, for example, through a points system or cash payment. It is also necessary to provide a detailed explanation of how the poster submits their sentences and the submission process. From the perspective of privacy protection, it is also necessary to explain how the AI ​​handles data when conducting evaluations. To protect privacy, it is important to clarify how the poster's personal information will be handled and protected. This allows the Japanese evaluation system to promote the use of correct Japanese and provide incentives to contributors.

[0029] The Japanese language evaluation system according to the embodiment includes a reception unit, an evaluation unit, a ranking unit, and a payment unit. The reception unit provides a procedure for a poster to post a sentence containing correct Japanese. For example, the reception unit provides a posting form through which the poster can input a sentence. The reception unit can also provide an interface through which the poster can check and submit the content of the post. The evaluation unit evaluates the posted sentence. The evaluation involves using AI to determine the accuracy and appropriateness of the sentence. For example, the evaluation unit can use a grammar check tool to evaluate the accuracy of grammar. The evaluation unit can also evaluate the appropriateness of honorific language based on honorific usage rules and appropriate honorific expressions. The evaluation unit can also evaluate the flow of the sentence based on logical structure and sentence connections. The ranking unit creates a ranking based on the results of the evaluation by the evaluation unit. The ranking is based on the evaluation score. For example, the ranking unit can sort the posts in descending order of evaluation score and create a ranking. The payment unit pays the poster a fee based on the ranking. Payment methods include a point system, cash payment, etc. For example, the payment unit can use a point system to award points to the poster. The payment unit can also pay cash using bank transfer or electronic money. In this way, the Japanese language evaluation system according to the embodiment can promote the use of correct Japanese and provide incentives to the poster.

[0030] The evaluation unit can evaluate the accuracy of grammar, the accuracy of honorific language, and the flow of the sentence. For example, the evaluation unit uses a grammar check tool to evaluate the accuracy of grammar. For example, the evaluation unit uses the grammar check tool to detect and correct grammatical errors in the sentence. The evaluation unit can also evaluate the accuracy of honorific language based on honorific usage rules and appropriate honorific expressions. For example, the evaluation unit evaluates whether honorific expressions in the sentence are appropriate based on the honorific usage rules. The evaluation unit can also evaluate the flow of the sentence based on the logical structure and connection of the sentences. For example, the evaluation unit evaluates the logical structure of the sentence and determines whether the connection of the sentences is natural. This evaluates the accuracy and appropriateness of the sentence, thereby improving the accuracy of the ranking. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can input the sentence into AI, which evaluates the accuracy of grammar, the appropriateness of honorific language, and the flow of the sentence.

[0031] The ranking unit can create a ranking based on the results of the evaluation by the evaluation unit. The ranking unit, for example, sorts the posts based on the evaluation scores and creates a ranking. For example, the ranking unit sorts the posts in descending order of evaluation score and creates a ranking. The ranking unit can also create a ranking of posters based on the evaluation scores. For example, the ranking unit ranks posters with high evaluation scores higher and creates a ranking. This enables accurate ranking by creating a ranking based on the evaluation results. Some or all of the above-mentioned processing in the ranking unit may be performed using AI, for example, or may be performed without using AI. For example, the ranking unit can input the evaluation scores into AI, which then creates a ranking.

[0032] The payment unit can pay the consideration using a point system or cash payment. The payment unit, for example, uses a point system to award points to the poster. For example, the payment unit awards points to the poster based on the evaluation score. The payment unit can also pay cash using bank transfer or electronic money. For example, the payment unit pays cash by transferring money to the poster's bank account. The payment unit can also pay cash to the poster using electronic money. This provides a variety of payment methods, improving convenience for posters. Some or all of the above-mentioned processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can input the evaluation score into AI, which can determine how to award points or how to pay cash.

[0033] The reception unit can provide a procedure for a poster to post a message. The reception unit, for example, provides a posting form, and the poster can input a message. For example, the reception unit can use the posting form to allow the poster to input and send a message. The reception unit can also provide an interface for the poster to check and send the content of the post. For example, the reception unit provides an interface for the poster to check and modify the content of the post. This allows the poster to easily post a message. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the posting form into AI, which can check and send the content entered by the poster.

[0034] The evaluation unit can protect privacy when handling data when the AI ​​makes an evaluation. The evaluation unit, for example, anonymizes the data. For example, the evaluation unit anonymizes the poster's personal information and uses it as evaluation data. The evaluation unit can also restrict access. For example, the evaluation unit restricts access to the evaluation data so that only specific users can access it. This protects the poster's privacy. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the evaluation data into AI, which can anonymize the data and restrict access.

[0035] The reception unit can analyze the poster's past posting history and select the optimal reception method. The reception unit, for example, analyzes the time period during which the poster frequently posted in the past and prioritizes receiving posts from those time periods. For example, the reception unit can analyze the poster's past posting history and prioritizes receiving posts from the time period during which the poster frequently posted. The reception unit can also prioritize suggesting a posting method (text, voice, etc.) that the poster has used in the past. For example, the reception unit can analyze the poster's past posting history and prioritizes suggesting the used posting method. The reception unit can also analyze the content of the poster's past posts and prioritize receiving posts on related topics. For example, the reception unit can analyze the content of the poster's past posts and prioritize receiving posts on related topics. This makes it possible to provide the optimal reception method based on the past posting history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the poster's past posting history into AI, which can select the optimal reception method.

[0036] When receiving a post, the reception unit can filter the posts based on the poster's current areas of interest. For example, the reception unit preferentially receives posts related to topics in which the poster is currently interested. For example, the reception unit analyzes the poster's current areas of interest and preferentially receives posts on related topics. The reception unit can also filter related posts based on keywords recently searched by the poster. For example, the reception unit analyzes the poster's recent search history and filters related posts. The reception unit can also preferentially receive related posts based on accounts and groups followed by the poster. For example, the reception unit analyzes the accounts and groups followed by the poster and preferentially receives related posts. This makes it possible to receive appropriate posts based on the poster's areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the poster's area of ​​interest data into AI, which then performs filtering.

[0037] When receiving a post, the reception unit can prioritize receiving highly relevant posts by taking into account the poster's geographical location information. For example, if the poster is in a specific area, the reception unit prioritizes receiving posts related to that area. For example, the reception unit analyzes the poster's geographical location information and prioritizes receiving posts related to that area. Furthermore, if the poster is traveling, the reception unit can prioritize receiving posts related to the travel destination. For example, the reception unit analyzes the poster's geographical location information and prioritizes receiving posts related to the travel destination. Furthermore, if the poster is at home, the reception unit can prioritize receiving posts related to local news and events. For example, the reception unit analyzes the poster's geographical location information and prioritizes receiving posts related to local news and events when the poster is at home. This allows highly relevant posts to be prioritized based on the geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the poster's geographical location information into AI, which can then prioritize receiving highly relevant posts.

[0038] When receiving a post, the reception unit can analyze the poster's social media activity and receive related posts. For example, the reception unit prioritizes receiving content related to posts that the poster recently "liked." For example, the reception unit analyzes the poster's social media activity and prioritizes receiving content related to posts that the poster recently "liked." The reception unit can also prioritize receiving content related to posts from accounts the poster follows. For example, the reception unit analyzes the poster's social media activity and prioritizes receiving content related to posts from the accounts the poster follows. The reception unit can also prioritize receiving content related to groups or events in which the poster participates. For example, the reception unit analyzes the poster's social media activity and prioritizes receiving content related to groups or events the poster participates in. This makes it possible to receive related posts based on social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the poster's social media activity data into AI, which then receives related posts.

[0039] During evaluation, the evaluation unit can adjust the level of detail of the evaluation based on the importance of the sentence. For example, the evaluation unit provides a detailed evaluation for important sentences and provides specific improvements. For example, the evaluation unit evaluates the importance of the sentence and provides a detailed evaluation for important sentences. The evaluation unit can also provide a concise evaluation for general sentences and provide basic feedback. For example, the evaluation unit evaluates the importance of the sentence and provides a concise evaluation for general sentences. The evaluation unit can also provide a summary evaluation for short sentences and provide concise feedback. For example, the evaluation unit evaluates the importance of the sentence and provides a summary evaluation for short sentences. This makes it possible to provide an appropriate evaluation according to the importance of the sentence. Some or all of the above-described processing in the evaluation unit may be performed using, or without, AI. For example, the evaluation unit can input sentence importance data into AI, which can adjust the level of detail of the evaluation.

[0040] During evaluation, the evaluation unit can apply different evaluation algorithms depending on the category of the text. For example, the evaluation unit applies an algorithm to evaluate the appropriateness of technical terminology and formatting to a business document. For example, the evaluation unit identifies the category of the business document and applies an algorithm to evaluate the appropriateness of technical terminology and formatting. The evaluation unit can also apply an algorithm to evaluate the appropriateness of natural expressions and honorific language to everyday conversation. For example, the evaluation unit identifies the category of everyday conversation and applies an algorithm to evaluate the appropriateness of natural expressions and honorific language. The evaluation unit can also apply an algorithm to evaluate the logical consistency and grammatical accuracy to an academic paper. For example, the evaluation unit identifies the category of an academic paper and applies an algorithm to evaluate the logical consistency and grammatical accuracy. This makes it possible to provide an appropriate evaluation according to the category of the text. Some or all of the above-mentioned processing in the evaluation unit may be performed using, or without, AI. For example, the evaluation unit can input text category data into AI, which then applies different evaluation algorithms.

[0041] During evaluation, the evaluation unit can determine the priority of evaluation based on the time of submission of the text. For example, the evaluation unit prioritizes evaluation of texts with an approaching deadline. For example, the evaluation unit analyzes the time of submission of the texts and prioritizes evaluation of texts with an approaching deadline. The evaluation unit can also determine the priority of evaluation based on the order of submission. For example, the evaluation unit analyzes the order of submission of the texts and prioritizes evaluation based on the order of submission. The evaluation unit can also prioritize evaluation of texts related to important events. For example, the evaluation unit analyzes the time of submission of the texts and prioritizes evaluation of texts related to important events. This allows evaluation to be performed with appropriate priority based on the time of submission. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on the time of submission of the texts into AI, and the AI ​​can determine the priority of evaluation.

[0042] During evaluation, the evaluation unit can adjust the order of evaluation based on the relevance of the sentences. For example, the evaluation unit evaluates sentences related to the same topic together. For example, the evaluation unit analyzes the relevance of the sentences and evaluates sentences related to the same topic together. The evaluation unit can also prioritize evaluation of sentences containing important keywords. For example, the evaluation unit analyzes the relevance of the sentences and prioritizes evaluation of sentences containing important keywords. The evaluation unit can also prioritize evaluation of sentences related to the poster's past posts. For example, the evaluation unit analyzes the poster's past posts and prioritizes evaluation of related sentences. This allows evaluation to be performed in an appropriate order based on relevance. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input sentence relevance data into AI, which can adjust the order of evaluation.

[0043] The ranking unit can improve the accuracy of the ranking by taking into account the interrelationships between sentences when ranking. The ranking unit, for example, groups sentences related to the same topic to improve the accuracy of the ranking. For example, the ranking unit analyzes the interrelationships between sentences and groups sentences related to the same topic. The ranking unit can also improve the accuracy of the ranking by taking into account the citation relationships between sentences. For example, the ranking unit analyzes the interrelationships between sentences and performs ranking by taking into account the citation relationships. The ranking unit can also analyze the cross-references between sentences to improve the accuracy of the ranking. For example, the ranking unit analyzes the interrelationships between sentences and performs ranking by taking into account the cross-references. In this way, the accuracy of the ranking is improved by taking the interrelationships between sentences into account. Some or all of the above-mentioned processing in the ranking unit may be performed using, for example, AI, or may be performed without using AI. For example, the ranking unit can input data on the interrelationships between sentences into AI, which can improve the accuracy of the ranking.

[0044] The ranking unit can perform ranking taking into consideration the attribute information of posters when performing ranking. The ranking unit, for example, performs ranking based on the specialty of the poster. For example, the ranking unit analyzes the attribute information of the posters and performs ranking based on the specialty. The ranking unit can also perform ranking based on the years of experience of the posters. For example, the ranking unit analyzes the attribute information of the posters and performs ranking based on the years of experience. The ranking unit can also perform ranking based on the posters' past evaluation results. For example, the ranking unit analyzes the attribute information of the posters and performs ranking based on the past evaluation results. This allows appropriate ranking to be performed based on the posters' attribute information. Some or all of the above-mentioned processing in the ranking unit may be performed using, for example, AI, or may be performed without using AI. For example, the ranking unit can input the attribute information data of the posters into AI, and the AI ​​can perform ranking.

[0045] The ranking unit can perform ranking taking into consideration the geographical distribution of texts. For example, the ranking unit prioritizes ranking texts related to a specific region. For example, the ranking unit analyzes the geographical distribution of texts and prioritizes ranking texts related to a specific region. The ranking unit can also group and rank texts written by geographically close contributors. For example, the ranking unit analyzes the geographical distribution of texts and groups and ranks texts written by geographically close contributors. The ranking unit can also perform ranking taking into consideration trends by region. For example, the ranking unit analyzes the geographical distribution of texts and performs ranking taking into consideration trends by region. This allows appropriate ranking to be performed based on geographical distribution. Some or all of the above-described processing in the ranking unit may be performed using, for example, AI, or may be performed without using AI. For example, the ranking unit can input geographical distribution data of texts into AI, and the AI ​​can perform ranking.

[0046] The ranking unit can improve the accuracy of the ranking by referring to related literature of the text when ranking. The ranking unit, for example, refers to citations of the text to improve the accuracy of the ranking. For example, the ranking unit analyzes related literature of the text and performs the ranking by referring to the citations. The ranking unit can also improve the accuracy of the ranking by referring to related academic papers. For example, the ranking unit analyzes related literature of the text and performs the ranking by referring to the related academic papers. The ranking unit can also analyze references of the text to improve the accuracy of the ranking. For example, the ranking unit analyzes related literature of the text and performs the ranking by referring to the references. In this way, the accuracy of the ranking is improved by referring to the related literature. Some or all of the above-mentioned processing in the ranking unit may be performed using, for example, AI, or may be performed without using AI. For example, the ranking unit can input related literature data of the text into AI, which can improve the accuracy of the ranking.

[0047] At the time of payment, the payment unit can select the optimal payment method by analyzing the poster's past posting history. For example, the payment unit prioritizes suggesting payment methods that the poster has used in the past. For example, the payment unit analyzes the poster's past posting history and prioritizes suggesting payment methods that the poster has used. The payment unit can also suggest the optimal timing for payment based on the poster's past posting history. For example, the payment unit analyzes the poster's past posting history and suggests the optimal timing for payment. The payment unit can also select an appropriate payment method based on the content of the poster's past posts. For example, the payment unit analyzes the poster's past posting history and selects an appropriate payment method based on the content of the posts. This makes it possible to provide the optimal payment method based on the past posting history. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can input the poster's past posting history data into AI, which can select the optimal payment method.

[0048] The payment unit can customize the payment method based on the poster's current living situation at the time of payment. For example, if the poster is a student, the payment unit provides a payment method that applies a student discount. For example, the payment unit analyzes the poster's living situation and provides a payment method that applies a student discount if the poster is a student. The payment unit can also provide a flexible payment schedule if the poster is a freelancer. For example, the payment unit analyzes the poster's living situation and provides a flexible payment schedule if the poster is a freelancer. The payment unit can also provide a stable payment method if the poster has a regular job. For example, the payment unit analyzes the poster's living situation and provides a stable payment method if the poster has a regular job. This makes it possible to provide an appropriate payment method according to the poster's living situation. Some or all of the above-mentioned processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can input the poster's living situation data into AI, which can customize the payment method.

[0049] At the time of payment, the payment unit can select the optimal payment method by taking into account the poster's geographic location information. For example, if the poster is in a specific region, the payment unit provides payment methods available in that region. For example, the payment unit analyzes the poster's geographic location information and provides payment methods available in that region. The payment unit can also provide payment methods available internationally if the poster is traveling. For example, the payment unit analyzes the poster's geographic location information and provides payment methods available internationally if the poster is traveling. The payment unit can also provide payment methods using local banks or payment services if the poster is at home. For example, the payment unit analyzes the poster's geographic location information and provides payment methods using local banks or payment services if the poster is at home. This makes it possible to provide the optimal payment method based on the geographic location information. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can input the poster's geographic location data into AI, which can select the optimal payment method.

[0050] At the time of payment, the payment unit can analyze the poster's social media activity to suggest a payment method. For example, the payment unit can suggest a payment service that the poster frequently uses on social media. For example, the payment unit can analyze the poster's social media activity and suggest a payment service that the poster frequently uses. The payment unit can also suggest a payment method related to a brand or service that the poster follows on social media. For example, the payment unit can analyze the poster's social media activity and suggest a payment method related to the brand or service that the poster follows. The payment unit can also suggest the optimal timing for payment based on the poster's social media activity. For example, the payment unit can analyze the poster's social media activity and suggest the optimal timing for payment. This makes it possible to provide an appropriate payment method based on the social media activity. Some or all of the above-mentioned processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can input the poster's social media activity data into AI, which then suggests a payment method.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The Japanese evaluation system may further include a feedback unit. The feedback unit provides the poster with feedback based on the evaluation results. For example, the feedback unit may provide specific feedback on grammatical errors or the use of honorific language in the text posted by the poster. The feedback unit may also specify areas where the poster should improve and provide useful advice for the next post. Furthermore, the feedback unit may refer to feedback the poster has received in the past and provide support to encourage continuous improvement. This allows the poster to obtain specific guidelines for improving their writing skills.

[0053] The evaluation department can collect readers' reactions to the poster's writing and reflect them in the evaluation. For example, the evaluation department can analyze the comments and reviews that readers have made on the post and make a comprehensive judgment on the quality of the writing. The evaluation department can also evaluate the appeal and readability of the writing based on the readers' reactions. Furthermore, the evaluation department can provide readers' feedback to the poster and help the poster improve their writing by taking readers' opinions into consideration. This allows the evaluation department to understand how the poster's writing is actually being received and to make a more accurate evaluation.

[0054] The ranking unit can rank posts based on the theme or genre of the contributor's writing. For example, the ranking unit can create rankings for different categories, such as business documents, everyday conversations, and academic papers. The ranking unit can also collect posts related to a specific theme and rank them. Furthermore, the ranking unit can provide customized rankings based on the contributor's specialty or interests. This allows contributors to know how their writing is evaluated in each category or theme, and to receive more specific feedback.

[0055] The payment unit can provide bonuses to contributors according to their contributions. For example, the payment unit can provide bonus points to contributors who receive many high ratings within a certain period of time. The payment unit can also provide special rewards for excellent writing posted during a specific campaign period. Furthermore, when a contributor invites other users, the payment unit can provide rewards according to the activities of the invited users. This allows contributors to have their efforts and contributions recognized and receive further incentives.

[0056] The reception unit can provide a function for checking grammar and honorific language in real time when a poster posts a sentence. For example, the reception unit can immediately point out grammatical errors and inappropriate use of honorific language when a poster enters a sentence. The reception unit can also provide specific advice for the poster to make corrections. Furthermore, the reception unit can predict the poster's overall evaluation score and indicate areas where there is room for improvement before the poster submits the sentence. This allows the poster to prepare for a higher evaluation before posting their sentence.

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

[0058] Step 1: The reception unit provides a procedure for the poster to post a text containing correct Japanese. For example, it provides a posting form and an interface for the poster to input the text, confirm the content of the post, and submit it. Step 2: The evaluation department evaluates the submitted text. The evaluation uses AI to determine the accuracy and appropriateness of the text. For example, a grammar check tool is used to evaluate the accuracy of grammar, honorific usage rules and appropriate honorific expressions are used to evaluate the appropriateness of honorific language, and the flow of the text is evaluated based on logical structure and sentence connections. Step 3: The ranking section creates a ranking based on the results evaluated by the evaluation section. The ranking ranks posts based on their evaluation scores and sorts them in descending order of their evaluation scores. Step 4: The payment unit pays the poster based on the ranking. Payment methods include a point system, cash payment, etc. For example, points can be awarded to the poster using a point system, or cash can be paid via bank transfer or electronic money.

[0059] (Example 2) The Japanese language evaluation system according to an embodiment of the present invention evaluates and ranks correct Japanese (e.g., humble language and polite language), and rewards posters for their submissions. In this Japanese language evaluation system, posters submit sentences containing correct Japanese, and the system evaluates and ranks them. For evaluation, AI is used to determine the accuracy and appropriateness of the sentences. Finally, the posters are paid based on the rankings. For example, a poster submits sentences containing correct Japanese. Next, the system evaluates and ranks the sentences. For evaluation, AI is used to determine the accuracy and appropriateness of the sentences. Possible evaluation criteria include grammatical accuracy, appropriateness of honorific language, and sentence flow. It is also necessary to clarify how the AI ​​evaluates these criteria. Finally, the posters are paid based on the rankings. It is important to clearly state how the reward will be paid, for example, through a points system or cash payment. It is also necessary to provide a detailed explanation of how the poster submits their sentences and the submission process. From the perspective of privacy protection, it is also necessary to explain how the AI ​​handles data when conducting evaluations. To protect privacy, it is important to clarify how the poster's personal information will be handled and protected. This allows the Japanese evaluation system to promote the use of correct Japanese and provide incentives to contributors.

[0060] The Japanese language evaluation system according to the embodiment includes a reception unit, an evaluation unit, a ranking unit, and a payment unit. The reception unit provides a procedure for a poster to post a sentence containing correct Japanese. For example, the reception unit provides a posting form through which the poster can input a sentence. The reception unit can also provide an interface through which the poster can check and submit the content of the post. The evaluation unit evaluates the posted sentence. The evaluation involves using AI to determine the accuracy and appropriateness of the sentence. For example, the evaluation unit can use a grammar check tool to evaluate the accuracy of grammar. The evaluation unit can also evaluate the appropriateness of honorific language based on honorific usage rules and appropriate honorific expressions. The evaluation unit can also evaluate the flow of the sentence based on logical structure and sentence connections. The ranking unit creates a ranking based on the results of the evaluation by the evaluation unit. The ranking is based on the evaluation score. For example, the ranking unit can sort the posts in descending order of evaluation score and create a ranking. The payment unit pays the poster a fee based on the ranking. Payment methods include a point system, cash payment, etc. For example, the payment unit can use a point system to award points to the poster. The payment unit can also pay cash using bank transfer or electronic money. In this way, the Japanese language evaluation system according to the embodiment can promote the use of correct Japanese and provide incentives to the poster.

[0061] The evaluation unit can evaluate the accuracy of grammar, the accuracy of honorific language, and the flow of the sentence. For example, the evaluation unit uses a grammar check tool to evaluate the accuracy of grammar. For example, the evaluation unit uses the grammar check tool to detect and correct grammatical errors in the sentence. The evaluation unit can also evaluate the accuracy of honorific language based on honorific usage rules and appropriate honorific expressions. For example, the evaluation unit evaluates whether honorific expressions in the sentence are appropriate based on the honorific usage rules. The evaluation unit can also evaluate the flow of the sentence based on the logical structure and connection of the sentences. For example, the evaluation unit evaluates the logical structure of the sentence and determines whether the connection of the sentences is natural. This evaluates the accuracy and appropriateness of the sentence, thereby improving the accuracy of the ranking. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can input the sentence into AI, which evaluates the accuracy of grammar, the appropriateness of honorific language, and the flow of the sentence.

[0062] The ranking unit can create a ranking based on the results of the evaluation by the evaluation unit. The ranking unit, for example, sorts the posts based on the evaluation scores and creates a ranking. For example, the ranking unit sorts the posts in descending order of evaluation score and creates a ranking. The ranking unit can also create a ranking of posters based on the evaluation scores. For example, the ranking unit ranks posters with high evaluation scores higher and creates a ranking. This enables accurate ranking by creating a ranking based on the evaluation results. Some or all of the above-mentioned processing in the ranking unit may be performed using AI, for example, or may be performed without using AI. For example, the ranking unit can input the evaluation scores into AI, which then creates a ranking.

[0063] The payment unit can pay the consideration using a point system or cash payment. The payment unit, for example, uses a point system to award points to the poster. For example, the payment unit awards points to the poster based on the evaluation score. The payment unit can also pay cash using bank transfer or electronic money. For example, the payment unit pays cash by transferring money to the poster's bank account. The payment unit can also pay cash to the poster using electronic money. This provides a variety of payment methods, improving convenience for posters. Some or all of the above-mentioned processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can input the evaluation score into AI, which can determine how to award points or how to pay cash.

[0064] The reception unit can provide a procedure for a poster to post a message. The reception unit, for example, provides a posting form, and the poster can input a message. For example, the reception unit can use the posting form to allow the poster to input and send a message. The reception unit can also provide an interface for the poster to check and send the content of the post. For example, the reception unit provides an interface for the poster to check and modify the content of the post. This allows the poster to easily post a message. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the posting form into AI, which can check and send the content entered by the poster.

[0065] The evaluation unit can protect privacy when handling data when the AI ​​makes an evaluation. The evaluation unit, for example, anonymizes the data. For example, the evaluation unit anonymizes the poster's personal information and uses it as evaluation data. The evaluation unit can also restrict access. For example, the evaluation unit restricts access to the evaluation data so that only specific users can access it. This protects the poster's privacy. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the evaluation data into AI, which can anonymize the data and restrict access.

[0066] The reception unit can estimate the poster's emotions and adjust the timing of post acceptance based on the estimated emotions of the poster. For example, if the poster is feeling stressed, the reception unit allows the system to quickly accept the post, thereby reducing the burden on the poster. For example, the reception unit can estimate the poster's emotions and, if the poster is feeling stressed, quickly accept the post. Furthermore, if the poster is relaxed, the reception unit can allow the system to accept the post at a normal timing, thereby maintaining a natural flow. For example, the reception unit can estimate the poster's emotions and, if the poster is relaxed, accept the post at a normal timing. Furthermore, if the poster is in a hurry, the reception unit can allow the system to immediately accept the post, thereby saving the poster's time. For example, the reception unit can estimate the poster's emotions and, if the poster is in a hurry, immediately accept the post. This allows the post to be accepted at an appropriate timing according to the poster's emotions. Emotion estimation is realized using an emotion estimation function, 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. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the poster's emotional data into AI, which may infer the emotion and adjust the timing of receiving the post.

[0067] The reception unit can analyze the poster's past posting history and select the optimal reception method. The reception unit, for example, analyzes the time period during which the poster frequently posted in the past and prioritizes receiving posts from those time periods. For example, the reception unit can analyze the poster's past posting history and prioritizes receiving posts from the time period during which the poster frequently posted. The reception unit can also prioritize suggesting a posting method (text, voice, etc.) that the poster has used in the past. For example, the reception unit can analyze the poster's past posting history and prioritizes suggesting the used posting method. The reception unit can also analyze the content of the poster's past posts and prioritize receiving posts on related topics. For example, the reception unit can analyze the content of the poster's past posts and prioritize receiving posts on related topics. This makes it possible to provide the optimal reception method based on the past posting history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the poster's past posting history into AI, which can select the optimal reception method.

[0068] When receiving a post, the reception unit can filter the posts based on the poster's current areas of interest. For example, the reception unit preferentially receives posts related to topics in which the poster is currently interested. For example, the reception unit analyzes the poster's current areas of interest and preferentially receives posts on related topics. The reception unit can also filter related posts based on keywords recently searched by the poster. For example, the reception unit analyzes the poster's recent search history and filters related posts. The reception unit can also preferentially receive related posts based on accounts and groups followed by the poster. For example, the reception unit analyzes the accounts and groups followed by the poster and preferentially receives related posts. This makes it possible to receive appropriate posts based on the poster's areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the poster's area of ​​interest data into AI, which then performs filtering.

[0069] The reception unit can estimate the poster's emotions and determine the priority of posts to be received based on the estimated emotions of the poster. For example, if the poster is excited, the reception unit prioritizes receiving the post and processes it quickly. For example, the reception unit estimates the poster's emotions and prioritizes receiving the post if the poster is excited. The reception unit can also receive posts with normal priority if the poster is calm. For example, the reception unit estimates the poster's emotions and prioritizes receiving the post if the poster is calm. The reception unit can also prioritize receiving posts if the poster is anxious and respond to them quickly. For example, the reception unit estimates the poster's emotions and prioritizes receiving the post if the poster is anxious. This allows posts to be received in priority order according to the poster's emotions. Emotion estimation is achieved using an emotion estimation function, 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. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the poster's emotional data into AI, which may infer the emotions and determine the priority of the posts.

[0070] When receiving a post, the reception unit can prioritize receiving highly relevant posts by taking into account the poster's geographical location information. For example, if the poster is in a specific area, the reception unit prioritizes receiving posts related to that area. For example, the reception unit analyzes the poster's geographical location information and prioritizes receiving posts related to that area. Furthermore, if the poster is traveling, the reception unit can prioritize receiving posts related to the travel destination. For example, the reception unit analyzes the poster's geographical location information and prioritizes receiving posts related to the travel destination. Furthermore, if the poster is at home, the reception unit can prioritize receiving posts related to local news and events. For example, the reception unit analyzes the poster's geographical location information and prioritizes receiving posts related to local news and events when the poster is at home. This allows highly relevant posts to be prioritized based on the geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the poster's geographical location information into AI, which can then prioritize receiving highly relevant posts.

[0071] When receiving a post, the reception unit can analyze the poster's social media activity and receive related posts. For example, the reception unit prioritizes receiving content related to posts that the poster recently "liked." For example, the reception unit analyzes the poster's social media activity and prioritizes receiving content related to posts that the poster recently "liked." The reception unit can also prioritize receiving content related to posts from accounts the poster follows. For example, the reception unit analyzes the poster's social media activity and prioritizes receiving content related to posts from the accounts the poster follows. The reception unit can also prioritize receiving content related to groups or events in which the poster participates. For example, the reception unit analyzes the poster's social media activity and prioritizes receiving content related to groups or events the poster participates in. This makes it possible to receive related posts based on social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the poster's social media activity data into AI, which then receives related posts.

[0072] The evaluation unit can estimate the poster's emotions and adjust the way the evaluation is expressed based on the estimated emotions of the poster. For example, if the poster is nervous, the evaluation unit expresses the evaluation gently to reduce the poster's anxiety. For example, the evaluation unit estimates the poster's emotions and, if the poster is nervous, expresses the evaluation gently. The evaluation unit can also provide detailed feedback to deepen understanding of the poster if the poster is relaxed. For example, the evaluation unit estimates the poster's emotions and, if the poster is relaxed, provides detailed feedback. The evaluation unit can also provide a concise and to-the-point evaluation if the poster is in a hurry. For example, the evaluation unit estimates the poster's emotions and, if the poster is in a hurry, provides a concise and to-the-point evaluation. This makes it possible to provide an appropriate evaluation according to the poster's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit may input the poster's emotional data into AI, which may infer the emotions and adjust the way the evaluation is expressed.

[0073] During evaluation, the evaluation unit can adjust the level of detail of the evaluation based on the importance of the sentence. For example, the evaluation unit provides a detailed evaluation for important sentences and provides specific improvements. For example, the evaluation unit evaluates the importance of the sentence and provides a detailed evaluation for important sentences. The evaluation unit can also provide a concise evaluation for general sentences and provide basic feedback. For example, the evaluation unit evaluates the importance of the sentence and provides a concise evaluation for general sentences. The evaluation unit can also provide a summary evaluation for short sentences and provide concise feedback. For example, the evaluation unit evaluates the importance of the sentence and provides a summary evaluation for short sentences. This makes it possible to provide an appropriate evaluation according to the importance of the sentence. Some or all of the above-described processing in the evaluation unit may be performed using, or without, AI. For example, the evaluation unit can input sentence importance data into AI, which can adjust the level of detail of the evaluation.

[0074] During evaluation, the evaluation unit can apply different evaluation algorithms depending on the category of the text. For example, the evaluation unit applies an algorithm to evaluate the appropriateness of technical terminology and formatting to a business document. For example, the evaluation unit identifies the category of the business document and applies an algorithm to evaluate the appropriateness of technical terminology and formatting. The evaluation unit can also apply an algorithm to evaluate the appropriateness of natural expressions and honorific language to everyday conversation. For example, the evaluation unit identifies the category of everyday conversation and applies an algorithm to evaluate the appropriateness of natural expressions and honorific language. The evaluation unit can also apply an algorithm to evaluate the logical consistency and grammatical accuracy to an academic paper. For example, the evaluation unit identifies the category of an academic paper and applies an algorithm to evaluate the logical consistency and grammatical accuracy. This makes it possible to provide an appropriate evaluation according to the category of the text. Some or all of the above-mentioned processing in the evaluation unit may be performed using, or without, AI. For example, the evaluation unit can input text category data into AI, which then applies different evaluation algorithms.

[0075] The evaluation unit can estimate the poster's emotions and adjust the length of the evaluation based on the estimated emotions of the poster. For example, if the poster is in a hurry, the evaluation unit provides a short and to-the-point evaluation. For example, if the poster is in a hurry, the evaluation unit provides a short and to-the-point evaluation. The evaluation unit can also provide detailed feedback if the poster is relaxed. For example, the evaluation unit can estimate the poster's emotions and provide detailed feedback if the poster is relaxed. The evaluation unit can also provide a gentle and concise evaluation if the poster is nervous. For example, the evaluation unit can estimate the poster's emotions and provide a gentle and concise evaluation if the poster is nervous. This allows the evaluation length to be appropriate for the poster'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. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI or without AI. For example, the evaluation department can input the poster's emotional data into the AI, which can then estimate the emotion and adjust the length of the evaluation.

[0076] During evaluation, the evaluation unit can determine the priority of evaluation based on the time of submission of the text. For example, the evaluation unit prioritizes evaluation of texts with an approaching deadline. For example, the evaluation unit analyzes the time of submission of the texts and prioritizes evaluation of texts with an approaching deadline. The evaluation unit can also determine the priority of evaluation based on the order of submission. For example, the evaluation unit analyzes the order of submission of the texts and prioritizes evaluation based on the order of submission. The evaluation unit can also prioritize evaluation of texts related to important events. For example, the evaluation unit analyzes the time of submission of the texts and prioritizes evaluation of texts related to important events. This allows evaluation to be performed with appropriate priority based on the time of submission. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on the time of submission of the texts into AI, and the AI ​​can determine the priority of evaluation.

[0077] During evaluation, the evaluation unit can adjust the order of evaluation based on the relevance of the sentences. For example, the evaluation unit evaluates sentences related to the same topic together. For example, the evaluation unit analyzes the relevance of the sentences and evaluates sentences related to the same topic together. The evaluation unit can also prioritize evaluation of sentences containing important keywords. For example, the evaluation unit analyzes the relevance of the sentences and prioritizes evaluation of sentences containing important keywords. The evaluation unit can also prioritize evaluation of sentences related to the poster's past posts. For example, the evaluation unit analyzes the poster's past posts and prioritizes evaluation of related sentences. This allows evaluation to be performed in an appropriate order based on relevance. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input sentence relevance data into AI, which can adjust the order of evaluation.

[0078] The ranking unit can estimate the poster's emotions and adjust the ranking criteria based on the estimated emotions of the poster. For example, if the poster is nervous, the ranking unit relaxes the strictness of the evaluation and adjusts the ranking criteria. For example, the ranking unit estimates the poster's emotions and relaxes the strictness of the evaluation when the poster is nervous. The ranking unit can also rank the poster using normal criteria when the poster is relaxed. For example, the ranking unit estimates the poster's emotions and relaxes the strictness of the evaluation when the poster is relaxed. The ranking unit can also strengthen the strictness of the evaluation and adjust the ranking criteria when the poster is excited. For example, the ranking unit estimates the poster's emotions and strengthens the strictness of the evaluation when the poster is excited. This makes it possible to provide appropriate ranking criteria according to the poster's emotions. 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. Some or all of the above-described processing in the ranking unit may be performed using, for example, AI, or may be performed without using AI. For example, the ranking unit may input the poster's emotional data into AI, which may estimate the emotions and adjust the ranking criteria.

[0079] The ranking unit can improve the accuracy of the ranking by taking into account the interrelationships between sentences when ranking. The ranking unit, for example, groups sentences related to the same topic to improve the accuracy of the ranking. For example, the ranking unit analyzes the interrelationships between sentences and groups sentences related to the same topic. The ranking unit can also improve the accuracy of the ranking by taking into account the citation relationships between sentences. For example, the ranking unit analyzes the interrelationships between sentences and performs ranking by taking into account the citation relationships. The ranking unit can also analyze the cross-references between sentences to improve the accuracy of the ranking. For example, the ranking unit analyzes the interrelationships between sentences and performs ranking by taking into account the cross-references. In this way, the accuracy of the ranking is improved by taking the interrelationships between sentences into account. Some or all of the above-mentioned processing in the ranking unit may be performed using, for example, AI, or may be performed without using AI. For example, the ranking unit can input data on the interrelationships between sentences into AI, which can improve the accuracy of the ranking.

[0080] The ranking unit can perform ranking taking into consideration the attribute information of posters when performing ranking. The ranking unit, for example, performs ranking based on the specialty of the poster. For example, the ranking unit analyzes the attribute information of the posters and performs ranking based on the specialty. The ranking unit can also perform ranking based on the years of experience of the posters. For example, the ranking unit analyzes the attribute information of the posters and performs ranking based on the years of experience. The ranking unit can also perform ranking based on the posters' past evaluation results. For example, the ranking unit analyzes the attribute information of the posters and performs ranking based on the past evaluation results. This allows appropriate ranking to be performed based on the posters' attribute information. Some or all of the above-mentioned processing in the ranking unit may be performed using, for example, AI, or may be performed without using AI. For example, the ranking unit can input the attribute information data of the posters into AI, and the AI ​​can perform ranking.

[0081] The ranking unit can estimate the poster's emotions and adjust the order in which the ranking results are displayed based on the estimated emotions of the poster. For example, if the poster is nervous, the ranking unit displays the ranking results in a gentle manner to reduce the poster's anxiety. For example, the ranking unit estimates the poster's emotions and displays the ranking results in a gentle manner when the poster is nervous. The ranking unit can also display the ranking results in a normal order when the poster is relaxed. For example, the ranking unit estimates the poster's emotions and displays the ranking results in a normal order when the poster is relaxed. The ranking unit can also highlight the ranking results when the poster is excited. For example, the ranking unit estimates the poster's emotions and displays the ranking results in a highlighted manner when the poster is excited. This allows the ranking results to be displayed in an appropriate order according to the poster'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. Some or all of the above-mentioned processing in the ranking unit may be performed using, for example, AI, or without AI. For example, the ranking unit can input the poster's emotional data into the AI, which can then estimate the emotion and adjust the order in which the ranking results are displayed.

[0082] The ranking unit can perform ranking taking into consideration the geographical distribution of texts. For example, the ranking unit prioritizes ranking texts related to a specific region. For example, the ranking unit analyzes the geographical distribution of texts and prioritizes ranking texts related to a specific region. The ranking unit can also group and rank texts written by geographically close contributors. For example, the ranking unit analyzes the geographical distribution of texts and groups and ranks texts written by geographically close contributors. The ranking unit can also perform ranking taking into consideration trends by region. For example, the ranking unit analyzes the geographical distribution of texts and performs ranking taking into consideration trends by region. This allows appropriate ranking to be performed based on geographical distribution. Some or all of the above-described processing in the ranking unit may be performed using, for example, AI, or may be performed without using AI. For example, the ranking unit can input geographical distribution data of texts into AI, and the AI ​​can perform ranking.

[0083] The ranking unit can improve the accuracy of the ranking by referring to related literature of the text when ranking. The ranking unit, for example, refers to citations of the text to improve the accuracy of the ranking. For example, the ranking unit analyzes related literature of the text and performs the ranking by referring to the citations. The ranking unit can also improve the accuracy of the ranking by referring to related academic papers. For example, the ranking unit analyzes related literature of the text and performs the ranking by referring to the related academic papers. The ranking unit can also analyze references of the text to improve the accuracy of the ranking. For example, the ranking unit analyzes related literature of the text and performs the ranking by referring to the references. In this way, the accuracy of the ranking is improved by referring to the related literature. Some or all of the above-mentioned processing in the ranking unit may be performed using, for example, AI, or may be performed without using AI. For example, the ranking unit can input related literature data of the text into AI, which can improve the accuracy of the ranking.

[0084] The payment unit can estimate the poster's emotions and adjust the payment method based on the estimated emotions of the poster. For example, if the poster is relaxed, the payment unit provides a normal payment method. For example, if the poster is relaxed, the payment unit can estimate the poster's emotions and provide a normal payment method. Furthermore, if the poster is in a hurry, the payment unit can provide a quick payment method. For example, if the poster is anxious, the payment unit can provide a payment method that provides a sense of security. For example, if the poster is anxious, the payment unit can estimate the poster's emotions and provide a payment method that provides a sense of security. This makes it possible to provide an appropriate payment method according to the poster's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-mentioned processing in the payment unit can be performed, for example, using AI or without AI. For example, the payment department can input the poster's emotional data into AI, which can then infer the emotion and adjust the payment method.

[0085] At the time of payment, the payment unit can select the optimal payment method by analyzing the poster's past posting history. For example, the payment unit prioritizes suggesting payment methods that the poster has used in the past. For example, the payment unit analyzes the poster's past posting history and prioritizes suggesting payment methods that the poster has used. The payment unit can also suggest the optimal timing for payment based on the poster's past posting history. For example, the payment unit analyzes the poster's past posting history and suggests the optimal timing for payment. The payment unit can also select an appropriate payment method based on the content of the poster's past posts. For example, the payment unit analyzes the poster's past posting history and selects an appropriate payment method based on the content of the posts. This makes it possible to provide the optimal payment method based on the past posting history. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can input the poster's past posting history data into AI, which can select the optimal payment method.

[0086] The payment unit can customize the payment method based on the poster's current living situation at the time of payment. For example, if the poster is a student, the payment unit provides a payment method that applies a student discount. For example, the payment unit analyzes the poster's living situation and provides a payment method that applies a student discount if the poster is a student. The payment unit can also provide a flexible payment schedule if the poster is a freelancer. For example, the payment unit analyzes the poster's living situation and provides a flexible payment schedule if the poster is a freelancer. The payment unit can also provide a stable payment method if the poster has a regular job. For example, the payment unit analyzes the poster's living situation and provides a stable payment method if the poster has a regular job. This makes it possible to provide an appropriate payment method according to the poster's living situation. Some or all of the above-mentioned processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can input the poster's living situation data into AI, which can customize the payment method.

[0087] The payment unit can estimate the poster's emotions and determine the priority of payments based on the estimated emotions of the poster. For example, if the poster is in a hurry, the payment unit prioritizes the payment. For example, the payment unit estimates the poster's emotions and prioritizes the payment if the poster is in a hurry. Furthermore, the payment unit can also make payments with normal priority if the poster is relaxed. For example, the payment unit estimates the poster's emotions and prioritizes the payment if the poster is relaxed. Furthermore, the payment unit can quickly make payments to provide a sense of security if the poster is feeling anxious. For example, the payment unit estimates the poster's emotions and quickly makes payments if the poster is feeling anxious. This allows payments to be made with appropriate priority according to the poster's emotions. Emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-mentioned processing in the payment unit can be performed using, for example, AI, or without AI. For example, the payment department can input the poster's emotional data into AI, which can then infer the emotions and determine payment priorities.

[0088] At the time of payment, the payment unit can select the optimal payment method by taking into account the poster's geographic location information. For example, if the poster is in a specific region, the payment unit provides payment methods available in that region. For example, the payment unit analyzes the poster's geographic location information and provides payment methods available in that region. The payment unit can also provide payment methods available internationally if the poster is traveling. For example, the payment unit analyzes the poster's geographic location information and provides payment methods available internationally if the poster is traveling. The payment unit can also provide payment methods using local banks or payment services if the poster is at home. For example, the payment unit analyzes the poster's geographic location information and provides payment methods using local banks or payment services if the poster is at home. This makes it possible to provide the optimal payment method based on the geographic location information. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can input the poster's geographic location data into AI, which can select the optimal payment method.

[0089] At the time of payment, the payment unit can analyze the poster's social media activity to suggest a payment method. For example, the payment unit can suggest a payment service that the poster frequently uses on social media. For example, the payment unit can analyze the poster's social media activity and suggest a payment service that the poster frequently uses. The payment unit can also suggest a payment method related to a brand or service that the poster follows on social media. For example, the payment unit can analyze the poster's social media activity and suggest a payment method related to the brand or service that the poster follows. The payment unit can also suggest the optimal timing for payment based on the poster's social media activity. For example, the payment unit can analyze the poster's social media activity and suggest the optimal timing for payment. This makes it possible to provide an appropriate payment method based on the social media activity. Some or all of the above-mentioned processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can input the poster's social media activity data into AI, which then suggests a payment method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, evaluation unit, ranking unit, and payment 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 is realized by the control unit 46A of the smart device 14 and provides an interface for the poster to input and submit text. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the accuracy and appropriateness of the text using AI. The ranking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and ranks the text based on the evaluation score. The payment unit is realized, for example, by the control unit 46A of the smart device 14 and pays compensation to the poster using a point system, cash payment, or other methods. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, evaluation unit, ranking unit, and payment 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 is realized by the control unit 46A of the smart glasses 214 and provides an interface for the poster to input and submit text. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the accuracy and appropriateness of the text using AI. The ranking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and ranks the text based on the evaluation score. The payment unit is realized, for example, by the control unit 46A of the smart glasses 214 and pays compensation to the poster using a point system, cash payment, or other methods. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, evaluation unit, ranking unit, and payment 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 is realized by the control unit 46A of the headset type terminal 314 and provides an interface for the poster to input and send text. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the accuracy and appropriateness of the text using AI. The ranking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and ranks the text based on the evaluation score. The payment unit is realized, for example, by the control unit 46A of the headset type terminal 314 and pays compensation to the poster using a point system, cash payment, or other methods. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, evaluation unit, ranking unit, and payment unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and provides an interface for the poster to input and submit a text. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the accuracy and appropriateness of the text using AI. The ranking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and ranks the text based on the evaluation score. The payment unit is realized, for example, by the control unit 46A of the robot 414 and pays compensation to the poster using a point system, cash payment, or other methods.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The Japanese evaluation system may further include a feedback unit. The feedback unit provides the poster with feedback based on the evaluation results. For example, the feedback unit may provide specific feedback on grammatical errors or the use of honorific language in the text posted by the poster. The feedback unit may also specify areas where the poster should improve and provide useful advice for the next post. Furthermore, the feedback unit may refer to feedback the poster has received in the past and provide support to encourage continuous improvement. This allows the poster to obtain specific guidelines for improving their writing skills.

[0092] The evaluation department can collect readers' reactions to the poster's writing and reflect them in the evaluation. For example, the evaluation department can analyze the comments and reviews that readers have made on the post and make a comprehensive judgment on the quality of the writing. The evaluation department can also evaluate the appeal and readability of the writing based on the readers' reactions. Furthermore, the evaluation department can provide readers' feedback to the poster and help the poster improve their writing by taking readers' opinions into consideration. This allows the evaluation department to understand how the poster's writing is actually being received and to make a more accurate evaluation.

[0093] The ranking unit can rank posts based on the theme or genre of the contributor's writing. For example, the ranking unit can create rankings for different categories, such as business documents, everyday conversations, and academic papers. The ranking unit can also collect posts related to a specific theme and rank them. Furthermore, the ranking unit can provide customized rankings based on the contributor's specialty or interests. This allows contributors to know how their writing is evaluated in each category or theme, and to receive more specific feedback.

[0094] The payment unit can provide bonuses to contributors according to their contributions. For example, the payment unit can provide bonus points to contributors who receive many high ratings within a certain period of time. The payment unit can also provide special rewards for excellent writing posted during a specific campaign period. Furthermore, when a contributor invites other users, the payment unit can provide rewards according to the activities of the invited users. This allows contributors to have their efforts and contributions recognized and receive further incentives.

[0095] The reception unit can provide a function for checking grammar and honorific language in real time when a poster posts a sentence. For example, the reception unit can immediately point out grammatical errors and inappropriate use of honorific language when a poster enters a sentence. The reception unit can also provide specific advice for the poster to make corrections. Furthermore, the reception unit can predict the poster's overall evaluation score and indicate areas where there is room for improvement before the poster submits the sentence. This allows the poster to prepare for a higher evaluation before posting their sentence.

[0096] The evaluation unit can estimate the poster's emotions and customize the evaluation feedback based on the estimated emotions of the poster. For example, if the poster is feeling down, the evaluation unit can provide feedback that includes encouraging words. If the poster is feeling confident, the evaluation unit can also provide feedback that highlights specific areas for improvement. Furthermore, if the poster is feeling nervous, the evaluation unit can provide gentle and polite feedback. This allows the poster to receive appropriate feedback according to their emotions and improve their writing skills while maintaining their motivation.

[0097] The ranking unit can estimate the poster's emotions and adjust the display method of the ranking results based on the estimated emotions of the poster. For example, if the poster is nervous, the ranking unit can display the ranking results in a gentle manner to reduce the poster's anxiety. Furthermore, if the poster is relaxed, the ranking unit can display the ranking results in a normal order. Furthermore, if the poster is excited, the ranking unit can display the ranking results in an emphasized manner. This makes it possible to provide ranking results in an appropriate display method according to the poster's emotions.

[0098] The payment unit can estimate the poster's emotions and adjust the timing of payment based on the estimated emotions of the poster. For example, the payment unit can make a quick payment if the poster is in a hurry. Alternatively, the payment unit can make a payment at a normal timing if the poster is relaxed. Furthermore, the payment unit can make a quick payment to provide a sense of security if the poster is feeling anxious. This allows payments to be made at appropriate timing according to the poster's emotions.

[0099] The evaluation unit can estimate the poster's emotions and adjust the level of detail in the evaluation based on the estimated emotions of the poster. For example, if the poster is in a hurry, the evaluation unit can provide a concise and to-the-point evaluation. If the poster is relaxed, the evaluation unit can also provide detailed feedback. Furthermore, if the poster is nervous, the evaluation unit can provide a gentle and concise evaluation. This makes it possible to provide an appropriate level of detail in the evaluation according to the poster's emotions.

[0100] The payment unit can estimate the poster's emotions and customize the payment method based on the estimated emotions of the poster. For example, if the poster is relaxed, the payment unit can provide a regular payment method. If the poster is in a hurry, the payment unit can also provide a quick payment method. Furthermore, if the poster is feeling anxious, the payment unit can also provide a payment method that gives a sense of security. In this way, it is possible to provide an appropriate payment method according to the poster's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception unit provides a procedure for the poster to post a text containing correct Japanese. For example, it provides a posting form and an interface for the poster to input the text, confirm the content of the post, and submit it. Step 2: The evaluation department evaluates the submitted text. The evaluation uses AI to determine the accuracy and appropriateness of the text. For example, a grammar check tool is used to evaluate the accuracy of grammar, honorific usage rules and appropriate honorific expressions are used to evaluate the appropriateness of honorific language, and the flow of the text is evaluated based on logical structure and sentence connections. Step 3: The ranking section creates a ranking based on the results evaluated by the evaluation section. The ranking ranks posts based on their evaluation scores and sorts them in descending order of their evaluation scores. Step 4: The payment unit pays the poster based on the ranking. Payment methods include a point system, cash payment, etc. For example, points can be awarded to the poster using a point system, or cash can be paid via bank transfer or electronic money.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 where contributors can submit sentences containing correct Japanese; an evaluation unit that evaluates the text received by the reception unit; a ranking unit that performs ranking based on the results of the evaluation by the evaluation unit; a payment unit that pays a fee based on the ranking created by the ranking unit. A system characterized by:

2. The evaluation unit Evaluate grammatical accuracy, honorific language accuracy, and sentence flow The system of claim 1 .

3. The ranking unit A ranking is created based on the results of the evaluation by the evaluation unit. The system of claim 1 .

4. The payment unit Pay by points system or cash payment method The system of claim 1 .

5. The reception unit Provide instructions for contributors to submit their writing The system of claim 1 .

6. The evaluation unit Protect privacy regarding the handling of data when AI makes evaluations The system of claim 1 .

7. The reception unit Estimate the poster's emotions and adjust the timing of accepting posts based on the estimated emotions of the poster. The system of claim 1 .

8. The reception unit Analyze the poster's past posting history and select the most appropriate reception method The system of claim 1 .

Images