system

Abstract

An object of a system according to an embodiment is to reduce a fire risk of SNS posting and perform optimal posting according to a trend.SOLUTION: A system includes a text check unit, a burning degree determination unit, a text change suggestion unit, a tag suggestion unit, and a photograph correction unit. The text check unit automatically checks the text. A burning degree determination part determines the burning degree of the text checked by the text check part. The text change proposal unit makes a text change proposal on the basis of the degree of inflammation determined by the inflammation degree determination unit. The tag proposal unit proposes a tag according to the trend. The photograph correction unit automatically corrects the photograph.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to prevent the risk of social media posts causing outrage, and it was difficult to make optimal posts in line with trends.

[0005] The system according to the embodiment aims to reduce the risk of social media posts causing outrage and to make optimal posts in line with trends. [Means for solving the problem]

[0006] The system according to the embodiment includes a text check unit, a controversy level determination unit, a text change suggestion unit, a tag suggestion unit, and a photo correction unit. The text check unit automatically checks the text. The controversy level determination unit determines the controversy level of the text checked by the text check unit. The text change suggestion unit suggests changes to the text based on the controversy level determined by the controversy level determination unit. The tag suggestion unit suggests tags according to trends. The photo correction unit automatically corrects photos. [Effects of the Invention]

[0007] The system according to the embodiment reduces the risk of social media posts causing outrage and enables optimal posts in line with trends. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 SNS posting support system according to the embodiment of the present invention is a system that automatically checks text and photos, determines the degree of controversy of the posted content, suggests text and tags according to trends, and automatically corrects photos. As a result, the SNS posting support system allows users to post according to trends and prevent controversy from occurring.

[0029] An SNS posting support system according to an embodiment includes a text checker, a flame rating determination unit, a text change suggestion unit, a tag suggestion unit, and a photo correction unit. The text checker automatically checks text. For example, the text checker analyzes text entered by a user to check whether it contains offensive language or discriminatory expressions. The text checker can also perform sentiment analysis of the text to evaluate the user's emotional state. The flame rating determination unit determines the flame rating of the text checked by the text checker. For example, the flame rating determination unit evaluates the flame rating based on the number of offensive language or discriminatory expressions contained in the text. The flame rating determination unit can also evaluate the flame rating based on the sentiment analysis results of the text. The text change suggestion unit proposes changes to the text based on the flame rating determined by the flame rating determination unit. For example, the text change suggestion unit proposes removing offensive language or discriminatory expressions. The text change suggestion unit can also propose changes to the text in accordance with trends. The tag suggestion unit proposes tags in accordance with trends. For example, the tag suggestion unit analyzes current trends and suggests appropriate hashtags. The tag suggestion unit can also analyze tags used in a user's past posts and suggest tags that match the user's posting style. The photo correction unit automatically corrects photos. For example, if a photo contains inappropriate content, the photo correction unit blurs that part. The photo correction unit can also suggest adjusting the color tone and composition of the photo. This allows the SNS posting support system according to the embodiment to enable users to post in line with trends and prevent flame wars. For example, if the text a user is about to post has a high risk of causing flame wars, the generation AI warns of the risk and suggests text and tags that are in line with the trend. Furthermore, if the photo contains inappropriate content, the generation AI automatically corrects it. This allows users to use SNS with peace of mind.

[0030] The text checking unit can create a database of historical cases of text that have caused a flare-up and compare it with past cases to determine whether or not there is a risk of a flare-up. The text checking unit, for example, creates a database of historical cases of text that have caused a flare-up and compares it with past cases to determine the risk of a flare-up. For example, if a text contains expressions similar to those that have caused a flare-up in the past, the risk of a flare-up is evaluated as high. The text checking unit also creates a database of historical cases of text that have caused a flare-up and compares it with past cases to determine the risk of a flare-up. For example, if a specific keyword or phrase matches a past case of a flare-up, the risk of a flare-up is evaluated as high. The text checking unit also creates a database of historical cases of text that have caused a flare-up and compares it with past cases to determine the risk of a flare-up. For example, the text checking unit analyzes patterns from past cases of a flare-up and determines that text containing similar patterns has a high risk of a flare-up. This makes it possible to determine the risk of a flare-up by comparing it with past cases of a flare-up.

[0031] The text checking unit can evaluate the degree of influence on a specific community or group based on the content of the text and determine the risk of a flame war. The text checking unit, for example, evaluates the degree of influence on a specific community or group based on the content of the text and determines the risk of a flame war. For example, if an offensive expression against a specific group is included, the risk of a flame war is evaluated as high. The text checking unit also evaluates the degree of influence on a specific community or group based on the content of the text and determines the risk of a flame war. For example, if a negative comment against a specific community is included, the risk of a flame war is evaluated as high. The text checking unit also evaluates the degree of influence on a specific community or group based on the content of the text and determines the risk of a flame war. For example, if a discriminatory expression against a specific group is included, the risk of a flame war is evaluated as high. In this way, the risk of a flame war can be evaluated and the risk of a flame war can be determined.

[0032] The text checking unit can determine the risk of a controversy based on the time period or day of the week when the post is scheduled to be posted, in addition to checking the text. The text checking unit, for example, determines the risk of a controversy based on the time period or day of the week when the post is scheduled to be posted, in addition to checking the text. For example, text posted at night or on weekends is evaluated as having a high risk of controversy. The text checking unit also determines the risk of a controversy based on the time period or day of the week when the post is scheduled to be posted. For example, text posted at a specific time period or day of the week is evaluated as having a high risk of controversy because users are more sensitive to such text. The text checking unit also determines the risk of a controversy based on the time period or day of the week when the post is scheduled to be posted, in addition to checking the text. For example, posts related to specific events or holidays are evaluated as having a high risk of controversy. This makes it possible to determine the risk of a controversy based on the time period or day of the week when the post is scheduled to be posted.

[0033] The text checking unit can analyze the poster's past posting history while checking the text, and comprehensively evaluate whether there is a risk of a controversy. The text checking unit, for example, can analyze the poster's past posting history while checking the text, and comprehensively evaluate the risk of a controversy. For example, the text of a user who has posted many controversial posts in the past is evaluated as having a high risk of a controversy. The text checking unit also analyzes the poster's past posting history and comprehensively evaluates the risk of a controversy. For example, the text of a poster who has received many negative reactions in the past is evaluated as having a high risk of a controversy. The text checking unit can also analyze the poster's past posting history while checking the text, and comprehensively evaluate the risk of a controversy. For example, the text of a poster who has made many problematic comments in the past is evaluated as having a high risk of a controversy. In this way, the poster's past posting history can be analyzed and the risk of a controversy can be comprehensively evaluated.

[0034] The text change suggestion unit can analyze the content of a user's past posts and suggest multiple trending messages that match the user's posting style. In the text change suggestion unit, for example, a generation AI analyzes the content of a user's past posts and suggests trending messages that match the user's posting style. For example, trending messages are generated based on expressions and phrases used in past posts. The text change suggestion unit also analyzes the content of a user's past posts and suggests trending messages that match the user's posting style. For example, trending messages that incorporate features of messages that were popular in past posts are suggested. In addition, the text change suggestion unit can analyze the content of a user's past posts and suggest trending messages that match the user's posting style. For example, trending messages are generated based on patterns of messages that received many responses in past posts. This makes it possible to suggest trending messages that match the user's posting style.

[0035] The text change proposal unit can collect trend information in real time and make multiple text change proposals based on the latest trends. The text change proposal unit, for example, collects trend information in real time and makes text change proposals based on the latest trends. For example, it proposes text that incorporates current trend keywords. The text change proposal unit also proposes text changes that match the latest trends based on the trend information collected in real time. For example, it proposes text that incorporates topics and events related to the trend. The text change proposal unit also collects trend information in real time and makes text change proposals based on the latest trends. For example, it proposes text aimed at users who are sensitive to trends. This makes it possible to propose text changes based on the latest trends.

[0036] The text change suggestion unit can make suggestions regarding the timing or frequency of posting in addition to suggesting changes to the text. For example, the text change suggestion unit can make suggestions regarding the timing and frequency of posting in addition to suggesting changes to the text. For example, it can suggest posting during times when the trend is most active. The text change suggestion unit can also make suggestions regarding the timing and frequency of posting in addition to suggesting changes to the text. For example, it can suggest that posting on a specific day of the week or at a specific time will get more responses. The text change suggestion unit can also make suggestions regarding the timing and frequency of posting in addition to suggesting changes to the text. For example, it can suggest attracting attention early by posting before the trend reaches its peak. This makes it possible to make suggestions regarding the timing and frequency of posting.

[0037] The text change suggestion unit can suggest related images or videos in addition to suggesting trending text. For example, the text change suggestion unit can automatically select images or videos related to the trend and suggest attaching them to a post. In addition to suggesting trending text, the text change suggestion unit can also suggest related images or videos. For example, the text change suggestion unit can automatically generate visual content related to the trend and suggest attaching it to a post. In addition to suggesting trending text, the text change suggestion unit can also suggest related images or videos. For example, the text change suggestion unit can suggest GIFs or short video clips related to the trend and suggest attaching them to a post. This makes it possible to suggest related images and videos.

[0038] The text change suggestion unit can make multiple text change suggestions from a global perspective based on trend information from different languages ​​and cultural spheres. The text change suggestion unit makes text change suggestions from a global perspective based on, for example, trend information from different languages ​​and cultural spheres. For example, it proposes text that matches international trends. The text change suggestion unit also makes text change suggestions from a global perspective based on trend information from different cultural spheres. For example, it proposes text that incorporates expressions and phrases that are popular in a particular region. The text change suggestion unit also makes text change suggestions from a global perspective based on trend information from different languages. For example, it proposes text related to trends in multiple languages ​​to accommodate international users. This makes it possible to make text change suggestions from a global perspective.

[0039] The tag suggestion unit can analyze tags used in a user's past posts and suggest multiple trending tags that match the user's posting style. For example, the tag suggestion unit uses a generation AI to analyze tags used in a user's past posts and suggest trending tags that match the user's posting style. For example, trending tags are selected based on tags that have been used frequently in the past. The tag suggestion unit can also analyze tags used in a user's past posts and suggest trending tags that match the user's posting style. For example, trending tags are suggested based on tags that have received a lot of responses in past posts. The tag suggestion unit can also analyze tags used in a user's past posts and suggest trending tags that match the user's posting style. For example, trending tags that incorporate the characteristics of tags that were popular in past posts are suggested. This makes it possible to suggest trending tags that match the user's posting style.

[0040] The tag suggestion unit can propose multiple effective tag strategies by combining tags in addition to proposing trending tags. For example, the tag suggestion unit proposes an effective tag strategy by combining tags in addition to proposing trending tags. For example, it makes a suggestion to maximize the exposure of a post by combining multiple trending tags. Furthermore, the tag suggestion unit proposes an effective tag strategy by combining tags in addition to proposing trending tags. For example, it makes a suggestion to appeal to a specific user group by combining highly related tags. Furthermore, the tag suggestion unit proposes an effective tag strategy by combining tags in addition to proposing trending tags. For example, it makes a suggestion to reach a wide range of users by combining trending tags and niche tags. This makes it possible to propose an effective tag strategy.

[0041] The tag suggestion unit can suggest a priority order of tags based on the frequency of tag use or popularity in addition to suggesting trending tags. For example, the tag suggestion unit suggests a priority order of tags based on the frequency of tag use or popularity in addition to suggesting trending tags. For example, it preferentially suggests tags that are used frequently. Furthermore, the tag suggestion unit suggests a priority order of tags based on the frequency of tag use or popularity in addition to suggesting trending tags. For example, it preferentially suggests tags that are popular. Furthermore, the tag suggestion unit suggests a priority order of tags based on the frequency of tag use or popularity in addition to suggesting trending tags. For example, it preferentially suggests tags that are both high in frequency of tag use and popularity. This makes it possible to suggest a priority order of tags based on the frequency of tag use or popularity.

[0042] The tag suggestion unit can provide related hashtag campaign or event information in addition to suggesting trending tags. For example, the tag suggestion unit provides related hashtag campaign or event information in addition to suggesting trending tags. For example, it suggests tags related to a campaign currently being held. Also, the tag suggestion unit provides related hashtag campaign or event information in addition to suggesting trending tags. For example, it suggests tags related to a specific event. Also, the tag suggestion unit provides related hashtag campaign or event information in addition to suggesting trending tags. For example, it suggests tags related to an event that is scheduled to be held in the future. This makes it possible to provide related hashtag campaign or event information.

[0043] The tag suggestion unit can make multiple tag suggestions from a global perspective based on trending tags in different languages ​​and cultural spheres. The tag suggestion unit makes tag suggestions from a global perspective based on trending tags in different languages ​​and cultural spheres, for example. For example, tags that match international trends are suggested. The tag suggestion unit also makes tag suggestions from a global perspective based on trending tags in different cultural spheres, for example. Tags that are popular in a specific region are suggested. The tag suggestion unit also makes tag suggestions from a global perspective based on trending tags in different languages, for example. Tags related to trends are suggested in multiple languages ​​to accommodate international users. This enables tag suggestions from a global perspective.

[0044] In addition to correcting a photo, the photo correction unit can also make suggestions regarding the composition or color tone of the photo. For example, in addition to correcting a photo, the photo correction unit can also make suggestions regarding the composition and color tone of the photo. For example, it can make suggestions for cropping to improve the composition of the photo. In addition to correcting a photo, the photo correction unit can also make suggestions regarding the composition and color tone of the photo. For example, it can make suggestions for adjusting the color tone of the photo to make it more visually appealing. In addition to correcting a photo, the photo correction unit can also make suggestions regarding the composition and color tone of the photo. For example, it can make suggestions for adjusting the brightness or contrast of the photo. This makes it possible to make suggestions regarding the composition and color tone of the photo.

[0045] In addition to correcting the photo, the photo correction unit can also suggest a caption or description related to the photo. For example, the photo correction unit can automatically generate an appropriate caption based on the content of the photo. In addition to correcting the photo, the photo correction unit can also suggest a caption or description related to the photo. For example, the photo correction unit can automatically generate an appropriate caption based on the content of the photo. In addition to correcting the photo, the photo correction unit can also suggest a caption or description related to the photo. For example, the photo correction unit can suggest a description that matches the theme of the photo. In addition to correcting the photo, the photo correction unit can also suggest a caption or description related to the photo. For example, the photo correction unit can suggest a description that explains the background information of the photo. This makes it possible to suggest a caption or description related to the photo.

[0046] The photo modification unit may suggest related images or videos in addition to modifying a photo. For example, the photo modification unit may suggest related images or videos in addition to modifying a photo. For example, the photo modification unit may automatically select images or videos related to the modified photo and suggest attaching them to a post. In addition to modifying a photo, the photo modification unit may suggest related images or videos in addition to modifying a photo. For example, the photo modification unit may automatically generate visual content related to the modified photo and suggest attaching it to a post. In addition to modifying a photo, the photo modification unit may suggest related images or videos in addition to modifying a photo. For example, the photo modification unit may suggest GIFs or short video clips related to the modified photo and suggest attaching them to a post. This makes it possible to suggest related images and videos.

[0047] The photo modification unit can make modifications to photos from the perspective of different cultures or regions. The photo modification unit, for example, makes modifications to photos from the perspective of different cultures or regions. For example, modifications are made to remove elements that are considered inappropriate in a particular culture. The photo modification unit also makes modifications to photos from the perspective of different cultures or regions. For example, modifications are made to match the color tones and composition preferred in a particular region. The photo modification unit also makes modifications to photos from the perspective of different cultures or regions. For example, modifications are made that take into consideration the particular culture or region. This makes it possible to make modifications to photos from the perspective of different cultures or regions.

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

[0049] The SNS posting support system can also analyze a user's past posting history and suggest multiple trending messages that match the user's posting style. For example, trending messages can be generated based on expressions and phrases used in past posts. It can also suggest trending messages that incorporate the characteristics of messages that were popular in past posts. It can also generate trending messages based on patterns of messages that received many responses in past posts. This makes it possible to suggest trending messages that match the user's posting style.

[0050] The SNS posting support system can also determine the risk of a flaming incident based on the time of day and day of the week when the post is scheduled to be posted. For example, messages posted at night or on weekends can be assessed as having a high risk of flaming incidents. Messages posted at specific times of day or on specific days of the week can also be assessed as having a high risk of flaming incidents because users are more sensitive to such messages. Furthermore, posts related to specific events or holidays can be assessed as having a high risk of flaming incidents. This makes it possible to determine the risk of a flaming incident based on the time of day and day of the week when the post is scheduled to be posted.

[0051] The SNS posting support system can also collect trend information in real time and make multiple suggestions for text changes based on the latest trends. For example, it can suggest text that incorporates current trending keywords. It can also suggest text that incorporates topics and events related to trends. It can also suggest text aimed at trend-conscious users. This makes it possible to suggest text changes based on the latest trends.

[0052] The SNS posting support system can also suggest tag priorities based on the frequency of tag use and popularity. For example, it can prioritize tags that are used frequently. It can also prioritize tags that are popular. It can also prioritize tags that are both used frequently and popular. This makes it possible to suggest tag priorities based on the frequency of tag use and popularity.

[0053] The SNS posting support system can also suggest multiple tags from a global perspective based on trending tags in different languages ​​and cultural spheres. For example, it can suggest tags that match international trends. It can also suggest tags that are popular in a specific region. It can also suggest tags related to trends in multiple languages ​​to accommodate international users. This makes it possible to suggest tags from a global perspective.

[0054] In addition to correcting photos, the SNS posting support system can also make suggestions regarding the composition and color tone of a photo. For example, it can make suggestions for cropping to improve the composition of a photo. It can also make suggestions for adjusting the color tone of a photo to make it more visually appealing. It can also make suggestions for adjusting the brightness and contrast of a photo. This makes it possible to make suggestions regarding the composition and color tone of a photo.

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

[0056] Step 1: The text checker automatically checks the text. For example, it analyzes the text entered by the user and checks whether it contains offensive language or discriminatory expressions. The text checker can also perform sentiment analysis of the text and evaluate the user's emotional state. Step 2: The flame risk assessment unit assesses the flame risk of the text checked by the text checking unit. For example, it assesses the flame risk based on the number of offensive words or discriminatory expressions contained in the text. It can also assess the flame risk based on the results of sentiment analysis of the text. Step 3: The text change suggestion unit proposes changes to the text based on the degree of inciting controversy determined by the degree of inciting controversy determination unit. For example, it may propose removing offensive language or discriminatory expressions. It may also propose changes to the text based on trends. Step 4: The tag suggestion unit suggests tags based on trends. For example, it analyzes current trends and suggests appropriate hashtags. It can also analyze tags used in a user's past posts and suggest tags that match the user's posting style. Step 5: The photo correction module automatically corrects your photos. For example, if a photo contains inappropriate content, it will blur it. It can also suggest adjusting the color tone and composition of your photo.

[0057] (Example 2) The SNS posting support system according to the embodiment of the present invention is a system that automatically checks text and photos, determines the degree of controversy of the posted content, suggests text and tags according to trends, and automatically corrects photos. As a result, the SNS posting support system allows users to post according to trends and prevent controversy from occurring.

[0058] An SNS posting support system according to an embodiment includes a text checker, a flame rating determination unit, a text change suggestion unit, a tag suggestion unit, and a photo correction unit. The text checker automatically checks text. For example, the text checker analyzes text entered by a user to check whether it contains offensive language or discriminatory expressions. The text checker can also perform sentiment analysis of the text to evaluate the user's emotional state. The flame rating determination unit determines the flame rating of the text checked by the text checker. For example, the flame rating determination unit evaluates the flame rating based on the number of offensive language or discriminatory expressions contained in the text. The flame rating determination unit can also evaluate the flame rating based on the sentiment analysis results of the text. The text change suggestion unit proposes changes to the text based on the flame rating determined by the flame rating determination unit. For example, the text change suggestion unit proposes removing offensive language or discriminatory expressions. The text change suggestion unit can also propose changes to the text in accordance with trends. The tag suggestion unit proposes tags in accordance with trends. For example, the tag suggestion unit analyzes current trends and suggests appropriate hashtags. The tag suggestion unit can also analyze tags used in a user's past posts and suggest tags that match the user's posting style. The photo correction unit automatically corrects photos. For example, if a photo contains inappropriate content, the photo correction unit blurs that part. The photo correction unit can also suggest adjusting the color tone and composition of the photo. This allows the SNS posting support system according to the embodiment to enable users to post in line with trends and prevent flame wars. For example, if the text a user is about to post has a high risk of causing flame wars, the generation AI warns of the risk and suggests text and tags that are in line with the trend. Furthermore, if the photo contains inappropriate content, the generation AI automatically corrects it. This allows users to use SNS with peace of mind.

[0059] The text checking unit can perform emotional analysis of the text and determine whether there is a risk of a controversy based on the user's emotional state. In the text checking unit, for example, a generation AI performs emotional analysis of the text and determines the risk of a controversy based on the user's emotional state. For example, the generation AI analyzes the emotional expressions contained in the text and evaluates the risk of a controversy based on the user's emotional state. For example, the generation AI analyzes the emotional expressions contained in the text and evaluates the risk of a controversy based on the user's emotional state. For example, the generation AI analyzes the emotional expressions contained in the text and evaluates the risk of a controversy based on the user's emotional state. For example, the generation AI extracts emotional keywords contained in the text and evaluates the risk of a controversy based on their intensity. This makes it possible to determine the risk of a controversy based on the user's emotional state.

[0060] The text checking unit can create a database of historical cases of text that have caused a flare-up and compare it with past cases to determine whether or not there is a risk of a flare-up. The text checking unit, for example, creates a database of historical cases of text that have caused a flare-up and compares it with past cases to determine the risk of a flare-up. For example, if a text contains expressions similar to those that have caused a flare-up in the past, the risk of a flare-up is evaluated as high. The text checking unit also creates a database of historical cases of text that have caused a flare-up and compares it with past cases to determine the risk of a flare-up. For example, if a specific keyword or phrase matches a past case of a flare-up, the risk of a flare-up is evaluated as high. The text checking unit also creates a database of historical cases of text that have caused a flare-up and compares it with past cases to determine the risk of a flare-up. For example, the text checking unit analyzes patterns from past cases of a flare-up and determines that text containing similar patterns has a high risk of a flare-up. This makes it possible to determine the risk of a flare-up by comparing it with past cases of a flare-up.

[0061] The text checking unit can evaluate the degree of influence on a specific community or group based on the content of the text and determine the risk of a flame war. The text checking unit, for example, evaluates the degree of influence on a specific community or group based on the content of the text and determines the risk of a flame war. For example, if an offensive expression against a specific group is included, the risk of a flame war is evaluated as high. The text checking unit also evaluates the degree of influence on a specific community or group based on the content of the text and determines the risk of a flame war. For example, if a negative comment against a specific community is included, the risk of a flame war is evaluated as high. The text checking unit also evaluates the degree of influence on a specific community or group based on the content of the text and determines the risk of a flame war. For example, if a discriminatory expression against a specific group is included, the risk of a flame war is evaluated as high. In this way, the risk of a flame war can be evaluated and the risk of a flame war can be determined.

[0062] The text checking unit can determine the risk of a controversy based on the time period or day of the week when the post is scheduled to be posted, in addition to checking the text. The text checking unit, for example, determines the risk of a controversy based on the time period or day of the week when the post is scheduled to be posted, in addition to checking the text. For example, text posted at night or on weekends is evaluated as having a high risk of controversy. The text checking unit also determines the risk of a controversy based on the time period or day of the week when the post is scheduled to be posted. For example, text posted at a specific time period or day of the week is evaluated as having a high risk of controversy because users are more sensitive to such text. The text checking unit also determines the risk of a controversy based on the time period or day of the week when the post is scheduled to be posted, in addition to checking the text. For example, posts related to specific events or holidays are evaluated as having a high risk of controversy. This makes it possible to determine the risk of a controversy based on the time period or day of the week when the post is scheduled to be posted.

[0063] The text checking unit can analyze the poster's past posting history while checking the text, and comprehensively evaluate whether there is a risk of a controversy. The text checking unit, for example, can analyze the poster's past posting history while checking the text, and comprehensively evaluate the risk of a controversy. For example, the text of a user who has posted many controversial posts in the past is evaluated as having a high risk of a controversy. The text checking unit also analyzes the poster's past posting history and comprehensively evaluates the risk of a controversy. For example, the text of a poster who has received many negative reactions in the past is evaluated as having a high risk of a controversy. The text checking unit can also analyze the poster's past posting history while checking the text, and comprehensively evaluate the risk of a controversy. For example, the text of a poster who has made many problematic comments in the past is evaluated as having a high risk of a controversy. In this way, the poster's past posting history can be analyzed and the risk of a controversy can be comprehensively evaluated.

[0064] The text check unit can use the emotion estimation function to predict other users' emotional reactions to text that the user is about to post and determine whether there is a risk of a flame war. The text check unit, for example, uses the emotion estimation function to predict other users' emotional reactions to text that the user is about to post and determine the risk of a flame war. For example, text that is predicted to have a high number of negative emotional reactions is evaluated as having a high risk of a flame war. The text check unit also predicts other users' emotional reactions to text that the user is about to post and determines the risk of a flame war. For example, text that is likely to cause anger or annoyance is evaluated as having a high risk of a flame war. The text check unit also uses the emotion estimation function to predict other users' emotional reactions to text that the user is about to post and determines the risk of a flame war. For example, text with a low emotion score is evaluated as having a high risk of a flame war. In this way, the emotional reactions of other users can be predicted and the risk of a flame war can be determined.

[0065] The text change suggestion unit can analyze the content of a user's past posts and suggest multiple trending messages that match the user's posting style. In the text change suggestion unit, for example, a generation AI analyzes the content of a user's past posts and suggests trending messages that match the user's posting style. For example, trending messages are generated based on expressions and phrases used in past posts. The text change suggestion unit also analyzes the content of a user's past posts and suggests trending messages that match the user's posting style. For example, trending messages that incorporate features of messages that were popular in past posts are suggested. In addition, the text change suggestion unit can analyze the content of a user's past posts and suggest trending messages that match the user's posting style. For example, trending messages are generated based on patterns of messages that received many responses in past posts. This makes it possible to suggest trending messages that match the user's posting style.

[0066] The text change proposal unit can collect trend information in real time and make multiple text change proposals based on the latest trends. The text change proposal unit, for example, collects trend information in real time and makes text change proposals based on the latest trends. For example, it proposes text that incorporates current trend keywords. The text change proposal unit also proposes text changes that match the latest trends based on the trend information collected in real time. For example, it proposes text that incorporates topics and events related to the trend. The text change proposal unit also collects trend information in real time and makes text change proposals based on the latest trends. For example, it proposes text aimed at users who are sensitive to trends. This makes it possible to propose text changes based on the latest trends.

[0067] The text change suggestion unit can make suggestions regarding the timing or frequency of posting in addition to suggesting changes to the text. For example, the text change suggestion unit can make suggestions regarding the timing and frequency of posting in addition to suggesting changes to the text. For example, it can suggest posting during times when the trend is most active. The text change suggestion unit can also make suggestions regarding the timing and frequency of posting in addition to suggesting changes to the text. For example, it can suggest that posting on a specific day of the week or at a specific time will get more responses. The text change suggestion unit can also make suggestions regarding the timing and frequency of posting in addition to suggesting changes to the text. For example, it can suggest attracting attention early by posting before the trend reaches its peak. This makes it possible to make suggestions regarding the timing and frequency of posting.

[0068] The text change suggestion unit can suggest related images or videos in addition to suggesting trending text. For example, the text change suggestion unit can automatically select images or videos related to the trend and suggest attaching them to a post. In addition to suggesting trending text, the text change suggestion unit can also suggest related images or videos. For example, the text change suggestion unit can automatically generate visual content related to the trend and suggest attaching it to a post. In addition to suggesting trending text, the text change suggestion unit can also suggest related images or videos. For example, the text change suggestion unit can suggest GIFs or short video clips related to the trend and suggest attaching them to a post. This makes it possible to suggest related images and videos.

[0069] The text change suggestion unit can make multiple text change suggestions from a global perspective based on trend information from different languages ​​and cultural spheres. The text change suggestion unit makes text change suggestions from a global perspective based on, for example, trend information from different languages ​​and cultural spheres. For example, it proposes text that matches international trends. The text change suggestion unit also makes text change suggestions from a global perspective based on trend information from different cultural spheres. For example, it proposes text that incorporates expressions and phrases that are popular in a particular region. The text change suggestion unit also makes text change suggestions from a global perspective based on trend information from different languages. For example, it proposes text related to trends in multiple languages ​​to accommodate international users. This makes it possible to make text change suggestions from a global perspective.

[0070] The text change suggestion unit can use the emotion estimation function to make multiple text change suggestions that will evoke the most positive emotion for the user. The text change suggestion unit, for example, uses the emotion estimation function to make text change suggestions that will evoke the most positive emotion for the user. For example, it prioritizes suggesting text with a high positive emotion score. The text change suggestion unit also uses the emotion estimation function to make text change suggestions that will evoke the most positive emotion for the user. For example, it suggests text that incorporates positive expressions and phrases. The text change suggestion unit also uses the emotion estimation function to make text change suggestions that will evoke the most positive emotion for the user. For example, it makes suggestions based on trending text with a high emotion score. This makes it possible to make text change suggestions that will evoke the most positive emotion for the user.

[0071] The tag suggestion unit can analyze tags used in a user's past posts and suggest multiple trending tags that match the user's posting style. For example, the tag suggestion unit uses a generation AI to analyze tags used in a user's past posts and suggest trending tags that match the user's posting style. For example, trending tags are selected based on tags that have been used frequently in the past. The tag suggestion unit can also analyze tags used in a user's past posts and suggest trending tags that match the user's posting style. For example, trending tags are suggested based on tags that have received a lot of responses in past posts. The tag suggestion unit can also analyze tags used in a user's past posts and suggest trending tags that match the user's posting style. For example, trending tags that incorporate the characteristics of tags that were popular in past posts are suggested. This makes it possible to suggest trending tags that match the user's posting style.

[0072] The tag suggestion unit can propose multiple effective tag strategies by combining tags in addition to proposing trending tags. For example, the tag suggestion unit proposes an effective tag strategy by combining tags in addition to proposing trending tags. For example, it makes a suggestion to maximize the exposure of a post by combining multiple trending tags. Furthermore, the tag suggestion unit proposes an effective tag strategy by combining tags in addition to proposing trending tags. For example, it makes a suggestion to appeal to a specific user group by combining highly related tags. Furthermore, the tag suggestion unit proposes an effective tag strategy by combining tags in addition to proposing trending tags. For example, it makes a suggestion to reach a wide range of users by combining trending tags and niche tags. This makes it possible to propose an effective tag strategy.

[0073] The tag suggestion unit can suggest a priority order of tags based on the frequency of tag use or popularity in addition to suggesting trending tags. For example, the tag suggestion unit suggests a priority order of tags based on the frequency of tag use or popularity in addition to suggesting trending tags. For example, it preferentially suggests tags that are used frequently. Furthermore, the tag suggestion unit suggests a priority order of tags based on the frequency of tag use or popularity in addition to suggesting trending tags. For example, it preferentially suggests tags that are popular. Furthermore, the tag suggestion unit suggests a priority order of tags based on the frequency of tag use or popularity in addition to suggesting trending tags. For example, it preferentially suggests tags that are both high in frequency of tag use and popularity. This makes it possible to suggest a priority order of tags based on the frequency of tag use or popularity.

[0074] The tag suggestion unit can provide related hashtag campaign or event information in addition to suggesting trending tags. For example, the tag suggestion unit provides related hashtag campaign or event information in addition to suggesting trending tags. For example, it suggests tags related to a campaign currently being held. Also, the tag suggestion unit provides related hashtag campaign or event information in addition to suggesting trending tags. For example, it suggests tags related to a specific event. Also, the tag suggestion unit provides related hashtag campaign or event information in addition to suggesting trending tags. For example, it suggests tags related to an event that is scheduled to be held in the future. This makes it possible to provide related hashtag campaign or event information.

[0075] The tag suggestion unit can make multiple tag suggestions from a global perspective based on trending tags in different languages ​​and cultural spheres. The tag suggestion unit makes tag suggestions from a global perspective based on trending tags in different languages ​​and cultural spheres, for example. For example, tags that match international trends are suggested. The tag suggestion unit also makes tag suggestions from a global perspective based on trending tags in different cultural spheres, for example. Tags that are popular in a specific region are suggested. The tag suggestion unit also makes tag suggestions from a global perspective based on trending tags in different languages, for example. Tags related to trends are suggested in multiple languages ​​to accommodate international users. This enables tag suggestions from a global perspective.

[0076] The tag suggestion unit can use the emotion estimation function to suggest multiple tags that evoke the most positive emotion for the user. The tag suggestion unit, for example, uses the emotion estimation function to suggest tags that evoke the most positive emotion for the user. For example, tags with a high positive emotion score are preferentially suggested. The tag suggestion unit also uses the emotion estimation function to suggest tags that evoke the most positive emotion for the user. For example, tags that incorporate positive expressions and phrases are suggested. The tag suggestion unit also uses the emotion estimation function to suggest tags that evoke the most positive emotion for the user. For example, suggestions are made based on trending tags with a high emotion score. This makes it possible to suggest tags that evoke the most positive emotion for the user.

[0077] The photo modification unit can perform modifications according to the user's emotional state based on the content of the photo. For example, the generation AI performs modifications according to the user's emotional state based on the content of the photo. For example, modifications are made to remove negative elements contained in the photo. The photo modification unit also performs modifications according to the user's emotional state based on the content of the photo. For example, modifications are made to adjust the color tone of the photo to bring out positive emotions. The photo modification unit also performs modifications according to the user's emotional state based on the content of the photo. For example, modifications are made to blur unpleasant elements contained in the photo. This makes it possible to modify photos according to the user's emotional state.

[0078] In addition to correcting a photo, the photo correction unit can also make suggestions regarding the composition or color tone of the photo. For example, in addition to correcting a photo, the photo correction unit can also make suggestions regarding the composition and color tone of the photo. For example, it can make suggestions for cropping to improve the composition of the photo. In addition to correcting a photo, the photo correction unit can also make suggestions regarding the composition and color tone of the photo. For example, it can make suggestions for adjusting the color tone of the photo to make it more visually appealing. In addition to correcting a photo, the photo correction unit can also make suggestions regarding the composition and color tone of the photo. For example, it can make suggestions for adjusting the brightness or contrast of the photo. This makes it possible to make suggestions regarding the composition and color tone of the photo.

[0079] In addition to correcting the photo, the photo correction unit can also suggest a caption or description related to the photo. For example, the photo correction unit can automatically generate an appropriate caption based on the content of the photo. In addition to correcting the photo, the photo correction unit can also suggest a caption or description related to the photo. For example, the photo correction unit can automatically generate an appropriate caption based on the content of the photo. In addition to correcting the photo, the photo correction unit can also suggest a caption or description related to the photo. For example, the photo correction unit can suggest a description that matches the theme of the photo. In addition to correcting the photo, the photo correction unit can also suggest a caption or description related to the photo. For example, the photo correction unit can suggest a description that explains the background information of the photo. This makes it possible to suggest a caption or description related to the photo.

[0080] The photo modification unit may suggest related images or videos in addition to modifying a photo. For example, the photo modification unit may suggest related images or videos in addition to modifying a photo. For example, the photo modification unit may automatically select images or videos related to the modified photo and suggest attaching them to a post. In addition to modifying a photo, the photo modification unit may suggest related images or videos in addition to modifying a photo. For example, the photo modification unit may automatically generate visual content related to the modified photo and suggest attaching it to a post. In addition to modifying a photo, the photo modification unit may suggest related images or videos in addition to modifying a photo. For example, the photo modification unit may suggest GIFs or short video clips related to the modified photo and suggest attaching them to a post. This makes it possible to suggest related images and videos.

[0081] The photo modification unit can make modifications to photos from the perspective of different cultures or regions. The photo modification unit, for example, makes modifications to photos from the perspective of different cultures or regions. For example, modifications are made to remove elements that are considered inappropriate in a particular culture. The photo modification unit also makes modifications to photos from the perspective of different cultures or regions. For example, modifications are made to match the color tones and composition preferred in a particular region. The photo modification unit also makes modifications to photos from the perspective of different cultures or regions. For example, modifications are made that take into consideration the particular culture or region. This makes it possible to make modifications to photos from the perspective of different cultures or regions.

[0082] The photo modification unit can use the emotion estimation function to make multiple photo modification suggestions that will evoke the most positive emotion for the user. For example, the photo modification unit uses the emotion estimation function to make photo modification suggestions that will evoke the most positive emotion for the user. For example, modifications with a high positive emotion score are preferentially suggested. The photo modification unit also uses the emotion estimation function to make photo modification suggestions that will evoke the most positive emotion for the user. For example, modifications that incorporate positive expressions or colors are suggested. The photo modification unit also uses the emotion estimation function to make photo modification suggestions that will evoke the most positive emotion for the user. For example, suggestions are made based on modifications with a high emotion score. This makes it possible to make photo modification suggestions that will evoke the most positive emotion for the user.

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

[0084] The SNS posting support system can also analyze a user's past posting history and suggest multiple trending messages that match the user's posting style. For example, trending messages can be generated based on expressions and phrases used in past posts. It can also suggest trending messages that incorporate the characteristics of messages that were popular in past posts. It can also generate trending messages based on patterns of messages that received many responses in past posts. This makes it possible to suggest trending messages that match the user's posting style.

[0085] The SNS posting support system can also determine the risk of a flaming incident based on the time of day and day of the week when the post is scheduled to be posted. For example, messages posted at night or on weekends can be assessed as having a high risk of flaming incidents. Messages posted at specific times of day or on specific days of the week can also be assessed as having a high risk of flaming incidents because users are more sensitive to such messages. Furthermore, posts related to specific events or holidays can be assessed as having a high risk of flaming incidents. This makes it possible to determine the risk of a flaming incident based on the time of day and day of the week when the post is scheduled to be posted.

[0086] The SNS posting support system can also use an emotion estimation function to predict other users' emotional reactions to text that a user is about to post, and determine the risk of it becoming a controversy. For example, text that is predicted to evoke a high number of negative emotional reactions can be evaluated as having a high risk of becoming a controversy. Text that is likely to provoke anger or discomfort can also be evaluated as having a high risk of becoming a controversy. Furthermore, text with a low emotion score can also be evaluated as having a high risk of becoming a controversy. This makes it possible to predict other users' emotional reactions and determine the risk of it becoming a controversy.

[0087] The SNS posting support system can also collect trend information in real time and make multiple suggestions for text changes based on the latest trends. For example, it can suggest text that incorporates current trending keywords. It can also suggest text that incorporates topics and events related to trends. It can also suggest text aimed at trend-conscious users. This makes it possible to suggest text changes based on the latest trends.

[0088] The SNS posting support system can also use its emotion estimation function to suggest multiple changes to text that will evoke the most positive emotions in users. For example, it can prioritize suggestions for text with a high positive emotion score. It can also suggest text that incorporates positive expressions and phrases. It can also make suggestions based on trending text with a high emotion score. This makes it possible to suggest changes to text that will evoke the most positive emotions in users.

[0089] The SNS posting support system can also suggest tag priorities based on the frequency of tag use and popularity. For example, it can prioritize tags that are used frequently. It can also prioritize tags that are popular. It can also prioritize tags that are both used frequently and popular. This makes it possible to suggest tag priorities based on the frequency of tag use and popularity.

[0090] The SNS posting support system can also use its emotion estimation function to suggest multiple tags that evoke the most positive emotions in users. For example, it can prioritize suggestions of tags with a high positive emotion score. It can also suggest tags that incorporate positive expressions and phrases. It can also make suggestions based on trending tags with high emotion scores. This makes it possible to suggest tags that evoke the most positive emotions in users.

[0091] The SNS posting support system can also suggest multiple tags from a global perspective based on trending tags in different languages ​​and cultural spheres. For example, it can suggest tags that match international trends. It can also suggest tags that are popular in a specific region. It can also suggest tags related to trends in multiple languages ​​to accommodate international users. This makes it possible to suggest tags from a global perspective.

[0092] The SNS posting support system can also use the emotion estimation function to suggest multiple photo edits that will evoke the most positive emotions in the user. For example, it can prioritize edits with a high positive emotion score. It can also suggest edits that incorporate positive expressions and colors. It can also make suggestions based on edits with a high emotion score. This makes it possible to suggest photo edits that will evoke the most positive emotions in the user.

[0093] In addition to correcting photos, the SNS posting support system can also make suggestions regarding the composition and color tone of a photo. For example, it can make suggestions for cropping to improve the composition of a photo. It can also make suggestions for adjusting the color tone of a photo to make it more visually appealing. It can also make suggestions for adjusting the brightness and contrast of a photo. This makes it possible to make suggestions regarding the composition and color tone of a photo.

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

[0095] Step 1: The text checker automatically checks the text. For example, it analyzes the text entered by the user and checks whether it contains offensive language or discriminatory expressions. The text checker can also perform sentiment analysis of the text and evaluate the user's emotional state. Step 2: The flame risk assessment unit assesses the flame risk of the text checked by the text checking unit. For example, it assesses the flame risk based on the number of offensive words or discriminatory expressions contained in the text. It can also assess the flame risk based on the results of sentiment analysis of the text. Step 3: The text change suggestion unit proposes changes to the text based on the degree of inciting controversy determined by the degree of inciting controversy determination unit. For example, it may propose removing offensive language or discriminatory expressions. It may also propose changes to the text based on trends. Step 4: The tag suggestion unit suggests tags based on trends. For example, it analyzes current trends and suggests appropriate hashtags. It can also analyze tags used in a user's past posts and suggest tags that match the user's posting style. Step 5: The photo correction module automatically corrects your photos. For example, if a photo contains inappropriate content, it will blur it. It can also suggest adjusting the color tone and composition of your photo.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0098] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] 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.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] 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.

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] 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.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0140] In the robot 414, the processor 46 performs the identification process. 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 robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0141] 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.

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0163] 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 text checking section that automatically checks the text, a flame-flame level determination unit that determines the flame-flame level of the text checked by the text check unit; a text change suggestion unit that suggests changes to the text based on the degree of excitement determined by the degree of excitement determination unit; a tag suggestion unit that suggests tags according to trends; A photo correction unit that automatically corrects photos. A system characterized by:

2. The text checking unit Analyzes the sentiment of the text and determines whether it poses a risk of flame war based on the user's emotional state 2. The system of claim 1.

3. The text checking unit In addition to checking the content, we also assess the risk of a post becoming a hot topic based on the time of day or day of the week it is posted.

2. The system of claim 1.

4. The text change suggestion unit Analyzes the content of a user's past posts and suggests multiple trending messages that match the user's posting style.

2. The system of claim 1.

5. The tag suggestion unit Analyzes tags used in a user's past posts and suggests multiple trending tags that match the user's posting style.

2. The system of claim 1.

6. The photo modification unit Modifying the user's emotional state based on the content of the photo 2. The system of claim 1.

7. The text checking unit Predicts other users' emotional reactions to the text a user is about to post and determines whether it poses a risk of flame wars.

2. The system of claim 1.

8. The text change suggestion unit Propose multiple changes to the text that will evoke the most positive feelings from the user 2. The system of claim 1.

Images