System

Abstract

An object of a system according to an embodiment is to collate content generated by a generation AI with an existing database and to safely operate the content.SOLUTION: A system includes a generation unit, a collation unit, and a notification unit. The generation unit generates content by the generation AI. The collation unit collates the content generated by the generation unit with an existing database. The notification unit notifies the user of a result obtained by the collation unit.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, there was a problem that content generated by generative AI could infringe copyright or trademark rights, making it difficult to operate safely.

[0005] The system according to the embodiment aims to ensure safe operation by matching content generated by the generation AI with existing databases. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a matching unit, and a notification unit. The generation unit generates content using a generation AI. The matching unit matches the content generated by the generation unit with an existing database. The notification unit notifies the user of the results obtained by the matching unit. [Effects of the Invention]

[0007] In the system according to the embodiment, content generated by the generation AI is compared with an existing database, allowing for safe operation. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The generation AI support system according to an embodiment of the present invention is a system that checks whether there is similar content on the market to prevent copyright or trademark infringement of content generated by the generation AI. This allows the generation AI support system to check whether the generated content overlaps with existing copyrighted works or trademarks, ensuring safe use.

[0029] A generation AI support system according to an embodiment includes a generation unit, a matching unit, and a notification unit. The generation unit generates content using a generation AI. For example, the generation AI generates content such as a name, a catchphrase, or music lyrics in response to user instructions. The generation AI receives prompts containing instructions on what the user wants the generation AI to do, and the generation AI generates content based on the prompts. The matching unit matches the content generated by the generation unit with an existing database. For example, it checks whether the generated product name overlaps with existing trademarks. The matching unit uses a dedicated tool to match the generated content with an existing database and checks whether the generated content is similar to these. The notification unit notifies the user of the results obtained by the matching unit. For example, it displays a warning such as, "This product name is similar to an existing trademark. Please think of a different name." This allows the generated content to be used safely, ensuring that it does not overlap with existing copyrighted works or trademarks. For example, it checks whether the generated content overlaps with existing copyrighted works or trademarks, ensuring safe use.

[0030] The generation unit can generate content based on the user's past preference data. For example, the generation unit uses a generation AI to analyze the user's past preference data and generate names and catchphrases based on that data. For example, it incorporates words and phrases that the user liked in the past. When generating music lyrics, the generation unit generates personalized lyrics based on the user's past music preference data. For example, it reflects the user's favorite music genres and themes. The generation unit also uses the generation AI to generate content tailored to a specific theme or tone based on the user's past preference data. For example, it reflects the user's favorite tone (e.g., humorous, serious). This makes it possible to generate personalized content based on the user's past preferences.

[0031] The generation unit can generate multilingual content that corresponds to different cultural spheres and languages. For example, when the generation AI generates names and catchphrases, the generation unit automatically translates them into different languages ​​and incorporates expressions appropriate for each language. For example, it generates lyrics in multiple languages ​​simultaneously, such as English, French, and Chinese. When generating music lyrics, the generation unit generates multilingual lyrics that incorporate the sensibilities and expressions of different cultural spheres. For example, it uses themes and words that are easily relatable in each cultural sphere. The generation unit also generates names and catchphrases that are appropriate for each culture based on data from different cultural spheres, using expressions that take cultural backgrounds and customs into consideration. This makes it possible to generate multilingual content that corresponds to different cultural spheres and languages.

[0032] The generation unit simultaneously generates visual elements, enabling the provision of a unified brand image. For example, when the generation AI generates a name or catchphrase, the generation unit simultaneously generates a logo and design, providing a unified brand image. For example, it unifies colors and fonts. In addition, when generating music lyrics, the generation unit simultaneously generates album covers and artwork, providing a visually unified feel. For example, it incorporates designs that match the theme of the lyrics. In addition, the generation unit simultaneously generates presentation materials incorporating visual elements for the content generated by the generation AI. For example, it unifies slide designs and graphics. This allows the generation of visual elements simultaneously, enabling the provision of a unified brand image.

[0033] The generation unit can generate audio versions of catchphrases and lyrics using speech synthesis technology. For example, the generation unit generates an audio version of a catchphrase generated by the generation AI using speech synthesis technology and provides it to the user. For example, it generates audio catchphrases for advertisements. When generating music lyrics, the generation unit also generates an audio file of sung lyrics using speech synthesis technology. For example, it is used as a demo audio source. The generation unit also generates an audio version of the name or catchphrase generated by the generation AI using speech synthesis technology and uses it for presentations and advertisements. For example, it generates an audio file for radio advertisements. This allows for the generation of audio versions of catchphrases and lyrics.

[0034] The matching unit can detect visually similar logos and designs using image recognition technology. For example, during database matching, the matching unit uses image recognition technology to detect visually similar logos and designs. For example, it identifies logos with the same design elements. Furthermore, when matching the generated logo or design with the database, the matching unit uses image recognition technology to detect visually similar existing designs. For example, it identifies designs with the same colors or shapes. Furthermore, during database matching, the matching unit uses image recognition technology to preferentially display visually similar designs. For example, it suggests content that matches the design desired by the user. This makes it possible to detect visually similar logos and designs.

[0035] The matching unit can detect phonetically similar catchphrases and lyrics using speech recognition technology. For example, when matching against a database, the matching unit uses speech recognition technology to detect phonetically similar catchphrases. For example, it identifies catchphrases with the same rhythm and intonation. Furthermore, when matching music lyrics against a database, the matching unit uses speech recognition technology to detect phonetically similar lyrics. For example, it identifies lyrics with the same melody and rhythm. Furthermore, when matching against a database, the matching unit uses speech recognition technology to preferentially display phonetically similar content. For example, it suggests content that matches the voice the user is looking for. This makes it possible to detect phonetically similar catchphrases and lyrics.

[0036] The matching unit can integrate databases from different industries or fields to perform a more extensive similarity check. For example, when performing database matching, the matching unit integrates databases from different industries to perform a more extensive similarity check. For example, databases from the technical field and the consumer market are integrated. The matching unit also integrates databases from different fields to check whether the generated content is similar in other fields. For example, databases from the medical field and the entertainment field are integrated. The matching unit also integrates databases from different industries or fields in real time when performing database matching, to perform a similarity check based on the latest information. For example, newly added data is immediately reflected. This allows databases from different industries or fields to be integrated to perform a more extensive similarity check.

[0037] The matching unit can instantly reflect newly added data in real time. The matching unit, for example, builds a system that instantly reflects newly added data in real time when matching a database. For example, newly registered trademarks and copyrighted works are instantly added to the database. The matching unit also updates the database in real time so that generated content is matched with the latest data. For example, newly added data is automatically reflected in the database. The matching unit also develops a system that prioritizes checking newly added data in real time when matching a database. For example, similarity is determined based on the newly added data. This makes it possible to instantly reflect newly added data in real time.

[0038] The notification unit can display the results of the similarity assessment in a visually easy-to-understand graph or chart. The notification unit, for example, builds a system that displays the results of the similarity assessment in a visually easy-to-understand graph or chart. For example, the degree of similarity is indicated using a bar graph or a pie chart. The notification unit also visually displays the results of the similarity assessment so that the user can intuitively understand them. For example, a color-coded chart or a heat map is used. The notification unit also displays the results of the similarity assessment using a visually easy-to-understand graph or chart. For example, items with high similarity are highlighted. This makes it possible to display the results of the similarity assessment in a visually easy-to-understand graph or chart.

[0039] The notification unit can notify the result of the similarity determination by voice using voice synthesis technology. The notification unit, for example, builds a system that notifies the result of the similarity determination by voice using voice synthesis technology. For example, if the generated content is similar to an existing copyrighted work, it issues a warning by voice. The notification unit also notifies the user of the result of the similarity determination by voice using voice synthesis technology. For example, if the generated product name overlaps with an existing trademark, it notifies the user by voice. The notification unit also develops a system that notifies the result of the similarity determination by voice, allowing the user to understand the result even when they cannot confirm it visually. For example, the notification is given by voice while driving or working. In this way, the result of the similarity determination can be notified by voice using voice synthesis technology.

[0040] The notification unit can synchronize and display the results of the similarity assessment on different devices. The notification unit, for example, builds a system that synchronizes and displays the results of the similarity assessment on different devices. For example, the results checked on a smartphone are simultaneously displayed on a PC. The notification unit also synchronizes the results of the similarity assessment between different devices in real time. For example, the results checked on a tablet are instantly displayed on a smartphone. The notification unit also develops a system that synchronizes and displays the results of the similarity assessment on multiple devices used by a user. For example, the same results can be viewed on a PC in the office and a tablet at home. This allows the results of the similarity assessment to be synchronized and displayed on different devices.

[0041] The notification unit can compare the results with the user's past preference data to make optimal suggestions. The notification unit, for example, compares the results of the similarity determination with the user's past preference data to build a system that makes optimal suggestions. For example, suggestions are made based on names and catchphrases that the user liked in the past. The notification unit also analyzes the user's past preference data to suggest optimal content based on the results of the similarity determination. For example, it reflects the musical styles that the user liked in the past. The notification unit also compares the results of the similarity determination with the user's past preference data to develop a system that makes personalized suggestions. For example, it suggests optimal product names based on the user's past preferences. This makes it possible to make optimal suggestions by comparing with the user's past preference data.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The generation unit can generate content based on the user's past preference data. For example, the generation AI analyzes the user's past preference data and generates names and catchphrases based on that data. For example, it incorporates words and phrases that the user liked in the past. When generating music lyrics, the generation unit generates personalized lyrics based on the user's past music preference data. For example, it reflects the user's favorite music genres and themes. The generation unit also generates content tailored to a specific theme or tone based on the user's past preference data. For example, it reflects the user's favorite tone (e.g., humorous, serious). This makes it possible to generate personalized content based on the user's past preferences.

[0044] The generation unit can generate multilingual content that corresponds to different cultural spheres and languages. For example, when the generation AI generates names and catchphrases, it automatically translates them into different languages ​​and incorporates expressions appropriate for each language. For example, it can generate lyrics in multiple languages ​​simultaneously, such as English, French, and Chinese. When generating music lyrics, the generation unit generates multilingual lyrics that incorporate the sensibilities and expressions of different cultural spheres. For example, it uses themes and words that are easily relatable in each cultural sphere. The generation unit also generates names and catchphrases that are appropriate for each culture based on data from different cultural spheres, using expressions that take cultural backgrounds and customs into consideration. This makes it possible to generate multilingual content that corresponds to different cultural spheres and languages.

[0045] The generation unit simultaneously generates visual elements, enabling the provision of a unified brand image. For example, when the generation AI generates a name or catchphrase, it simultaneously generates a logo and design, providing a unified brand image. For example, it unifies colors and fonts. Similarly, when generating music lyrics, the generation unit simultaneously generates album covers and artwork, providing a visually unified feel. For example, it incorporates designs that match the theme of the lyrics. Additionally, the generation unit simultaneously generates presentation materials incorporating visual elements for the content generated by the generation AI. For example, it unifies slide designs and graphics. This allows the generation of visual elements simultaneously, enabling the provision of a unified brand image.

[0046] The generation unit can generate audio versions of catchphrases and lyrics using speech synthesis technology. For example, the generation unit can generate an audio version of a catchphrase generated by the generation AI using speech synthesis technology and provide it to the user. For example, it can generate audio catchphrases for advertisements. When generating music lyrics, the generation unit can also generate an audio file of the lyrics sung using speech synthesis technology. For example, it can be used as a demo audio source. The generation unit can also generate an audio version of the name or catchphrase generated by the generation AI using speech synthesis technology and use it in presentations and advertisements. For example, it can generate an audio file for radio advertisements. This allows it to generate audio versions of catchphrases and lyrics.

[0047] The matching unit can detect visually similar logos and designs using image recognition technology. For example, when matching against a database, the matching unit uses image recognition technology to detect visually similar logos and designs. For example, it identifies logos with the same design elements. Furthermore, when matching a generated logo or design against a database, the matching unit uses image recognition technology to detect visually similar existing designs. For example, it identifies designs with the same colors or shapes. Furthermore, when matching against a database, the matching unit uses image recognition technology to preferentially display visually similar designs. For example, it suggests content that matches the design desired by the user. This makes it possible to detect visually similar logos and designs.

[0048] The matching unit can detect phonetically similar catchphrases and lyrics using speech recognition technology. For example, when matching against a database, speech recognition technology is used to detect phonetically similar catchphrases. For example, catchphrases with the same rhythm and intonation are identified. Furthermore, when matching music lyrics against a database, the matching unit uses speech recognition technology to detect phonetically similar lyrics. For example, lyrics with the same melody and rhythm are identified. Furthermore, when matching against a database, the matching unit uses speech recognition technology to preferentially display phonetically similar content. For example, content that matches the voice desired by the user is suggested. This makes it possible to detect phonetically similar catchphrases and lyrics.

[0049] The matching unit can integrate databases from different industries or fields to perform a more extensive similarity check. For example, when performing database matching, databases from different industries are integrated to perform a more extensive similarity check. For example, databases from the technical field and the consumer market are integrated. The matching unit can also integrate databases from different fields to check whether the generated content is similar in other fields. For example, databases from the medical field and the entertainment field are integrated. When performing database matching, the matching unit can also integrate databases from different industries or fields in real time to perform a similarity check based on the latest information. For example, newly added data can be reflected immediately. This allows databases from different industries or fields to be integrated to perform a more extensive similarity check.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The generation unit generates content using a generation AI. For example, the generation AI receives instructions from the user and generates content such as a name, a catchphrase, or music lyrics. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates content based on that prompt. Step 2: The matching unit checks the content generated by the generation unit against an existing database. For example, it checks whether the generated product name overlaps with an existing trademark. The matching unit uses a dedicated tool to check whether the generated content is similar to the existing database. Step 3: The notification unit notifies the user of the results obtained by the matching unit. For example, it displays a warning such as, "This product name is similar to an existing trademark. Please think of a different name." This allows the user to check whether the generated content overlaps with existing copyrighted works or trademarks, ensuring safe use.

[0052] (Example 2) The generation AI support system according to an embodiment of the present invention is a system that checks whether there is similar content on the market to prevent copyright or trademark infringement of content generated by the generation AI. This allows the generation AI support system to check whether the generated content overlaps with existing copyrighted works or trademarks, ensuring safe use.

[0053] A generation AI support system according to an embodiment includes a generation unit, a matching unit, and a notification unit. The generation unit generates content using a generation AI. For example, the generation AI generates content such as a name, a catchphrase, or music lyrics in response to user instructions. The generation AI receives prompts containing instructions on what the user wants the generation AI to do, and the generation AI generates content based on the prompts. The matching unit matches the content generated by the generation unit with an existing database. For example, it checks whether the generated product name overlaps with existing trademarks. The matching unit uses a dedicated tool to match the generated content with an existing database and checks whether the generated content is similar to these. The notification unit notifies the user of the results obtained by the matching unit. For example, it displays a warning such as, "This product name is similar to an existing trademark. Please think of a different name." This allows the generated content to be used safely, ensuring that it does not overlap with existing copyrighted works or trademarks. For example, it checks whether the generated content overlaps with existing copyrighted works or trademarks, ensuring safe use.

[0054] The generation unit can generate content based on the user's emotional data. For example, the generation unit analyzes the user's emotional data in real time and incorporates expressions that evoke positive emotions into the names and catchphrases generated by the generation AI. For example, it selects words that make the user feel happy. When generating music lyrics, the generation unit analyzes the user's emotional state and generates lyrics that match those emotions. For example, if the user is feeling sad, it generates lyrics that comfort them. The generation unit also generates personalized content based on the user's past emotional data for the content generated by the generation AI. For example, it incorporates expressions and words that the user liked in the past. This makes it possible to generate content that matches the user's emotions.

[0055] The generation unit can generate content based on the user's past preference data. For example, the generation unit uses a generation AI to analyze the user's past preference data and generate names and catchphrases based on that data. For example, it incorporates words and phrases that the user liked in the past. When generating music lyrics, the generation unit generates personalized lyrics based on the user's past music preference data. For example, it reflects the user's favorite music genres and themes. The generation unit also uses the generation AI to generate content tailored to a specific theme or tone based on the user's past preference data. For example, it reflects the user's favorite tone (e.g., humorous, serious). This makes it possible to generate personalized content based on the user's past preferences.

[0056] The generation unit can generate multilingual content that corresponds to different cultural spheres and languages. For example, when the generation AI generates names and catchphrases, the generation unit automatically translates them into different languages ​​and incorporates expressions appropriate for each language. For example, it generates lyrics in multiple languages ​​simultaneously, such as English, French, and Chinese. When generating music lyrics, the generation unit generates multilingual lyrics that incorporate the sensibilities and expressions of different cultural spheres. For example, it uses themes and words that are easily relatable in each cultural sphere. The generation unit also generates names and catchphrases that are appropriate for each culture based on data from different cultural spheres, using expressions that take cultural backgrounds and customs into consideration. This makes it possible to generate multilingual content that corresponds to different cultural spheres and languages.

[0057] The generation unit simultaneously generates visual elements, enabling the provision of a unified brand image. For example, when the generation AI generates a name or catchphrase, the generation unit simultaneously generates a logo and design, providing a unified brand image. For example, it unifies colors and fonts. In addition, when generating music lyrics, the generation unit simultaneously generates album covers and artwork, providing a visually unified feel. For example, it incorporates designs that match the theme of the lyrics. In addition, the generation unit simultaneously generates presentation materials incorporating visual elements for the content generated by the generation AI. For example, it unifies slide designs and graphics. This allows the generation of visual elements simultaneously, enabling the provision of a unified brand image.

[0058] The generation unit can generate audio versions of catchphrases and lyrics using speech synthesis technology. For example, the generation unit generates an audio version of a catchphrase generated by the generation AI using speech synthesis technology and provides it to the user. For example, it generates audio catchphrases for advertisements. When generating music lyrics, the generation unit also generates an audio file of sung lyrics using speech synthesis technology. For example, it is used as a demo audio source. The generation unit also generates an audio version of the name or catchphrase generated by the generation AI using speech synthesis technology and uses it for presentations and advertisements. For example, it generates an audio file for radio advertisements. This allows for the generation of audio versions of catchphrases and lyrics.

[0059] The generation unit can use the emotion estimation function to generate music and sound effects that correspond to the user's emotions. For example, the generation unit generates appropriate music and sound effects based on the user's emotional data for the name and catchphrase generated by the generation AI. For example, it selects music that elicits positive emotions. When generating music lyrics, the generation unit also analyzes the user's emotional state and generates music and sound effects that match those emotions. For example, it generates music that is suitable for when you want to relax. The generation unit also generates personalized music and sound effects for content generated by the generation AI based on the user's past emotional data. For example, it incorporates musical styles that the user has preferred in the past. This makes it possible to generate music and sound effects that correspond to the user's emotions.

[0060] The matching unit can detect emotionally similar content using the emotion estimation function. For example, during database matching, the matching unit uses the emotion estimation function to detect emotionally similar names or catchphrases. For example, it identifies expressions that evoke the same emotion. Furthermore, during database matching of music lyrics, the matching unit uses the emotion estimation function to detect emotionally similar lyrics. For example, it identifies lyrics that express the same emotion. Furthermore, during database matching, the matching unit uses the emotion estimation function to preferentially display emotionally similar content. For example, it suggests content that matches the emotion desired by the user. This makes it possible to detect emotionally similar content.

[0061] The matching unit can detect visually similar logos and designs using image recognition technology. For example, during database matching, the matching unit uses image recognition technology to detect visually similar logos and designs. For example, it identifies logos with the same design elements. Furthermore, when matching the generated logo or design with the database, the matching unit uses image recognition technology to detect visually similar existing designs. For example, it identifies designs with the same colors or shapes. Furthermore, during database matching, the matching unit uses image recognition technology to preferentially display visually similar designs. For example, it suggests content that matches the design desired by the user. This makes it possible to detect visually similar logos and designs.

[0062] The matching unit can detect phonetically similar catchphrases and lyrics using speech recognition technology. For example, when matching against a database, the matching unit uses speech recognition technology to detect phonetically similar catchphrases. For example, it identifies catchphrases with the same rhythm and intonation. Furthermore, when matching music lyrics against a database, the matching unit uses speech recognition technology to detect phonetically similar lyrics. For example, it identifies lyrics with the same melody and rhythm. Furthermore, when matching against a database, the matching unit uses speech recognition technology to preferentially display phonetically similar content. For example, it suggests content that matches the voice the user is looking for. This makes it possible to detect phonetically similar catchphrases and lyrics.

[0063] The matching unit can integrate databases from different industries or fields to perform a more extensive similarity check. For example, when performing database matching, the matching unit integrates databases from different industries to perform a more extensive similarity check. For example, databases from the technical field and the consumer market are integrated. The matching unit also integrates databases from different fields to check whether the generated content is similar in other fields. For example, databases from the medical field and the entertainment field are integrated. The matching unit also integrates databases from different industries or fields in real time when performing database matching, to perform a similarity check based on the latest information. For example, newly added data is immediately reflected. This allows databases from different industries or fields to be integrated to perform a more extensive similarity check.

[0064] The matching unit can instantly reflect newly added data in real time. The matching unit, for example, builds a system that instantly reflects newly added data in real time when matching a database. For example, newly registered trademarks and copyrighted works are instantly added to the database. The matching unit also updates the database in real time so that generated content is matched with the latest data. For example, newly added data is automatically reflected in the database. The matching unit also develops a system that prioritizes checking newly added data in real time when matching a database. For example, similarity is determined based on the newly added data. This makes it possible to instantly reflect newly added data in real time.

[0065] The matching unit can use the emotion estimation function to preferentially display similarities that the user is most interested in. For example, the matching unit uses the emotion estimation function to build a system that preferentially displays similarities that the user is most interested in when performing database matching. For example, the matching unit determines similarities based on the user's emotion score. Furthermore, the matching unit uses the emotion estimation function to analyze similarities that the user is interested in in real time and preferentially display them. For example, the matching unit determines similarities based on the user's emotion data. Furthermore, the matching unit uses the emotion estimation function to develop a system that preferentially displays similarities that the user is most interested in when performing database matching. For example, the similarities are determined based on the user's emotional reactions. This makes it possible to preferentially display similarities that the user is most interested in.

[0066] The notification unit can use the emotion estimation function to display the result of the similarity determination in a format that corresponds to the user's emotion. The notification unit, for example, builds a system that uses the emotion estimation function to display the result of the similarity determination in a format that corresponds to the user's emotion. For example, the result is displayed using expressions that elicit positive emotions. The notification unit also displays the result of the similarity determination in a personalized format based on the user's emotion data. For example, when the user is relaxed, the result is displayed using calm expressions. The notification unit also uses the emotion estimation function to display the result of the similarity determination in a format that corresponds to the user's emotion. For example, when the user is feeling anxious, the result is displayed using expressions that give a sense of security. In this way, the result of the similarity determination can be displayed in a format that corresponds to the user's emotion.

[0067] The notification unit can display the results of the similarity assessment in a visually easy-to-understand graph or chart. The notification unit, for example, builds a system that displays the results of the similarity assessment in a visually easy-to-understand graph or chart. For example, the degree of similarity is indicated using a bar graph or a pie chart. The notification unit also visually displays the results of the similarity assessment so that the user can intuitively understand them. For example, a color-coded chart or a heat map is used. The notification unit also displays the results of the similarity assessment using a visually easy-to-understand graph or chart. For example, items with high similarity are highlighted. This makes it possible to display the results of the similarity assessment in a visually easy-to-understand graph or chart.

[0068] The notification unit can notify the result of the similarity determination by voice using voice synthesis technology. The notification unit, for example, builds a system that notifies the result of the similarity determination by voice using voice synthesis technology. For example, if the generated content is similar to an existing copyrighted work, it issues a warning by voice. The notification unit also notifies the user of the result of the similarity determination by voice using voice synthesis technology. For example, if the generated product name overlaps with an existing trademark, it notifies the user by voice. The notification unit also develops a system that notifies the result of the similarity determination by voice, allowing the user to understand the result even when they cannot confirm it visually. For example, the notification is given by voice while driving or working. In this way, the result of the similarity determination can be notified by voice using voice synthesis technology.

[0069] The notification unit can synchronize and display the results of the similarity assessment on different devices. The notification unit, for example, builds a system that synchronizes and displays the results of the similarity assessment on different devices. For example, the results checked on a smartphone are simultaneously displayed on a PC. The notification unit also synchronizes the results of the similarity assessment between different devices in real time. For example, the results checked on a tablet are instantly displayed on a smartphone. The notification unit also develops a system that synchronizes and displays the results of the similarity assessment on multiple devices used by a user. For example, the same results can be viewed on a PC in the office and a tablet at home. This allows the results of the similarity assessment to be synchronized and displayed on different devices.

[0070] The notification unit can compare the results with the user's past preference data to make optimal suggestions. The notification unit, for example, compares the results of the similarity determination with the user's past preference data to build a system that makes optimal suggestions. For example, suggestions are made based on names and catchphrases that the user liked in the past. The notification unit also analyzes the user's past preference data to suggest optimal content based on the results of the similarity determination. For example, it reflects the musical styles that the user liked in the past. The notification unit also compares the results of the similarity determination with the user's past preference data to develop a system that makes personalized suggestions. For example, it suggests optimal product names based on the user's past preferences. This makes it possible to make optimal suggestions by comparing with the user's past preference data.

[0071] The notification unit can provide feedback according to the user's emotion using the emotion estimation function. The notification unit, for example, builds a system that provides feedback according to the user's emotion using the emotion estimation function based on the result of the similarity determination. For example, feedback that elicits positive emotions is provided. The notification unit also uses the emotion estimation function to provide feedback according to the user's emotion in real time. For example, if the user is feeling anxious, feedback that gives a sense of security is provided. The notification unit also uses the emotion estimation function to provide feedback according to the user's emotion based on the result of the similarity determination. For example, feedback is provided using an expression that makes the user feel happy. This makes it possible to provide feedback according to the user's emotion.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The generation unit can generate content based on the user's emotional data. For example, the generation AI can analyze the user's emotional data in real time and incorporate expressions that evoke positive emotions into the names and catchphrases it generates. For example, it can select words that make the user feel happy. When generating music lyrics, the generation unit can analyze the user's emotional state and generate lyrics that match those emotions. For example, if the user is feeling sad, it can generate lyrics that comfort them. The generation unit can also generate personalized content based on the user's past emotional data for the content generated by the generation AI. For example, it can incorporate expressions and words that the user liked in the past. This makes it possible to generate content that matches the user's emotions.

[0074] The generation unit can generate content based on the user's past preference data. For example, the generation AI analyzes the user's past preference data and generates names and catchphrases based on that data. For example, it incorporates words and phrases that the user liked in the past. When generating music lyrics, the generation unit generates personalized lyrics based on the user's past music preference data. For example, it reflects the user's favorite music genres and themes. The generation unit also generates content tailored to a specific theme or tone based on the user's past preference data. For example, it reflects the user's favorite tone (e.g., humorous, serious). This makes it possible to generate personalized content based on the user's past preferences.

[0075] The generation unit can generate multilingual content that corresponds to different cultural spheres and languages. For example, when the generation AI generates names and catchphrases, it automatically translates them into different languages ​​and incorporates expressions appropriate for each language. For example, it can generate lyrics in multiple languages ​​simultaneously, such as English, French, and Chinese. When generating music lyrics, the generation unit generates multilingual lyrics that incorporate the sensibilities and expressions of different cultural spheres. For example, it uses themes and words that are easily relatable in each cultural sphere. The generation unit also generates names and catchphrases that are appropriate for each culture based on data from different cultural spheres, using expressions that take cultural backgrounds and customs into consideration. This makes it possible to generate multilingual content that corresponds to different cultural spheres and languages.

[0076] The generation unit simultaneously generates visual elements, enabling the provision of a unified brand image. For example, when the generation AI generates a name or catchphrase, it simultaneously generates a logo and design, providing a unified brand image. For example, it unifies colors and fonts. Similarly, when generating music lyrics, the generation unit simultaneously generates album covers and artwork, providing a visually unified feel. For example, it incorporates designs that match the theme of the lyrics. Additionally, the generation unit simultaneously generates presentation materials incorporating visual elements for the content generated by the generation AI. For example, it unifies slide designs and graphics. This allows the generation of visual elements simultaneously, enabling the provision of a unified brand image.

[0077] The generation unit can generate audio versions of catchphrases and lyrics using speech synthesis technology. For example, the generation unit can generate an audio version of a catchphrase generated by the generation AI using speech synthesis technology and provide it to the user. For example, it can generate audio catchphrases for advertisements. When generating music lyrics, the generation unit can also generate an audio file of the lyrics sung using speech synthesis technology. For example, it can be used as a demo audio source. The generation unit can also generate an audio version of the name or catchphrase generated by the generation AI using speech synthesis technology and use it in presentations and advertisements. For example, it can generate an audio file for radio advertisements. This allows it to generate audio versions of catchphrases and lyrics.

[0078] The generation unit can use the emotion estimation function to generate music and sound effects that correspond to the user's emotions. For example, for a name or catchphrase generated by the generation AI, appropriate music and sound effects are generated based on the user's emotional data. For example, music that elicits positive emotions is selected. When generating music lyrics, the generation unit also analyzes the user's emotional state and generates music and sound effects that match those emotions. For example, it generates music that is suitable for when you want to relax. The generation unit also generates personalized music and sound effects for content generated by the generation AI based on the user's past emotional data. For example, it incorporates musical styles that the user has preferred in the past. This makes it possible to generate music and sound effects that correspond to the user's emotions.

[0079] The matching unit can detect emotionally similar content using the emotion estimation function. For example, when performing database matching, the emotion estimation function is used to detect emotionally similar names or catchphrases. For example, expressions that evoke the same emotion are identified. Furthermore, when matching music lyrics with a database, the matching unit uses the emotion estimation function to detect emotionally similar lyrics. For example, lyrics that express the same emotion are identified. Furthermore, when performing database matching, the matching unit uses the emotion estimation function to preferentially display emotionally similar content. For example, content that matches the emotion desired by the user is suggested. This makes it possible to detect emotionally similar content.

[0080] The matching unit can detect visually similar logos and designs using image recognition technology. For example, when matching against a database, the matching unit uses image recognition technology to detect visually similar logos and designs. For example, it identifies logos with the same design elements. Furthermore, when matching a generated logo or design against a database, the matching unit uses image recognition technology to detect visually similar existing designs. For example, it identifies designs with the same colors or shapes. Furthermore, when matching against a database, the matching unit uses image recognition technology to preferentially display visually similar designs. For example, it suggests content that matches the design desired by the user. This makes it possible to detect visually similar logos and designs.

[0081] The matching unit can detect phonetically similar catchphrases and lyrics using speech recognition technology. For example, when matching against a database, speech recognition technology is used to detect phonetically similar catchphrases. For example, catchphrases with the same rhythm and intonation are identified. Furthermore, when matching music lyrics against a database, the matching unit uses speech recognition technology to detect phonetically similar lyrics. For example, lyrics with the same melody and rhythm are identified. Furthermore, when matching against a database, the matching unit uses speech recognition technology to preferentially display phonetically similar content. For example, content that matches the voice desired by the user is suggested. This makes it possible to detect phonetically similar catchphrases and lyrics.

[0082] The matching unit can integrate databases from different industries or fields to perform a more extensive similarity check. For example, when performing database matching, databases from different industries are integrated to perform a more extensive similarity check. For example, databases from the technical field and the consumer market are integrated. The matching unit can also integrate databases from different fields to check whether the generated content is similar in other fields. For example, databases from the medical field and the entertainment field are integrated. When performing database matching, the matching unit can also integrate databases from different industries or fields in real time to perform a similarity check based on the latest information. For example, newly added data can be reflected immediately. This allows databases from different industries or fields to be integrated to perform a more extensive similarity check.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The generation unit generates content using a generation AI. For example, the generation AI receives instructions from the user and generates content such as a name, a catchphrase, or music lyrics. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates content based on that prompt. Step 2: The matching unit checks the content generated by the generation unit against an existing database. For example, it checks whether the generated product name overlaps with an existing trademark. The matching unit uses a dedicated tool to check whether the generated content is similar to the existing database. Step 3: The notification unit notifies the user of the results obtained by the matching unit. For example, it displays a warning such as, "This product name is similar to an existing trademark. Please think of a different name." This allows the user to check whether the generated content overlaps with existing copyrighted works or trademarks, ensuring safe use.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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 generation unit that generates content using a generation AI; a collation unit that compares the content generated by the generation unit with an existing database; a notification unit that notifies a user of the result obtained by the matching unit. A system characterized by:

2. The generation unit Generate content based on the user's emotion data 2. The system of claim 1.

3. The generation unit Generate content based on the user's past preference data.

2. The system of claim 1.

4. The generation unit Generate multilingual content for different cultures and languages 2. The system of claim 1.

5. The generation unit Simultaneous generation of visual elements to provide a unified brand image 2. The system of claim 1.

Images