System

Abstract

An object of the system according to the embodiment is to generate and provide an autobiographical novel based on specific experience and values of a user.SOLUTION: A system includes an input unit, an analysis unit, a generation unit, and a provision unit. The input unit inputs information related to specific experience and values of the user. The analysis unit analyzes the information input by the input unit. The generation part generates an autobiographical novel on the basis of the information analyzed by the analysis part. The providing section provides the autobiographical novel generated by the generating section.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 technology has made it difficult to automatically generate autobiographical novels based on a user's specific experiences and values, resulting in a lack of self-understanding and sharing with others.

[0005] The system according to the embodiment aims to generate and provide an autobiographical novel based on the specific experiences and values ​​of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a generation unit, and a provision unit. The input unit inputs information relating to a user's specific experiences and values. The analysis unit analyzes the information input by the input unit. The generation unit generates an autobiography based on the information analyzed by the analysis unit. The provision unit provides the autobiography generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate and provide an autobiographical novel based on the specific experiences and values ​​of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An autobiographical novel generation system according to an embodiment of the present invention generates and provides an autobiographical novel based on a user's life experiences and values. This system not only provides users with opportunities to deepen their self-understanding and help them plan their future lives, but also allows them to share their scenarios with others and explore different people's lifestyles and perspectives. For example, a user inputs information about their life experiences and values, such as past events, important decisions, values, and goals. This information is then input into an AI. The AI ​​then analyzes the input information and generates an autobiographical novel based on the user's life experiences and values. The AI ​​then creates an autobiographical novel that reflects the user's life, constructing a story and setting characters based on the user's information. The generated autobiographical novel is then provided to the user. The user can reflect on their life and deepen their self-understanding. The generated autobiographical novel can also help them plan their future lives. For example, they can use lessons learned from past experiences to set future goals and plans. Furthermore, the user can share the generated autobiographical novel with others, thereby enabling them to explore different people's lifestyles and perspectives. For example, by reading other users' autobiographical stories, users can learn about different values ​​and experiences and gain a new perspective on their own lives. This allows the autobiographical story generation system to not only deepen users' self-understanding and help them plan their future lives, but also provides an opportunity to gain different perspectives through interactions with others. For example, users can share their autobiographical stories on social media and receive feedback from other users, thereby gaining a deeper self-understanding.

[0029] An autobiographical novel generation system according to an embodiment includes an input unit, an analysis unit, a generation unit, and a provision unit. The input unit inputs information about a user's specific experiences and values. Examples of specific experiences and values ​​include, but are not limited to, work experiences, life events, and personal beliefs. The input unit allows the user to input, for example, past events and important decisions. The input unit can also input the user's values ​​and goals. The analysis unit analyzes the information input by the input unit. The analysis can be performed using, for example, text analysis, sentiment analysis, data mining, or other methods, but is not limited to these examples. The analysis unit analyzes the user's input information using, for example, text analysis technology. The analysis unit can also analyze the user's emotions using sentiment analysis technology. The analysis unit can also analyze the user's information using data mining technology. The generation unit generates an autobiographical novel based on the information analyzed by the analysis unit. The autobiographical novel is generated in a format, for example, based on sentence length, story structure, language used, and the like, but is not limited to these examples. The generation unit generates a story based on the user's life experiences, for example. The generation unit can also set a character that reflects the user's values. The generation unit can also create episodes that reflect the user's goals. The provision unit provides the autobiographical novel generated by the generation unit. The provision unit, for example, displays the generated autobiographical novel to the user. The provision unit can also share the generated autobiographical novel with other users. The provision unit can also allow other users to view autobiographical novels. The provision unit can also receive feedback from other users. In this way, the autobiographical novel generation system according to the embodiment can generate and provide an autobiographical novel based on the user's life experiences and values.

[0030] The providing unit includes a sharing unit that shares the generated autobiographical novel with other users. The sharing unit shares the generated autobiographical novel with other users. Sharing is performed by, for example, sharing on social media, sharing with a specific user group, or the like, but is not limited to these examples. For example, the sharing unit shares the generated autobiographical novel on an SNS. The sharing unit can also share with other users using a dedicated platform. The sharing unit can also share the generated autobiographical novel with a specific user group. In this way, the generated autobiographical novel can be shared with other users.

[0031] The providing unit includes a viewing unit that views the autobiographical novels of other users. The viewing unit views the autobiographical novels of other users. The viewing is performed, for example, by viewing on a web browser or a dedicated app, but is not limited to these examples. For example, the viewing unit views the autobiographical novels of other users on a web browser. The viewing unit can also view the autobiographical novels of other users using a dedicated app. The viewing unit can also download and view the autobiographical novels of other users. This allows the autobiographical novels of other users to be viewed.

[0032] The providing unit includes a feedback unit that receives feedback from other users. The feedback unit receives feedback from other users. Feedback is provided by, for example, a comment function, a rating function, a questionnaire, or the like, but is not limited to these examples. The feedback unit receives comments from other users, for example. The feedback unit can also receive ratings from other users. The feedback unit can also receive questionnaires from other users. This makes it possible to receive feedback from other users.

[0033] The input unit can analyze the user's past input history and select an appropriate input method. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The input unit can also predict and suggest an input method to be used in a specific time period based on the user's past input history. The input unit can also customize the input method based on information the user has previously input. This makes it possible to select the optimal input method based on the user's past input history. The analysis of the input history may be performed, for example, using AI or without using AI.

[0034] The input unit can filter information based on the user's current living situation and areas of interest at the time of input. For example, the input unit filters information so that the user inputs only relevant information based on the user's current living situation. The input unit can also select information to be input based on the user's areas of interest. The input unit can also determine the priority of information to be input based on the user's current living situation and areas of interest. This makes it possible to filter input information based on the user's current living situation and areas of interest. The filtering process can be performed using AI, for example, or without AI.

[0035] The input unit can select an appropriate 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 input unit performs input using voice recognition technology. Furthermore, if the user selects text input, the input unit can also prioritize keyboard input. Furthermore, if the user selects image input, the input unit can also perform input using image recognition technology. This makes it possible to select the optimal input means depending on the user's input method. The selection of the input means may be performed using, for example, AI, or may be performed without using AI.

[0036] The input unit can, at the time of input, preferentially input highly relevant information taking into account the user's geographical location information. For example, when the user is in a specific area, the input unit preferentially inputs information related to that area. Furthermore, when the user is traveling, the input unit can also preferentially input information related to the travel destination. Furthermore, when the user is at home, the input unit can also preferentially input information related to the user's home. In this way, highly relevant information can be preferentially input based on the user's geographical location information. Consideration of the geographical location information may be performed, for example, using AI or may be performed without using AI.

[0037] The input unit can analyze the user's social media activity and input related information at the time of input. The input unit can input related information based on, for example, information shared by the user on social media. The input unit can also analyze the user's social media activity and input related information. The input unit can also input related information with reference to the activity of the user's friends on social media. In this way, related information can be input based on the user's social media activity. The analysis of social media activity may be performed using, for example, AI, or may be performed without using AI.

[0038] The input unit can customize the input method by reflecting the user's past feedback when inputting. The input unit customizes the input method based on, for example, feedback provided by the user in the past. The input unit can also suggest an optimal input method based on the user's past feedback. The input unit can also improve the input interface by reflecting the user's past feedback. This makes it possible to customize the input method based on the user's past feedback. Reflecting the feedback may be performed using AI, for example, or may be performed without using AI.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on important information. The analysis unit can also perform a concise analysis on general information. The analysis unit can also perform a detailed analysis on information that is of great interest to the user. This makes it possible to adjust the level of detail of the analysis based on the importance of the information. The evaluation of the importance of the information may be performed using AI, for example, or may be performed without using AI.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies a specific analysis algorithm to information about the user's life experiences. The analysis unit can also apply a different analysis algorithm to information about the user's values. The analysis unit can also apply yet another analysis algorithm to information about the user's goals. This makes it possible to apply different analysis algorithms depending on the category of information. The application of the analysis algorithm may be performed using AI, for example, or may be performed without using AI.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the current analysis, for example, based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm from the user's past analysis results. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results. Referring to the past analysis results may be performed, for example, using AI or without using AI.

[0042] During analysis, the analysis unit can determine the priority of analysis based on the time when the information was submitted. For example, the analysis unit prioritizes analyzing the most recent information. The analysis unit can also prioritize analyzing information from a time specified by the user. The analysis unit can also prioritize analyzing important information from the past. This makes it possible to determine the priority of analysis based on the time when the information was submitted. Consideration of the time when the information was submitted may be performed using, for example, AI, or may be performed without using AI.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also prioritize analysis of information that is of great interest to the user. The analysis unit can also optimize the order of analysis based on the relevance of the information. This makes it possible to adjust the order of analysis based on the relevance of the information. The evaluation of the relevance of the information may be performed using AI, for example, or may be performed without using AI.

[0044] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the analysis according to the user's level of expertise. The adjustment of the use of technical terms may be performed, for example, using AI or without using AI.

[0045] The generation unit can adjust the level of detail of the autobiography based on the importance of the information during generation. For example, the generation unit provides a detailed description for important information. The generation unit can also provide a concise description for general information. The generation unit can also provide a detailed description for information that is of high interest to the user. This makes it possible to adjust the level of detail of the autobiography based on the importance of the information. The evaluation of the importance of the information may be performed using AI, for example, or may be performed without using AI.

[0046] The generation unit can apply different generation algorithms depending on the category of information during generation. For example, the generation unit applies a specific generation algorithm to information about the user's life experiences. The generation unit can also apply a different generation algorithm to information about the user's values. The generation unit can also apply yet another generation algorithm to information about the user's goals. In this way, different generation algorithms can be applied depending on the category of information. The application of the generation algorithm may be performed using AI, for example, or may be performed without using AI.

[0047] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit can improve the accuracy of current generation, for example, based on the user's past generation results. The generation unit can also optimize the generation algorithm from the user's past generation results. The generation unit can also adjust the level of detail of generation by referring to the user's past generation results. This makes it possible to improve the accuracy of generation by referring to the user's past generation results. Referring to the past generation results may be performed, for example, using AI or without using AI.

[0048] The generation unit can determine the priority of the autobiographical novel based on the time of submission of information at the time of generation. The generation unit, for example, preferentially reflects the latest information in the autobiographical novel. The generation unit can also preferentially reflect information from a time period specified by the user in the autobiographical novel. The generation unit can also preferentially reflect important information from the past in the autobiographical novel. This makes it possible to determine the priority of the autobiographical novel based on the time of submission of information. Consideration of the time of submission of information may be performed, for example, using AI or without using AI.

[0049] The generation unit can adjust the order of the autobiographical novel based on the relevance of the information during generation. For example, the generation unit preferentially reflects highly relevant information in the autobiographical novel. The generation unit can also preferentially reflect information that is of high interest to the user in the autobiographical novel. The generation unit can also optimize the order of the autobiographical novel based on the relevance of the information. This makes it possible to adjust the order of the autobiographical novel based on the relevance of the information. The evaluation of the relevance of the information may be performed, for example, using AI or may be performed without using AI.

[0050] The generation unit can adjust the use of technical terms in the autobiographical novel during generation according to the user's level of expertise. For example, if the user has technical expertise, the generation unit uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the generation unit can provide the autobiographical novel in simple language. Furthermore, the generation unit can adjust the use of technical terms in the autobiographical novel according to the user's level of expertise. In this way, the use of technical terms in the autobiographical novel can be adjusted according to the user's level of expertise. The adjustment of the use of technical terms may be performed, for example, using AI or without using AI.

[0051] The providing unit can select the optimal providing method by referring to the user's past providing history at the time of providing. For example, the providing unit preferentially provides a display method that the user has previously preferred. The providing unit can also suggest the optimal display method from the user's past providing history. The providing unit can also customize the providing method based on the user's past providing history. This makes it possible to select the optimal providing method based on the user's past providing history. The providing history can be referred to, for example, using AI or without using AI.

[0052] The providing unit can customize the provided content based on the user's current living situation at the time of providing the content. For example, if the user is busy, the providing unit can prioritize concise provided content. Furthermore, if the user is relaxed, the providing unit can also prioritize detailed provided content. Furthermore, the providing unit can customize the provided content based on the user's current living situation. This makes it possible to customize the provided content based on the user's current living situation. Customization of the provided content may be performed, for example, using AI or without using AI.

[0053] The providing unit can improve the providing method by reflecting user feedback at the time of providing. The providing unit improves the providing method based on, for example, user feedback. The providing unit can also suggest an optimal providing method based on user feedback. The providing unit can also improve the providing interface by reflecting user feedback. This makes it possible to improve the providing method based on user feedback. Reflecting feedback may be performed using AI, for example, or may be performed without using AI.

[0054] The providing unit can select the optimal providing method by taking into consideration the user's geographical location information when providing information. For example, when the user is in a specific area, the providing unit can provide information related to that area preferentially. Furthermore, when the user is traveling, the providing unit can also provide information related to the travel destination preferentially. Furthermore, when the user is at home, the providing unit can also provide information related to the user's home preferentially. This makes it possible to select the optimal providing method based on the user's geographical location information. Consideration of the geographical location information may be performed, for example, using AI or without using AI.

[0055] The providing unit can customize the content of the information provided by analyzing the user's social media activity at the time of providing the information. The providing unit can provide related information based on, for example, information shared by the user on social media. The providing unit can also analyze the user's social media activity and provide related information. The providing unit can also provide related information by referring to the activity of the user's friends on social media. This makes it possible to customize the content of the information provided based on the user's social media activity. The analysis of social media activity may be performed, for example, using AI or may be performed without using AI.

[0056] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the information. The providing unit customizes the delivery method, for example, based on feedback provided by the user in the past. The providing unit can also suggest an optimal delivery method based on the user's past feedback. The providing unit can also improve the delivery interface by reflecting the user's past feedback. This makes it possible to customize the delivery method based on the user's past feedback. Reflecting the feedback may be performed, for example, using AI or without using AI.

[0057] When sharing, the sharing unit can select the optimal sharing method by referring to the user's past sharing history. For example, the sharing unit preferentially provides sharing methods that the user has previously preferred. The sharing unit can also suggest the optimal sharing method based on the user's past sharing history. The sharing unit can also customize the sharing method based on the user's past sharing history. This makes it possible to select the optimal sharing method based on the user's past sharing history. Referencing the sharing history may be performed using AI, for example, or may be performed without using AI.

[0058] The sharing unit can customize the shared content based on the user's current living situation when sharing. For example, if the user is busy, the sharing unit can prioritize concise shared content. Also, if the user is relaxed, the sharing unit can prioritize detailed shared content. Also, the sharing unit can customize the shared content based on the user's current living situation. This makes it possible to customize the shared content based on the user's current living situation. Customization of the shared content may be performed, for example, using AI or without using AI.

[0059] The sharing unit can improve the sharing method by reflecting user feedback during sharing. The sharing unit improves the sharing method based on user feedback, for example. The sharing unit can also suggest an optimal sharing method based on user feedback. The sharing unit can also improve the sharing interface by reflecting user feedback. This makes it possible to improve the sharing method based on user feedback. Reflecting feedback may be performed using AI, for example, or may be performed without using AI.

[0060] When sharing, the sharing unit can select the optimal sharing method by taking into account the user's geographical location information. For example, if the user is in a specific area, the sharing unit can prioritize sharing information related to that area. Furthermore, if the user is traveling, the sharing unit can also prioritize sharing information related to the travel destination. Furthermore, if the user is at home, the sharing unit can also prioritize sharing information related to the home. This makes it possible to select the optimal sharing method based on the user's geographical location information. Consideration of the geographical location information may be performed, for example, using AI or without using AI.

[0061] The sharing unit can customize the shared content by analyzing the user's social media activity at the time of sharing. For example, the sharing unit shares related information based on information shared by the user on social media. The sharing unit can also analyze the user's social media activity and share related information. The sharing unit can also share related information by referring to the activity of the user's friends on social media. This makes it possible to customize the shared content based on the user's social media activity. The analysis of social media activity may be performed, for example, using AI or may be performed without using AI.

[0062] The sharing unit can customize the sharing method by reflecting the user's past feedback when sharing. The sharing unit customizes the sharing method based on, for example, feedback provided by the user in the past. The sharing unit can also suggest an optimal sharing method based on the user's past feedback. The sharing unit can also improve the sharing interface by reflecting the user's past feedback. This makes it possible to customize the sharing method based on the user's past feedback. Reflecting the feedback may be performed using AI, for example, or may be performed without using AI.

[0063] The browsing unit can select the optimal browsing method by referring to the user's past browsing history when browsing. For example, the browsing unit preferentially provides a display method that the user has previously preferred. The browsing unit can also suggest the optimal display method based on the user's past browsing history. The browsing unit can also customize the browsing method based on the user's past browsing history. This makes it possible to select the optimal browsing method based on the user's past browsing history. The browsing history can be referenced using, for example, AI or without AI.

[0064] The viewing unit can customize the viewing content based on the user's current living situation during viewing. For example, if the user is busy, the viewing unit can prioritize brief viewing content. Also, if the user is relaxed, the viewing unit can prioritize detailed viewing content. Also, the viewing unit can customize the viewing content based on the user's current living situation. This allows the viewing content to be customized based on the user's current living situation. Customization of the viewing content may be performed, for example, using AI or without using AI.

[0065] The viewing unit can improve the viewing method by reflecting user feedback during viewing. The viewing unit improves the viewing method based on user feedback, for example. The viewing unit can also suggest an optimal viewing method based on user feedback. The viewing unit can also improve the viewing interface by reflecting user feedback. This makes it possible to improve the viewing method based on user feedback. Reflecting feedback may be done using AI, for example, or may be done without using AI.

[0066] The browsing unit can select the optimal browsing method during browsing by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the browsing unit prioritizes browsing of information related to that area. Furthermore, when the user is traveling, the browsing unit can also prioritize browsing of information related to the travel destination. Furthermore, when the user is at home, the browsing unit can also prioritize browsing of information related to the user's home. This makes it possible to select the optimal browsing method based on the user's geographical location information. Consideration of the geographical location information may be performed, for example, using AI or without using AI.

[0067] The viewing unit can analyze the user's social media activity during viewing and customize the viewing content. The viewing unit, for example, views related information based on information shared by the user on social media. The viewing unit can also analyze the user's social media activity and view related information. The viewing unit can also view related information with reference to the activity of the user's friends on social media. This makes it possible to customize the viewing content based on the user's social media activity. The analysis of social media activity may be performed, for example, using AI or without using AI.

[0068] The browsing unit can customize the browsing method by reflecting the user's past feedback during browsing. The browsing unit customizes the browsing method based on, for example, feedback provided by the user in the past. The browsing unit can also suggest an optimal browsing method based on the user's past feedback. The browsing unit can also improve the browsing interface by reflecting the user's past feedback. This makes it possible to customize the browsing method based on the user's past feedback. Reflecting the feedback may be performed using AI, for example, or may be performed without using AI.

[0069] When providing feedback, the feedback unit can select the optimal feedback method by referring to the user's past feedback history. For example, the feedback unit can preferentially provide feedback methods that the user has previously preferred. The feedback unit can also suggest the optimal feedback method from the user's past feedback history. The feedback unit can also customize the feedback method based on the user's past feedback history. This makes it possible to select the optimal feedback method based on the user's past feedback history. Referring to the feedback history may be performed using AI, for example, or may be performed without using AI.

[0070] The feedback unit may customize the feedback content based on the user's current living situation when providing feedback. For example, if the user is busy, the feedback unit may prioritize brief feedback content. Alternatively, if the user is relaxed, the feedback unit may prioritize detailed feedback content. Alternatively, the feedback unit may customize the feedback content based on the user's current living situation. This allows the feedback content to be customized based on the user's current living situation. Customization of the feedback content may be performed using, for example, AI or without using AI.

[0071] The feedback unit can improve the feedback method by reflecting the user's feedback at the time of feedback. The feedback unit can improve the feedback method based on, for example, the user's feedback. The feedback unit can also suggest an optimal feedback method based on the user's feedback. The feedback unit can also improve the feedback interface by reflecting the user's feedback. This makes it possible to improve the feedback method based on the user's feedback. Reflecting the feedback may be performed using, for example, AI or without using AI.

[0072] The feedback unit can select an optimal feedback method by taking into consideration the geographical location information of the user when providing feedback. For example, when the user is in a specific area, the feedback unit can prioritize providing feedback related to the area. Furthermore, when the user is traveling, the feedback unit can prioritize providing feedback related to the travel destination. Furthermore, when the user is at home, the feedback unit can prioritize providing feedback related to the home. This makes it possible to select an optimal feedback method based on the geographical location information of the user. Consideration of the geographical location information may be performed using AI, for example, or may be performed without using AI.

[0073] When providing feedback, the feedback unit can analyze the user's social media activity and customize the feedback content. The feedback unit can provide relevant feedback based on, for example, information shared by the user on social media. The feedback unit can also analyze the user's social media activity and provide relevant feedback. The feedback unit can also provide relevant feedback based on the activity of the user's friends on social media. This makes it possible to customize the feedback content based on the user's social media activity. The analysis of social media activity can be performed, for example, using AI or without using AI.

[0074] The feedback unit can customize the feedback method by reflecting the user's past feedback when providing feedback. The feedback unit customizes the feedback method based on, for example, feedback provided by the user in the past. The feedback unit can also suggest an optimal feedback method based on the user's past feedback. The feedback unit can also improve the feedback interface by reflecting the user's past feedback. This makes it possible to customize the feedback method based on the user's past feedback. Reflecting the feedback may be performed using AI, for example, or may be performed without using AI.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The input unit can analyze the user's past input history and select an appropriate input method. For example, it can preferentially suggest input methods (voice, text, etc.) that the user has frequently used in the past. The input unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. The input unit can also customize the input method based on information the user has previously input. This makes it possible to select the optimal input method based on the user's past input history. The analysis of the input history can be performed, for example, using AI or without using AI.

[0077] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, a detailed analysis is performed for important information. The analysis unit can also perform a concise analysis for general information. The analysis unit can also perform a detailed analysis for information that is of great interest to the user. This makes it possible to adjust the level of detail of the analysis based on the importance of the information. The evaluation of the importance of the information may be performed, for example, using AI or without using AI.

[0078] The providing unit can select the optimal providing method by referring to the user's past providing history at the time of providing. For example, the display method that the user has previously preferred can be preferentially provided. The providing unit can also suggest the optimal display method based on the user's past providing history. The providing unit can also customize the providing method based on the user's past providing history. This makes it possible to select the optimal providing method based on the user's past providing history. The providing history can be referred to, for example, using AI or without using AI.

[0079] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, a specific analysis algorithm can be applied to information about the user's life experiences. The analysis unit can also apply a different analysis algorithm to information about the user's values. The analysis unit can also apply yet another analysis algorithm to information about the user's goals. This makes it possible to apply different analysis algorithms depending on the category of information. The application of the analysis algorithm can be performed using, for example, AI or without using AI.

[0080] The input unit can filter information based on the user's current living situation and areas of interest at the time of input. For example, the input unit filters information so that only relevant information is input based on the user's current living situation. The input unit can also select information to be input based on the user's areas of interest. The input unit can also determine the priority of information to be input based on the user's current living situation and areas of interest. This makes it possible to filter input information based on the user's current living situation and areas of interest. The filtering process can be performed using AI, for example, or without AI.

[0081] The providing unit can customize the provided content based on the user's current living situation at the time of providing the content. For example, if the user is busy, the providing unit can prioritize brief provided content. Furthermore, if the user is relaxed, the providing unit can prioritize detailed provided content. Furthermore, the providing unit can customize the provided content based on the user's current living situation. This allows the provided content to be customized based on the user's current living situation. Customization of the provided content may be performed, for example, using AI or without using AI.

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

[0083] Step 1: The input section inputs information about the user's specific experiences and values. Specific experiences and values ​​include work experiences, life events, personal beliefs, etc. The user can input past events, important decisions, values, and goals. Step 2: The analysis unit analyzes the information input by the input unit. The analysis is performed using methods such as text analysis, sentiment analysis, and data mining. For example, the information input by the user is analyzed using text analysis technology, sentiment analysis technology, and data mining technology. Step 3: The generator generates an autobiography based on the information analyzed by the analyzer. The autobiography is generated in a format that takes into account things like sentence length, story structure, and language used. For example, it creates a story based on the user's life experiences, characters that reflect the user's values, and episodes that reflect the user's goals. Step 4: The providing unit provides the autobiographical novel generated by the generating unit. The generated autobiographical novel can be displayed to the user and shared with other users. The user can also view other users' autobiographical novels and receive feedback.

[0084] (Example 2) An autobiographical novel generation system according to an embodiment of the present invention generates and provides an autobiographical novel based on a user's life experiences and values. This system not only provides users with opportunities to deepen their self-understanding and help them plan their future lives, but also allows them to share their scenarios with others and explore different people's lifestyles and perspectives. For example, a user inputs information about their life experiences and values, such as past events, important decisions, values, and goals. This information is then input into an AI. The AI ​​then analyzes the input information and generates an autobiographical novel based on the user's life experiences and values. The AI ​​then creates an autobiographical novel that reflects the user's life, constructing a story and setting characters based on the user's information. The generated autobiographical novel is then provided to the user. The user can reflect on their life and deepen their self-understanding. The generated autobiographical novel can also help them plan their future lives. For example, they can use lessons learned from past experiences to set future goals and plans. Furthermore, the user can share the generated autobiographical novel with others, thereby enabling them to explore different people's lifestyles and perspectives. For example, by reading other users' autobiographical stories, users can learn about different values ​​and experiences and gain a new perspective on their own lives. This allows the autobiographical story generation system to not only deepen users' self-understanding and help them plan their future lives, but also provides an opportunity to gain different perspectives through interactions with others. For example, users can share their autobiographical stories on social media and receive feedback from other users, thereby gaining a deeper self-understanding.

[0085] An autobiographical novel generation system according to an embodiment includes an input unit, an analysis unit, a generation unit, and a provision unit. The input unit inputs information about a user's specific experiences and values. Examples of specific experiences and values ​​include, but are not limited to, work experiences, life events, and personal beliefs. The input unit allows the user to input, for example, past events and important decisions. The input unit can also input the user's values ​​and goals. The analysis unit analyzes the information input by the input unit. The analysis can be performed using, for example, text analysis, sentiment analysis, data mining, or other methods, but is not limited to these examples. The analysis unit analyzes the user's input information using, for example, text analysis technology. The analysis unit can also analyze the user's emotions using sentiment analysis technology. The analysis unit can also analyze the user's information using data mining technology. The generation unit generates an autobiographical novel based on the information analyzed by the analysis unit. The autobiographical novel is generated in a format, for example, based on sentence length, story structure, language used, and the like, but is not limited to these examples. The generation unit generates a story based on the user's life experiences, for example. The generation unit can also set a character that reflects the user's values. The generation unit can also create episodes that reflect the user's goals. The provision unit provides the autobiographical novel generated by the generation unit. The provision unit, for example, displays the generated autobiographical novel to the user. The provision unit can also share the generated autobiographical novel with other users. The provision unit can also allow other users to view autobiographical novels. The provision unit can also receive feedback from other users. In this way, the autobiographical novel generation system according to the embodiment can generate and provide an autobiographical novel based on the user's life experiences and values.

[0086] The providing unit includes a sharing unit that shares the generated autobiographical novel with other users. The sharing unit shares the generated autobiographical novel with other users. Sharing is performed by, for example, sharing on social media, sharing with a specific user group, or the like, but is not limited to these examples. For example, the sharing unit shares the generated autobiographical novel on an SNS. The sharing unit can also share with other users using a dedicated platform. The sharing unit can also share the generated autobiographical novel with a specific user group. In this way, the generated autobiographical novel can be shared with other users.

[0087] The providing unit includes a viewing unit that views the autobiographical novels of other users. The viewing unit views the autobiographical novels of other users. The viewing is performed, for example, by viewing on a web browser or a dedicated app, but is not limited to these examples. For example, the viewing unit views the autobiographical novels of other users on a web browser. The viewing unit can also view the autobiographical novels of other users using a dedicated app. The viewing unit can also download and view the autobiographical novels of other users. This allows the autobiographical novels of other users to be viewed.

[0088] The providing unit includes a feedback unit that receives feedback from other users. The feedback unit receives feedback from other users. Feedback is provided by, for example, a comment function, a rating function, a questionnaire, or the like, but is not limited to these examples. The feedback unit receives comments from other users, for example. The feedback unit can also receive ratings from other users. The feedback unit can also receive questionnaires from other users. This makes it possible to receive feedback from other users.

[0089] The input unit can estimate the user's emotions and adjust the timing of input based on the estimated user emotions. For example, if the user is feeling stressed, the input unit can delay the timing of input to provide a relaxing environment. Furthermore, if the user is relaxed, the input unit can also speed up the timing of input to provide a smooth input experience. Furthermore, if the user is in a hurry, the input unit can optimize the timing of input to enable quick information input. This allows the input timing to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or without AI.

[0090] The input unit can analyze the user's past input history and select an appropriate input method. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The input unit can also predict and suggest an input method to be used in a specific time period based on the user's past input history. The input unit can also customize the input method based on information the user has previously input. This makes it possible to select the optimal input method based on the user's past input history. The analysis of the input history may be performed, for example, using AI or without using AI.

[0091] The input unit can filter information based on the user's current living situation and areas of interest at the time of input. For example, the input unit filters information so that the user inputs only relevant information based on the user's current living situation. The input unit can also select information to be input based on the user's areas of interest. The input unit can also determine the priority of information to be input based on the user's current living situation and areas of interest. This makes it possible to filter input information based on the user's current living situation and areas of interest. The filtering process can be performed using AI, for example, or without AI.

[0092] The input unit can select an appropriate 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 input unit performs input using voice recognition technology. Furthermore, if the user selects text input, the input unit can also prioritize keyboard input. Furthermore, if the user selects image input, the input unit can also perform input using image recognition technology. This makes it possible to select the optimal input means depending on the user's input method. The selection of the input means may be performed using, for example, AI, or may be performed without using AI.

[0093] The input unit can estimate the user's emotions and determine the priority of information to be input based on the estimated user emotions. For example, when the user is feeling stressed, the input unit prioritizes input of important information. Furthermore, when the user is relaxed, the input unit can also prioritize input of detailed information. Furthermore, when the user is in a hurry, the input unit can also prioritize input of the minimum necessary information. This makes it possible to determine the priority of information to be input according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI.

[0094] The input unit can, at the time of input, preferentially input highly relevant information taking into account the user's geographical location information. For example, when the user is in a specific area, the input unit preferentially inputs information related to that area. Furthermore, when the user is traveling, the input unit can also preferentially input information related to the travel destination. Furthermore, when the user is at home, the input unit can also preferentially input information related to the user's home. In this way, highly relevant information can be preferentially input based on the user's geographical location information. Consideration of the geographical location information may be performed, for example, using AI or may be performed without using AI.

[0095] The input unit can analyze the user's social media activity and input related information at the time of input. The input unit can input related information based on, for example, information shared by the user on social media. The input unit can also analyze the user's social media activity and input related information. The input unit can also input related information with reference to the activity of the user's friends on social media. In this way, related information can be input based on the user's social media activity. The analysis of social media activity may be performed using, for example, AI, or may be performed without using AI.

[0096] The input unit can customize the input method by reflecting the user's past feedback when inputting. The input unit customizes the input method based on, for example, feedback provided by the user in the past. The input unit can also suggest an optimal input method based on the user's past feedback. The input unit can also improve the input interface by reflecting the user's past feedback. This makes it possible to customize the input method based on the user's past feedback. Reflecting the feedback may be performed using AI, for example, or may be performed without using AI.

[0097] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. If the user is excited, the analysis unit can also provide visually stimulating analysis results. This makes it possible to adjust the way the analysis is presented depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI.

[0098] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on important information. The analysis unit can also perform a concise analysis on general information. The analysis unit can also perform a detailed analysis on information that is of great interest to the user. This makes it possible to adjust the level of detail of the analysis based on the importance of the information. The evaluation of the importance of the information may be performed using AI, for example, or may be performed without using AI.

[0099] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies a specific analysis algorithm to information about the user's life experiences. The analysis unit can also apply a different analysis algorithm to information about the user's values. The analysis unit can also apply yet another analysis algorithm to information about the user's goals. This makes it possible to apply different analysis algorithms depending on the category of information. The application of the analysis algorithm may be performed using AI, for example, or may be performed without using AI.

[0100] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the current analysis, for example, based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm from the user's past analysis results. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results. Referring to the past analysis results may be performed, for example, using AI or without using AI.

[0101] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis. The analysis unit can also provide a detailed analysis if the user is relaxed. The analysis unit can also provide a visually stimulating analysis if the user is excited. This allows the length of the analysis to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI.

[0102] During analysis, the analysis unit can determine the priority of analysis based on the time when the information was submitted. For example, the analysis unit prioritizes analyzing the most recent information. The analysis unit can also prioritize analyzing information from a time specified by the user. The analysis unit can also prioritize analyzing important information from the past. This makes it possible to determine the priority of analysis based on the time when the information was submitted. Consideration of the time when the information was submitted may be performed using, for example, AI, or may be performed without using AI.

[0103] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also prioritize analysis of information that is of great interest to the user. The analysis unit can also optimize the order of analysis based on the relevance of the information. This makes it possible to adjust the order of analysis based on the relevance of the information. The evaluation of the relevance of the information may be performed using AI, for example, or may be performed without using AI.

[0104] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the analysis according to the user's level of expertise. The adjustment of the use of technical terms may be performed, for example, using AI or without using AI.

[0105] The generation unit can estimate the user's emotions and adjust the expression style of the generated autobiography based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate an autobiography that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate an autobiography that emphasizes the shortest route. If the user is excited, the generation unit can also generate an autobiography that adds visually stimulating effects. This allows the expression style of the autobiography to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed, for example, using AI or without AI.

[0106] The generation unit can adjust the level of detail of the autobiography based on the importance of the information during generation. For example, the generation unit provides a detailed description for important information. The generation unit can also provide a concise description for general information. The generation unit can also provide a detailed description for information that is of high interest to the user. This makes it possible to adjust the level of detail of the autobiography based on the importance of the information. The evaluation of the importance of the information may be performed using AI, for example, or may be performed without using AI.

[0107] The generation unit can apply different generation algorithms depending on the category of information during generation. For example, the generation unit applies a specific generation algorithm to information about the user's life experiences. The generation unit can also apply a different generation algorithm to information about the user's values. The generation unit can also apply yet another generation algorithm to information about the user's goals. In this way, different generation algorithms can be applied depending on the category of information. The application of the generation algorithm may be performed using AI, for example, or may be performed without using AI.

[0108] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit can improve the accuracy of current generation, for example, based on the user's past generation results. The generation unit can also optimize the generation algorithm from the user's past generation results. The generation unit can also adjust the level of detail of generation by referring to the user's past generation results. This makes it possible to improve the accuracy of generation by referring to the user's past generation results. Referring to the past generation results may be performed, for example, using AI or without using AI.

[0109] The generation unit can determine the priority of the autobiographical novel based on the time of submission of information at the time of generation. The generation unit, for example, preferentially reflects the latest information in the autobiographical novel. The generation unit can also preferentially reflect information from a time period specified by the user in the autobiographical novel. The generation unit can also preferentially reflect important information from the past in the autobiographical novel. This makes it possible to determine the priority of the autobiographical novel based on the time of submission of information. Consideration of the time of submission of information may be performed, for example, using AI or without using AI.

[0110] The generation unit can adjust the order of the autobiographical novel based on the relevance of the information during generation. For example, the generation unit preferentially reflects highly relevant information in the autobiographical novel. The generation unit can also preferentially reflect information that is of high interest to the user in the autobiographical novel. The generation unit can also optimize the order of the autobiographical novel based on the relevance of the information. This makes it possible to adjust the order of the autobiographical novel based on the relevance of the information. The evaluation of the relevance of the information may be performed, for example, using AI or may be performed without using AI.

[0111] The generation unit can adjust the use of technical terms in the autobiographical novel during generation according to the user's level of expertise. For example, if the user has technical expertise, the generation unit uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the generation unit can provide the autobiographical novel in simple language. Furthermore, the generation unit can adjust the use of technical terms in the autobiographical novel according to the user's level of expertise. In this way, the use of technical terms in the autobiographical novel can be adjusted according to the user's level of expertise. The adjustment of the use of technical terms may be performed, for example, using AI or without using AI.

[0112] The providing unit can estimate the user's emotions and adjust the display method of the autobiographical novel to be provided based on the estimated user's emotions. For example, when the user is relaxed, the providing unit can provide a detailed display method. When the user is in a hurry, the providing unit can also provide a concise display method that focuses on the main points. When the user is excited, the providing unit can also provide a visually stimulating display method. This makes it possible to adjust the display method of the autobiographical novel according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or can be performed without using AI.

[0113] The providing unit can select the optimal providing method by referring to the user's past providing history at the time of providing. For example, the providing unit preferentially provides a display method that the user has previously preferred. The providing unit can also suggest the optimal display method from the user's past providing history. The providing unit can also customize the providing method based on the user's past providing history. This makes it possible to select the optimal providing method based on the user's past providing history. The providing history can be referred to, for example, using AI or without using AI.

[0114] The providing unit can customize the provided content based on the user's current living situation at the time of providing the content. For example, if the user is busy, the providing unit can prioritize concise provided content. Furthermore, if the user is relaxed, the providing unit can also prioritize detailed provided content. Furthermore, the providing unit can customize the provided content based on the user's current living situation. This makes it possible to customize the provided content based on the user's current living situation. Customization of the provided content may be performed, for example, using AI or without using AI.

[0115] The providing unit can improve the providing method by reflecting user feedback at the time of providing. The providing unit improves the providing method based on, for example, user feedback. The providing unit can also suggest an optimal providing method based on user feedback. The providing unit can also improve the providing interface by reflecting user feedback. This makes it possible to improve the providing method based on user feedback. Reflecting feedback may be performed using AI, for example, or may be performed without using AI.

[0116] The providing unit can estimate the user's emotions and determine the priority of the autobiographical novels to be provided based on the estimated user's emotions. For example, when the user is feeling stressed, the providing unit can prioritize providing important information. Furthermore, when the user is relaxed, the providing unit can prioritize providing detailed information. Furthermore, when the user is in a hurry, the providing unit can prioritize providing the minimum necessary information. This makes it possible to determine the priority of the autobiographical novels according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0117] The providing unit can select the optimal providing method by taking into consideration the user's geographical location information when providing information. For example, when the user is in a specific area, the providing unit can provide information related to that area preferentially. Furthermore, when the user is traveling, the providing unit can also provide information related to the travel destination preferentially. Furthermore, when the user is at home, the providing unit can also provide information related to the user's home preferentially. This makes it possible to select the optimal providing method based on the user's geographical location information. Consideration of the geographical location information may be performed, for example, using AI or without using AI.

[0118] The providing unit can customize the content of the information provided by analyzing the user's social media activity at the time of providing the information. The providing unit can provide related information based on, for example, information shared by the user on social media. The providing unit can also analyze the user's social media activity and provide related information. The providing unit can also provide related information by referring to the activity of the user's friends on social media. This makes it possible to customize the content of the information provided based on the user's social media activity. The analysis of social media activity may be performed, for example, using AI or may be performed without using AI.

[0119] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the information. The providing unit customizes the delivery method, for example, based on feedback provided by the user in the past. The providing unit can also suggest an optimal delivery method based on the user's past feedback. The providing unit can also improve the delivery interface by reflecting the user's past feedback. This makes it possible to customize the delivery method based on the user's past feedback. Reflecting the feedback may be performed, for example, using AI or without using AI.

[0120] The sharing unit can estimate the user's emotions and adjust the display method of the autobiographical novel to be shared based on the estimated user's emotions. For example, the sharing unit can provide a detailed display method when the user is relaxed. Furthermore, the sharing unit can provide a concise display method that focuses on the main points when the user is in a hurry. Furthermore, the sharing unit can provide a visually stimulating display method when the user is excited. This makes it possible to adjust the display method of the autobiographical novel according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the sharing unit can be performed, for example, using AI, or can be performed without using AI.

[0121] When sharing, the sharing unit can select the optimal sharing method by referring to the user's past sharing history. For example, the sharing unit preferentially provides sharing methods that the user has previously preferred. The sharing unit can also suggest the optimal sharing method based on the user's past sharing history. The sharing unit can also customize the sharing method based on the user's past sharing history. This makes it possible to select the optimal sharing method based on the user's past sharing history. Referencing the sharing history may be performed using AI, for example, or may be performed without using AI.

[0122] The sharing unit can customize the shared content based on the user's current living situation when sharing. For example, if the user is busy, the sharing unit can prioritize concise shared content. Also, if the user is relaxed, the sharing unit can prioritize detailed shared content. Also, the sharing unit can customize the shared content based on the user's current living situation. This makes it possible to customize the shared content based on the user's current living situation. Customization of the shared content may be performed, for example, using AI or without using AI.

[0123] The sharing unit can improve the sharing method by reflecting user feedback during sharing. The sharing unit improves the sharing method based on user feedback, for example. The sharing unit can also suggest an optimal sharing method based on user feedback. The sharing unit can also improve the sharing interface by reflecting user feedback. This makes it possible to improve the sharing method based on user feedback. Reflecting feedback may be performed using AI, for example, or may be performed without using AI.

[0124] The sharing unit can estimate the user's emotions and determine the priority of the autobiographical novels to be shared based on the estimated user's emotions. For example, if the user is feeling stressed, the sharing unit can prioritize sharing important information. Furthermore, if the user is relaxed, the sharing unit can prioritize sharing detailed information. Furthermore, if the user is in a hurry, the sharing unit can prioritize sharing the minimum necessary information. This makes it possible to determine the priority of the autobiographical novels according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI or without AI.

[0125] When sharing, the sharing unit can select the optimal sharing method by taking into account the user's geographical location information. For example, if the user is in a specific area, the sharing unit can prioritize sharing information related to that area. Furthermore, if the user is traveling, the sharing unit can also prioritize sharing information related to the travel destination. Furthermore, if the user is at home, the sharing unit can also prioritize sharing information related to the home. This makes it possible to select the optimal sharing method based on the user's geographical location information. Consideration of the geographical location information may be performed, for example, using AI or without using AI.

[0126] The sharing unit can customize the shared content by analyzing the user's social media activity at the time of sharing. For example, the sharing unit shares related information based on information shared by the user on social media. The sharing unit can also analyze the user's social media activity and share related information. The sharing unit can also share related information by referring to the activity of the user's friends on social media. This makes it possible to customize the shared content based on the user's social media activity. The analysis of social media activity may be performed, for example, using AI or may be performed without using AI.

[0127] The sharing unit can customize the sharing method by reflecting the user's past feedback when sharing. The sharing unit customizes the sharing method based on, for example, feedback provided by the user in the past. The sharing unit can also suggest an optimal sharing method based on the user's past feedback. The sharing unit can also improve the sharing interface by reflecting the user's past feedback. This makes it possible to customize the sharing method based on the user's past feedback. Reflecting the feedback may be performed using AI, for example, or may be performed without using AI.

[0128] The viewing unit can estimate the user's emotions and adjust the display method of the autobiographical novel being viewed based on the estimated user emotions. For example, the viewing unit can provide a detailed display method when the user is relaxed. Furthermore, the viewing unit can provide a concise display method that focuses on the main points when the user is in a hurry. Furthermore, the viewing unit can provide a visually stimulating display method when the user is excited. This allows the display method of the autobiographical novel to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the viewing unit can be performed, for example, using AI, or can be performed without using AI.

[0129] The browsing unit can select the optimal browsing method by referring to the user's past browsing history when browsing. For example, the browsing unit preferentially provides a display method that the user has previously preferred. The browsing unit can also suggest the optimal display method based on the user's past browsing history. The browsing unit can also customize the browsing method based on the user's past browsing history. This makes it possible to select the optimal browsing method based on the user's past browsing history. The browsing history can be referenced using, for example, AI or without AI.

[0130] The viewing unit can customize the viewing content based on the user's current living situation during viewing. For example, if the user is busy, the viewing unit can prioritize brief viewing content. Also, if the user is relaxed, the viewing unit can prioritize detailed viewing content. Also, the viewing unit can customize the viewing content based on the user's current living situation. This allows the viewing content to be customized based on the user's current living situation. Customization of the viewing content may be performed, for example, using AI or without using AI.

[0131] The viewing unit can improve the viewing method by reflecting user feedback during viewing. The viewing unit improves the viewing method based on user feedback, for example. The viewing unit can also suggest an optimal viewing method based on user feedback. The viewing unit can also improve the viewing interface by reflecting user feedback. This makes it possible to improve the viewing method based on user feedback. Reflecting feedback may be done using AI, for example, or may be done without using AI.

[0132] The viewing unit can estimate the user's emotions and determine the priority of the autobiographical novels to be viewed based on the estimated user emotions. For example, when the user is feeling stressed, the viewing unit prioritizes viewing important information. Furthermore, when the user is relaxed, the viewing unit can prioritize viewing detailed information. Furthermore, when the user is in a hurry, the viewing unit can prioritize viewing the minimum necessary information. This allows the priority of the autobiographical novels to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the viewing unit may be performed, for example, using AI or without AI.

[0133] The browsing unit can select the optimal browsing method during browsing by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the browsing unit prioritizes browsing of information related to that area. Furthermore, when the user is traveling, the browsing unit can also prioritize browsing of information related to the travel destination. Furthermore, when the user is at home, the browsing unit can also prioritize browsing of information related to the user's home. This makes it possible to select the optimal browsing method based on the user's geographical location information. Consideration of the geographical location information may be performed, for example, using AI or without using AI.

[0134] The viewing unit can analyze the user's social media activity during viewing and customize the viewing content. The viewing unit, for example, views related information based on information shared by the user on social media. The viewing unit can also analyze the user's social media activity and view related information. The viewing unit can also view related information with reference to the activity of the user's friends on social media. This makes it possible to customize the viewing content based on the user's social media activity. The analysis of social media activity may be performed, for example, using AI or without using AI.

[0135] The browsing unit can customize the browsing method by reflecting the user's past feedback during browsing. The browsing unit customizes the browsing method based on, for example, feedback provided by the user in the past. The browsing unit can also suggest an optimal browsing method based on the user's past feedback. The browsing unit can also improve the browsing interface by reflecting the user's past feedback. This makes it possible to customize the browsing method based on the user's past feedback. Reflecting the feedback may be performed using AI, for example, or may be performed without using AI.

[0136] The feedback unit can estimate the user's emotions and adjust the feedback display method based on the estimated user's emotions. For example, the feedback unit can provide detailed feedback when the user is relaxed. Furthermore, the feedback unit can provide concise feedback that focuses on the main points when the user is in a hurry. Furthermore, the feedback unit can provide visually stimulating feedback when the user is excited. This makes it possible to adjust the feedback display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI or without using AI.

[0137] When providing feedback, the feedback unit can select the optimal feedback method by referring to the user's past feedback history. For example, the feedback unit can preferentially provide feedback methods that the user has previously preferred. The feedback unit can also suggest the optimal feedback method from the user's past feedback history. The feedback unit can also customize the feedback method based on the user's past feedback history. This makes it possible to select the optimal feedback method based on the user's past feedback history. Referring to the feedback history may be performed using AI, for example, or may be performed without using AI.

[0138] The feedback unit may customize the feedback content based on the user's current living situation when providing feedback. For example, if the user is busy, the feedback unit may prioritize brief feedback content. Alternatively, if the user is relaxed, the feedback unit may prioritize detailed feedback content. Alternatively, the feedback unit may customize the feedback content based on the user's current living situation. This allows the feedback content to be customized based on the user's current living situation. Customization of the feedback content may be performed using, for example, AI or without using AI.

[0139] The feedback unit can improve the feedback method by reflecting the user's feedback at the time of feedback. The feedback unit can improve the feedback method based on, for example, the user's feedback. The feedback unit can also suggest an optimal feedback method based on the user's feedback. The feedback unit can also improve the feedback interface by reflecting the user's feedback. This makes it possible to improve the feedback method based on the user's feedback. Reflecting the feedback may be performed using, for example, AI or without using AI.

[0140] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, if the user is feeling stressed, the feedback unit can prioritize providing important feedback. Furthermore, if the user is relaxed, the feedback unit can prioritize providing detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can prioritize providing the minimum necessary feedback. This makes it possible to determine the priority of feedback according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI or without AI.

[0141] The feedback unit can select an optimal feedback method by taking into consideration the geographical location information of the user when providing feedback. For example, when the user is in a specific area, the feedback unit can prioritize providing feedback related to the area. Furthermore, when the user is traveling, the feedback unit can prioritize providing feedback related to the travel destination. Furthermore, when the user is at home, the feedback unit can prioritize providing feedback related to the home. This makes it possible to select an optimal feedback method based on the geographical location information of the user. Consideration of the geographical location information may be performed using AI, for example, or may be performed without using AI.

[0142] When providing feedback, the feedback unit can analyze the user's social media activity and customize the feedback content. The feedback unit can provide relevant feedback based on, for example, information shared by the user on social media. The feedback unit can also analyze the user's social media activity and provide relevant feedback. The feedback unit can also provide relevant feedback based on the activity of the user's friends on social media. This makes it possible to customize the feedback content based on the user's social media activity. The analysis of social media activity can be performed, for example, using AI or without using AI.

[0143] The feedback unit can customize the feedback method by reflecting the user's past feedback when providing feedback. The feedback unit customizes the feedback method based on, for example, feedback provided by the user in the past. The feedback unit can also suggest an optimal feedback method based on the user's past feedback. The feedback unit can also improve the feedback interface by reflecting the user's past feedback. This makes it possible to customize the feedback method based on the user's past feedback. Reflecting the feedback may be performed using AI, for example, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit can input information about the user's experiences and values ​​using the reception device 38 of the smart device 14. The analysis unit analyzes the input information using the specific processing unit 290 of the data processing device 12. The generation unit generates the autobiography based on the analysis results using the specific processing unit 290 of the data processing device 12. The provision unit provides the generated autobiography to the user using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit can input information related to the user's experiences and values ​​using the microphone 238 of the smart glasses 214. The analysis unit analyzes the input information using the specific processing unit 290 of the data processing device 12. The generation unit generates an autobiography based on the analysis results using the specific processing unit 290 of the data processing device 12. The provision unit provides the generated autobiography to the user using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the input unit can input information about the user's experiences and values ​​using the microphone 238 of the headset type terminal 314. The analysis unit analyzes the input information using the specific processing unit 290 of the data processing device 12. The generation unit generates an autobiography based on the analysis results using the specific processing unit 290 of the data processing device 12. The provision unit provides the generated autobiography to the user using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit can input information about the user's experiences and values ​​using the microphone 238 of the robot 414. The analysis unit analyzes the input information using the specific processing unit 290 of the data processing device 12. The generation unit generates an autobiography based on the analysis results using the specific processing unit 290 of the data processing device 12. The provision unit provides the generated autobiography to the user using the speaker 240 of the robot 414.

[0144] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0145] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, important information can be analyzed with priority. Also, if the user is relaxed, detailed information can be analyzed with priority. Also, if the user is in a hurry, the minimum necessary information can be analyzed with priority. This allows the analysis priority to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI.

[0146] The providing unit can estimate the user's emotions and adjust the display method of the autobiographical novel to be provided based on the estimated user's emotions. For example, if the user is relaxed, a detailed display method can be provided. If the user is in a hurry, a concise display method that focuses on the main points can be provided. If the user is excited, a visually stimulating display method can be provided. This makes it possible to adjust the display method of the autobiographical novel according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or can be performed without using AI.

[0147] The input unit can analyze the user's past input history and select an appropriate input method. For example, it can preferentially suggest input methods (voice, text, etc.) that the user has frequently used in the past. The input unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. The input unit can also customize the input method based on information the user has previously input. This makes it possible to select the optimal input method based on the user's past input history. The analysis of the input history can be performed, for example, using AI or without using AI.

[0148] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, a detailed analysis is performed for important information. The analysis unit can also perform a concise analysis for general information. The analysis unit can also perform a detailed analysis for information that is of great interest to the user. This makes it possible to adjust the level of detail of the analysis based on the importance of the information. The evaluation of the importance of the information may be performed, for example, using AI or without using AI.

[0149] The generation unit can estimate the user's emotions and adjust the expression style of the generated autobiography based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate an autobiography that progresses at a leisurely pace. If the user is in a hurry, the generation unit can generate an autobiography that emphasizes the shortest route. If the user is excited, the generation unit can generate an autobiography that adds visually stimulating effects. This allows the expression style of the autobiography to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed, for example, using AI or without AI.

[0150] The providing unit can select the optimal providing method by referring to the user's past providing history at the time of providing. For example, the display method that the user has previously preferred can be preferentially provided. The providing unit can also suggest the optimal display method based on the user's past providing history. The providing unit can also customize the providing method based on the user's past providing history. This makes it possible to select the optimal providing method based on the user's past providing history. The providing history can be referred to, for example, using AI or without using AI.

[0151] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, a specific analysis algorithm can be applied to information about the user's life experiences. The analysis unit can also apply a different analysis algorithm to information about the user's values. The analysis unit can also apply yet another analysis algorithm to information about the user's goals. This makes it possible to apply different analysis algorithms depending on the category of information. The application of the analysis algorithm can be performed using, for example, AI or without using AI.

[0152] The input unit can filter information based on the user's current living situation and areas of interest at the time of input. For example, the input unit filters information so that only relevant information is input based on the user's current living situation. The input unit can also select information to be input based on the user's areas of interest. The input unit can also determine the priority of information to be input based on the user's current living situation and areas of interest. This makes it possible to filter input information based on the user's current living situation and areas of interest. The filtering process can be performed using AI, for example, or without AI.

[0153] The providing unit can customize the provided content based on the user's current living situation at the time of providing the content. For example, if the user is busy, the providing unit can prioritize brief provided content. Furthermore, if the user is relaxed, the providing unit can prioritize detailed provided content. Furthermore, the providing unit can customize the provided content based on the user's current living situation. This allows the provided content to be customized based on the user's current living situation. Customization of the provided content may be performed, for example, using AI or without using AI.

[0154] The feedback unit can estimate the user's emotions and adjust the feedback display method based on the estimated user's emotions. For example, if the user is relaxed, detailed feedback can be provided. If the user is in a hurry, the feedback unit can also provide concise feedback that focuses on the main points. If the user is excited, the feedback unit can also provide visually stimulating feedback. This makes it possible to adjust the feedback display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI or without using AI.

[0155] The processing flow of the second embodiment will be briefly explained below.

[0156] Step 1: The input section inputs information about the user's specific experiences and values. Specific experiences and values ​​include work experiences, life events, personal beliefs, etc. The user can input past events, important decisions, values, and goals. Step 2: The analysis unit analyzes the information input by the input unit. The analysis is performed using methods such as text analysis, sentiment analysis, and data mining. For example, the information input by the user is analyzed using text analysis technology, sentiment analysis technology, and data mining technology. Step 3: The generator generates an autobiography based on the information analyzed by the analyzer. The autobiography is generated in a format that takes into account things like sentence length, story structure, and language used. For example, it creates a story based on the user's life experiences, characters that reflect the user's values, and episodes that reflect the user's goals. Step 4: The providing unit provides the autobiographical novel generated by the generating unit. The generated autobiographical novel can be displayed to the user and shared with other users. The user can also view other users' autobiographical novels and receive feedback.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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 AI 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.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0178] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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 AI 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.

[0191] 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.

[0192] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0193] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0194] 7, a 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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).

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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 AI 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.

[0208] 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.

[0209] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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).

[0214] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0215] 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."

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] [Explanation of symbols]

[0229] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input unit for inputting information about a user's specific experiences and values; an analysis unit that analyzes the information input by the input unit; a generation unit that generates an autobiographical novel based on the information analyzed by the analysis unit; a providing unit that provides the autobiographical novel generated by the generating unit; Equipped with A system characterized by:

2. The providing unit It has a sharing section that allows users to share the generated autobiographical novel with other users.

2. The system of claim 1.

3. The providing unit It has a viewing section for viewing other users' autobiographical novels.

2. The system of claim 1.

4. The providing unit A feedback section is provided to receive feedback from other users.

2. The system of claim 1.

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

2. The system of claim 1.

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

7. The input unit As you type, it filters based on your current life situation and interests.

2. The system of claim 1.

8. The input unit When inputting, select the appropriate input method depending on the user's input method 2. The system of claim 1.

9. The input unit Estimate the user's emotions and prioritize the information to be input based on the estimated user emotions.

2. The system of claim 1.

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