System

Abstract

An object of a system according to an embodiment is to enable a user to easily create a diary.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives an input from a user. The generation unit analyzes the input received by the reception unit and generates a detailed diary. The providing unit provides the diary generated by the generating unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult for users to easily create diaries, and it takes time and effort.

[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 a receiving unit, a generating unit, and a providing unit. The receiving unit receives input from a user. The generating unit analyzes the input received by the receiving unit and generates a detailed diary. The providing unit provides the diary generated by the generating unit to the user. [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 diary generation system according to an embodiment of the present invention allows a user to easily create and save a diary entry. The diary generation system accepts input from a user, and a generation AI analyzes the input content to generate a detailed diary entry and provide it to the user. For example, the user briefly inputs the events and thoughts of the day. For example, the user inputs a short sentence such as, "Today, I went to a cafe with a friend." This input is sent to the generation AI. The diary generation system then analyzes the input content using the generation AI to generate a detailed diary entry. The generation AI complements the sentence based on the user's input, creating a more detailed and easy-to-read diary entry. For example, the generation AI complements the sentence, such as, "Today, I went to a cafe with a friend. We had a great time drinking delicious coffee." The generated diary entry is provided to the user. The user can review the generated diary entry and make corrections as necessary. Finally, the user can save the completed diary entry. This allows the diary generation system to easily generate and save a diary entry, even for people who find keeping a diary tedious. This allows the user to create a detailed diary entry with only short inputs. Furthermore, the created diary can be modified to suit the user's preferences, so that it can meet individual needs.

[0029] A diary generation system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives input from a user. The user input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, a keyboard input interface for receiving text input. The reception unit can also provide a microphone input interface for receiving voice input. The reception unit can also provide a camera interface for receiving image input. For example, the reception unit receives text input from a user and transmits it to a generation AI. The generation unit uses the generation AI to analyze the input received by the reception unit and generate a detailed diary. The generation AI analyzes the user input and extracts relevant information using a model such as GPT-4 (registered trademark) or Gemini. For example, the generation AI analyzes the user input and extracts keywords and context. The generation unit completes the sentences based on the extracted information and generates a detailed diary. For example, the generation AI generates grammatically accurate and contextually consistent sentences based on the user input. The providing unit provides the diary generated by the generating unit to the user. For example, the providing unit provides an interface that allows the user to preview the generated diary and make corrections as necessary. The providing unit also provides a function for saving the diary after corrections have been completed. For example, the providing unit displays the generated diary to the user and provides a text editor for the user to make corrections. Furthermore, the providing unit provides a function for saving the diary after corrections have been completed in the cloud or locally. This allows the diary generation system according to the embodiment to allow the user to easily generate and save a diary. Some or all of the above-described processing in the generating unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generating unit may input user input to the generation AI and cause the generation AI to generate a detailed diary.

[0030] The generation unit can analyze the user's input using a generation AI and extract relevant information. For example, the generation AI analyzes the user's input and extracts relevant information. The generation AI analyzes the user's input using a model such as GPT-4 or Gemini. The generation unit can also extract important keywords from the user's input using keyword extraction technology. For example, the generation AI extracts frequently occurring keywords and important phrases based on the user's input. The generation unit can also analyze the context of the user's input using context analysis technology. For example, the generation AI analyzes the context of the user's input and extracts information to maintain contextual consistency. This allows the generation AI to analyze the user's input and extract relevant information, thereby generating a detailed diary. Some or all of the above-described processing in the generation unit can be performed using, or without, the generation AI. For example, the generation unit can input the user's input to the generation AI and have the generation AI extract relevant information.

[0031] The generation unit can complete sentences based on the extracted information and generate a detailed diary. For example, the generation unit allows a generation AI to complete sentences based on the extracted information and generate a detailed diary. The generation AI generates sentences based on the extracted information using a model such as GPT-4 or Gemini. The generation unit can also generate sentences that are grammatically correct and contextually consistent. For example, the generation AI generates grammatically accurate sentences based on user input. The generation unit can also generate sentences taking into account the context before and after the sentences to maintain contextual consistency. For example, the generation AI generates sentences that maintain contextual consistency by taking into account the context of the user input. This allows a detailed diary to be generated based on the extracted information. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the extracted information into the generation AI and cause the generation AI to generate a detailed diary.

[0032] The providing unit can provide an interface that allows the user to preview the generated diary and make corrections as necessary. The providing unit, for example, provides an interface that allows the user to preview the generated diary and make corrections as necessary. The providing unit, for example, provides a real-time preview function that allows the user to instantly check the generated diary. The providing unit can also provide a partial preview function that allows the user to check only a specific portion. For example, the providing unit can display a portion of the generated diary and allow the user to correct that portion. The providing unit can also provide a text editor for making corrections. For example, the providing unit can provide a text editor that allows the user to correct the generated diary and allow the user to freely edit the text. This allows the user to check the generated diary and make corrections. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the generated diary into AI and cause the AI ​​to provide a preview and correction interface.

[0033] The providing unit can provide a function for saving a diary entry whose corrections have been completed. The providing unit, for example, provides a function for saving a diary entry whose corrections have been completed. The providing unit, for example, provides a cloud saving function, allowing a user to save a created diary entry on the cloud. The providing unit can also provide a local saving function, allowing a user to save a created diary entry on their own device. For example, the providing unit saves the created diary entry on a cloud storage service. The providing unit can also save the created diary entry as a local file on the user's device. This allows a user to save a diary entry whose corrections have been completed. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the diary entry whose corrections have been completed into AI, and have the AI ​​execute the saving process.

[0034] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically suggest expressions and phrases that the user has frequently used in the past. The reception unit can also suggest related topics based on the content the user has previously input. The reception unit can also predict and suggest content that the user will input during a specific time period from the user's past input history. For example, the reception unit can analyze text data previously input by the user and extract frequently occurring expressions and phrases. The reception unit can also use an algorithm to suggest related topics based on the user's past input content. Furthermore, the reception unit can analyze the user's input history in chronological order and predict content that will be input during a specific time period. This improves input efficiency by suggesting the optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's past input history data into a generation AI and cause the generation AI to suggest the optimal input method.

[0035] The reception unit can filter the input content based on the user's current activity status and areas of interest when the input is made. For example, when the input is made, the reception unit filters the input content based on the user's current activity status and areas of interest. For example, when the user is participating in a sporting event, the reception unit can prioritize suggesting related topics. Furthermore, when the user is traveling, the reception unit can also suggest travel-related input content. Furthermore, when the user is working, the reception unit can prioritize suggesting work-related topics. For example, the reception unit acquires the user's current activity status using GPS information or an activity log. Furthermore, the reception unit can acquire the user's areas of interest from past search history or social media activity. By filtering the input content based on the user's current activity status and areas of interest, a more relevant diary entry can be generated. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's activity status data and area of ​​interest data to a generation AI and cause the generation AI to filter the input content.

[0036] The reception unit can select the optimal input means depending on the user's input method at the time of input. For example, the reception unit selects the optimal input means depending on the user's input method (voice, text, image, etc.) at the time of input. For example, if the user selects voice input, the reception unit converts the input content into text using voice recognition technology. Furthermore, if the user uploads an image, the reception unit can generate related text using image analysis technology. Furthermore, if the user selects text input, the reception unit can support keyboard input. For example, the reception unit can convert the user's voice input into text data using voice recognition software. Furthermore, the reception unit can generate related text from an image uploaded by the user using image analysis technology. Furthermore, if the user selects text input, the reception unit provides a keyboard input interface to enable the user to efficiently input text. This improves input convenience by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input method data to a generation AI and cause the generation AI to select the optimal input means.

[0037] The reception unit can prioritize input of highly relevant content based on the user's geographical location information during input. For example, the reception unit prioritizes input of highly relevant content based on the user's geographical location information during input. For example, when the user is in a specific location, the reception unit prioritizes suggesting topics related to that location. Furthermore, when the user is traveling, the reception unit can prompt the user to prioritize input of content related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize suggesting topics related to the user's home. For example, the reception unit acquires the user's geographical location information using GPS data or a location information service. Furthermore, the reception unit can use an algorithm that suggests highly relevant content based on the user's geographical location information. This allows for the generation of a more appropriate diary entry by preferentially inputting highly relevant content based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to a generation AI and cause the generation AI to suggest highly relevant content.

[0038] The reception unit can analyze the user's social media activity and input related content at the time of input. For example, the reception unit can analyze the user's social media activity and input related content at the time of input. For example, the reception unit can suggest related topics based on content shared by the user on social media. The reception unit can also analyze the user's social media posts and prompt the user to input related events. The reception unit can also suggest related content based on the activities of the user's friends on social media. For example, the reception unit can analyze the user's social media posts and extract related keywords and topics. The reception unit can also use an algorithm that suggests related content based on the activities of the user's friends on social media. This allows for the input of related content based on the user's social media activity to generate a more relevant diary entry. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's social media activity data to a generation AI and cause the generation AI to suggest related content.

[0039] The reception unit can customize the input method by reflecting the user's past feedback at the time of input. The reception unit, for example, customizes the input method by reflecting the user's past feedback at the time of input. For example, the reception unit preferentially suggests input methods that the user has used favorably in the past. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also optimize input content suggestions by reflecting the user's past feedback. For example, the reception unit analyzes feedback provided by the user in the past and extracts a preferred input method. The reception unit can also use an algorithm to customize the input interface based on the user's feedback. This improves input convenience by customizing the input method by reflecting the user's past feedback. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data to a generation AI and cause the generation AI to customize the input method.

[0040] The generation unit can adjust the level of detail of the diary to be generated based on the importance of the input content at the time of generation. For example, the generation unit adjusts the level of detail of the diary to be generated based on the importance of the input content at the time of generation. For example, the generation unit generates a diary by adding detailed descriptions to important events. The generation unit can also generate a diary by adding brief descriptions to minor events. The generation unit can also generate a diary by adding detailed descriptions to content that the user particularly wants to emphasize. For example, the generation unit evaluates the importance based on the user's input content and adds detailed descriptions to important content. The generation unit can also use an algorithm that adds brief descriptions to minor content. In this way, by adjusting the level of detail of the diary based on the importance of the input content, a more appropriate diary can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's input content data to the generation AI and cause the generation AI to adjust the level of detail of the diary.

[0041] The generation unit can apply different generation algorithms depending on the category of the input content during generation. For example, the generation unit can apply different generation algorithms depending on the category of the input content during generation. For example, the generation unit can generate a diary using travelogue-style expressions for travel-related content. The generation unit can also generate a diary using business-like expressions for work-related content. The generation unit can also generate a diary using warm expressions for family-related content. For example, the generation unit classifies categories based on the user's input content and applies a generation algorithm corresponding to each category. The generation unit can also use an algorithm that uses a different writing style or expression method for each category. In this way, by applying different generation algorithms depending on the category of the input content, a more appropriate diary can be generated. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI. For example, the generation unit can input the user's input content data to the generation AI and cause the generation AI to apply a generation algorithm according to the category.

[0042] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit can refer to the style of diaries the user has previously generated and generate a diary in a similar style. The generation unit can also extract preferred expressions from the user's past generation results and reflect them in the diary. The generation unit can also analyze the user's past generation results and optimize the generation algorithm. For example, the generation unit extracts preferred expressions and styles based on the user's past generation results. The generation unit can also use an algorithm that adjusts parameters of the generation algorithm based on the past generation results. This improves the accuracy of generation by referring to the user's past generation results. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0043] The generation unit can determine the priority of the diaries to be generated based on the submission time of the input content at the time of generation. For example, the generation unit determines the priority of the diaries to be generated based on the submission time of the input content at the time of generation. For example, the generation unit prioritizes generating a diary for content with an approaching deadline. The generation unit can also postpone generating a diary for content with a distant submission time. The generation unit can also prioritize generating a diary for content for which the user is particularly urgent. For example, the generation unit evaluates the submission time based on the user's input content and prioritizes generating a diary for content with a close submission time. The generation unit can also use an algorithm to generate a diary for content with a distant submission time at the end. In this way, by determining the priority of the diaries based on the submission time of the input content, a more appropriate diary can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's input content data to the generation AI and cause the generation AI to determine the priority of the diaries.

[0044] The generation unit can adjust the order of the diary entries to be generated based on the relevance of the input content during generation. The generation unit, for example, adjusts the order of the diary entries to be generated based on the relevance of the input content during generation. For example, the generation unit prioritizes placing highly relevant content at the beginning of the diary. The generation unit can also place less relevant content at the end of the diary. The generation unit can also place content that the user particularly wants to emphasize at the center of the diary. For example, the generation unit evaluates relevance based on the user's input content and places highly relevant content at the beginning of the diary. The generation unit can also use an algorithm that places less relevant content at the end of the diary. In this way, by adjusting the order of the diary entries based on the relevance of the input content, a more appropriate diary can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's input content data to the generation AI and cause the generation AI to adjust the order of the diary entries.

[0045] The generation unit can adjust the use of technical terms in the diary to be generated according to the user's level of expertise at the time of generation. For example, the generation unit can adjust the use of technical terms in the diary to be generated according to the user's level of expertise at the time of generation. For example, if the user has technical expertise, the generation unit generates a diary using a lot of technical terms. Furthermore, if the user does not have technical expertise, the generation unit can also generate a diary using simple language. The generation unit can also generate a diary by selecting appropriate technical terms according to the user's level of expertise. For example, the generation unit evaluates the level of expertise based on the user's input content and uses a lot of technical terms for users with technical expertise. Furthermore, the generation unit can use an algorithm that uses simple language for users without technical expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, a more appropriate diary can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0046] The providing unit can select the optimal display method by referring to the user's past operation history when providing the data. For example, the providing unit selects the optimal display method by referring to the user's past operation history when providing the data. For example, the providing unit preferentially provides display methods that the user has previously preferred. The providing unit can also customize the display interface based on the user's past operation history. The providing unit can also optimize display content suggestions by reflecting the user's past operation history. For example, the providing unit analyzes display methods that the user has previously used and extracts a preferred display method. The providing unit can also use an algorithm to customize the display interface based on the user's operation history. This improves display convenience by selecting the optimal display method based on the user's past operation history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past operation history data to the generation AI and cause the generation AI to select the optimal display method.

[0047] The providing unit can customize the display content according to the user's current task when providing the content. For example, the providing unit customizes the display content according to the user's current task when providing the content. For example, if the user is at work, the providing unit can prioritize displaying work-related content. Furthermore, if the user is on vacation, the providing unit can prioritize displaying relaxing content. Furthermore, if the user is working on a specific project, the providing unit can prioritize displaying content related to the project. For example, the providing unit can suggest related content based on the user's current task. Furthermore, the providing unit can use an algorithm to customize the display content according to the user's task. This enables more appropriate display by customizing the display content according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task data into a generation AI and cause the generation AI to customize the display content.

[0048] The providing unit can improve the display method by reflecting user feedback when providing the data. For example, the providing unit improves the display method by reflecting user feedback when providing the data. For example, the providing unit improves the display interface based on feedback previously provided by the user. The providing unit can also optimize display content suggestions by reflecting user feedback. The providing unit can also customize the display method based on user feedback. For example, the providing unit analyzes feedback previously provided by the user and extracts areas for improvement. The providing unit can also use an algorithm to improve the display interface based on user feedback. This improves the display method by reflecting user feedback, thereby improving the convenience of the display. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data to a generation AI and cause the generation AI to improve the display method.

[0049] The providing unit can select the optimal display method based on the user's device information at the time of providing. For example, the providing unit selects the optimal display method based on the user's device information at the time of providing. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can provide a display method optimized for a wide screen. For example, the providing unit can evaluate the device type and OS version based on the user's device information. Furthermore, the providing unit can use an algorithm to select a display method appropriate for the device. This improves display convenience by selecting the optimal display method based on the user's device information. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's device information data into a generation AI and cause the generation AI to select the optimal display method.

[0050] The providing unit can make the display content multilingual according to the user's language setting when providing the content. For example, the providing unit can make the display content multilingual according to the user's language setting when providing the content. The providing unit can automatically translate the display content based on the language setting of the user's device, for example. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. For example, the providing unit uses an algorithm that automatically translates the display content based on the user's language setting. The providing unit can also provide an interface that provides a language switching function when the user uses multiple languages. This improves the convenience of the display by making the display content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's language setting data to a generation AI and cause the generation AI to provide multilingual display content.

[0051] The providing unit can customize the display method by reflecting the user's past feedback when providing the data. For example, the providing unit customizes the display method by reflecting the user's past feedback when providing the data. For example, the providing unit improves the display interface based on feedback previously provided by the user. The providing unit can also optimize display content suggestions by reflecting the user's feedback. The providing unit can also customize the display method based on the user's feedback. For example, the providing unit analyzes feedback previously provided by the user and extracts a preferred display method. The providing unit can also use an algorithm to customize the display interface based on the user's feedback. This improves the convenience of the display by customizing the display method by reflecting the user's past feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the display method.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The reception unit can automatically suggest related images and videos based on the user's input. For example, if the user inputs "I went to a cafe with friends today," the reception unit can suggest photos of the cafe and photos of the friend. If the user inputs "I went on a trip," the reception unit can suggest videos of the scenery and tourist spots at the travel destination. Furthermore, if the user inputs "I played sports," the reception unit can suggest sports highlight videos and related images. This allows the user to easily add visual content related to the input content, enriching the content of the diary.

[0054] The generator can automatically search for and quote relevant news articles and blog posts based on the user's input. For example, if a user inputs "I went to a new restaurant," the generator can search for and quote reviews about that restaurant. If a user inputs "I saw a movie," the generator can search for and quote reviews about that movie. Furthermore, if a user inputs "I bought a new gadget," the generator can search for and quote technology blog posts about that gadget. This makes the user's diary more informative and helpful.

[0055] The generation unit can suggest related music and podcasts based on the user's input. For example, if the user inputs "I want to relax," the generation unit can suggest relaxing music and podcasts. If the user inputs "I exercised," the generation unit can suggest music suitable for exercise and podcasts related to fitness. Furthermore, if the user inputs "I studied," the generation unit can suggest music that improves concentration and podcasts related to studying. This allows the user to discover new music and podcasts through their diary.

[0056] The providing unit can provide templates for customizing the created diary according to the user's preferences. For example, if the user prefers a simple design, a simple template can be provided. Also, if the user prefers a colorful design, a colorful template can be provided. Furthermore, if the user prefers a design based on a specific theme (e.g., travel, sports, cooking, etc.), a template matching that theme can be provided. This allows the user to customize the design of the diary to suit their preferences.

[0057] The providing unit can provide a social media linking function for sharing the created diary. For example, the providing unit can enable the user to easily share the created diary on social media such as Facebook, Twitter, and Instagram. The providing unit can also provide a private sharing function for the user to share the diary with specific friends or family. Furthermore, the providing unit can provide a function for the user to publish the diary in blog format. This allows the user to easily share the created diary with others, thereby expanding the scope of use of the diary.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit receives input from a user. The input from a user includes text input, voice input, image input, etc. For example, the reception unit provides a keyboard input interface, a microphone input interface, and a camera interface. Step 2: The generator analyzes the input received by the receiver and generates a detailed diary entry. The generator uses a generative AI (e.g., GPT-4 or Gemini) to analyze the user's input, extract keywords and context, and generate grammatically correct and contextually consistent sentences. Step 3: The providing unit provides the diary generated by the generating unit to the user. The providing unit provides an interface for the user to preview the generated diary and make corrections as necessary. The providing unit also provides a function for saving the diary after corrections are completed in the cloud or locally.

[0060] (Example 2) A diary generation system according to an embodiment of the present invention allows a user to easily create and save a diary entry. The diary generation system accepts input from a user, and a generation AI analyzes the input content to generate a detailed diary entry and provide it to the user. For example, the user briefly inputs the events and thoughts of the day. For example, the user inputs a short sentence such as, "Today, I went to a cafe with a friend." This input is sent to the generation AI. The diary generation system then analyzes the input content using the generation AI to generate a detailed diary entry. The generation AI complements the sentence based on the user's input, creating a more detailed and easy-to-read diary entry. For example, the generation AI complements the sentence, such as, "Today, I went to a cafe with a friend. We had a great time drinking delicious coffee." The generated diary entry is provided to the user. The user can review the generated diary entry and make corrections as necessary. Finally, the user can save the completed diary entry. This allows the diary generation system to easily generate and save a diary entry, even for people who find keeping a diary tedious. This allows the user to create a detailed diary entry with only short inputs. Furthermore, the created diary can be modified to suit the user's preferences, so that it can meet individual needs.

[0061] A diary generation system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives input from a user. The user input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, a keyboard input interface for receiving text input. The reception unit can also provide a microphone input interface for receiving voice input. The reception unit can also provide a camera interface for receiving image input. For example, the reception unit receives text input from a user and transmits it to a generation AI. The generation unit uses the generation AI to analyze the input received by the reception unit and generate a detailed diary. The generation AI analyzes the user input and extracts relevant information using a model such as GPT-4 or Gemini. For example, the generation AI analyzes the user input and extracts keywords and context. The generation unit completes the sentences based on the extracted information and generates a detailed diary. For example, the generation AI generates grammatically accurate and contextually consistent sentences based on the user input. The providing unit provides the diary generated by the generating unit to the user. For example, the providing unit provides an interface that allows the user to preview the generated diary and make corrections as necessary. The providing unit also provides a function for saving the diary after corrections have been completed. For example, the providing unit displays the generated diary to the user and provides a text editor for the user to make corrections. Furthermore, the providing unit provides a function for saving the diary after corrections have been completed in the cloud or locally. This allows the diary generation system according to the embodiment to allow the user to easily generate and save a diary. Some or all of the above-described processing in the generating unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generating unit may input user input to the generation AI and cause the generation AI to generate a detailed diary.

[0062] The generation unit can analyze the user's input using a generation AI and extract relevant information. For example, the generation AI analyzes the user's input and extracts relevant information. The generation AI analyzes the user's input using a model such as GPT-4 or Gemini. The generation unit can also extract important keywords from the user's input using keyword extraction technology. For example, the generation AI extracts frequently occurring keywords and important phrases based on the user's input. The generation unit can also analyze the context of the user's input using context analysis technology. For example, the generation AI analyzes the context of the user's input and extracts information to maintain contextual consistency. This allows the generation AI to analyze the user's input and extract relevant information, thereby generating a detailed diary. Some or all of the above-described processing in the generation unit can be performed using, or without, the generation AI. For example, the generation unit can input the user's input to the generation AI and have the generation AI extract relevant information.

[0063] The generation unit can complete sentences based on the extracted information and generate a detailed diary. For example, the generation unit allows a generation AI to complete sentences based on the extracted information and generate a detailed diary. The generation AI generates sentences based on the extracted information using a model such as GPT-4 or Gemini. The generation unit can also generate sentences that are grammatically correct and contextually consistent. For example, the generation AI generates grammatically accurate sentences based on user input. The generation unit can also generate sentences taking into account the context before and after the sentences to maintain contextual consistency. For example, the generation AI generates sentences that maintain contextual consistency by taking into account the context of the user input. This allows a detailed diary to be generated based on the extracted information. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the extracted information into the generation AI and cause the generation AI to generate a detailed diary.

[0064] The providing unit can provide an interface that allows the user to preview the generated diary and make corrections as necessary. The providing unit, for example, provides an interface that allows the user to preview the generated diary and make corrections as necessary. The providing unit, for example, provides a real-time preview function that allows the user to instantly check the generated diary. The providing unit can also provide a partial preview function that allows the user to check only a specific portion. For example, the providing unit can display a portion of the generated diary and allow the user to correct that portion. The providing unit can also provide a text editor for making corrections. For example, the providing unit can provide a text editor that allows the user to correct the generated diary and allow the user to freely edit the text. This allows the user to check the generated diary and make corrections. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the generated diary into AI and cause the AI ​​to provide a preview and correction interface.

[0065] The providing unit can provide a function for saving a diary entry whose corrections have been completed. The providing unit, for example, provides a function for saving a diary entry whose corrections have been completed. The providing unit, for example, provides a cloud saving function, allowing a user to save a created diary entry on the cloud. The providing unit can also provide a local saving function, allowing a user to save a created diary entry on their own device. For example, the providing unit saves the created diary entry on a cloud storage service. The providing unit can also save the created diary entry as a local file on the user's device. This allows a user to save a diary entry whose corrections have been completed. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the diary entry whose corrections have been completed into AI, and have the AI ​​execute the saving process.

[0066] The reception unit can estimate the user's emotions and adjust the level of detail of the input content based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the level of detail of the input content based on the estimated user emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the reception unit may encourage a user to enter brief input and avoid detailed descriptions when the user is sad. The reception unit may also encourage a user to enter detailed events and impressions when the user is happy. The reception unit may also encourage a user to enter short inputs to simplify input when the user is tired. This allows a more appropriate diary entry to be generated by adjusting the level of detail of the input content according to the user's emotions. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the level of detail of the input content.

[0067] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically suggest expressions and phrases that the user has frequently used in the past. The reception unit can also suggest related topics based on the content the user has previously input. The reception unit can also predict and suggest content that the user will input during a specific time period from the user's past input history. For example, the reception unit can analyze text data previously input by the user and extract frequently occurring expressions and phrases. The reception unit can also use an algorithm to suggest related topics based on the user's past input content. Furthermore, the reception unit can analyze the user's input history in chronological order and predict content that will be input during a specific time period. This improves input efficiency by suggesting the optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's past input history data into a generation AI and cause the generation AI to suggest the optimal input method.

[0068] The reception unit can filter the input content based on the user's current activity status and areas of interest when the input is made. For example, when the input is made, the reception unit filters the input content based on the user's current activity status and areas of interest. For example, when the user is participating in a sporting event, the reception unit can prioritize suggesting related topics. Furthermore, when the user is traveling, the reception unit can also suggest travel-related input content. Furthermore, when the user is working, the reception unit can prioritize suggesting work-related topics. For example, the reception unit acquires the user's current activity status using GPS information or an activity log. Furthermore, the reception unit can acquire the user's areas of interest from past search history or social media activity. By filtering the input content based on the user's current activity status and areas of interest, a more relevant diary entry can be generated. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's activity status data and area of ​​interest data to a generation AI and cause the generation AI to filter the input content.

[0069] The reception unit can select the optimal input means depending on the user's input method at the time of input. For example, the reception unit selects the optimal input means depending on the user's input method (voice, text, image, etc.) at the time of input. For example, if the user selects voice input, the reception unit converts the input content into text using voice recognition technology. Furthermore, if the user uploads an image, the reception unit can generate related text using image analysis technology. Furthermore, if the user selects text input, the reception unit can support keyboard input. For example, the reception unit can convert the user's voice input into text data using voice recognition software. Furthermore, the reception unit can generate related text from an image uploaded by the user using image analysis technology. Furthermore, if the user selects text input, the reception unit provides a keyboard input interface to enable the user to efficiently input text. This improves input convenience by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input method data to a generation AI and cause the generation AI to select the optimal input means.

[0070] The reception unit can estimate the user's emotions and prioritize the input contents based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and prioritizes the input contents based on the estimated user emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the reception unit may prompt the user to prioritize input of important content when the user is stressed. The reception unit may also prompt the user to prioritize input of detailed content when the user is relaxed. The reception unit may also prompt the user to prioritize input of concise content when the user is in a hurry. In this way, the priority of the input contents can be determined according to the user's emotions, allowing important content to be input preferentially. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's emotion data into the generation AI and have the generation AI determine the priority of the input contents.

[0071] The reception unit can prioritize input of highly relevant content based on the user's geographical location information during input. For example, the reception unit prioritizes input of highly relevant content based on the user's geographical location information during input. For example, when the user is in a specific location, the reception unit prioritizes suggesting topics related to that location. Furthermore, when the user is traveling, the reception unit can prompt the user to prioritize input of content related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize suggesting topics related to the user's home. For example, the reception unit acquires the user's geographical location information using GPS data or a location information service. Furthermore, the reception unit can use an algorithm that suggests highly relevant content based on the user's geographical location information. This allows for the generation of a more appropriate diary entry by preferentially inputting highly relevant content based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to a generation AI and cause the generation AI to suggest highly relevant content.

[0072] The reception unit can analyze the user's social media activity and input related content at the time of input. For example, the reception unit can analyze the user's social media activity and input related content at the time of input. For example, the reception unit can suggest related topics based on content shared by the user on social media. The reception unit can also analyze the user's social media posts and prompt the user to input related events. The reception unit can also suggest related content based on the activities of the user's friends on social media. For example, the reception unit can analyze the user's social media posts and extract related keywords and topics. The reception unit can also use an algorithm that suggests related content based on the activities of the user's friends on social media. This allows for the input of related content based on the user's social media activity to generate a more relevant diary entry. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's social media activity data to a generation AI and cause the generation AI to suggest related content.

[0073] The reception unit can customize the input method by reflecting the user's past feedback at the time of input. The reception unit, for example, customizes the input method by reflecting the user's past feedback at the time of input. For example, the reception unit preferentially suggests input methods that the user has used favorably in the past. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also optimize input content suggestions by reflecting the user's past feedback. For example, the reception unit analyzes feedback provided by the user in the past and extracts a preferred input method. The reception unit can also use an algorithm to customize the input interface based on the user's feedback. This improves input convenience by customizing the input method by reflecting the user's past feedback. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data to a generation AI and cause the generation AI to customize the input method.

[0074] The generation unit can estimate the user's emotions and adjust the expression style of the diary entry to be generated based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the expression style of the diary entry to be generated based on the estimated user emotions. The emotion estimation is realized, for example, using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit may generate a diary entry using a gentle expression when the user is sad. The generation unit may also generate a diary entry using a cheerful expression when the user is happy. The generation unit may also generate a diary entry using a concise and easy-to-read expression when the user is tired. This allows a more appropriate diary entry to be generated by adjusting the expression style of the diary entry according to the user's emotions. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the expression style of the diary entry.

[0075] The generation unit can adjust the level of detail of the diary to be generated based on the importance of the input content at the time of generation. For example, the generation unit adjusts the level of detail of the diary to be generated based on the importance of the input content at the time of generation. For example, the generation unit generates a diary by adding detailed descriptions to important events. The generation unit can also generate a diary by adding brief descriptions to minor events. The generation unit can also generate a diary by adding detailed descriptions to content that the user particularly wants to emphasize. For example, the generation unit evaluates the importance based on the user's input content and adds detailed descriptions to important content. The generation unit can also use an algorithm that adds brief descriptions to minor content. In this way, by adjusting the level of detail of the diary based on the importance of the input content, a more appropriate diary can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's input content data to the generation AI and cause the generation AI to adjust the level of detail of the diary.

[0076] The generation unit can apply different generation algorithms depending on the category of the input content during generation. For example, the generation unit can apply different generation algorithms depending on the category of the input content during generation. For example, the generation unit can generate a diary using travelogue-style expressions for travel-related content. The generation unit can also generate a diary using business-like expressions for work-related content. The generation unit can also generate a diary using warm expressions for family-related content. For example, the generation unit classifies categories based on the user's input content and applies a generation algorithm corresponding to each category. The generation unit can also use an algorithm that uses a different writing style or expression method for each category. In this way, by applying different generation algorithms depending on the category of the input content, a more appropriate diary can be generated. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI. For example, the generation unit can input the user's input content data to the generation AI and cause the generation AI to apply a generation algorithm according to the category.

[0077] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit can refer to the style of diaries the user has previously generated and generate a diary in a similar style. The generation unit can also extract preferred expressions from the user's past generation results and reflect them in the diary. The generation unit can also analyze the user's past generation results and optimize the generation algorithm. For example, the generation unit extracts preferred expressions and styles based on the user's past generation results. The generation unit can also use an algorithm that adjusts parameters of the generation algorithm based on the past generation results. This improves the accuracy of generation by referring to the user's past generation results. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0078] The generation unit can estimate the user's emotions and adjust the length of the diary entry to be generated based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the length of the diary entry to be generated based on the estimated user emotions. The emotion estimation is realized, for example, using an emotion engine or a generation AI, using an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit can generate a short and concise diary entry when the user is sad. The generation unit can also generate a detailed and longer diary entry when the user is happy. The generation unit can also generate a short and easy-to-read diary entry when the user is tired. This allows for the generation of a more appropriate diary entry by adjusting the length of the diary entry according to the user's emotions. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the diary entry.

[0079] The generation unit can determine the priority of the diaries to be generated based on the submission time of the input content at the time of generation. For example, the generation unit determines the priority of the diaries to be generated based on the submission time of the input content at the time of generation. For example, the generation unit prioritizes generating a diary for content with an approaching deadline. The generation unit can also postpone generating a diary for content with a distant submission time. The generation unit can also prioritize generating a diary for content for which the user is particularly urgent. For example, the generation unit evaluates the submission time based on the user's input content and prioritizes generating a diary for content with a close submission time. The generation unit can also use an algorithm to generate a diary for content with a distant submission time at the end. In this way, by determining the priority of the diaries based on the submission time of the input content, a more appropriate diary can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's input content data to the generation AI and cause the generation AI to determine the priority of the diaries.

[0080] The generation unit can adjust the order of the diary entries to be generated based on the relevance of the input content during generation. The generation unit, for example, adjusts the order of the diary entries to be generated based on the relevance of the input content during generation. For example, the generation unit prioritizes placing highly relevant content at the beginning of the diary. The generation unit can also place less relevant content at the end of the diary. The generation unit can also place content that the user particularly wants to emphasize at the center of the diary. For example, the generation unit evaluates relevance based on the user's input content and places highly relevant content at the beginning of the diary. The generation unit can also use an algorithm that places less relevant content at the end of the diary. In this way, by adjusting the order of the diary entries based on the relevance of the input content, a more appropriate diary can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's input content data to the generation AI and cause the generation AI to adjust the order of the diary entries.

[0081] The generation unit can adjust the use of technical terms in the diary to be generated according to the user's level of expertise at the time of generation. For example, the generation unit can adjust the use of technical terms in the diary to be generated according to the user's level of expertise at the time of generation. For example, if the user has technical expertise, the generation unit generates a diary using a lot of technical terms. Furthermore, if the user does not have technical expertise, the generation unit can also generate a diary using simple language. The generation unit can also generate a diary by selecting appropriate technical terms according to the user's level of expertise. For example, the generation unit evaluates the level of expertise based on the user's input content and uses a lot of technical terms for users with technical expertise. Furthermore, the generation unit can use an algorithm that uses simple language for users without technical expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, a more appropriate diary can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0082] The providing unit can estimate the user's emotions and adjust the display method of the diary entry based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the display method of the diary entry based on the estimated user emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the providing unit may display the diary entry in subdued colors if the user is sad. Furthermore, the providing unit may display the diary entry in bright colors if the user is happy. Furthermore, the providing unit may provide a simple, highly visible display method if the user is tired. This allows for a more appropriate display by adjusting the display method of the diary entry according to the user's emotions. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the diary entry.

[0083] The providing unit can select the optimal display method by referring to the user's past operation history when providing the data. For example, the providing unit selects the optimal display method by referring to the user's past operation history when providing the data. For example, the providing unit preferentially provides display methods that the user has previously preferred. The providing unit can also customize the display interface based on the user's past operation history. The providing unit can also optimize display content suggestions by reflecting the user's past operation history. For example, the providing unit analyzes display methods that the user has previously used and extracts a preferred display method. The providing unit can also use an algorithm to customize the display interface based on the user's operation history. This improves display convenience by selecting the optimal display method based on the user's past operation history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past operation history data to the generation AI and cause the generation AI to select the optimal display method.

[0084] The providing unit can customize the display content according to the user's current task when providing the content. For example, the providing unit customizes the display content according to the user's current task when providing the content. For example, if the user is at work, the providing unit can prioritize displaying work-related content. Furthermore, if the user is on vacation, the providing unit can prioritize displaying relaxing content. Furthermore, if the user is working on a specific project, the providing unit can prioritize displaying content related to the project. For example, the providing unit can suggest related content based on the user's current task. Furthermore, the providing unit can use an algorithm to customize the display content according to the user's task. This enables more appropriate display by customizing the display content according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task data into a generation AI and cause the generation AI to customize the display content.

[0085] The providing unit can improve the display method by reflecting user feedback when providing the data. For example, the providing unit improves the display method by reflecting user feedback when providing the data. For example, the providing unit improves the display interface based on feedback previously provided by the user. The providing unit can also optimize display content suggestions by reflecting user feedback. The providing unit can also customize the display method based on user feedback. For example, the providing unit analyzes feedback previously provided by the user and extracts areas for improvement. The providing unit can also use an algorithm to improve the display interface based on user feedback. This improves the display method by reflecting user feedback, thereby improving the convenience of the display. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data to a generation AI and cause the generation AI to improve the display method.

[0086] The providing unit can estimate the user's emotions and adjust the display order of the diary entries based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the display order of the diary entries based on the estimated user emotions. The emotion estimation is realized, for example, by using an emotion estimation function with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is sad, the providing unit may prioritize displaying positive content. Furthermore, if the user is happy, the providing unit may also adjust the display order taking into account the overall flow. Furthermore, if the user is tired, the providing unit may display important content first and detailed content later. This allows for more appropriate display by adjusting the display order of the diary entries according to the user's emotions. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the display order of the diary entries.

[0087] The providing unit can select the optimal display method based on the user's device information at the time of providing. For example, the providing unit selects the optimal display method based on the user's device information at the time of providing. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can provide a display method optimized for a wide screen. For example, the providing unit can evaluate the device type and OS version based on the user's device information. Furthermore, the providing unit can use an algorithm to select a display method appropriate for the device. This improves display convenience by selecting the optimal display method based on the user's device information. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's device information data into a generation AI and cause the generation AI to select the optimal display method.

[0088] The providing unit can make the display content multilingual according to the user's language setting when providing the content. For example, the providing unit can make the display content multilingual according to the user's language setting when providing the content. The providing unit can automatically translate the display content based on the language setting of the user's device, for example. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. For example, the providing unit uses an algorithm that automatically translates the display content based on the user's language setting. The providing unit can also provide an interface that provides a language switching function when the user uses multiple languages. This improves the convenience of the display by making the display content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's language setting data to a generation AI and cause the generation AI to provide multilingual display content.

[0089] The providing unit can customize the display method by reflecting the user's past feedback when providing the data. For example, the providing unit customizes the display method by reflecting the user's past feedback when providing the data. For example, the providing unit improves the display interface based on feedback previously provided by the user. The providing unit can also optimize display content suggestions by reflecting the user's feedback. The providing unit can also customize the display method based on the user's feedback. For example, the providing unit analyzes feedback previously provided by the user and extracts a preferred display method. The providing unit can also use an algorithm to customize the display interface based on the user's feedback. This improves the convenience of the display by customizing the display method by reflecting the user's past feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the display method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives input from the user using the touch panel 38A or microphone 38B of the smart device 14. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's input using a generation AI to generate a detailed diary. The provision unit provides the generated diary to the user using, for example, the display 40A of the smart device 14, and provides an interface for making corrections. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives voice input from the user using the microphone 238 of the smart glasses 214. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's input using a generation AI to generate a detailed diary. The provision unit provides the generated diary to the user using, for example, the display of the smart glasses 214, and provides an interface for making corrections. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives voice input from the user using the microphone 238 of the headset type terminal 314. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's input using a generation AI to generate a detailed diary. The provision unit provides the generated diary to the user using, for example, the display 343 of the headset type terminal 314, and provides an interface for making corrections. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives voice input from the user using the microphone 238 of the robot 414. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's input using a generation AI to generate a detailed diary. The provision unit provides the generated diary to the user using, for example, a display of the robot 414, and provides an interface for making corrections.

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

[0091] The reception unit can automatically suggest related images and videos based on the user's input. For example, if the user inputs "I went to a cafe with friends today," the reception unit can suggest photos of the cafe and photos of the friend. If the user inputs "I went on a trip," the reception unit can suggest videos of the scenery and tourist spots at the travel destination. Furthermore, if the user inputs "I played sports," the reception unit can suggest sports highlight videos and related images. This allows the user to easily add visual content related to the input content, enriching the content of the diary.

[0092] The generator can automatically search for and quote relevant news articles and blog posts based on the user's input. For example, if a user inputs "I went to a new restaurant," the generator can search for and quote reviews about that restaurant. If a user inputs "I saw a movie," the generator can search for and quote reviews about that movie. Furthermore, if a user inputs "I bought a new gadget," the generator can search for and quote technology blog posts about that gadget. This makes the user's diary more informative and helpful.

[0093] The generation unit can suggest related music and podcasts based on the user's input. For example, if the user inputs "I want to relax," the generation unit can suggest relaxing music and podcasts. If the user inputs "I exercised," the generation unit can suggest music suitable for exercise and podcasts related to fitness. Furthermore, if the user inputs "I studied," the generation unit can suggest music that improves concentration and podcasts related to studying. This allows the user to discover new music and podcasts through their diary.

[0094] The providing unit can provide templates for customizing the created diary according to the user's preferences. For example, if the user prefers a simple design, a simple template can be provided. Also, if the user prefers a colorful design, a colorful template can be provided. Furthermore, if the user prefers a design based on a specific theme (e.g., travel, sports, cooking, etc.), a template matching that theme can be provided. This allows the user to customize the design of the diary to suit their preferences.

[0095] The providing unit can provide a social media linking function for sharing the created diary. For example, the providing unit can enable the user to easily share the created diary on social media such as Facebook, Twitter, and Instagram. The providing unit can also provide a private sharing function for the user to share the diary with specific friends or family. Furthermore, the providing unit can provide a function for the user to publish the diary in blog format. This allows the user to easily share the created diary with others, thereby expanding the scope of use of the diary.

[0096] The reception unit can estimate the user's emotions and provide feedback on the input content based on the estimated user emotions. For example, if the user is sad, the reception unit can display an encouraging message. If the user is happy, the reception unit can display a congratulatory message. Furthermore, if the user is feeling stressed, the reception unit can provide advice on how to relax. This allows the user to receive feedback according to their emotions, making diary entry more enjoyable.

[0097] The reception unit can estimate the user's emotions and adjust the tone of the input content based on the estimated user emotions. For example, if the user is angry, the reception unit can prompt the user to input in a calm tone. Also, if the user is excited, the reception unit can prompt the user to input in a tone that reflects the user's excitement. Furthermore, if the user is calm, the reception unit can prompt the user to input in a calm tone. This allows the user to input in a tone that corresponds to their emotions, making the diary content more natural and consistent.

[0098] The generation unit can estimate the user's emotions and adjust the style of the diary entry to be generated based on the estimated user emotions. For example, if the user is sad, the generation unit can generate a diary entry using kind language. If the user is happy, the generation unit can generate a diary entry using cheerful and positive language. Furthermore, if the user is tired, the generation unit can generate a diary entry in a concise and easy-to-read style. This allows the generation of a diary entry in a style that corresponds to the user's emotions, making the diary entry more relatable.

[0099] The providing unit can estimate the user's emotions and customize the display method of the diary based on the estimated user's emotions. For example, if the user is sad, the diary can be displayed in subdued colors. If the user is happy, the diary can be displayed in bright colors. Furthermore, if the user is tired, a simple, highly visible display method can be provided. This makes it possible to provide the diary in a display method that suits the user's emotions, thereby improving user satisfaction.

[0100] The providing unit can estimate the user's emotions and suggest a method for sharing the diary based on the estimated user's emotions. For example, if the user is happy, the providing unit can suggest sharing the diary on social media. Also, if the user is sad, the providing unit can suggest saving the diary privately. Furthermore, if the user is stressed, the providing unit can suggest sharing the diary with specific friends or family. In this way, by suggesting a sharing method according to the user's emotions, the use of the diary becomes more effective.

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

[0102] Step 1: The reception unit receives input from a user. The input from a user includes text input, voice input, image input, etc. For example, the reception unit provides a keyboard input interface, a microphone input interface, and a camera interface. Step 2: The generator analyzes the input received by the receiver and generates a detailed diary entry. The generator uses a generative AI (e.g., GPT-4 or Gemini) to analyze the user's input, extract keywords and context, and generate grammatically correct and contextually consistent sentences. Step 3: The providing unit provides the diary generated by the generating unit to the user. The providing unit provides an interface for the user to preview the generated diary and make corrections as necessary. The providing unit also provides a function for saving the diary after corrections are completed in the cloud or locally.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0140] 7, 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

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

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input from a user; a generation unit that analyzes the input received by the reception unit and generates a detailed diary; a providing unit that provides a user with the diary created by the creating unit; Equipped with A system characterized by:

2. The generation unit Generative AI analyzes user input and extracts relevant information 2. The system of claim 1.

3. The generation unit Based on the extracted information, the sentences are completed and a detailed diary is generated.

2. The system of claim 1.

4. The providing unit Provide an interface that allows users to preview the generated diary and make corrections as needed.

2. The system of claim 1.

5. The providing unit Provides the ability to save edited journal entries 2. The system of claim 1.

6. The reception unit Inferring user emotions and adjusting the level of detail of input content based on the estimated user emotions 2. The system of claim 1.

7. The reception unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

8. The reception unit As you type, filter your input based on your current activity and interests 2. The system of claim 1.

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