system

Abstract

The system according to the embodiment aims to provide a simple visual representation of the user's daily events. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of an event from a user. The analysis unit analyzes the event received by the reception unit. The generation unit generates a four-panel manga based on the content analyzed by the analysis unit. The provision unit provides the four-panel manga generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately provide users with a means to easily visualize everyday events, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a simple visual representation of the user's daily events. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of an event from a user. The analysis unit analyzes the event received by the reception unit. The generation unit generates a four-panel manga based on the content analyzed by the analysis unit. The provision unit provides the four-panel manga generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can easily visually represent the daily happenings of a user. [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) In an embodiment of the present invention, a system allows parents to record the events of their day, and an AI generates a four-panel manga based on the recorded events. In this system, users input text about the events of the day, and the AI ​​analyzes the text and understands the content of the events. The AI ​​generates a four-panel manga story based on the analyzed content and automatically draws characters and backgrounds. The generated four-panel manga can be published and shared by users on social media. For example, if a user inputs content such as "Today was my baby's first laugh," the AI ​​analyzes the content and extracts key points. The AI ​​then generates a four-panel manga story based on the extracted points and draws characters and backgrounds. The generated four-panel manga can be downloaded by users or posted to social media. This allows parents to easily record and share their daily events. Furthermore, because the AI ​​automatically generates the manga, parents can easily preserve their memories. This system allows parents to easily record and share their daily events. Furthermore, because the AI ​​automatically generates the manga, parents can easily preserve their memories.

[0029] A four-panel manga generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of events from a user. The user can input events that occurred that day, for example, in text format. The reception unit provides, for example, a text input form, allowing the user to easily input events. The reception unit can also receive voice input and image input. The analysis unit analyzes the events received by the reception unit. The analysis unit understands the content of the text using, for example, natural language processing technology and extracts important points of the events. The analysis unit can, for example, segment the text using morphological analysis, perform grammatical analysis, and perform semantic analysis. The analysis unit can also perform sentiment analysis to extract emotional elements of the events. The generation unit generates a four-panel manga based on the content analyzed by the analysis unit. The generation unit, for example, automatically draws characters and backgrounds. The generation unit can, for example, generate a story using text generation AI (e.g., LLM) and draw characters and backgrounds using multimodal generation AI. The generation unit can also change the style of the characters and background according to the user's preferences. The provision unit provides the four-panel manga generated by the generation unit. For example, the provision unit allows the user to publish and share the generated four-panel manga on a social networking site (SNS). For example, the provision unit provides a posting function to a social networking site (SNS) so that the user can easily share the four-panel manga. The provision unit can also make the generated four-panel manga available for download. In this way, the four-panel manga generation system according to the embodiment can efficiently convert the user's events into a four-panel manga and provide it.

[0030] The analysis unit can use natural language processing technology to understand the content of the text and extract important points of the event. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit can segment the text using morphological analysis, perform grammatical analysis, and perform semantic analysis. The analysis unit can also perform sentiment analysis to extract emotional elements of the event. For example, the analysis unit can extract frequently occurring keywords in the text and identify important points based on the keywords. The analysis unit can also evaluate the importance of the context and extract important points. Furthermore, the analysis unit can summarize the content of the text and extract important points. As a result, the use of natural language processing technology can accurately extract important points of the event.

[0031] The generation unit can automatically draw characters and backgrounds. Characters include, for example, human characters, animal characters, and abstract characters. The generation unit can, for example, generate a story using a text generation AI (e.g., LLM) and draw characters and backgrounds using a multimodal generation AI. Backgrounds include, for example, urban backgrounds, natural backgrounds, and abstract backgrounds. The generation unit can also change the style of characters and backgrounds according to a user's preferences. For example, the generation unit can draw characters based on a character style selected by the user. The generation unit can also draw backgrounds based on a background style selected by the user. Furthermore, the generation unit can automatically adjust the colors of characters and backgrounds. This makes it more efficient to generate four-panel manga by automatically drawing characters and backgrounds.

[0032] The providing unit can enable users to publish and share the generated four-panel manga on social networking sites. Publishing and sharing includes, for example, posting to social networking sites, downloading, and sending by email. The providing unit, for example, enables users to publish and share the generated four-panel manga on social networking sites. The providing unit, for example, provides a function for posting to social networking sites, allowing users to easily share the four-panel manga. The providing unit can also enable users to download the generated four-panel manga. For example, the providing unit can enable users to download the generated four-panel manga in PDF format or image format. The providing unit can also provide a function for sending the generated four-panel manga by email. This allows users to easily share the generated four-panel manga on social networking sites.

[0033] The reception unit can analyze the user's past input history and select the optimal input method. The input history includes, for example, past input content, input frequency, input timing, etc. For example, if the user has frequently used text input in the past, the reception unit can preferentially suggest text input. Furthermore, if the user has frequently used voice input in the past, the reception unit can preferentially suggest voice input. Furthermore, if the user has frequently used image input in the past, the reception unit can preferentially suggest image input. For example, the reception unit analyzes the user's past input history and selects the optimal input method. This makes it possible to suggest the optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's past input history to a generation AI and cause the generation AI to select the optimal input method.

[0034] When inputting events, the reception unit can filter the events based on the user's current living situation and areas of interest. Examples of living situations include work status, family status, and health status. Examples of areas of interest include hobbies, topics of interest, and areas of expertise. For example, if the user is raising a child, the reception unit can suggest that events related to childcare be input preferentially. Also, if the user is working, the reception unit can suggest that events related to work be input preferentially. Furthermore, if the user is interested in hobbies, the reception unit can suggest that events related to the hobbies be input preferentially. For example, the reception unit can filter the events based on the user's current living situation and areas of interest. This can promote the input of events according to the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input data on the user's living situation and areas of interest to the generation AI and cause the generation AI to perform filtering.

[0035] When inputting an event, the reception unit can select the optimal input means depending on the user's input method. Input methods include, for example, voice input, text input, and image input. For example, if the user selects voice input, the reception unit can input the event using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also input the event using keyboard input. Furthermore, if the user selects image input, the reception unit can also input the event using image analysis technology. For example, the reception unit selects the optimal input means depending on the user's input method. This allows the event to be input using the optimal means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data of the user's input method to the generation AI and cause the generation AI to select the optimal input means.

[0036] When inputting events, the reception unit can prioritize inputting highly relevant events by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data, address information, location accuracy, etc. For example, when the user is in a specific location, the reception unit can suggest that the user prioritize inputting events related to that location. Furthermore, when the user is traveling, the reception unit can suggest that the user prioritize inputting events related to the travel destination. Furthermore, when the user is at home, the reception unit can suggest that the user prioritize inputting events related to the home. For example, the reception unit prioritizes inputting highly relevant events by taking into account the user's geographical location information. This allows highly relevant events to be input based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data of the user's geographical location information to a generation AI and cause the generation AI to select highly relevant events.

[0037] When an event is input, the reception unit can analyze the user's social media activity and input related events. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. The reception unit, for example, automatically inputs events shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input related events. Furthermore, the reception unit can input related events based on the activity of the user's friends on social media. For example, the reception unit analyzes the user's social media activity and inputs related events. In this way, related events can be input based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's social media activity to a generation AI and cause the generation AI to select related events.

[0038] The reception unit can customize the input method by reflecting the user's past feedback when inputting an event. The feedback includes, for example, the user's ratings, comments, usage history, etc. The reception unit can improve the input method, for example, based on the user's past feedback. The reception unit can also suggest an optimal input means based on the user's past feedback. Furthermore, the reception unit can customize the input interface by referring to the user's past feedback. For example, the reception unit customizes the input method by reflecting the user's past feedback. This allows the input method to be customized based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the input method.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the event. The importance includes, for example, the impact of the event and the user's level of interest. For example, the analysis unit provides detailed analysis results for important events. The analysis unit can also provide concise analysis results for general events. Furthermore, the analysis unit can provide detailed analysis results for events in which the user is particularly interested. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the event. This allows the level of detail of the analysis to be adjusted according to the importance of the event. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on the importance of the event to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the event. Categories include, for example, the type of event and the type of emotion. For example, in the case of an event related to childcare, the analysis unit applies an analysis algorithm dedicated to childcare. In addition, in the case of an event related to work, the analysis unit can also apply an analysis algorithm dedicated to work. Furthermore, in the case of an event related to a hobby, the analysis unit can also apply an analysis algorithm dedicated to a hobby. For example, the analysis unit applies different analysis algorithms depending on the category of the event. This makes it possible to apply an analysis algorithm depending on the category of the event. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the category of the event to the generation AI and cause the generation AI to apply the analysis algorithm.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, for example, past analysis data and user feedback. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also extract important points from the user's past analysis results to improve the accuracy of the analysis. Furthermore, the analysis unit can adjust the level of detail of the analysis by referring to the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input data of the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0042] During analysis, the analysis unit can determine the priority of analysis based on the time of occurrence of an event. The time of occurrence includes, for example, date and time, season, timing of the event, etc. The analysis unit, for example, prioritizes analysis of events that occurred recently. The analysis unit can also prioritize analysis of events that occurred during a period in which the user is particularly interested. Furthermore, the analysis unit can prioritize analysis of the time when an important event occurred. For example, the analysis unit determines the priority of analysis based on the time of occurrence of an event. This allows the priority of analysis to be determined based on the time of occurrence of an event. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of occurrence of an event to the generation AI and have the generation AI determine the priority of analysis.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of events. Relevance includes, for example, commonalities between events, related topics, etc. The analysis unit, for example, prioritizes the analysis of highly related events. The analysis unit can also prioritize the analysis of events in which the user is particularly interested. Furthermore, the analysis unit can prioritize the analysis of important events. For example, the analysis unit adjusts the order of analysis based on the relevance of events. This makes it possible to adjust the order of analysis based on the relevance of events. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of events to the generation AI and cause the generation AI to adjust the order of analysis.

[0044] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. Expertise levels include, for example, beginner, intermediate, and expert. For example, if the user has expert knowledge, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user has general knowledge, the analysis unit can provide concise and easy-to-understand analysis results. Furthermore, if the user is a beginner, the analysis unit can provide analysis results that avoid technical terminology. For example, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. This allows the use of technical terminology in the analysis to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0045] During generation, the generation unit can adjust the level of detail of the four-panel manga based on the importance of the event. Importance includes, for example, the impact of the event and the user's level of interest. For example, the generation unit generates a four-panel manga that includes a detailed depiction for an important event. The generation unit can also generate a four-panel manga that includes a concise depiction for a general event. Furthermore, the generation unit can generate a four-panel manga that includes a detailed depiction for an event in which the user is particularly interested. For example, the generation unit adjusts the level of detail of the four-panel manga based on the importance of the event. This allows the level of detail of the four-panel manga to be adjusted according to the importance of the event. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input data on the importance of the event into the generation AI and cause the generation AI to adjust the level of detail of the four-panel manga.

[0046] During generation, the generation unit can apply different generation algorithms depending on the category of the event. Categories include, for example, the type of event and the type of emotion. For example, in the case of an event related to childcare, the generation unit applies a generation algorithm dedicated to childcare. Furthermore, in the case of an event related to work, the generation unit can also apply a generation algorithm dedicated to work. Furthermore, in the case of an event related to a hobby, the generation unit can also apply a generation algorithm dedicated to a hobby. For example, the generation unit applies different generation algorithms depending on the category of the event. This makes it possible to apply a generation algorithm depending on the category of the event. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the category of the event into the generation AI and cause the generation AI to apply the generation algorithm.

[0047] During generation, the generation unit can improve the accuracy of the four-panel manga by referring to the user's past generation results. Past generation results include, for example, past manga data and user feedback. The generation unit, for example, adjusts the generation algorithm based on the user's past generation results. The generation unit can also extract important points from the user's past generation results to improve the accuracy of the four-panel manga. Furthermore, the generation unit can adjust the level of detail of the four-panel manga by referring to the user's past generation results. For example, the generation unit can improve the accuracy of the four-panel manga by referring to the user's past generation results. This allows the accuracy of the four-panel manga to be improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed, for example, using AI or without AI. For example, the generation unit can input data of the user's past generation results into the generation AI and cause the generation AI to improve the accuracy of the four-panel manga.

[0048] During generation, the generation unit can determine the priority of four-panel manga based on the time of occurrence of an event. The time of occurrence includes, for example, date and time, season, timing of the event, etc. The generation unit, for example, prioritizes recent events to create four-panel manga. The generation unit can also prioritize events that occurred during a period in which the user is particularly interested to create four-panel manga. The generation unit can also prioritize times when important events occurred to create four-panel manga. For example, the generation unit determines the priority of four-panel manga based on the time of occurrence of an event. This allows the priority of four-panel manga to be determined based on the time of occurrence of an event. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the time of occurrence of an event into the generation AI and have the generation AI determine the priority of the four-panel manga.

[0049] The generation unit can adjust the order of the four-panel manga based on the relevance of the events during generation. Relevance includes, for example, commonalities between the events and related topics. For example, the generation unit can prioritize highly related events to create four-panel manga. The generation unit can also prioritize events in which the user is particularly interested to create four-panel manga. Furthermore, the generation unit can prioritize important events to create four-panel manga. For example, the generation unit adjusts the order of the four-panel manga based on the relevance of the events. This allows the order of the four-panel manga to be adjusted based on the relevance of the events. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the relevance of the events into the generation AI and have the generation AI adjust the order of the four-panel manga.

[0050] During generation, the generation unit can adjust the use of technical terms in the four-panel manga according to the user's level of expertise. Expertise levels include, for example, beginner, intermediate, and expert. For example, if the user has expert knowledge, the generation unit generates a four-panel manga that uses a lot of technical terms. Furthermore, if the user has general knowledge, the generation unit can generate a concise and easy-to-understand four-panel manga. Furthermore, if the user is a beginner, the generation unit can generate a four-panel manga that avoids technical terms. For example, the generation unit adjusts the use of technical terms in the four-panel manga according to the user's level of expertise. This allows the use of technical terms in the four-panel manga to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without AI. For example, the generation unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms.

[0051] The providing unit can select the optimal delivery method by referring to the user's past delivery history when providing content. The delivery history includes, for example, past delivery content, delivery frequency, and user responses. The providing unit, for example, preferentially suggests delivery methods that the user has used favorably in the past. The providing unit can also select the optimal delivery means from the user's past delivery history. Furthermore, the providing unit can also customize the delivery interface by referring to the user's past delivery history. For example, the providing unit selects the optimal delivery method by referring to the user's past delivery history. This allows the optimal delivery method to be selected based on the user's past delivery history. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input data of the user's past delivery history into the generation AI and cause the generation AI to select the optimal delivery method.

[0052] The providing unit can customize the provided content based on the user's current living situation at the time of providing. Living situations include, for example, work situation, family situation, health condition, etc. For example, if the user is raising a child, the providing unit can prioritize providing four-panel comics related to child-rearing. Furthermore, if the user is working, the providing unit can prioritize providing four-panel comics related to work. Furthermore, if the user is interested in a hobby, the providing unit can prioritize providing four-panel comics related to the hobby. For example, the providing unit customizes the provided content based on the user's current living situation. This allows the provided content to be customized based on the user's current living situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the user's living situation into a generation AI and cause the generation AI to customize the provided content.

[0053] The providing unit can improve the providing method by reflecting user feedback at the time of providing. The feedback includes, for example, user ratings, comments, and usage history. The providing unit improves the providing method, for example, based on the user feedback. The providing unit can also suggest an optimal providing means based on the user feedback. Furthermore, the providing unit can customize the providing interface by referring to the user feedback. For example, the providing unit improves the providing method by reflecting user feedback. In this way, the providing method can be improved based on the user feedback. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input user feedback data into the generating AI and cause the generating AI to improve the providing method.

[0054] The providing unit can select the optimal delivery method by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data, address information, and location accuracy. For example, if the user is in a specific location, the providing unit can prioritize providing four-panel manga related to that location. Also, if the user is traveling, the providing unit can prioritize providing four-panel manga related to the user's travel destination. Furthermore, if the user is at home, the providing unit can prioritize providing four-panel manga related to the user's home. For example, the providing unit selects the optimal delivery method by taking into account the user's geographical location information. This allows the optimal delivery method to be selected based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal delivery method.

[0055] At the time of provision, the providing unit can analyze the user's social media activity and suggest a means of provision. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. The providing unit can, for example, provide a related four-panel comic based on events shared by the user on social media. The providing unit can also analyze the content of the user's social media posts and provide a related four-panel comic. Furthermore, the providing unit can provide a related four-panel comic based on the activity of the user's friends on social media. For example, the providing unit analyzes the user's social media activity and suggests a means of provision. This makes it possible to suggest the optimal means of provision based on the user's social media activity. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input data on the user's social media activity into a generation AI and have the generation AI execute a suggestion of a means of provision.

[0056] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the service. The feedback includes, for example, the user's ratings, comments, and usage history. The providing unit can improve the delivery method, for example, based on the user's past feedback. The providing unit can also suggest an optimal delivery method based on the user's past feedback. Furthermore, the providing unit can customize the delivery interface by referring to the user's past feedback. For example, the providing unit customizes the delivery method by reflecting the user's past feedback. This allows the delivery method to be customized based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data of the user's past feedback into the generation AI and cause the generation AI to customize the delivery method.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The reception unit can automatically suggest related past events based on the user's input. For example, if the user inputs "Today was the first time my baby laughed," the reception unit can suggest events related to "baby growth" that the user previously input. Also, if the user inputs "I went to the park with my family," the reception unit can suggest events related to "family outings" that the user previously input. Furthermore, if the user inputs "I gave a presentation at work," the reception unit can suggest events related to "work events" that the user previously input. This allows the user to input new events while looking back on past events.

[0059] The analysis unit can provide related news and trend information based on the user's input. For example, if the user inputs "Today my baby laughed for the first time," the analysis unit can provide the latest news and trend information related to childcare. Also, if the user inputs "I went to the park with my family," the analysis unit can provide the latest news and trend information related to parks and outdoor activities. Furthermore, if the user inputs "I gave a presentation at work," the analysis unit can provide the latest news and trend information related to business and presentations. This allows the user to obtain the latest information related to the input content.

[0060] The generation unit can add sound to characters in a four-panel cartoon based on user input. For example, if a user inputs, "Today was the first time my baby laughed," the generation unit can add the sound of a baby laughing. If a user inputs, "I went to the park with my family," the generation unit can add the natural sounds of the park and family conversations. Furthermore, if a user inputs, "I gave a presentation at work," the generation unit can add the sound of the presentation. This adds sound to the four-panel cartoon, making it possible to create a more realistic expression.

[0061] The provider can link the generated four-panel comic to the user's calendar app. For example, if the user inputs "Today was the first time my baby laughed," the provider can automatically add that event to the user's calendar. Also, if the user inputs "I went to the park with my family," that event can also be added to the calendar. Furthermore, if the user inputs "I gave a presentation at work," that event can also be added to the calendar. This allows the user to easily manage their daily events on the calendar.

[0062] The analysis unit can recommend related books and movies based on the user's input. For example, if the user inputs "Today my baby laughed for the first time," the analysis unit can recommend books and movies related to childcare. If the user inputs "I went to the park with my family," the analysis unit can recommend books and movies related to the outdoors and family. Furthermore, if the user inputs "I gave a presentation at work," the analysis unit can recommend books and movies related to business and presentations. This allows the user to find books and movies related to the input content.

[0063] The generator can add interactive elements to the four-panel comic based on the user's input. For example, if the user inputs "Today was the first time my baby laughed," the generator can add an animation of the baby laughing when the user clicks. Alternatively, if the user inputs "We went to the park with my family," the generator can add an animation of the park scenery changing when the user clicks. Furthermore, if the user inputs "I gave a presentation at work," the generator can add an animation of the presentation slides being displayed when the user clicks. This adds an interactive element to the four-panel comic, making it even more enjoyable for users.

[0064] The providing unit can link the generated four-panel comic to the user's smart device. For example, if the user inputs "Today was the first time my baby laughed," the providing unit can display that event as a notification on the smartphone. Also, if the user inputs "I went to the park with my family," the smart device can be notified of that event as well. Furthermore, if the user inputs "I gave a presentation at work," the smart device can be notified of that event as well. This allows the user to easily check the generated four-panel comic on their smart device.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit receives input of events from the user. The user can input the events that occurred that day in text format, for example. The reception unit provides, for example, a text input form, allowing the user to easily input the events. The reception unit can also receive voice input or image input. Step 2: The analysis unit analyzes the event received by the reception unit. The analysis unit understands the content of the text using, for example, natural language processing technology and extracts important points of the event. The analysis unit can, for example, divide the text using morphological analysis, perform grammatical analysis, and perform semantic analysis. The analysis unit can also perform sentiment analysis to extract emotional elements of the event. Step 3: The generation unit generates a four-panel manga based on the content analyzed by the analysis unit. The generation unit, for example, automatically draws characters and backgrounds. For example, the generation unit can generate a story using a text generation AI (e.g., LLM) and draw characters and backgrounds using a multimodal generation AI. The generation unit can also change the style of characters and backgrounds according to the user's preferences. Step 4: The providing unit provides the four-panel manga generated by the generating unit. The providing unit, for example, allows users to publish and share the generated four-panel manga on a social networking site. The providing unit, for example, provides a function for posting to a social networking site, allowing users to easily share the four-panel manga. The providing unit may also make the generated four-panel manga available for download.

[0067] (Example 2) In an embodiment of the present invention, a system allows parents to record the events of their day, and an AI generates a four-panel manga based on the recorded events. In this system, users input text about the events of the day, and the AI ​​analyzes the text and understands the content of the events. The AI ​​generates a four-panel manga story based on the analyzed content and automatically draws characters and backgrounds. The generated four-panel manga can be published and shared by users on social media. For example, if a user inputs content such as "Today was my baby's first laugh," the AI ​​analyzes the content and extracts key points. The AI ​​then generates a four-panel manga story based on the extracted points and draws characters and backgrounds. The generated four-panel manga can be downloaded by users or posted to social media. This allows parents to easily record and share their daily events. Furthermore, because the AI ​​automatically generates the manga, parents can easily preserve their memories. This system allows parents to easily record and share their daily events. Furthermore, because the AI ​​automatically generates the manga, parents can easily preserve their memories.

[0068] A four-panel manga generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of events from a user. The user can input events that occurred that day, for example, in text format. The reception unit provides, for example, a text input form, allowing the user to easily input events. The reception unit can also receive voice input and image input. The analysis unit analyzes the events received by the reception unit. The analysis unit understands the content of the text using, for example, natural language processing technology and extracts important points of the events. The analysis unit can, for example, segment the text using morphological analysis, perform grammatical analysis, and perform semantic analysis. The analysis unit can also perform sentiment analysis to extract emotional elements of the events. The generation unit generates a four-panel manga based on the content analyzed by the analysis unit. The generation unit, for example, automatically draws characters and backgrounds. The generation unit can, for example, generate a story using text generation AI (e.g., LLM) and draw characters and backgrounds using multimodal generation AI. The generation unit can also change the style of the characters and background according to the user's preferences. The provision unit provides the four-panel manga generated by the generation unit. For example, the provision unit allows the user to publish and share the generated four-panel manga on a social networking site (SNS). For example, the provision unit provides a posting function to a social networking site (SNS) so that the user can easily share the four-panel manga. The provision unit can also make the generated four-panel manga available for download. In this way, the four-panel manga generation system according to the embodiment can efficiently convert the user's events into a four-panel manga and provide it.

[0069] The analysis unit can use natural language processing technology to understand the content of the text and extract important points of the event. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit can segment the text using morphological analysis, perform grammatical analysis, and perform semantic analysis. The analysis unit can also perform sentiment analysis to extract emotional elements of the event. For example, the analysis unit can extract frequently occurring keywords in the text and identify important points based on the keywords. The analysis unit can also evaluate the importance of the context and extract important points. Furthermore, the analysis unit can summarize the content of the text and extract important points. As a result, the use of natural language processing technology can accurately extract important points of the event.

[0070] The generation unit can automatically draw characters and backgrounds. Characters include, for example, human characters, animal characters, and abstract characters. The generation unit can, for example, generate a story using a text generation AI (e.g., LLM) and draw characters and backgrounds using a multimodal generation AI. Backgrounds include, for example, urban backgrounds, natural backgrounds, and abstract backgrounds. The generation unit can also change the style of characters and backgrounds according to a user's preferences. For example, the generation unit can draw characters based on a character style selected by the user. The generation unit can also draw backgrounds based on a background style selected by the user. Furthermore, the generation unit can automatically adjust the colors of characters and backgrounds. This makes it more efficient to generate four-panel manga by automatically drawing characters and backgrounds.

[0071] The providing unit can enable users to publish and share the generated four-panel manga on social networking sites. Publishing and sharing includes, for example, posting to social networking sites, downloading, and sending by email. The providing unit, for example, enables users to publish and share the generated four-panel manga on social networking sites. The providing unit, for example, provides a function for posting to social networking sites, allowing users to easily share the four-panel manga. The providing unit can also enable users to download the generated four-panel manga. For example, the providing unit can enable users to download the generated four-panel manga in PDF format or image format. The providing unit can also provide a function for sending the generated four-panel manga by email. This allows users to easily share the generated four-panel manga on social networking sites.

[0072] The reception unit can estimate the user's emotions and adjust the timing of event input based on the estimated user emotions. Specific methods for estimating emotions include, for example, facial expression analysis, voice analysis, and text analysis. For example, the reception unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression analysis algorithm. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the reception unit can analyze text input by the user to estimate the emotions. For example, if the user is feeling stressed, the reception unit can reduce the notifications prompting input and prompt the user to input when they are relaxed. Furthermore, if the user is relaxed, the reception unit can actively send notifications prompting input to encourage the user to input events. Furthermore, if the user is busy, the reception unit can temporarily stop the notifications prompting input and send them again later. This allows the user to input events at the optimal timing depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0073] The reception unit can analyze the user's past input history and select the optimal input method. The input history includes, for example, past input content, input frequency, input timing, etc. For example, if the user has frequently used text input in the past, the reception unit can preferentially suggest text input. Furthermore, if the user has frequently used voice input in the past, the reception unit can preferentially suggest voice input. Furthermore, if the user has frequently used image input in the past, the reception unit can preferentially suggest image input. For example, the reception unit analyzes the user's past input history and selects the optimal input method. This makes it possible to suggest the optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's past input history to a generation AI and cause the generation AI to select the optimal input method.

[0074] When inputting events, the reception unit can filter the events based on the user's current living situation and areas of interest. Examples of living situations include work status, family status, and health status. Examples of areas of interest include hobbies, topics of interest, and areas of expertise. For example, if the user is raising a child, the reception unit can suggest that events related to childcare be input preferentially. Also, if the user is working, the reception unit can suggest that events related to work be input preferentially. Furthermore, if the user is interested in hobbies, the reception unit can suggest that events related to the hobbies be input preferentially. For example, the reception unit can filter the events based on the user's current living situation and areas of interest. This can promote the input of events according to the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input data on the user's living situation and areas of interest to the generation AI and cause the generation AI to perform filtering.

[0075] When inputting an event, the reception unit can select the optimal input means depending on the user's input method. Input methods include, for example, voice input, text input, and image input. For example, if the user selects voice input, the reception unit can input the event using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also input the event using keyboard input. Furthermore, if the user selects image input, the reception unit can also input the event using image analysis technology. For example, the reception unit selects the optimal input means depending on the user's input method. This allows the event to be input using the optimal means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data of the user's input method to the generation AI and cause the generation AI to select the optimal input means.

[0076] The reception unit can estimate the user's emotions and determine the priority of events to be input based on the estimated user emotions. Specific methods for estimating emotions include, for example, facial expression analysis, voice analysis, and text analysis. For example, the reception unit can capture the user's facial expressions with a camera and estimate emotions using a facial expression analysis algorithm. The reception unit can also record the user's voice and estimate emotions using voice analysis technology. Furthermore, the reception unit can analyze text input by the user to estimate emotions. For example, if the user is happy, the reception unit can suggest that the user prioritize input of positive events. If the user is sad, the reception unit can suggest that the user prioritize input of negative events. Furthermore, if the user is excited, the reception unit can suggest that the user prioritize input of emotional events. This allows the priority of events to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] When inputting events, the reception unit can prioritize inputting highly relevant events by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data, address information, location accuracy, etc. For example, when the user is in a specific location, the reception unit can suggest that the user prioritize inputting events related to that location. Furthermore, when the user is traveling, the reception unit can suggest that the user prioritize inputting events related to the travel destination. Furthermore, when the user is at home, the reception unit can suggest that the user prioritize inputting events related to the home. For example, the reception unit prioritizes inputting highly relevant events by taking into account the user's geographical location information. This allows highly relevant events to be input based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data of the user's geographical location information to a generation AI and cause the generation AI to select highly relevant events.

[0078] When an event is input, the reception unit can analyze the user's social media activity and input related events. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. The reception unit, for example, automatically inputs events shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input related events. Furthermore, the reception unit can input related events based on the activity of the user's friends on social media. For example, the reception unit analyzes the user's social media activity and inputs related events. In this way, related events can be input based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's social media activity to a generation AI and cause the generation AI to select related events.

[0079] The reception unit can customize the input method by reflecting the user's past feedback when inputting an event. The feedback includes, for example, the user's ratings, comments, usage history, etc. The reception unit can improve the input method, for example, based on the user's past feedback. The reception unit can also suggest an optimal input means based on the user's past feedback. Furthermore, the reception unit can customize the input interface by referring to the user's past feedback. For example, the reception unit customizes the input method by reflecting the user's past feedback. This allows the input method to be customized based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the input method.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. Specific methods for estimating emotions include, for example, facial expression analysis, voice analysis, and text analysis. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using a facial expression analysis algorithm. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. Furthermore, the analysis unit can analyze text entered by the user to estimate emotions. For example, if the user is relaxed, the analysis unit can present the analysis results in soft expressions. If the user is nervous, the analysis unit can present the analysis results in simple and clear expressions. Furthermore, if the user is excited, the analysis unit can present the analysis results in visually stimulating expressions. This allows the presentation method of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the event. The importance includes, for example, the impact of the event and the user's level of interest. For example, the analysis unit provides detailed analysis results for important events. The analysis unit can also provide concise analysis results for general events. Furthermore, the analysis unit can provide detailed analysis results for events in which the user is particularly interested. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the event. This allows the level of detail of the analysis to be adjusted according to the importance of the event. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on the importance of the event to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the event. Categories include, for example, the type of event and the type of emotion. For example, in the case of an event related to childcare, the analysis unit applies an analysis algorithm dedicated to childcare. In addition, in the case of an event related to work, the analysis unit can also apply an analysis algorithm dedicated to work. Furthermore, in the case of an event related to a hobby, the analysis unit can also apply an analysis algorithm dedicated to a hobby. For example, the analysis unit applies different analysis algorithms depending on the category of the event. This makes it possible to apply an analysis algorithm depending on the category of the event. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the category of the event to the generation AI and cause the generation AI to apply the analysis algorithm.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, for example, past analysis data and user feedback. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also extract important points from the user's past analysis results to improve the accuracy of the analysis. Furthermore, the analysis unit can adjust the level of detail of the analysis by referring to the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input data of the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. Specific methods for estimating emotions include, for example, facial expression analysis, voice analysis, and text analysis. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using a facial expression analysis algorithm. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. The analysis unit can also analyze text entered by the user to estimate emotions. For example, the analysis unit can provide a short and concise analysis result if the user is in a hurry. The analysis unit can also provide a detailed analysis result if the user is relaxed. The analysis unit can also provide a visually stimulating analysis result if the user is excited. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] During analysis, the analysis unit can determine the priority of analysis based on the time of occurrence of an event. The time of occurrence includes, for example, date and time, season, timing of the event, etc. The analysis unit, for example, prioritizes analysis of events that occurred recently. The analysis unit can also prioritize analysis of events that occurred during a period in which the user is particularly interested. Furthermore, the analysis unit can prioritize analysis of the time when an important event occurred. For example, the analysis unit determines the priority of analysis based on the time of occurrence of an event. This allows the priority of analysis to be determined based on the time of occurrence of an event. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of occurrence of an event to the generation AI and have the generation AI determine the priority of analysis.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of events. Relevance includes, for example, commonalities between events, related topics, etc. The analysis unit, for example, prioritizes the analysis of highly related events. The analysis unit can also prioritize the analysis of events in which the user is particularly interested. Furthermore, the analysis unit can prioritize the analysis of important events. For example, the analysis unit adjusts the order of analysis based on the relevance of events. This makes it possible to adjust the order of analysis based on the relevance of events. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of events to the generation AI and cause the generation AI to adjust the order of analysis.

[0087] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. Expertise levels include, for example, beginner, intermediate, and expert. For example, if the user has expert knowledge, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user has general knowledge, the analysis unit can provide concise and easy-to-understand analysis results. Furthermore, if the user is a beginner, the analysis unit can provide analysis results that avoid technical terminology. For example, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. This allows the use of technical terminology in the analysis to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0088] The generation unit can estimate the user's emotions and adjust the generation method of the four-panel cartoon based on the estimated user emotions. Specific methods for estimating emotions include, for example, facial expression analysis, voice analysis, and text analysis. For example, the generation unit can capture the user's facial expressions with a camera and estimate emotions using a facial expression analysis algorithm. The generation unit can also record the user's voice and estimate emotions using voice analysis technology. Furthermore, the generation unit can analyze text entered by the user to estimate emotions. For example, the generation unit can generate a four-panel cartoon with a soft touch if the user is relaxed. Furthermore, the generation unit can generate a simple and clear four-panel cartoon if the user is nervous. Furthermore, the generation unit can generate a visually stimulating four-panel cartoon if the user is excited. This allows the generation method of the four-panel cartoon to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] During generation, the generation unit can adjust the level of detail of the four-panel manga based on the importance of the event. Importance includes, for example, the impact of the event and the user's level of interest. For example, the generation unit generates a four-panel manga that includes a detailed depiction for an important event. The generation unit can also generate a four-panel manga that includes a concise depiction for a general event. Furthermore, the generation unit can generate a four-panel manga that includes a detailed depiction for an event in which the user is particularly interested. For example, the generation unit adjusts the level of detail of the four-panel manga based on the importance of the event. This allows the level of detail of the four-panel manga to be adjusted according to the importance of the event. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input data on the importance of the event into the generation AI and cause the generation AI to adjust the level of detail of the four-panel manga.

[0090] During generation, the generation unit can apply different generation algorithms depending on the category of the event. Categories include, for example, the type of event and the type of emotion. For example, in the case of an event related to childcare, the generation unit applies a generation algorithm dedicated to childcare. Furthermore, in the case of an event related to work, the generation unit can also apply a generation algorithm dedicated to work. Furthermore, in the case of an event related to a hobby, the generation unit can also apply a generation algorithm dedicated to a hobby. For example, the generation unit applies different generation algorithms depending on the category of the event. This makes it possible to apply a generation algorithm depending on the category of the event. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the category of the event into the generation AI and cause the generation AI to apply the generation algorithm.

[0091] During generation, the generation unit can improve the accuracy of the four-panel manga by referring to the user's past generation results. Past generation results include, for example, past manga data and user feedback. The generation unit, for example, adjusts the generation algorithm based on the user's past generation results. The generation unit can also extract important points from the user's past generation results to improve the accuracy of the four-panel manga. Furthermore, the generation unit can adjust the level of detail of the four-panel manga by referring to the user's past generation results. For example, the generation unit can improve the accuracy of the four-panel manga by referring to the user's past generation results. This allows the accuracy of the four-panel manga to be improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed, for example, using AI or without AI. For example, the generation unit can input data of the user's past generation results into the generation AI and cause the generation AI to improve the accuracy of the four-panel manga.

[0092] The generation unit can estimate the user's emotions and adjust the length of the four-panel cartoon based on the estimated user emotions. Specific methods for estimating emotions include, for example, facial expression analysis, voice analysis, and text analysis. For example, the generation unit can capture the user's facial expressions with a camera and estimate emotions using a facial expression analysis algorithm. The generation unit can also record the user's voice and estimate emotions using voice analysis technology. The generation unit can also analyze text entered by the user to estimate emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point four-panel cartoon. If the user is relaxed, the generation unit can generate a longer four-panel cartoon with detailed descriptions. If the user is excited, the generation unit can generate a four-panel cartoon with visually stimulating effects. This allows the length of the four-panel cartoon to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0093] During generation, the generation unit can determine the priority of four-panel manga based on the time of occurrence of an event. The time of occurrence includes, for example, date and time, season, timing of the event, etc. The generation unit, for example, prioritizes recent events to create four-panel manga. The generation unit can also prioritize events that occurred during a period in which the user is particularly interested to create four-panel manga. The generation unit can also prioritize times when important events occurred to create four-panel manga. For example, the generation unit determines the priority of four-panel manga based on the time of occurrence of an event. This allows the priority of four-panel manga to be determined based on the time of occurrence of an event. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the time of occurrence of an event into the generation AI and have the generation AI determine the priority of the four-panel manga.

[0094] The generation unit can adjust the order of the four-panel manga based on the relevance of the events during generation. Relevance includes, for example, commonalities between the events and related topics. For example, the generation unit can prioritize highly related events to create four-panel manga. The generation unit can also prioritize events in which the user is particularly interested to create four-panel manga. Furthermore, the generation unit can prioritize important events to create four-panel manga. For example, the generation unit adjusts the order of the four-panel manga based on the relevance of the events. This allows the order of the four-panel manga to be adjusted based on the relevance of the events. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the relevance of the events into the generation AI and have the generation AI adjust the order of the four-panel manga.

[0095] During generation, the generation unit can adjust the use of technical terms in the four-panel manga according to the user's level of expertise. Expertise levels include, for example, beginner, intermediate, and expert. For example, if the user has expert knowledge, the generation unit generates a four-panel manga that uses a lot of technical terms. Furthermore, if the user has general knowledge, the generation unit can generate a concise and easy-to-understand four-panel manga. Furthermore, if the user is a beginner, the generation unit can generate a four-panel manga that avoids technical terms. For example, the generation unit adjusts the use of technical terms in the four-panel manga according to the user's level of expertise. This allows the use of technical terms in the four-panel manga to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without AI. For example, the generation unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms.

[0096] The providing unit can estimate the user's emotions and adjust the way the four-panel cartoon is presented based on the estimated user emotions. Specific methods for estimating emotions include, for example, facial expression analysis, voice analysis, and text analysis. For example, the providing unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression analysis algorithm. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the providing unit can analyze text entered by the user to estimate the emotions. For example, if the user is relaxed, the providing unit can provide a four-panel cartoon with a soft touch. If the user is nervous, the providing unit can provide a simple and clear four-panel cartoon. Furthermore, if the user is excited, the providing unit can provide a visually stimulating four-panel cartoon. This allows the way the four-panel cartoon is presented to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The providing unit can select the optimal delivery method by referring to the user's past delivery history when providing content. The delivery history includes, for example, past delivery content, delivery frequency, and user responses. The providing unit, for example, preferentially suggests delivery methods that the user has used favorably in the past. The providing unit can also select the optimal delivery means from the user's past delivery history. Furthermore, the providing unit can also customize the delivery interface by referring to the user's past delivery history. For example, the providing unit selects the optimal delivery method by referring to the user's past delivery history. This allows the optimal delivery method to be selected based on the user's past delivery history. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input data of the user's past delivery history into the generation AI and cause the generation AI to select the optimal delivery method.

[0098] The providing unit can customize the provided content based on the user's current living situation at the time of providing. Living situations include, for example, work situation, family situation, health condition, etc. For example, if the user is raising a child, the providing unit can prioritize providing four-panel comics related to child-rearing. Furthermore, if the user is working, the providing unit can prioritize providing four-panel comics related to work. Furthermore, if the user is interested in a hobby, the providing unit can prioritize providing four-panel comics related to the hobby. For example, the providing unit customizes the provided content based on the user's current living situation. This allows the provided content to be customized based on the user's current living situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the user's living situation into a generation AI and cause the generation AI to customize the provided content.

[0099] The providing unit can improve the providing method by reflecting user feedback at the time of providing. The feedback includes, for example, user ratings, comments, and usage history. The providing unit improves the providing method, for example, based on the user feedback. The providing unit can also suggest an optimal providing means based on the user feedback. Furthermore, the providing unit can customize the providing interface by referring to the user feedback. For example, the providing unit improves the providing method by reflecting user feedback. In this way, the providing method can be improved based on the user feedback. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input user feedback data into the generating AI and cause the generating AI to improve the providing method.

[0100] The providing unit can estimate the user's emotions and determine the priority of providing four-panel cartoons based on the estimated user emotions. Specific methods for estimating emotions include, for example, facial expression analysis, voice analysis, and text analysis. For example, the providing unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression analysis algorithm. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the providing unit can analyze text entered by the user to estimate the emotions. For example, if the user is relaxed, the providing unit can prioritize providing four-panel cartoons with a soft touch. If the user is nervous, the providing unit can prioritize providing simple and clear four-panel cartoons. Furthermore, if the user is excited, the providing unit can prioritize providing visually stimulating four-panel cartoons. This allows the priority of providing four-panel cartoons to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0101] The providing unit can select the optimal delivery method by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data, address information, and location accuracy. For example, if the user is in a specific location, the providing unit can prioritize providing four-panel manga related to that location. Also, if the user is traveling, the providing unit can prioritize providing four-panel manga related to the user's travel destination. Furthermore, if the user is at home, the providing unit can prioritize providing four-panel manga related to the user's home. For example, the providing unit selects the optimal delivery method by taking into account the user's geographical location information. This allows the optimal delivery method to be selected based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal delivery method.

[0102] At the time of provision, the providing unit can analyze the user's social media activity and suggest a means of provision. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. The providing unit can, for example, provide a related four-panel comic based on events shared by the user on social media. The providing unit can also analyze the content of the user's social media posts and provide a related four-panel comic. Furthermore, the providing unit can provide a related four-panel comic based on the activity of the user's friends on social media. For example, the providing unit analyzes the user's social media activity and suggests a means of provision. This makes it possible to suggest the optimal means of provision based on the user's social media activity. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input data on the user's social media activity into a generation AI and have the generation AI execute a suggestion of a means of provision.

[0103] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the service. The feedback includes, for example, the user's ratings, comments, and usage history. The providing unit can improve the delivery method, for example, based on the user's past feedback. The providing unit can also suggest an optimal delivery method based on the user's past feedback. Furthermore, the providing unit can customize the delivery interface by referring to the user's past feedback. For example, the providing unit customizes the delivery method by reflecting the user's past feedback. This allows the delivery method to be customized based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data of the user's past feedback into the generation AI and cause the generation AI to customize the delivery method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives text input from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the content of the text using natural language processing technology. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a four-panel comic based on the analyzed content. The provision unit is realized by the control unit 46A of the smart device 14 and allows users to share the generated four-panel comic on social networking sites. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives text input from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the text using natural language processing technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a four-panel comic based on the analyzed content. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and enables users to share the generated four-panel comic on social networking sites. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives text input from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the text using natural language processing technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a four-panel comic based on the analyzed content. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and allows users to share the generated four-panel comic on social networking sites. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives text input from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the text using natural language processing technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a four-panel comic based on the analyzed content. The provision unit is realized, for example, by the control unit 46A of the robot 414 and allows users to share the generated four-panel comic on social networking sites.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The reception unit can automatically suggest related past events based on the user's input. For example, if the user inputs "Today was the first time my baby laughed," the reception unit can suggest events related to "baby growth" that the user previously input. Also, if the user inputs "I went to the park with my family," the reception unit can suggest events related to "family outings" that the user previously input. Furthermore, if the user inputs "I gave a presentation at work," the reception unit can suggest events related to "work events" that the user previously input. This allows the user to input new events while looking back on past events.

[0106] The analysis unit can provide related news and trend information based on the user's input. For example, if the user inputs "Today my baby laughed for the first time," the analysis unit can provide the latest news and trend information related to childcare. Also, if the user inputs "I went to the park with my family," the analysis unit can provide the latest news and trend information related to parks and outdoor activities. Furthermore, if the user inputs "I gave a presentation at work," the analysis unit can provide the latest news and trend information related to business and presentations. This allows the user to obtain the latest information related to the input content.

[0107] The generation unit can add sound to characters in a four-panel cartoon based on user input. For example, if a user inputs, "Today was the first time my baby laughed," the generation unit can add the sound of a baby laughing. If a user inputs, "I went to the park with my family," the generation unit can add the natural sounds of the park and family conversations. Furthermore, if a user inputs, "I gave a presentation at work," the generation unit can add the sound of the presentation. This adds sound to the four-panel cartoon, making it possible to create a more realistic expression.

[0108] The provider can link the generated four-panel comic to the user's calendar app. For example, if the user inputs "Today was the first time my baby laughed," the provider can automatically add that event to the user's calendar. Also, if the user inputs "I went to the park with my family," that event can also be added to the calendar. Furthermore, if the user inputs "I gave a presentation at work," that event can also be added to the calendar. This allows the user to easily manage their daily events on the calendar.

[0109] The reception unit can estimate the user's emotion and provide feedback for the input content based on the estimated emotion. For example, if the user inputs "Today my baby laughed for the first time" and the emotion of joy is estimated, the reception unit can provide feedback such as "That's wonderful! Watching a baby grow is a truly special moment." If the user inputs "I gave a presentation at work" and the emotion of nervousness is estimated, the reception unit can provide feedback such as "Good job. Presentations are tough, but I'm sure you'll do well." If the user inputs "I went to the park with my family" and the emotion of relaxation is estimated, the reception unit can provide feedback such as "I'm glad you enjoyed spending time with your family." In this way, the user can receive feedback according to their emotion.

[0110] The analysis unit can recommend related books and movies based on the user's input. For example, if the user inputs "Today my baby laughed for the first time," the analysis unit can recommend books and movies related to childcare. If the user inputs "I went to the park with my family," the analysis unit can recommend books and movies related to the outdoors and family. Furthermore, if the user inputs "I gave a presentation at work," the analysis unit can recommend books and movies related to business and presentations. This allows the user to find books and movies related to the input content.

[0111] The generator can add interactive elements to the four-panel comic based on the user's input. For example, if the user inputs "Today was the first time my baby laughed," the generator can add an animation of the baby laughing when the user clicks. Alternatively, if the user inputs "We went to the park with my family," the generator can add an animation of the park scenery changing when the user clicks. Furthermore, if the user inputs "I gave a presentation at work," the generator can add an animation of the presentation slides being displayed when the user clicks. This adds an interactive element to the four-panel comic, making it even more enjoyable for users.

[0112] The providing unit can link the generated four-panel comic to the user's smart device. For example, if the user inputs "Today was the first time my baby laughed," the providing unit can display that event as a notification on the smartphone. Also, if the user inputs "I went to the park with my family," the smart device can be notified of that event as well. Furthermore, if the user inputs "I gave a presentation at work," the smart device can be notified of that event as well. This allows the user to easily check the generated four-panel comic on their smart device.

[0113] The reception unit can estimate the user's emotion and adjust the tone of the input content based on the estimated emotion. For example, if the user inputs "My baby laughed for the first time today" and the emotion of joy is estimated, the reception unit can display the input content in a bright tone. Also, if the user inputs "I gave a presentation at work" and the emotion of tension is estimated, the reception unit can display the input content in a calm tone. Furthermore, if the user inputs "I went to the park with my family" and the emotion of relaxation is estimated, the reception unit can display the input content in a relaxed tone. This allows the user to check the input content in a tone that corresponds to their emotion.

[0114] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user inputs "Today my baby laughed for the first time" and the emotion of joy is estimated, the analysis unit can adopt a colorful and positive display method. Alternatively, if the user inputs "I gave a presentation at work" and the emotion of tension is estimated, the analysis unit can adopt a simple and clear display method. Furthermore, if the user inputs "I went to the park with my family" and the emotion of relaxation is estimated, the analysis unit can adopt a relaxed display method. This allows the user to check the analysis results in a display method that corresponds to their emotions.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reception unit receives input of events from the user. The user can input the events that occurred that day in text format, for example. The reception unit provides, for example, a text input form, allowing the user to easily input the events. The reception unit can also receive voice input or image input. Step 2: The analysis unit analyzes the event received by the reception unit. The analysis unit understands the content of the text using, for example, natural language processing technology and extracts important points of the event. The analysis unit can, for example, divide the text using morphological analysis, perform grammatical analysis, and perform semantic analysis. The analysis unit can also perform sentiment analysis to extract emotional elements of the event. Step 3: The generation unit generates a four-panel manga based on the content analyzed by the analysis unit. The generation unit, for example, automatically draws characters and backgrounds. For example, the generation unit can generate a story using a text generation AI (e.g., LLM) and draw characters and backgrounds using a multimodal generation AI. The generation unit can also change the style of characters and backgrounds according to the user's preferences. Step 4: The providing unit provides the four-panel manga generated by the generating unit. The providing unit, for example, allows users to publish and share the generated four-panel manga on a social networking site. The providing unit, for example, provides a function for posting to a social networking site, allowing users to easily share the four-panel manga. The providing unit may also make the generated four-panel manga available for download.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0119] 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.

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

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 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.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the 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.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0135] 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.

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

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives an input of an event from a user; an analysis unit that analyzes the event received by the reception unit; a generation unit that generates a four-panel manga based on the content analyzed by the analysis unit; a providing unit that provides the four-panel manga generated by the generating unit. A system characterized by:

2. The analysis unit Using natural language processing technology to understand the content of the text and extract key points of events 2. The system of claim 1.

3. The generation unit Automatically draw characters and backgrounds 2. The system of claim 1.

4. The providing unit Users can publish and share the generated four-panel manga on social media.

2. The system of claim 1.

5. The reception unit Estimate the user's emotions and adjust the timing of inputting events based on the estimated user emotions.

2. The system of claim 1.

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

7. The reception unit Filter events based on the user's current life situation and interests as they are entered 2. The system of claim 1.

8. The reception unit When entering events, select the most appropriate input method depending on the user's input method.

2. The system of claim 1.

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