system

Abstract

The system according to the embodiment aims to enable a user to easily create a diary. [Solution] A system according to an embodiment includes an analysis unit, a generation unit, and a utilization unit. The analysis unit analyzes illustrations drawn by a user. The generation unit generates diary text based on the illustrations analyzed by the analysis unit. The generation unit generates illustrations based on text entered by the user. The utilization unit generates illustrations and text using location information or search history and photo data from the smartphone. The generation unit generates a picture diary based on the illustrations and text generated by the generation unit and utilization 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 have had the problem that it takes time and effort for users to manually create a diary.

[0005] The system according to the embodiment aims to enable a user to easily create a diary. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a generation unit, and a utilization unit. The analysis unit analyzes illustrations drawn by a user. The generation unit generates diary text based on the illustrations analyzed by the analysis unit. The generation unit generates illustrations based on text entered by the user. The utilization unit generates illustrations and text using location information or search history and photo data from the smartphone. The generation unit generates a picture diary based on the illustrations and text generated by the generation unit and utilization unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to easily create a diary. [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) A pictorial diary generation system according to an embodiment of the present invention generates diary entries from illustrations drawn by a user or from text to create a pictorial diary. This system automatically generates illustrations and text using smartphone location information, search history, photos, and other data, and can automatically create a pictorial diary. For example, a user inputs an illustration they have drawn. This illustration can be captured with a smartphone camera or entered using the smartphone's handwriting input function. The generation AI then analyzes the illustration and generates corresponding diary entries. For example, a user can generate text describing the day's events and impressions from a landscape drawing. Illustrations can also be generated from text entered by the user. For example, if a user inputs "I had a picnic in the park today," the generation AI generates an illustration of a picnic in the park based on the content. In this way, a pictorial diary combining text and illustrations can be created. Furthermore, illustrations and text can be automatically generated using smartphone location information, search history, photos, and other data. For example, the day's events can be expressed in illustrations and text based on the location information of places visited and photos taken by the user. This allows users to easily review their own activity history. This system organizes users' activity history and information, creating opportunities for communication. For example, sharing with family and friends can provide common topics of conversation and promote communication. Furthermore, automatically generating a diary allows users to effortlessly record their daily events. This allows the picture diary generation system to organize users' behavioral history and information, creating opportunities for communication.

[0029] The illustrated diary creation system according to the embodiment includes an analysis unit, a generation unit, and a utilization unit. The analysis unit analyzes illustrations drawn by a user. For example, the analysis unit can analyze the content of the illustration using image recognition technology. The analysis unit can also extract features of the illustration using pattern matching technology. The analysis unit can analyze the content of the illustration in detail using generation AI. For example, the analysis unit analyzes the color and shape of the illustration and generates diary text based on the content. The generation unit generates diary text based on the illustration analyzed by the analysis unit. For example, the generation unit can express the content of the illustration as text using natural language generation technology. The generation unit can also use generation AI to create detailed text for the illustration. The generation unit can also generate illustrations based on text entered by a user. For example, the generation unit generates illustrations based on the text using an image generation algorithm. The utilization unit generates illustrations and text using data such as smartphone location information, search history, and photos. For example, the utilization unit can obtain the user's location information using GPS data and generate illustrations and text based on that information. The utilization unit can also analyze search history and generate illustrations and text based on related information. Furthermore, the utilization unit can analyze photo data and generate illustrations and text based on the content. For example, the utilization unit analyzes photo metadata and generates illustrations and text based on the location and date / time of the photo. This allows the pictorial diary generation system according to the embodiment to automatically generate a pictorial diary using illustrations drawn by the user, text entered by the user, and smartphone data.

[0030] When analyzing an illustration, the analysis unit can improve the accuracy of the analysis by referring to the user's past illustration history. For example, the analysis unit can learn the style of illustrations the user has drawn in the past, and the generation AI can generate text that matches that style. The analysis unit can also refer to the themes of illustrations the user has drawn in the past, and the generation AI can generate text on related themes. Furthermore, the analysis unit can analyze the color usage of illustrations the user has drawn in the past, and the generation AI can generate text based on that color usage. This improves the accuracy of the analysis by referring to the past illustration history. Methods for obtaining and using the past illustration history include, for example, obtaining it from a database and storing the history. Some or all of the above-mentioned 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 past illustration data into the generation AI and have the generation AI improve its analysis accuracy.

[0031] When analyzing an illustration, the analysis unit can apply different analysis algorithms depending on the category of the illustration. For example, in the case of a landscape painting, the generation AI can apply an algorithm specialized for depicting nature to generate a sentence with a detailed description of the landscape. In addition, in the case of a portrait, the analysis unit can also apply an algorithm specialized for depicting the facial expressions and movements of the person to generate a sentence rich in emotion. Furthermore, in the case of an abstract painting, the generation AI can apply an algorithm specialized for color and shape to generate a sentence with an abstract expression. This improves the accuracy of the analysis by applying an analysis algorithm according to the category of the illustration. Examples of classification criteria and specific categories for illustration categories include animals, landscapes, and people. 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 illustration category data into the generation AI and have the generation AI apply the analysis algorithm.

[0032] When analyzing an illustration, the analysis unit can improve the accuracy of the analysis by taking into account the user's geographical location information. For example, if the user draws an illustration in a specific region, the analysis unit performs the analysis by taking into account the characteristics of that region. Furthermore, if the user draws an illustration while traveling, the analysis unit can also perform the analysis by referring to information about the travel destination. Furthermore, if the user draws an illustration at home, the analysis unit can also perform the analysis by taking into account information about the area around the home. In this way, by taking into account the geographical location information, the accuracy of the analysis is improved. Methods for acquiring and using the geographical location information include, for example, GPS data and Wi-Fi location information. 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 the geographical location data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0033] When analyzing illustrations, the analysis unit can analyze the user's social media activity and reflect related information in the analysis. For example, the analysis unit can analyze illustrations shared by the user on social media and generate text by referring to related comments and tags. The analysis unit can also reference information about accounts the user follows on social media to generate text on related themes. Furthermore, the analysis unit can reference information about events the user is participating in on social media to generate text with related content. This analysis of social media activity improves the accuracy of the analysis. Methods for acquiring and analyzing social media activity include, for example, analyzing posted content and analyzing followers. Some or all of the above-mentioned processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit can input social media data into a generation AI and cause the generation AI to improve the accuracy of the analysis.

[0034] During generation, the generation unit can improve generation accuracy by referring to the user's past input history. For example, the generation unit learns the style of text previously input by the user, and the generation AI generates text that matches that style. The generation unit can also refer to the themes of illustrations previously generated by the user, and the generation AI can generate illustrations on related themes. Furthermore, the generation unit can analyze keywords previously input by the user, and the generation AI can generate text based on those keywords. This improves generation accuracy by referring to the past input history. Methods for acquiring and using the past input history include, for example, acquiring it from a database and storing the history. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input past input data into the generation AI and cause the generation AI to improve generation accuracy.

[0035] The generation unit can apply different generation algorithms depending on the category of the input text or illustration during generation. For example, when text related to a landscape is input, the generation AI applies an algorithm specialized for depicting nature to generate a detailed illustration of the landscape. Furthermore, when text related to a person is input, the generation AI can apply an algorithm specialized for depicting the person's facial expressions and movements to generate an illustration rich in emotion. Furthermore, when abstract text is input, the generation unit can apply an algorithm specialized for color and shape to generate an illustration with an abstract expression. This improves generation accuracy by applying a generation algorithm according to the category. Examples of classification criteria and specific categories for text and illustration categories include animals, landscapes, and people. 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 text or illustration category data to the generation AI and have the generation AI apply a generation algorithm.

[0036] During generation, the generation unit can determine the generation priority based on the submission date of the input characters and illustrations. For example, the generation unit may prioritize generating characters and illustrations recently input by the user. The generation unit may also prioritize generating characters and illustrations related to a specific event. Furthermore, the generation unit may prioritize generating characters and illustrations that the user input in the past but have not yet been generated. This enables efficient generation by determining the priority based on the submission date. Methods for obtaining and using the submission date include, for example, a timestamp and a submission history. 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 may input submission date data into the generation AI and have the generation AI execute the generation priority.

[0037] During generation, the generation unit can adjust the order of generation based on the relevance of the input characters and illustrations. For example, the generation unit analyzes the relevance of the characters and illustrations input by the user and prioritizes generating those with high relevance. The generation unit can also analyze the theme of the characters and illustrations input by the user and adjust the order of generation based on the theme. Furthermore, the generation unit can analyze the content of the characters and illustrations input by the user and adjust the order of generation based on the content. This enables efficient generation by adjusting the order of generation based on relevance. Examples of evaluation criteria and specific evaluation methods for relevance include co-occurrence network analysis and relevance scores. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input relevance data to a generation AI and have the generation AI execute the order of generation.

[0038] The utilization unit can improve the accuracy of the usage data by referring to the user's past behavioral history during usage. For example, the utilization unit references information about places the user has visited in the past and selects related data. The utilization unit can also reference keywords the user has searched for in the past and select related data. Furthermore, the utilization unit can reference photos the user has taken in the past and select related data. This improves the accuracy of the usage data by referring to the past behavioral history. Methods for acquiring and using the past behavioral history include, for example, acquiring the history from a database and storing the history. Some or all of the above-described processing in the utilization unit may be performed using, for example, AI, or may be performed without using AI. For example, the utilization unit can input past behavioral data into a generation AI and have the generation AI improve the accuracy of the usage data.

[0039] During use, the utilization unit can apply different utilization algorithms depending on the category of data, such as the smartphone's location information, search history, and photos. For example, in the case of data based on location information, the utilization unit allows the generation AI to apply the utilization algorithm by taking geographical characteristics into consideration. Furthermore, in the case of data based on search history, the utilization unit can also allow the generation AI to apply the utilization algorithm by taking into consideration the relevance of search keywords. Furthermore, in the case of data based on photos, the generation AI can perform image analysis, extract related information, and apply the utilization algorithm. This improves the accuracy of the utilization data by applying utilization algorithms according to categories. Examples of data category classification criteria and specific categories include location information, search history, and photos. Some or all of the above-described processing in the utilization unit may be performed using AI, or may be performed without AI. For example, the utilization unit can input data category information into the generation AI and have the generation AI apply the utilization algorithm.

[0040] The utilization unit can prioritize the use of highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the utilization unit prioritizes the use of data related to that area. Furthermore, when the user is traveling, the utilization unit can prioritize the use of information about the user's travel destination. Furthermore, when the user is at home, the utilization unit can prioritize the use of information about the area around the user's home. This allows the use of highly relevant data to be prioritized by taking the geographical location information into consideration. Methods for acquiring and using the geographical location information include, for example, GPS data and Wi-Fi location information. Some or all of the above-described processing in the utilization unit may be performed using, or without, AI. For example, the utilization unit can input geographical location data to a generation AI and cause the generation AI to prioritize the use of highly relevant data.

[0041] During use, the utilization unit can analyze the user's social media activity and utilize the associated data. For example, the utilization unit can analyze information shared by the user on social media and utilize the associated data. The utilization unit can also analyze information on accounts the user follows on social media and utilize the associated data. Furthermore, the utilization unit can analyze information on events the user is participating in on social media and utilize the associated data. This allows highly relevant data to be utilized by analyzing social media activity. Methods for acquiring and analyzing social media activity include, for example, analyzing posted content and analyzing followers. Some or all of the above-described processing in the utilization unit may be performed using, for example, AI, or may be performed without using AI. For example, the utilization unit can input social media data into a generation AI and cause the generation AI to utilize the associated data.

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

[0043] The generation unit can learn the user's preferences and interests and reflect them in the illustrations and text it generates. For example, if a user likes the style of a particular artist, the generation AI can generate illustrations based on that style. Also, if a user likes books of a particular genre, the generation AI can generate text based on that genre. Furthermore, if a user likes a particular color scheme, the generation AI can generate illustrations based on that color scheme. This allows for customization according to the user's preferences.

[0044] The generation unit can refer to the user's reading history and reflect it in the illustrations and text it generates. For example, if the user is reading a mystery novel, the generation AI can generate text that matches that atmosphere. Also, if the user is reading a fantasy novel, the generation AI can generate text with fantastical content. Furthermore, if the user is reading a science book, the generation AI can generate text that deepens knowledge. In this way, by using the reading history, it is possible to provide more appropriate diary content.

[0045] The generation unit can reference the user's dietary data and reflect it in the illustrations and text it generates. For example, if the user eats a specific dish, the generation AI can generate an illustration based on that dish. Also, if the user eats a healthy diet, the generation AI can generate text based on that content. Furthermore, if the user eats at a special event, the generation AI can generate text based on that event. In this way, by using dietary data, it is possible to provide more appropriate diary content.

[0046] The utilization unit can refer to the user's travel history and generate illustrations and text based on information about travel destinations. For example, based on information about tourist spots visited by the user, the generation AI can generate illustrations of those tourist spots. Also, based on information about activities experienced by the user, the generation AI can generate text based on those activities. Furthermore, based on photos taken by the user, the generation AI can generate illustrations and text related to those photos. In this way, by utilizing the travel history, more appropriate diary content can be provided.

[0047] The analysis unit can refer to the user's learning history and generate illustrations and sentences based on the learning content. For example, based on the topic the user studied, the generation AI can generate sentences related to that topic. The generation AI can also generate illustrations related to the problem based on the content of the problem the user solved. Furthermore, based on information about the online course the user participated in, the generation AI can generate sentences related to that course. In this way, by using the learning history, it is possible to provide more appropriate diary content.

[0048] The generation unit can learn the user's hobbies and interests and reflect them in the illustrations and text it generates. For example, if a user likes a particular sport, the generation AI can generate illustrations based on that sport. Also, if a user likes a particular movie genre, the generation AI can generate text based on that genre. Furthermore, if a user likes a particular art style, the generation AI can generate illustrations based on that style. This allows for customization according to the user's hobbies and interests.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The analysis unit analyzes the illustration drawn by the user. For example, the analysis unit can analyze the content of the illustration using image recognition technology. The analysis unit can also extract the features of the illustration using pattern matching technology. Furthermore, the analysis unit can analyze the content of the illustration in detail using generative AI. Step 2: The generation unit generates diary entries based on the illustrations analyzed by the analysis unit. For example, the generation unit can use natural language generation technology to express the content of the illustrations as text. The generation unit can also use generation AI to create detailed text about the content of the illustrations. Step 3: The generator generates an illustration based on the characters entered by the user. For example, the generator uses an image generation algorithm to generate an illustration based on the content of the characters. Step 4: The utilization unit generates illustrations and text using data such as the smartphone's location information, search history, and photos. For example, the utilization unit can obtain the user's location information using GPS data and generate illustrations and text based on that information. The utilization unit can also analyze search history and generate illustrations and text based on related information. Furthermore, the utilization unit can analyze photo data and generate illustrations and text based on its contents. Step 5: A pictorial diary is generated based on the illustrations and text generated by the generation unit and the utilization unit. As a result, the pictorial diary generation system according to the embodiment can automatically generate a pictorial diary using illustrations drawn by the user, text entered by the user, and smartphone data.

[0051] (Example 2) A pictorial diary generation system according to an embodiment of the present invention generates diary entries from illustrations drawn by a user or from text to create a pictorial diary. This system automatically generates illustrations and text using smartphone location information, search history, photos, and other data, and can automatically create a pictorial diary. For example, a user inputs an illustration they have drawn. This illustration can be captured with a smartphone camera or entered using the smartphone's handwriting input function. The generation AI then analyzes the illustration and generates corresponding diary entries. For example, a user can generate text describing the day's events and impressions from a landscape drawing. Illustrations can also be generated from text entered by the user. For example, if a user inputs "I had a picnic in the park today," the generation AI generates an illustration of a picnic in the park based on the content. In this way, a pictorial diary combining text and illustrations can be created. Furthermore, illustrations and text can be automatically generated using smartphone location information, search history, photos, and other data. For example, the day's events can be expressed in illustrations and text based on the location information of places visited and photos taken by the user. This allows users to easily review their own activity history. This system organizes users' activity history and information, creating opportunities for communication. For example, sharing with family and friends can provide common topics of conversation and promote communication. Furthermore, automatically generating a diary allows users to effortlessly record their daily events. This allows the picture diary generation system to organize users' behavioral history and information, creating opportunities for communication.

[0052] The illustrated diary creation system according to the embodiment includes an analysis unit, a generation unit, and a utilization unit. The analysis unit analyzes illustrations drawn by a user. For example, the analysis unit can analyze the content of the illustration using image recognition technology. The analysis unit can also extract features of the illustration using pattern matching technology. The analysis unit can analyze the content of the illustration in detail using generation AI. For example, the analysis unit analyzes the color and shape of the illustration and generates diary text based on the content. The generation unit generates diary text based on the illustration analyzed by the analysis unit. For example, the generation unit can express the content of the illustration as text using natural language generation technology. The generation unit can also use generation AI to create detailed text for the illustration. The generation unit can also generate illustrations based on text entered by a user. For example, the generation unit generates illustrations based on the text using an image generation algorithm. The utilization unit generates illustrations and text using data such as smartphone location information, search history, and photos. For example, the utilization unit can obtain the user's location information using GPS data and generate illustrations and text based on that information. The utilization unit can also analyze search history and generate illustrations and text based on related information. Furthermore, the utilization unit can analyze photo data and generate illustrations and text based on the content. For example, the utilization unit analyzes photo metadata and generates illustrations and text based on the location and date / time of the photo. This allows the pictorial diary generation system according to the embodiment to automatically generate a pictorial diary using illustrations drawn by the user, text entered by the user, and smartphone data.

[0053] The analysis unit can estimate the user's emotions and adjust the illustration analysis method based on the estimated user emotions. For example, if the user is sad, the generation AI can generate text that reflects the user's emotions by taking into account the color and style of the illustration. Furthermore, if the user is happy, the analysis unit can generate positive text by emphasizing the brightness and content of the illustration. Furthermore, if the user is tired, the analysis unit can extract simple parts of the illustration and generate concise text. This allows for the generation of more appropriate diary entries by providing an analysis method that corresponds to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and text analysis. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0054] When analyzing an illustration, the analysis unit can improve the accuracy of the analysis by referring to the user's past illustration history. For example, the analysis unit can learn the style of illustrations the user has drawn in the past, and the generation AI can generate text that matches that style. The analysis unit can also refer to the themes of illustrations the user has drawn in the past, and the generation AI can generate text on related themes. Furthermore, the analysis unit can analyze the color usage of illustrations the user has drawn in the past, and the generation AI can generate text based on that color usage. This improves the accuracy of the analysis by referring to the past illustration history. Methods for obtaining and using the past illustration history include, for example, obtaining it from a database and storing the history. Some or all of the above-mentioned 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 past illustration data into the generation AI and have the generation AI improve its analysis accuracy.

[0055] When analyzing an illustration, the analysis unit can apply different analysis algorithms depending on the category of the illustration. For example, in the case of a landscape painting, the generation AI can apply an algorithm specialized for depicting nature to generate a sentence with a detailed description of the landscape. In addition, in the case of a portrait, the analysis unit can also apply an algorithm specialized for depicting the facial expressions and movements of the person to generate a sentence rich in emotion. Furthermore, in the case of an abstract painting, the generation AI can apply an algorithm specialized for color and shape to generate a sentence with an abstract expression. This improves the accuracy of the analysis by applying an analysis algorithm according to the category of the illustration. Examples of classification criteria and specific categories for illustration categories include animals, landscapes, and people. 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 illustration category data into the generation AI and have the generation AI apply the analysis algorithm.

[0056] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can cause the generation AI to provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can cause the generation AI to provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can cause the generation AI to provide a display method that focuses on the main points. This allows for more appropriate analysis results to be displayed by providing a display method that corresponds to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and text analysis. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0057] When analyzing an illustration, the analysis unit can improve the accuracy of the analysis by taking into account the user's geographical location information. For example, if the user draws an illustration in a specific region, the analysis unit performs the analysis by taking into account the characteristics of that region. Furthermore, if the user draws an illustration while traveling, the analysis unit can also perform the analysis by referring to information about the travel destination. Furthermore, if the user draws an illustration at home, the analysis unit can also perform the analysis by taking into account information about the area around the home. In this way, by taking into account the geographical location information, the accuracy of the analysis is improved. Methods for acquiring and using the geographical location information include, for example, GPS data and Wi-Fi location information. 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 the geographical location data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0058] When analyzing illustrations, the analysis unit can analyze the user's social media activity and reflect related information in the analysis. For example, the analysis unit can analyze illustrations shared by the user on social media and generate text by referring to related comments and tags. The analysis unit can also reference information about accounts the user follows on social media to generate text on related themes. Furthermore, the analysis unit can reference information about events the user is participating in on social media to generate text with related content. This analysis of social media activity improves the accuracy of the analysis. Methods for acquiring and analyzing social media activity include, for example, analyzing posted content and analyzing followers. Some or all of the above-mentioned processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit can input social media data into a generation AI and cause the generation AI to improve the accuracy of the analysis.

[0059] The generation unit can estimate the user's emotions and adjust the expression method of the generated text and illustrations based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can generate soft-touch illustrations and calm text. Furthermore, if the user is excited, the generation AI can generate brightly colored illustrations and lively text. Furthermore, if the user is sad, the generation AI can generate calmly colored illustrations and comforting text. This allows for the generation of more appropriate text and illustrations by providing an expression method appropriate to the user's emotions. Emotion estimation is performed using techniques such as facial expression recognition and text analysis. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0060] During generation, the generation unit can improve generation accuracy by referring to the user's past input history. For example, the generation unit learns the style of text previously input by the user, and the generation AI generates text that matches that style. The generation unit can also refer to the themes of illustrations previously generated by the user, and the generation AI can generate illustrations on related themes. Furthermore, the generation unit can analyze keywords previously input by the user, and the generation AI can generate text based on those keywords. This improves generation accuracy by referring to the past input history. Methods for acquiring and using the past input history include, for example, acquiring it from a database and storing the history. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input past input data into the generation AI and cause the generation AI to improve generation accuracy.

[0061] The generation unit can apply different generation algorithms depending on the category of the input text or illustration during generation. For example, when text related to a landscape is input, the generation AI applies an algorithm specialized for depicting nature to generate a detailed illustration of the landscape. Furthermore, when text related to a person is input, the generation AI can apply an algorithm specialized for depicting the person's facial expressions and movements to generate an illustration rich in emotion. Furthermore, when abstract text is input, the generation unit can apply an algorithm specialized for color and shape to generate an illustration with an abstract expression. This improves generation accuracy by applying a generation algorithm according to the category. Examples of classification criteria and specific categories for text and illustration categories include animals, landscapes, and people. 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 text or illustration category data to the generation AI and have the generation AI apply a generation algorithm.

[0062] The generation unit can estimate the user's emotions and adjust the length of the generated text and illustrations based on the estimated user emotions. For example, if the user is in a hurry, the generation AI can generate short, to-the-point text and illustrations. Furthermore, if the user is relaxed, the generation AI can generate longer text and illustrations with detailed explanations. Furthermore, if the user is excited, the generation AI can generate text and illustrations with visually stimulating effects. This allows for the generation of more appropriate text and illustrations by providing a length appropriate to the user's emotions. Emotion estimation is performed using, for example, techniques such as facial expression recognition and text analysis. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0063] During generation, the generation unit can determine the generation priority based on the submission date of the input characters and illustrations. For example, the generation unit may prioritize generating characters and illustrations recently input by the user. The generation unit may also prioritize generating characters and illustrations related to a specific event. Furthermore, the generation unit may prioritize generating characters and illustrations that the user input in the past but have not yet been generated. This enables efficient generation by determining the priority based on the submission date. Methods for obtaining and using the submission date include, for example, a timestamp and a submission history. 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 may input submission date data into the generation AI and have the generation AI execute the generation priority.

[0064] During generation, the generation unit can adjust the order of generation based on the relevance of the input characters and illustrations. For example, the generation unit analyzes the relevance of the characters and illustrations input by the user and prioritizes generating those with high relevance. The generation unit can also analyze the theme of the characters and illustrations input by the user and adjust the order of generation based on the theme. Furthermore, the generation unit can analyze the content of the characters and illustrations input by the user and adjust the order of generation based on the content. This enables efficient generation by adjusting the order of generation based on relevance. Examples of evaluation criteria and specific evaluation methods for relevance include co-occurrence network analysis and relevance scores. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input relevance data to a generation AI and have the generation AI execute the order of generation.

[0065] The utilization unit can estimate the user's emotions and select data to be used based on the estimated user emotions. For example, if the user is relaxed, the utilization unit causes the generation AI to select data with calm content. Furthermore, if the user is excited, the utilization unit can also cause the generation AI to select data with lively content. Furthermore, if the user is sad, the utilization unit can also cause the generation AI to select data with comforting content. By selecting data according to the user's emotions, more appropriate data can be used. The estimation of emotions is performed using techniques such as facial expression recognition and text analysis. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the utilization unit may be performed using, for example, AI, or without AI. For example, the utilization unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0066] The utilization unit can improve the accuracy of the usage data by referring to the user's past behavioral history during usage. For example, the utilization unit references information about places the user has visited in the past and selects related data. The utilization unit can also reference keywords the user has searched for in the past and select related data. Furthermore, the utilization unit can reference photos the user has taken in the past and select related data. This improves the accuracy of the usage data by referring to the past behavioral history. Methods for acquiring and using the past behavioral history include, for example, acquiring the history from a database and storing the history. Some or all of the above-described processing in the utilization unit may be performed using, for example, AI, or may be performed without using AI. For example, the utilization unit can input past behavioral data into a generation AI and have the generation AI improve the accuracy of the usage data.

[0067] During use, the utilization unit can apply different utilization algorithms depending on the category of data, such as the smartphone's location information, search history, and photos. For example, in the case of data based on location information, the utilization unit allows the generation AI to apply the utilization algorithm by taking geographical characteristics into consideration. Furthermore, in the case of data based on search history, the utilization unit can also allow the generation AI to apply the utilization algorithm by taking into consideration the relevance of search keywords. Furthermore, in the case of data based on photos, the generation AI can perform image analysis, extract related information, and apply the utilization algorithm. This improves the accuracy of the utilization data by applying utilization algorithms according to categories. Examples of data category classification criteria and specific categories include location information, search history, and photos. Some or all of the above-described processing in the utilization unit may be performed using AI, or may be performed without AI. For example, the utilization unit can input data category information into the generation AI and have the generation AI apply the utilization algorithm.

[0068] The utilization unit can estimate the user's emotions and determine the priority of data to be used based on the estimated user's emotions. For example, if the user is relaxed, the utilization unit can cause the generation AI to prioritize data with calm content. Furthermore, if the user is excited, the utilization unit can cause the generation AI to prioritize data with lively content. Furthermore, if the user is sad, the utilization unit can cause the generation AI to prioritize data with comforting content. This allows more appropriate data to be used by providing priorities according to the user's emotions. Emotion estimation is performed using techniques such as facial expression recognition and text analysis. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the utilization unit can be performed using, for example, AI, or without AI. For example, the utilization unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0069] The utilization unit can prioritize the use of highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the utilization unit prioritizes the use of data related to that area. Furthermore, when the user is traveling, the utilization unit can prioritize the use of information about the user's travel destination. Furthermore, when the user is at home, the utilization unit can prioritize the use of information about the area around the user's home. This allows the use of highly relevant data to be prioritized by taking the geographical location information into consideration. Methods for acquiring and using the geographical location information include, for example, GPS data and Wi-Fi location information. Some or all of the above-described processing in the utilization unit may be performed using, or without, AI. For example, the utilization unit can input geographical location data to a generation AI and cause the generation AI to prioritize the use of highly relevant data.

[0070] During use, the utilization unit can analyze the user's social media activity and utilize the associated data. For example, the utilization unit can analyze information shared by the user on social media and utilize the associated data. The utilization unit can also analyze information on accounts the user follows on social media and utilize the associated data. Furthermore, the utilization unit can analyze information on events the user is participating in on social media and utilize the associated data. This allows highly relevant data to be utilized by analyzing social media activity. Methods for acquiring and analyzing social media activity include, for example, analyzing posted content and analyzing followers. Some or all of the above-described processing in the utilization unit may be performed using, for example, AI, or may be performed without using AI. For example, the utilization unit can input social media data into a generation AI and cause the generation AI to utilize the associated data. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, and utilization unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit photographs an illustration drawn by the user using the camera 42 of the smart device 14, and the control unit 46A analyzes the content of the illustration using image recognition technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates diary text based on the analyzed illustration. The utilization unit generates illustrations and text using, for example, GPS data, search history, and photo data of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, and utilization unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit photographs an illustration drawn by a user using the camera 42 of the smart glasses 214, and the control unit 46A analyzes the content of the illustration using image recognition technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates diary text based on the analyzed illustration. The utilization unit generates illustrations and text using, for example, GPS data, search history, and photo data of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, and utilization unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit photographs an illustration drawn by the user using the camera 42 of the headset type terminal 314, and the control unit 46A analyzes the content of the illustration using image recognition technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates diary text based on the analyzed illustration. The utilization unit generates illustrations and text using, for example, GPS data, search history, and photo data of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, and utilization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit photographs an illustration drawn by the user using the camera 42 of the robot 414, and the control unit 46A analyzes the content of the illustration using image recognition technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates diary text based on the analyzed illustration. The utilization unit generates illustrations and text using, for example, GPS data, search history, and photo data of the robot 414.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The analysis unit analyzes the user's voice data and can infer emotions from the tone and speed of the voice. For example, if the user speaks in an excited tone, the generation AI can generate positive sentences based on that emotion. If the user speaks in a calm tone, the generation AI can also generate relaxed sentences. Furthermore, if the user speaks in a hurry, the generation AI can generate concise sentences that get to the point. This makes it possible to use voice data to generate more appropriate diary entries.

[0073] The generation unit can learn the user's preferences and interests and reflect them in the illustrations and text it generates. For example, if a user likes the style of a particular artist, the generation AI can generate illustrations based on that style. Also, if a user likes books of a particular genre, the generation AI can generate text based on that genre. Furthermore, if a user likes a particular color scheme, the generation AI can generate illustrations based on that color scheme. This allows for customization according to the user's preferences.

[0074] The utilization unit can refer to the user's health data and reflect this in the illustrations and text it generates. For example, if the user's heart rate is high, the generation AI can generate text encouraging relaxation. It can also refer to the user's sleep data and generate text encouraging rest if the user is sleep deprived. It can also refer to the user's exercise data and generate text that gives the user a sense of accomplishment after exercising. In this way, by using health data, it is possible to provide more appropriate diary content.

[0075] The analysis unit can refer to the user's music playback history and reflect it in the illustrations and text it generates. For example, if the user is listening to classical music, the generation AI can generate text that matches that atmosphere. Also, if the user is listening to rock music, the generation AI can generate text with energetic content. Furthermore, if the user is listening to relaxing music, the generation AI can generate text with calm content. In this way, by using the music playback history, it is possible to provide more appropriate diary content.

[0076] The generation unit can refer to the user's reading history and reflect it in the illustrations and text it generates. For example, if the user is reading a mystery novel, the generation AI can generate text that matches that atmosphere. Also, if the user is reading a fantasy novel, the generation AI can generate text with fantastical content. Furthermore, if the user is reading a science book, the generation AI can generate text that deepens knowledge. In this way, by using the reading history, it is possible to provide more appropriate diary content.

[0077] The analysis unit can analyze the user's exercise data and generate illustrations and text based on the type and intensity of the exercise. For example, if the user runs, the generation AI generates text based on that exercise. Also, if the user does yoga, the generation AI can generate text with a relaxing message. Furthermore, if the user does strength training, the generation AI can generate text with a sense of accomplishment. In this way, by using exercise data, it is possible to provide more appropriate diary content.

[0078] The generation unit can reference the user's dietary data and reflect it in the illustrations and text it generates. For example, if the user eats a specific dish, the generation AI can generate an illustration based on that dish. Also, if the user eats a healthy diet, the generation AI can generate text based on that content. Furthermore, if the user eats at a special event, the generation AI can generate text based on that event. In this way, by using dietary data, it is possible to provide more appropriate diary content.

[0079] The utilization unit can refer to the user's travel history and generate illustrations and text based on information about travel destinations. For example, based on information about tourist spots visited by the user, the generation AI can generate illustrations of those tourist spots. Also, based on information about activities experienced by the user, the generation AI can generate text based on those activities. Furthermore, based on photos taken by the user, the generation AI can generate illustrations and text related to those photos. In this way, by utilizing the travel history, more appropriate diary content can be provided.

[0080] The analysis unit can refer to the user's learning history and generate illustrations and sentences based on the learning content. For example, based on the topic the user studied, the generation AI can generate sentences related to that topic. The generation AI can also generate illustrations related to the problem based on the content of the problem the user solved. Furthermore, based on information about the online course the user participated in, the generation AI can generate sentences related to that course. In this way, by using the learning history, it is possible to provide more appropriate diary content.

[0081] The generation unit can learn the user's hobbies and interests and reflect them in the illustrations and text it generates. For example, if a user likes a particular sport, the generation AI can generate illustrations based on that sport. Also, if a user likes a particular movie genre, the generation AI can generate text based on that genre. Furthermore, if a user likes a particular art style, the generation AI can generate illustrations based on that style. This allows for customization according to the user's hobbies and interests.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The analysis unit analyzes the illustration drawn by the user. For example, the analysis unit can analyze the content of the illustration using image recognition technology. The analysis unit can also extract the features of the illustration using pattern matching technology. Furthermore, the analysis unit can analyze the content of the illustration in detail using generative AI. Step 2: The generation unit generates diary entries based on the illustrations analyzed by the analysis unit. For example, the generation unit can use natural language generation technology to express the content of the illustrations as text. The generation unit can also use generation AI to create detailed text about the content of the illustrations. Step 3: The generator generates an illustration based on the characters entered by the user. For example, the generator uses an image generation algorithm to generate an illustration based on the content of the characters. Step 4: The utilization unit generates illustrations and text using data such as the smartphone's location information, search history, and photos. For example, the utilization unit can obtain the user's location information using GPS data and generate illustrations and text based on that information. The utilization unit can also analyze search history and generate illustrations and text based on related information. Furthermore, the utilization unit can analyze photo data and generate illustrations and text based on its contents. Step 5: A pictorial diary is generated based on the illustrations and text generated by the generation unit and the utilization unit. As a result, the pictorial diary generation system according to the embodiment can automatically generate a pictorial diary using illustrations drawn by the user, text entered by the user, and smartphone data.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0086] 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.

[0087] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[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 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.

[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 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.

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

[0135] The data processing system 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.

[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] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] [Explanation of symbols]

[0156] 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. An analysis unit that analyzes illustrations drawn by users; a generation unit that generates diary sentences based on the illustrations analyzed by the analysis unit; a generation unit that generates an illustration based on characters input by a user; A utilization part generates illustrations and text using smartphone location information, search history, and photo data; a generation unit that generates a picture diary based on the illustrations and characters generated by the generation unit and the utilization unit; A system characterized by:

2. The analysis unit Estimate the user's emotions and adjust the analysis method of the illustration based on the estimated user emotions.

2. The system of claim 1.

3. The analysis unit When analyzing illustrations, the accuracy of the analysis can be improved by referring to the user's past illustration history.

2. The system of claim 1.

4. The analysis unit When analyzing illustrations, different analysis algorithms are applied depending on the illustration category.

2. The system of claim 1.

5. The analysis unit Inferring user emotions and adjusting the display of analysis results based on the estimated user emotions 2. The system of claim 1.

6. The analysis unit Improve accuracy of illustration analysis based on user geographic location information 2. The system of claim 1.

7. The analysis unit When analyzing illustrations, analyze users' social media activity and incorporate relevant information into the analysis.

2. The system of claim 1.

8. The generation unit Estimate the user's emotions and adjust the way the text and illustrations are generated based on the estimated user emotions.

2. The system of claim 1.

Images