system

Abstract

Provide a system. A means for inputting basic information from a user; A means for generating questions based on the input basic information; means for presenting the generated question to a user; a means for collecting user responses; A means of analyzing the collected responses and extracting keywords and episodes, A method for constructing a story based on extracted keywords and episodes, A means for generating questions to convert a user's negative memories into a positive perspective; A way to integrate all the episodes and generate a story, and a means for saving the final version of the story.

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] In modern society, many people rarely reflect on their own lives, and past experiences and lessons are not fully utilized. Furthermore, because it is difficult to see negative memories and experiences in a positive light, they are often left untouched. Furthermore, there are few ways for family and friends to organize the memories and stories of the deceased and leave them as a story. The present invention aims to solve these problems. [Means for solving the problem]

[0005] The present invention solves the above problems by providing a system that includes a means for inputting basic information from a user, a means for generating questions based on the input basic information, a means for presenting the generated questions to the user, a means for collecting user responses, a means for analyzing the collected responses and extracting keywords and episodes, a means for constructing a story based on the extracted keywords and episodes, a means for generating questions to convert the user's negative memories into a positive perspective, a means for integrating all episodes and generating a story, and a means for saving the final version of the story.

[0006] "User" refers to an individual who uses the system to enter basic information and responses.

[0007] "Basic information" refers to personal data about the user, such as name, date of birth, etc.

[0008] "Questions" refer to questions presented to users to elicit their past experiences and episodes.

[0009] "Generative means" refers to the process or method of generating new questions or stories based on the basic information entered or the answers collected.

[0010] "Presentation means" refers to a screen or interface for showing the generated question to the user.

[0011] "Means of collection" refers to the process or method for capturing user-entered responses.

[0012] An "answer" refers to the text or information a user enters in response to a posed question.

[0013] "Means of analysis" refers to the process or method of extracting and organizing keywords and episodes from the collected responses.

[0014] "Keywords" refer to important phrases or words extracted from the user's answers.

[0015] An "episode" refers to a user-provided description of a specific experience or event.

[0016] "Means of constructing a story" refers to the process or method of creating a coherent story based on extracted keywords and episodes.

[0017] "Negative memories" refer to past experiences or experiences that were painful or sad for the user.

[0018] "Positive perspective" refers to a way of reinterpreting negative memories in a positive light.

[0019] The "final story" refers to the completed story that incorporates user responses and feedback.

[0020] "Preservation measures" refers to the process or method of recording the final version of the story in a database or storage medium to enable future access. [Brief explanation of the drawings]

[0021] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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, a 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), and an APU (Accelerated Processing Unit).

[0025] 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.

[0026] 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.

[0027] 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), Bluetooth (registered trademark), etc.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0033] 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.

[0034] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0035] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The embodiments for carrying out the present invention will be described in detail below.

[0043] First, the user accesses the system using their own terminal. After accessing, an interface for entering basic information (such as name and date of birth) is displayed on the terminal. The user enters this basic information, and the terminal sends it to the server.

[0044] The server generates an appropriate list of questions based on the received basic information. This list of questions may relate to themes such as the user's memories from school, occupation, and hobbies. The generated questions are sent to the terminal, which then presents them to the user. The first question displayed may be, "Who was the teacher who had the greatest influence on you during your school days?"

[0045] The user inputs answers to the questions through the device. For example, they input a specific answer such as, "Professor Tanaka had the greatest influence on me." The device collects these answers and sends them to the server in real time.

[0046] The server stores the collected responses in a database and uses NLP (natural language processing) technology to extract keywords and important episodes. For example, keywords such as "Professor Tanaka," "I was influenced by him," and "I enjoyed the math class" can be extracted from the user's responses.

[0047] The server then constructs part of a story based on these keywords and episodes. Specifically, it generates a sentence about "My encounter with Professor Tanaka and its influence" and presents it to the user. The user can review this intermediate result through their device and provide corrections or additional information as needed. The device then collects feedback from the user and sends that information back to the server.

[0048] For negative memories, the server generates new questions and executes a process to convert them into a positive perspective. For example, a question such as, "How do you think your painful experiences have helped you today?" is generated. The user inputs a positive answer such as, "My painful experiences have made me more resilient," which is then collected by the device and sent to the server.

[0049] Finally, the server combines all episodes and responses into a coherent story that highlights the user's growth and learning. The final story is sent from the server to the device for review by the user. The user can review the final story and, if there are no problems, request that it be saved. The server saves the final story in its database for future access by the user. A link to download it as a PDF is also generated, if desired.

[0050] As a concrete example, consider the case where a user requests the system to "talk about memories from their school days." The user enters basic information and responds, "Professor Tanaka was the person who had the greatest influence on me." The server then analyzes "your encounter with Professor Tanaka and the impact he had on you" and constructs it as part of a story. Furthermore, in response to the question, "How have your difficult experiences helped you today?" the user responds, "My difficult experiences have given me perseverance," which is also integrated into the story.

[0051] In this way, the present invention provides a system for generating a story that allows users to look back on their own lives and view their personal growth and learning in a positive light.

[0052] The processing flow will be explained below.

[0053] Step 1:

[0054] User: Accesses the system using a terminal and enters basic information (such as name and date of birth) into the interface.

[0055] Step 2:

[0056] Terminal: Sends the entered basic information to the server.

[0057] Step 3:

[0058] Server: Generates a list of questions appropriate for the user based on the received basic information.

[0059] Step 4:

[0060] Server: Sends the generated question list to the terminal.

[0061] Step 5:

[0062] Terminal: Presents the list of questions to the user.

[0063] Step 6:

[0064] User: Enters answers to the questions presented.

[0065] Step 7:

[0066] Terminal: Collects user responses and sends them to the server in real time.

[0067] Step 8:

[0068] Server: Stores the received answers in a database and extracts keywords and episodes using NLP (natural language processing) technology.

[0069] Step 9:

[0070] Server: Constructs parts of a story based on extracted keywords and episodes.

[0071] Step 10:

[0072] Server: Sends the intermediate products of the constructed story to the terminal.

[0073] Step 11:

[0074] Terminal: Presents the intermediate product to the user.

[0075] Step 12:

[0076] User: Review the intermediate results and provide corrections or additional information as needed.

[0077] Step 13:

[0078] Terminal: Collects feedback from users and sends it to the server.

[0079] Step 14:

[0080] Server: Generates new questions for negative memories.

[0081] Step 15:

[0082] Server: Sends the generated question to the terminal.

[0083] Step 16:

[0084] Terminal: Presents the new question to the user.

[0085] Step 17:

[0086] User: Enter an answer to a new question.

[0087] Step 18:

[0088] Terminal: Collects the user's answers and sends them to the server.

[0089] Step 19:

[0090] Server: Analyze positive responses and integrate them into the narrative.

[0091] Step 20:

[0092] Server: Integrates all episodes and responses and generates a coherent story.

[0093] Step 21:

[0094] Server: Sends the final story to the device.

[0095] Step 22:

[0096] Terminal: Presents the final story to the user.

[0097] Step 23:

[0098] User: Review the final version of the story and, if there are no problems, request to save it.

[0099] Step 24:

[0100] Server: Stores the final story in a database for future access by users.

[0101] Step 25:

[0102] Server: If desired, generate a link to download the PDF.

[0103] Example 1

[0104] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0105] Conventional story generation systems have had difficulty generating a coherent story by appropriately utilizing the user's basic information and episodes. They also lacked the ability to provide a positive perspective on negative memories, making it difficult to incorporate user feedback and revise the story. Furthermore, there were limited ways for users to access and download the generated story.

[0106] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0107] In this invention, the server includes means for inputting basic information from a user, means for generating questions based on the input basic information, means for generating questions using a generative AI model, means for presenting the generated questions to the user, means for collecting user responses, means for analyzing the collected responses and extracting keywords and episodes, means for generating a story based on the extracted keywords and episodes, means for generating questions to convert the user's negative memories into a positive perspective, means for integrating all episodes and generating a story, means for regenerating the story based on feedback provided by the user, means for generating sentences based on the keywords and episodes using the generative AI model, means for accessing and downloading the generated story, and means for saving the final version of the story. This provides a system that utilizes the user's basic information and episodes to generate a consistent story, convert negative memories into positive ones, and further modifies the story based on user feedback, making it accessible and downloadable.

[0108] "Basic information" refers to information related to the user's profile, such as the user's name, date of birth, occupation, and hobbies.

[0109] A "question" is an inquiry related to a specific topic that is generated by the server based on the user's basic information.

[0110] A "generative AI model" refers to an algorithm that generates text using natural language processing technology, and includes, for example, GPT-4 (registered trademark).

[0111] "User's response" is the reply entered by the user in response to the question posed by the server.

[0112] "Keywords" are important words or phrases extracted from the user's answers.

[0113] An "episode" is a detail of a particular experience or event extracted from a user's responses.

[0114] A "story" is a coherent written story generated based on the user's basic information, keywords, and episodes.

[0115] "Negative memories" refer to unpleasant events or emotions that a user has experienced in the past.

[0116] A "positive perspective" is a way of finding a positive interpretation or impact on negative memories.

[0117] "Feedback" refers to corrections or additional information that users provide to the generated story.

[0118] "Regeneration" is the process of regenerating an existing story based on user feedback.

[0119] The present invention provides a system that allows users to have a positive perspective on their lives and generate a coherent narrative. This system operates in cooperation with users, terminals, and a server.

[0120] Hardware and software configuration

[0121] User terminal: Uses devices such as PCs, smartphones, tablets, etc. Access the system through browser software (e.g., GOOGLE CHROME (registered trademark), Safari).

[0122] Server: Built using Python frameworks (Django, Flask). Databases use MySQL (registered trademark) or PostgreSQL, and libraries such as NLTK and Spacy are used for natural language processing. GPT-4 is used as the generative AI model.

[0123] System Operation

[0124] First, the user accesses the system using a browser. The terminal displays an online interface and prompts the user to enter basic information (such as name, date of birth, etc.). After the user enters the required information, the terminal sends the information to the server in JSON format.

[0125] Next, the server uses a Python algorithm to analyze the user's basic information. Based on that, it automatically generates a list of questions appropriate to the user's profile. For example, questions related to "memories of school days," "occupation," and "hobbies" are generated. The server then sends this list of questions to the terminal in JSON format.

[0126] The device displays the received question to the user. The user enters an answer to the question (e.g., "Who was the teacher who had the greatest influence on you during your school days?" and answers "Professor Tanaka was the most influential"), which the device then sends to the server in real time.

[0127] The server stores the answers in a database and then uses natural language processing technology to extract keywords and important anecdotes from the answers. Based on the extracted information, a generative AI model (GPT-4) is used to generate parts of a story. The generated text is then presented to the user via their device.

[0128] The user checks the story generated on the device and enters corrections or additional information as necessary. The device then sends the user's feedback to the server and regenerates the story.

[0129] For negative memories, the server generates a new question (e.g., "How do you think your painful experiences have helped you today?") and executes a process to convert the memory into a positive perspective. The user inputs an answer such as "My painful experiences have made me more resilient," and the device sends this answer to the server.

[0130] Finally, the server integrates all episodes and responses into a single, coherent story that emphasizes the user's growth and learning. The user can review the final version of the story on their device and, if there are no problems, request that it be saved. The server saves the final version of the story in its database and, if desired, generates a download link in PDF format.

[0131] Examples of concrete examples and prompts

[0132] As a concrete example, consider the case where a user requests the system to "talk about memories from their school days." The user enters basic information and responds, "Professor Tanaka was the person who had the greatest influence on me." The server then analyzes "my encounter with Professor Tanaka and the impact he had on me" and constructs it as part of a story. Furthermore, in response to the question, "How have your difficult experiences helped you today?" the user responds, "My difficult experiences have given me perseverance," and this is also integrated into the story.

[0133] An example of a prompt is:

[0134] "Tell us about the teacher who had the greatest influence on you as a student. How did they influence you?"

[0135] "How have your painful experiences helped you become who you are today?"

[0136] Examples include:

[0137] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0138] Step 1:

[0139] A user accesses the system using their own terminal. When the user opens a browser and accesses the specified URL, the system's web page is displayed. The terminal provides the user with an interface for entering basic information. This information includes name, date of birth, occupation, hobbies, etc. When the user enters the basic information and clicks the send button, the terminal sends the information to the server in JSON format (input: basic information, output: transmitted data).

[0140] Step 2:

[0141] The server analyzes the basic information received from the user. Specifically, it uses a Python algorithm to analyze the information and create a user profile (input: basic information, output: user profile). The server then uses a generative AI model to generate a customized list of questions based on the user profile (input: user profile, output: list of questions). The generated questions are based on a theme, such as "memories from school" or "hobbies."

[0142] Step 3:

[0143] The server sends the generated question list to the terminal in JSON format (input: question list, output: transmitted data). The terminal displays the received question list to the user. The user enters answers to the displayed questions. For example, in response to the question "Who was the teacher who had the greatest influence on you during your student days?" the user might answer "Professor Tanaka had the greatest influence on me." When the user enters the answer and clicks the send button, the terminal collects the answer and transmits it to the server in real time (input: answer, output: transmitted data).

[0144] Step 4:

[0145] The server receives the user's answers and stores them in a database, typically a MySQL or PostgreSQL database (input: answers, output: saved data). The server then uses natural language processing techniques to analyze the saved answers. It uses libraries such as Python's NLTK and Spacy to extract important keywords and episodes from the answers (input: answer data, output: keywords, episodes).

[0146] Step 5:

[0147] The server generates part of a story based on the extracted keywords and episodes. Using a generative AI model (e.g., GPT-4), it generates part of a story about "Meeting with Professor Tanaka and its impact" (input: keywords, episodes, output: generated part of the story). The generated text is sent to the device.

[0148] Step 6:

[0149] The device displays part of the generated story to the user. The user checks the content and enters corrections or additional information as needed. For example, the user may add details such as "Professor Tanaka's class was particularly interesting in math" (Input: User feedback, Output: Corrected data). The device collects the user's feedback and sends it back to the server (Input: Corrected data, Output: Sent data).

[0150] Step 7:

[0151] The server generates a new question to convert the user's negative memories into a positive one. For example, it generates a question such as, "How do you think your painful experiences have helped you today?" (Input: User's negative answer, Output: New question). The user responds to this question by saying, "My painful experiences have given me more patience," and the device collects this answer and sends it to the server (Input: Positive answer, Output: Transmitted data).

[0152] Step 8:

[0153] The server integrates all episodes and responses to generate a coherent story. It uses a generative AI model to generate text that highlights the user's growth and learning (input: episodes and responses, output: final story). The final story is sent to the device for the user to review.

[0154] Step 9:

[0155] The terminal displays the final version of the story to the user. The user checks the final version of the story and, if there are no problems, requests that it be saved. The server saves the final version of the story in a database so that the user can access it in the future (input: final confirmed data, output: saved data). In addition, the server generates a download link in PDF format if desired (input: saved data, output: download link).

[0156] (Application example 1)

[0157] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0158] In recent years, there has been a demand for improving customer experiences in brick-and-mortar stores, but it is difficult to provide personalized product recommendations and services to each individual customer. Furthermore, technologies for effectively leveraging customers' past experiences and preferences to provide specific and engaging customer experiences are still in their infancy. Furthermore, there is a need for flexible systems that can adapt based on customer feedback.

[0159] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0160] In this invention, the server includes means for inputting basic information from a user, means for generating questions based on the input basic information, means for presenting the generated questions to the user, means for collecting user responses, means for analyzing the collected responses and extracting keywords and episodes, means for constructing a story based on the extracted keywords and episodes, means for generating questions to convert the user's negative memories into a positive perspective, means for integrating all episodes and generating a story, means for saving the final version of the story, means for analyzing the user's preferences and past experiences from the collected responses and generating a personalized recommended product list, and means for presenting the generated recommended product list. This makes it possible to significantly improve customer experience by providing personalized recommended product lists in physical stores.

[0161] "Basic information" is information for identifying an individual, such as the user's name, date of birth, gender, occupation, and hobbies.

[0162] "Means for generating questions" refers to technology that automatically creates a list of appropriate questions based on the user's basic information and past answers.

[0163] A "means for presenting a question" is an interface or device that displays the generated question to the user.

[0164] The "means for collecting answers" refers to a technique for collecting answers entered by users as data.

[0165] "Means of analyzing and extracting keywords and episodes" refers to a technique that uses natural language processing technology to identify important words and events from the collected responses.

[0166] "Means of constructing a story" refers to the technique of creating a coherent story based on extracted keywords and episodes.

[0167] The "means for generating questions to convert a user's negative memories into a positive perspective" is a technique for creating additional questions to help the user view negative memories in a positive light.

[0168] "Narrative generation" is the technique of integrating all collected episodes to create a final story.

[0169] The "means for storing the final story" is a technique for storing the generated story in a database so that users can access it later.

[0170] The "means for generating an individualized recommended product list" is a technology for creating a product list optimized for a specific user based on the user's preferences and past experiences.

[0171] The "means for presenting a recommended product list" refers to an interface or device that displays the generated recommended product list to the user.

[0172] The process of entering basic information from the user is carried out using a device such as a smartphone or PC. After the user accesses the system, an interface for entering basic information such as name, date of birth, gender, occupation, and hobbies is displayed on the device. Once the user enters the basic information, the device sends the information to the server.

[0173] The server generates an appropriate list of questions based on the received basic information. The questions may relate to themes such as the user's memories from their school days, their occupation, or their hobbies. The generated questions are then sent back to the device, which then presents them to the user. An example of the initial question displayed is, "Who was the teacher who had the greatest influence on you during your school days?"

[0174] The user inputs answers to the questions through the device, such as "My math teacher had the biggest impact on me," and the device sends the answers to the server in real time.

[0175] The server analyzes the collected responses and uses natural language processing (NLP) technology to extract keywords and important anecdotes. For example, keywords such as "math teacher," "influenced me," and "fun classes" can be extracted from user responses.

[0176] The server then constructs part of a story based on these keywords and episodes. For example, it generates a sentence about "My encounter with my math teacher and its impact" and presents it to the user. The user can check the draft of this story through their device and provide corrections or additional information as needed. User feedback is then sent back to the server via their device.

[0177] For negative memories, the server generates new questions and executes a process to transform the user's negative experiences into positive ones. For example, a question such as "How has your painful experience helped you?" is generated. The user can then respond with "That experience helped me develop perseverance," thereby integrating the positive episode into the story.

[0178] Finally, the server combines all episodes and responses to generate a coherent story. This story highlights the user's growth and learning, and is then edited into a final version. The user can review the final version of the story through their device and, if satisfied, request that it be saved. The server saves this final version of the story in a database for future access by the user. A link is also provided to download the story in PDF format if desired.

[0179] As an application example, we will explain the case where the present invention is applied to a "personal shopping assistant" application in a physical store. When a user visiting a store accesses the system using smart glasses or a smartphone, a personalized list of recommended products is displayed based on the user's past shopping experiences and answers. This allows the user to enjoy shopping according to their preferences.

[0180] As an example of how to use a generative AI model, we will use the following prompt:

[0181] "Generate a story based on the user's experiences. Keywords: math teacher, impact, fun class"

[0182] In this way, the present invention allows users to generate personalized stories and recommended product lists based on their experiences, enhancing their experience in brick-and-mortar stores and other settings.

[0183] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0184] Step 1:

[0185] A user accesses the system through a terminal and inputs basic information (such as name, date of birth, gender, occupation, hobbies, etc.). The input basic information is sent from the terminal to the server. The input of this step is the basic information entered by the user, and the output is the basic information sent from the terminal to the server.

[0186] Step 2:

[0187] The server generates an appropriate list of questions based on the received basic information. Here, the server analyzes the basic information and automatically generates questions related to the user's memories from school, occupation, hobbies, etc. The input for this step is the basic information, and the output is the generated list of questions.

[0188] Step 3:

[0189] The generated question list is sent to the terminal, which then presents it to the user. The user confirms the questions on the terminal. The input of this step is the question list sent from the server, and the output is the questions presented to the user.

[0190] Step 4:

[0191] The user inputs an answer to the question through the terminal. For example, they input a specific answer such as "My math teacher had the biggest impact on me." The input of this step is the user's answer, and the output is the answer sent from the terminal to the server.

[0192] Step 5:

[0193] The server analyzes the collected answers and extracts keywords and important episodes using natural language processing (NLP) technology. For example, keywords such as "math teacher," "influenced me," and "fun class" can be extracted from the answers. The input for this step is the user's answer, and the output is the extracted keywords and episodes.

[0194] Step 6:

[0195] The server generates part of a story based on the extracted keywords and episodes. Using the generative AI model, it creates a sentence about, for example, "My encounter with my math teacher and its impact" and presents it to the user. The input for this step is keywords and episodes, and the output is the generated part of the story.

[0196] Step 7:

[0197] The user checks the generated draft of the story through the terminal and provides corrections or additional information as necessary. The user's feedback is sent back to the server through the terminal. The input of this step is the user's feedback, and the output is a part of the revised story.

[0198] Step 8:

[0199] The server generates questions to convert the user's negative answers into positive ones. For example, it provides new questions such as, "How has your painful experience helped you?" The input of this step is the negative answer, and the output is a newly generated question.

[0200] Step 9:

[0201] The user inputs a positive answer through the terminal and sends it to the server from the terminal. For example, the user provides an answer such as "That experience helped me develop perseverance." The input of this step is the user's positive answer, and the output is the positive answer sent from the terminal to the server.

[0202] Step 10:

[0203] The server integrates all episodes and responses to generate the final story, building a coherent narrative that highlights the user's growth and learning. The input for this step is all episodes and responses, and the output is the final story.

[0204] Step 11:

[0205] The server stores the final story, allowing users to access and review it via their devices, and also generates a link to download it in PDF format if desired. The input to this step is the final story, and the output is the stored story and a download link.

[0206] As a concrete example, a personal shopping assistant in a brick-and-mortar store might show a list of recommended products based on past shopping experiences and individual preferences via smart glasses or a smartphone when the user enters the store, using, for example, the following prompt:

[0207] "Generate stories based on user experiences. Keywords: math teacher, influenced, fun lessons."

[0208] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0209] The embodiments for carrying out the present invention will be described in detail below.

[0210] Users access the system using their own terminals. Once accessed, an interface for entering basic information (such as name and date of birth) is displayed on the terminal. The user enters the basic information, and the terminal sends the information to the server.

[0211] Based on the received basic information, the server generates a list of questions suited to the user. This list of questions is related to the user's past experiences and interests. The generated list of questions is sent to the terminal, which then presents it to the user. For example, the user might be asked a question such as, "Who was the teacher who had the greatest influence on you when you were a student?"

[0212] The user inputs answers to questions posed through the device. For example, they input a specific answer such as, "Professor Tanaka had the greatest influence on me." The device collects these answers and sends them to the server in real time.

[0213] The server stores the received responses in a database and uses NLP (natural language processing) technology to extract keywords and important episodes. For example, keywords such as "Mr. Tanaka," "influenced me," and "I enjoyed the math class" are extracted. The server is also equipped with an emotion engine that recognizes the user's emotions when analyzing the collected responses. This emotion recognition makes it possible to grasp the emotional tone and nuances of the responses.

[0214] Next, the server constructs part of a story based on the extracted keywords, episodes, and the user's emotions. Specifically, it generates a sentence about "My encounter with Professor Tanaka and its impact" and presents it to the user. The user can review this intermediate result through their device and provide corrections or additional information as necessary. The device then collects feedback from the user and sends it back to the server.

[0215] For negative memories, the server generates new questions and executes a process to convert them into a positive perspective. This process utilizes the emotion engine again to present appropriate questions based on the user's emotions. For example, a question such as "How do you think your painful experiences have helped you today?" is generated. The user enters a positive answer such as "My painful experiences have made me more patient," which is then collected by the device and sent to the server.

[0216] Finally, the server integrates all episodes, responses, and emotional information recognized by the emotion engine to generate a coherent story. This story emphasizes the user's growth and learning. The final version of the story is sent from the server to the user's device for review. The user can review this final version of the story and, if there are no problems, request to save it. The server saves the final version of the story in a database for the user to access in the future. If desired, a link to download it in PDF format is also generated.

[0217] As a concrete example, consider the case where a user makes a request to the system saying, "I want to talk about my memories from my school days." The user enters basic information and responds, "Professor Tanaka was the person who had the greatest influence on me." The server then analyzes "my meeting with Professor Tanaka and the impact he had on me" and constructs it as part of a story. Furthermore, in response to the question, "How has your difficult experience helped you today?" the user responds, "My difficult experience taught me perseverance," which is also integrated into the story. The emotion engine recognizes the user's positive emotions and emphasizes feedback and episodes accordingly.

[0218] In this way, the present invention provides a system that generates stories that allow users to look back on their own lives and positively perceive their self-growth and learning. By combining it with an emotion engine, it has the effect of gaining deeper understanding and empathy.

[0219] The processing flow will be explained below.

[0220] Step 1:

[0221] User: Accesses the system using a terminal and enters basic information such as name and date of birth into the interface.

[0222] Step 2:

[0223] Terminal: Sends the entered basic information to the server.

[0224] Step 3:

[0225] Server: Generates a list of questions appropriate for the user based on the received basic information.

[0226] Step 4:

[0227] Server: Sends the generated question list to the terminal.

[0228] Step 5:

[0229] Terminal: Presents the list of questions to the user.

[0230] Step 6:

[0231] User: Enters answers to the questions presented.

[0232] Step 7:

[0233] Terminal: Collects user responses and sends them to the server in real time.

[0234] Step 8:

[0235] Server: Stores the received answers in a database and extracts keywords and episodes using NLP (natural language processing) technology.

[0236] Step 9:

[0237] Server: Uses an emotion engine to recognize the user's emotions from the collected answers. For example, text analysis determines whether the user has a positive emotion such as "It was fun."

[0238] Step 10:

[0239] Server: Constructs parts of a story based on extracted keywords and episodes, and recognized user emotions.

[0240] Step 11:

[0241] Server: Sends the intermediate products of the constructed story to the terminal.

[0242] Step 12:

[0243] Terminal: Presents the intermediate product to the user.

[0244] Step 13:

[0245] User: Review the intermediate results and provide corrections or additional information as needed.

[0246] Step 14:

[0247] Terminal: Collects feedback from users and sends it to the server.

[0248] Step 15:

[0249] Server: Uses the emotion engine to generate new questions to turn negative episodes into positive ones, such as, "What did you learn from that painful experience?"

[0250] Step 16:

[0251] Server: Sends the generated question to the terminal.

[0252] Step 17:

[0253] Terminal: Presents the new question to the user.

[0254] Step 18:

[0255] User: Enter an answer to a new question.

[0256] Step 19:

[0257] Terminal: Collects the user's answers and sends them to the server.

[0258] Step 20:

[0259] Server: Uses an emotion engine to analyze the positive responses entered by users and integrate them into the story.

[0260] Step 21:

[0261] Server: Integrates all episodes, responses, and recognized emotional information to generate a coherent story.

[0262] Step 22:

[0263] Server: Sends the final story to the device.

[0264] Step 23:

[0265] Terminal: Presents the final story to the user.

[0266] Step 24:

[0267] User: Review the final version of the story and, if there are no problems, request to save it.

[0268] Step 25:

[0269] Server: Stores the final story in a database for future access by users.

[0270] Step 26:

[0271] Server: If desired, generate a link to download the PDF.

[0272] Example 2

[0273] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0274] Previously, story generation systems based on users' life experiences had a linear process from inputting information to generating a story, which meant that they were unable to fully reflect the user's emotions and individual experiences. Furthermore, they lacked the ability to appropriately respond to negative memories and complex analysis to maintain the consistency of the story.

[0275] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting basic information from a user, a means for generating questions based on the input basic information, a means for presenting the generated questions to the user, a means for collecting user answers, a means for analyzing the collected answers and extracting keywords and topics, a means for constructing a story based on the extracted keywords and topics, a means for generating questions to convert the user's negative memories into a positive perspective, a means for integrating all topics and generating a story, a means for saving the final version of the story, a means for analyzing answers using NLP technology, and a means including an emotion engine for recognizing and analyzing the user's emotions. This makes it possible to generate a story that reflects the user's emotions and individual experiences in detail and provide a consistent story.

[0276] A "user" is an individual who uses the system to enter basic information and answers to questions, providing the data necessary to generate a story.

[0277] "Basic information" refers to basic personal information such as the user's name, date of birth, and email address.

[0278] A "question" is a question that is generated based on the user's basic information and presented to the user.

[0279] An "answer" is information that a user enters in response to a question.

[0280] "Collection" refers to the process by which a terminal or server compiles information entered by a user.

[0281] "Analysis" refers to the process in which the server analyzes the collected responses and extracts keywords and topics.

[0282] "Keywords" are important words or phrases extracted from the user's responses.

[0283] "Topics" refer to important episodes or themes extracted from users' responses.

[0284] A "story" is a series of sentences or descriptions constructed based on extracted keywords and topics.

[0285] "Negative memories" are memories associated with unpleasant events or emotions experienced by the user.

[0286] A "positive perspective" is a perspective that reevaluates negative memories from a positive perspective.

[0287] "Synthesis" refers to the process by which the server combines multiple episodes or topics into a single coherent story.

[0288] The "final story" is the story in its completed form, incorporating all episodes and feedback.

[0289] "Preservation" refers to the process of recording the final story in a database and making it available for reuse as needed.

[0290] "NLP technology" refers to natural language processing technology, which is used to analyze user responses and extract keywords and topics.

[0291] An "emotion engine" is a technology that recognizes emotions in the analysis of collected responses and assists in generating stories based on those emotions.

[0292] MODE FOR CARRYING OUT THE INVENTION

[0293] The present invention is a story generation system based on a user's life experiences, and aims to generate a story that reflects the user's emotions and individual experiences from a positive perspective. This system uses various hardware and software to collect and analyze information provided by the user and generate a story that takes emotions into consideration based on that information. Specific embodiments for implementing the present invention will be described below.

[0294] Hardware Configuration

[0295] User's device: A device that can connect to the internet, such as a PC, smartphone, or tablet.

[0296] Server: A high-performance server equipped with a database, natural language processing (NLP) technology, and an emotion engine

[0297] Software Configuration

[0298] Web interface: Provides an interface for users to enter basic information and answers.

[0299] Database: A database such as MongoDB that stores user information and responses.

[0300] NLP technology: Software such as SpaCy and NLTK that extracts keywords and topics from user responses

[0301] Emotion engine: An engine that recognizes and analyzes user emotions, such as IBM Watson (registered trademark) or Azure (registered trademark) Cognitive Services

[0302] System processing overview

[0303] When a user accesses the system using their own terminal, an interface for entering basic information is displayed on the terminal. The user enters basic information (such as name and date of birth), and the terminal sends this information to the server. Based on the received basic information, the server generates a list of questions appropriate for the user. This list of questions is related to the user's past experiences and interests. The generated list of questions is sent to the terminal and presented to the user through the terminal.

[0304] The user enters answers to the questions posed, and the device collects and sends these answers to the server. The server stores the received answers in a database and uses NLP technology to analyze the answers and extract keywords and topics. For example, keywords such as "Mr. Tanaka," "I was influenced by him," and "I enjoyed the math class" are extracted. The emotion engine also recognizes and analyzes the user's emotions and generates a story based on those emotions.

[0305] The server then constructs a part of the story based on the extracted keywords, topics, and the user's emotions and presents it to the user. When the user provides feedback, the device collects it and sends it back to the server, which then modifies and reconstructs the story based on the feedback.

[0306] Even for negative memories, the server generates new questions and executes a process to convert them into a positive perspective. For example, a question might be posed: "How do you think your painful experiences have helped you today?" The user can then input a positive answer, such as "My painful experiences have made me more resilient," which is then collected by the device and sent to the server.

[0307] Finally, the server integrates all episodes, responses, and emotional information to generate a coherent story. The final story is presented to the user via their device, and if the user is satisfied with the content, the server stores the story in its database. If desired, a download link in PDF format is also provided.

[0308] Specific examples

[0309] As a concrete example, consider the case where a user makes a request to the system saying, "I want to talk about my memories from my school days." The user enters basic information and responds, "Professor Tanaka was the person who had the greatest influence on me." The server then analyzes "my meeting with Professor Tanaka and the impact he had on me" and constructs it as part of a story. Furthermore, in response to the question, "How has your difficult experience helped you today?" the user responds, "My difficult experience taught me perseverance," which is also integrated into the story. The emotion engine recognizes the user's positive emotions and emphasizes feedback and episodes accordingly.

[0310] An example of this prompt is:

[0311] Example prompt:

[0312] Please enter basic information such as your name and date of birth.

[0313] Who was the teacher who had the greatest influence on you when you were a student?

[0314] "How do you think your painful experiences have helped you become who you are today?"

[0315] In this way, the present invention provides a system that generates stories that allow users to look back on their own lives and positively perceive their self-growth and learning. By combining an emotion engine with NLP technology, it has the effect of gaining deeper understanding and empathy.

[0316] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0317] Program processing flow and detailed explanation of each processing step

[0318] Step 1:

[0319] User access and basic information entry

[0320] Input: The user enters the URL when accessing.

[0321] How it works: A user opens a browser and enters the system's URL to access the system. The terminal displays an interface for entering the user's basic information (name, date of birth, etc.).

[0322] Data processing: The user enters basic information into the form and clicks the submit button.

[0323] Output: The basic information entered is formed and sent from the terminal to the server.

[0324] Step 2:

[0325] Submit basic information and generate a questionnaire

[0326] Input: The device sends the user's basic information to the server.

[0327] How it works: When you press the submit button on the form, the device sends an HTTP POST request to the server, which receives the request, stores the information in a database, and creates a user profile.

[0328] Data processing: The server analyzes the received basic information and generates a list of relevant questions.

[0329] Output: The list of questions is sent from the server to the terminal.

[0330] Step 3:

[0331] Present a list of questions and enter answers

[0332] Input: A list of questions is presented to the user via a terminal.

[0333] How it works: The device displays a list of questions received from the server to the user, such as "Who was your teacher who had the greatest influence on you during your school days?"

[0334] Data processing: The user enters answers to questions.

[0335] Output: The user's answer is entered into the terminal.

[0336] Step 4:

[0337] Submit and analyze responses

[0338] Input: The user's answer is sent from the device to the server.

[0339] How it works: The device sends the answers entered by the user to the server as an HTTP POST request. The server receives the answers and stores them in a database.

[0340] Data processing: The server applies NLP models to the stored answers to extract keywords and topics.

[0341] Output: Keywords and topics are generated on the server.

[0342] Step 5:

[0343] Story generation and presentation to users

[0344] Input: Extracted keywords and topics are available on the server.

[0345] Operation: The server generates a story about "Meeting with Professor Tanaka and its influence" and sends it to the device.

[0346] Data processing: The server uses a generative AI model to create a story based on extracted keywords, topics, and emotional information.

[0347] Output: A portion of the story is generated and sent to the device.

[0348] Step 6:

[0349] User feedback and rebuild

[0350] Input: User feedback is entered into the terminal.

[0351] What it does: The terminal displays an input field that says "Please enter additional information." The user enters the additional information: "Mr. Tanaka taught math classes."

[0352] Data processing: The device sends the input feedback to the server.

[0353] Output: Feedback is sent to the server and used to reconstruct the story.

[0354] Step 7:

[0355] Dealing with negative memories

[0356] Input: The user's negative answer is available on the server.

[0357] How it works: The server uses the emotion engine to generate a question: "How has your painful experience helped you today?" and sends it to the device.

[0358] Data manipulation: The user enters a positive answer to a new question.

[0359] Output: A positive response is sent from the device to the server.

[0360] Step 8:

[0361] Generate and save the final story

[0362] Input: All episodes, responses, and emotional information are integrated into the server.

[0363] How it works: The server uses that information to generate a final story that emphasizes the user's growth and learning.

[0364] Data processing: The server organizes the data collected at each step and generates a coherent narrative.

[0365] Output: The final story is generated and sent to the device for the user to review, after which it is stored in a database and a download link in PDF format is provided if desired.

[0366] (Application example 2)

[0367] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0368] Conventional story generation systems have difficulty effectively generating personalized stories based on a user's personal information and emotions. Furthermore, they lack the functionality to easily share the generated stories or output prompts. Therefore, a system that allows users to share their experiences in a rich way is needed.

[0369] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting basic information from a user, means for generating questions based on the input basic information, means for presenting the generated questions to the user, means for collecting user answers, means for analyzing the collected answers and extracting keywords and episodes, means for constructing a story based on the extracted keywords and episodes, means for generating questions to convert the user's negative memories into a positive perspective, means for integrating all episodes and generating a story, means for saving the final version of the story, means for the user to view and share the generated story on social media, and means for outputting the generated story as a prompt sentence. This makes it possible to generate and share personalized stories based on the user's individual experiences.

[0370] "Means for users to input basic information" refers to a mechanism that provides an interface for users to input personal information such as their name and date of birth.

[0371] The "means for generating questions" is a system that automatically generates questions appropriate for the user based on the basic information entered.

[0372] The "means for presenting a question to a user" is a function for displaying the generated question on the user's terminal so that the user can confirm it.

[0373] "Means for collecting user answers" refers to the process in which a user inputs answers to questions presented to them and the system collects those answers.

[0374] "Means for analyzing responses and extracting keywords and episodes" refers to a method for analyzing collected responses using natural language processing technology and extracting important keywords and episodes.

[0375] The "means of constructing a story" is a mechanism for generating a coherent story based on extracted keywords and episodes.

[0376] The "means for generating questions to convert a user's negative memories into positive ones" is a system that automatically creates appropriate questions to convert a user's negative memories into positive ones.

[0377] "Means of integrating all episodes and generating a narrative" refers to the process of integrating various episodes into a single story and ultimately completing a coherent narrative.

[0378] The "means for storing the final story" is the mechanism by which the final story, as reviewed and revised by the user, is stored in the database.

[0379] "Means for users to view stories and share them on social media" refers to a feature that allows users to view the generated stories within the application and share them on social media platforms.

[0380] "Means for outputting a story as a prompt sentence" is a mechanism for outputting the generated story in the form of a prompt sentence to be input into a generative AI model.

[0381] The present invention relates to a system that allows users to create and share personalized stories based on their personal experiences and emotions. The system includes a series of processes that collect basic information from users, present the users with customized questions based on the information, and analyze the users' answers to generate a story.

[0382] The entire system is mainly composed of a server, a user terminal, and a network environment. Each step will be explained in detail below.

[0383] Enter and collect basic information

[0384] First, the user enters basic information using a device. This basic information includes personal information such as name and date of birth. The user provides this information through an input interface on the user device and sends it to the server. The user can use a mobile device such as a smartphone or tablet to do this.

[0385] Question generation and presentation

[0386] Based on the received basic information, the server generates a list of questions suited to the user. This list of questions is related to the user's past experiences and interests. The generated list of questions is then sent back to the terminal and presented to the user. The user then enters answers to the questions.

[0387] Response collection and analysis

[0388] The user's answers are then sent in real time from the device to a server, which analyzes the collected answers and extracts important keywords and anecdotes using natural language processing (NLP) techniques. For example, the NLP model can use the Hugging Face Transformers library.

[0389] Story Building

[0390] The server constructs part of a story based on the extracted keywords and episodes. Furthermore, if negative memories are found in the user's answers, it generates additional questions to convert them into a positive perspective. This process uses an emotion engine to accurately recognize and analyze the user's emotions.

[0391] Creating and saving the final story

[0392] All episodes are integrated into a single coherent story. Users can review this final version of the story through their devices and provide corrections or additional information as needed. The final version of the story is stored on the server, allowing users to access it in the future.

[0393] Sharing and publishing stories

[0394] The generated stories can be viewed and reviewed by users, and can also be shared on social media.The system also has a function to output the stories as prompts to be input into generative AI models.

[0395] Specific examples

[0396] For example, if a user requests the system to "talk about memories from my school days," the user might respond by saying, "Professor Tanaka was the person who had the greatest influence on me." The server then analyzes "my meeting with Professor Tanaka and the impact he had on me" and constructs it as part of a story. Furthermore, in response to the question, "How have your difficult experiences helped you today?" the user might respond, "My difficult experiences have given me perseverance," and this would also be integrated into the story.

[0397] This system allows users to generate and share personalized stories based on their individual experiences. The generated stories are also output as prompts, such as:

[0398] A user requested to talk about his memories from his school days. He answered that Professor Tanaka had the greatest influence on him, and that his difficult experiences had given him perseverance.

[0399] This paragraph will be used as a prompt when inputting the user's story into the generative AI model.

[0400] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0401] Step 1:

[0402] A user accesses the system using their own terminal and enters basic information, such as name and date of birth. The terminal sends this information to the server. Input is done using a text field, and transmission is via an HTTP POST request. The output is the transfer of the basic information to the server.

[0403] Step 2:

[0404] The server generates a list of questions appropriate for the user based on the received basic information. During this process, the basic information is analyzed and questions related to the user's interests and past experiences are selected. Data processing involves querying the database for relevant questions and organizing them in a list format. The output is the generated list of questions.

[0405] Step 3:

[0406] The server sends the generated question list to the user's terminal. The user terminal displays an interface to present the question list to the user. This interface displays each question as text and provides a text box for entering an answer. The output is a question list displayed on the user's terminal.

[0407] Step 4:

[0408] The user inputs answers to the questions posed, and the terminal collects these answers. The collected answers are sent to the server in real time. The input is the user's text input, and the output is the transfer of the answer data to the server.

[0409] Step 5:

[0410] The server analyzes the received answers and extracts important keywords and episodes. This analysis uses natural language processing (NLP) techniques. Specifically, it processes the answer data using Hugging Face's Transformers library to perform entity recognition and sentiment analysis. The input is the user's answer data, and the output is the extracted keywords and episodes.

[0411] Step 6:

[0412] The server constructs part of a story based on the extracted keywords and episodes. If negative memories are detected during this process, it generates follow-up questions to convert them into positive perspectives. The output is an initial story proposal and, if necessary, follow-up questions.

[0413] Step 7:

[0414] The server sends the generated story idea to the user's device, where the user can review and modify it. Any additional information or modifications provided by the user are also sent to the server in real time. The input is the story idea sent from the server, and the output is the user's feedback.

[0415] Step 8:

[0416] The server generates the final version of the story based on user feedback. This involves data processing to integrate all episodes into a single coherent story. The generated story is stored in a database. The input is user feedback and the output is the final version of the story.

[0417] Step 9:

[0418] The server sends the final story to the user's device for viewing and sharing. Users can share the generated story on social media. The story is also output in the form of a prompt, which is used as input to the generative AI model. The input is the final story, and the output is the story share and prompt.

[0419] 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.

[0420] 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> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0421] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0422] [Second embodiment]

[0423] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0424] 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.

[0425] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0426] 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.

[0427] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0428] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0429] 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.

[0430] 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.

[0431] 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 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.

[0432] 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.

[0433] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0434] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0435] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The embodiments for carrying out the present invention will be described in detail below.

[0436] First, the user accesses the system using their own terminal. After accessing, an interface for entering basic information (such as name and date of birth) is displayed on the terminal. The user enters this basic information, and the terminal sends it to the server.

[0437] The server generates an appropriate list of questions based on the received basic information. This list of questions may relate to themes such as the user's memories from school, occupation, and hobbies. The generated questions are sent to the terminal, which then presents them to the user. The first question displayed may be, "Who was the teacher who had the greatest influence on you during your school days?"

[0438] The user inputs answers to the questions through the device. For example, they input a specific answer such as, "Professor Tanaka had the greatest influence on me." The device collects these answers and sends them to the server in real time.

[0439] The server stores the collected responses in a database and uses NLP (natural language processing) technology to extract keywords and important episodes. For example, keywords such as "Professor Tanaka," "I was influenced by him," and "I enjoyed the math class" can be extracted from the user's responses.

[0440] The server then constructs part of a story based on these keywords and episodes. Specifically, it generates a sentence about "My encounter with Professor Tanaka and its influence" and presents it to the user. The user can review this intermediate result through their device and provide corrections or additional information as needed. The device then collects feedback from the user and sends that information back to the server.

[0441] For negative memories, the server generates new questions and executes a process to convert them into a positive perspective. For example, a question such as, "How do you think your painful experiences have helped you today?" is generated. The user inputs a positive answer such as, "My painful experiences have made me more resilient," which is then collected by the device and sent to the server.

[0442] Finally, the server combines all episodes and responses into a coherent story that highlights the user's growth and learning. The final story is sent from the server to the device for review by the user. The user can review the final story and, if there are no problems, request that it be saved. The server saves the final story in its database for future access by the user. A link to download it as a PDF is also generated, if desired.

[0443] As a concrete example, consider the case where a user requests the system to "talk about memories from their school days." The user enters basic information and responds, "Professor Tanaka was the person who had the greatest influence on me." The server then analyzes "your encounter with Professor Tanaka and the impact he had on you" and constructs it as part of a story. Furthermore, in response to the question, "How have your difficult experiences helped you today?" the user responds, "My difficult experiences have given me perseverance," which is also integrated into the story.

[0444] In this way, the present invention provides a system for generating a story that allows users to look back on their own lives and view their personal growth and learning in a positive light.

[0445] The processing flow will be explained below.

[0446] Step 1:

[0447] User: Accesses the system using a terminal and enters basic information (such as name and date of birth) into the interface.

[0448] Step 2:

[0449] Terminal: Sends the entered basic information to the server.

[0450] Step 3:

[0451] Server: Generates a list of questions appropriate for the user based on the received basic information.

[0452] Step 4:

[0453] Server: Sends the generated question list to the terminal.

[0454] Step 5:

[0455] Terminal: Presents the list of questions to the user.

[0456] Step 6:

[0457] User: Enters answers to the questions presented.

[0458] Step 7:

[0459] Terminal: Collects user responses and sends them to the server in real time.

[0460] Step 8:

[0461] Server: Stores the received answers in a database and extracts keywords and episodes using NLP (natural language processing) technology.

[0462] Step 9:

[0463] Server: Constructs parts of a story based on extracted keywords and episodes.

[0464] Step 10:

[0465] Server: Sends the intermediate products of the constructed story to the terminal.

[0466] Step 11:

[0467] Terminal: Presents the intermediate product to the user.

[0468] Step 12:

[0469] User: Review the intermediate results and provide corrections or additional information as needed.

[0470] Step 13:

[0471] Terminal: Collects feedback from users and sends it to the server.

[0472] Step 14:

[0473] Server: Generates new questions for negative memories.

[0474] Step 15:

[0475] Server: Sends the generated question to the terminal.

[0476] Step 16:

[0477] Terminal: Presents the new question to the user.

[0478] Step 17:

[0479] User: Enter an answer to a new question.

[0480] Step 18:

[0481] Terminal: Collects the user's answers and sends them to the server.

[0482] Step 19:

[0483] Server: Analyze positive responses and integrate them into the narrative.

[0484] Step 20:

[0485] Server: Integrates all episodes and responses and generates a coherent story.

[0486] Step 21:

[0487] Server: Sends the final story to the device.

[0488] Step 22:

[0489] Terminal: Presents the final story to the user.

[0490] Step 23:

[0491] User: Review the final version of the story and, if there are no problems, request to save it.

[0492] Step 24:

[0493] Server: Stores the final story in a database for future access by users.

[0494] Step 25:

[0495] Server: If desired, generate a link to download the PDF.

[0496] Example 1

[0497] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0498] Conventional story generation systems have had difficulty generating a coherent story by appropriately utilizing the user's basic information and episodes. They also lacked the ability to provide a positive perspective on negative memories, making it difficult to incorporate user feedback and revise the story. Furthermore, there were limited ways for users to access and download the generated story.

[0499] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0500] In this invention, the server includes means for inputting basic information from a user, means for generating questions based on the input basic information, means for generating questions using a generative AI model, means for presenting the generated questions to the user, means for collecting user responses, means for analyzing the collected responses and extracting keywords and episodes, means for generating a story based on the extracted keywords and episodes, means for generating questions to convert the user's negative memories into a positive perspective, means for integrating all episodes and generating a story, means for regenerating the story based on feedback provided by the user, means for generating sentences based on the keywords and episodes using the generative AI model, means for accessing and downloading the generated story, and means for saving the final version of the story. This provides a system that utilizes the user's basic information and episodes to generate a consistent story, convert negative memories into positive ones, and further modifies the story based on user feedback, making it accessible and downloadable.

[0501] "Basic information" refers to information related to the user's profile, such as the user's name, date of birth, occupation, and hobbies.

[0502] A "question" is an inquiry related to a specific topic that is generated by the server based on the user's basic information.

[0503] A "generative AI model" refers to an algorithm that generates text using natural language processing technology, and includes, for example, GPT-4.

[0504] "User's response" is the reply entered by the user in response to the question posed by the server.

[0505] "Keywords" are important words or phrases extracted from the user's answers.

[0506] An "episode" is a detail of a particular experience or event extracted from a user's responses.

[0507] A "story" is a coherent written story generated based on the user's basic information, keywords, and episodes.

[0508] "Negative memories" refer to unpleasant events or emotions that a user has experienced in the past.

[0509] A "positive perspective" is a way of finding a positive interpretation or impact on negative memories.

[0510] "Feedback" refers to corrections or additional information that users provide to the generated story.

[0511] "Regeneration" is the process of regenerating an existing story based on user feedback.

[0512] The present invention provides a system that allows users to have a positive perspective on their lives and generate a coherent narrative. This system operates in cooperation with users, terminals, and a server.

[0513] Hardware and software configuration

[0514] User terminal: A device such as a PC, smartphone, or tablet is used to access the system through browser software (e.g., Google (registered trademark) Chrome, Safari).

[0515] Server: Built using Python frameworks (Django, Flask). Databases use MySQL or PostgreSQL, and libraries such as NLTK and Spacy are used for natural language processing. GPT-4 is used as the generative AI model.

[0516] System Operation

[0517] First, the user accesses the system using a browser. The terminal displays an online interface and prompts the user to enter basic information (such as name, date of birth, etc.). After the user enters the required information, the terminal sends the information to the server in JSON format.

[0518] Next, the server uses a Python algorithm to analyze the user's basic information. Based on that, it automatically generates a list of questions appropriate to the user's profile. For example, questions related to "memories of school days," "occupation," and "hobbies" are generated. The server then sends this list of questions to the terminal in JSON format.

[0519] The device displays the received question to the user. The user enters an answer to the question (e.g., "Who was the teacher who had the greatest influence on you during your school days?" and answers "Professor Tanaka was the most influential"), which the device then sends to the server in real time.

[0520] The server stores the answers in a database and then uses natural language processing technology to extract keywords and important anecdotes from the answers. Based on the extracted information, a generative AI model (GPT-4) is used to generate parts of a story. The generated text is then presented to the user via their device.

[0521] The user checks the story generated on the device and enters corrections or additional information as necessary. The device then sends the user's feedback to the server and regenerates the story.

[0522] For negative memories, the server generates a new question (e.g., "How do you think your painful experiences have helped you today?") and executes a process to convert the memory into a positive perspective. The user inputs an answer such as "My painful experiences have made me more resilient," and the device sends this answer to the server.

[0523] Finally, the server integrates all episodes and responses into a single, coherent story that emphasizes the user's growth and learning. The user can review the final version of the story on their device and, if there are no problems, request that it be saved. The server saves the final version of the story in its database and, if desired, generates a download link in PDF format.

[0524] Examples of concrete examples and prompts

[0525] As a concrete example, consider the case where a user requests the system to "talk about memories from their school days." The user enters basic information and responds, "Professor Tanaka was the person who had the greatest influence on me." The server then analyzes "my encounter with Professor Tanaka and the impact he had on me" and constructs it as part of a story. Furthermore, in response to the question, "How have your difficult experiences helped you today?" the user responds, "My difficult experiences have given me perseverance," and this is also integrated into the story.

[0526] An example of a prompt is:

[0527] "Tell us about the teacher who had the greatest influence on you as a student. How did they influence you?"

[0528] "How have your painful experiences helped you become who you are today?"

[0529] Examples include:

[0530] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0531] Step 1:

[0532] A user accesses the system using their own terminal. When the user opens a browser and accesses the specified URL, the system's web page is displayed. The terminal provides the user with an interface for entering basic information. This information includes name, date of birth, occupation, hobbies, etc. When the user enters the basic information and clicks the send button, the terminal sends the information to the server in JSON format (input: basic information, output: transmitted data).

[0533] Step 2:

[0534] The server analyzes the basic information received from the user. Specifically, it uses a Python algorithm to analyze the information and create a user profile (input: basic information, output: user profile). The server then uses a generative AI model to generate a customized list of questions based on the user profile (input: user profile, output: list of questions). The generated questions are based on a theme, such as "memories from school" or "hobbies."

[0535] Step 3:

[0536] The server sends the generated question list to the terminal in JSON format (input: question list, output: transmitted data). The terminal displays the received question list to the user. The user enters answers to the displayed questions. For example, in response to the question "Who was the teacher who had the greatest influence on you during your student days?" the user might answer "Professor Tanaka had the greatest influence on me." When the user enters the answer and clicks the send button, the terminal collects the answer and transmits it to the server in real time (input: answer, output: transmitted data).

[0537] Step 4:

[0538] The server receives the user's answers and stores them in a database, typically a MySQL or PostgreSQL database (input: answers, output: saved data). The server then uses natural language processing techniques to analyze the saved answers. It uses libraries such as Python's NLTK and Spacy to extract important keywords and episodes from the answers (input: answer data, output: keywords, episodes).

[0539] Step 5:

[0540] The server generates part of a story based on the extracted keywords and episodes. Using a generative AI model (e.g., GPT-4), it generates part of a story about "Meeting with Professor Tanaka and its impact" (input: keywords, episodes, output: generated part of the story). The generated text is sent to the device.

[0541] Step 6:

[0542] The device displays part of the generated story to the user. The user checks the content and enters corrections or additional information as needed. For example, the user may add details such as "Professor Tanaka's class was particularly interesting in math" (Input: User feedback, Output: Corrected data). The device collects the user's feedback and sends it back to the server (Input: Corrected data, Output: Sent data).

[0543] Step 7:

[0544] The server generates a new question to convert the user's negative memories into a positive one. For example, it generates a question such as, "How do you think your painful experiences have helped you today?" (Input: User's negative answer, Output: New question). The user responds to this question by saying, "My painful experiences have given me more patience," and the device collects this answer and sends it to the server (Input: Positive answer, Output: Transmitted data).

[0545] Step 8:

[0546] The server integrates all episodes and responses to generate a coherent story. It uses a generative AI model to generate text that highlights the user's growth and learning (input: episodes and responses, output: final story). The final story is sent to the device for the user to review.

[0547] Step 9:

[0548] The terminal displays the final version of the story to the user. The user checks the final version of the story and, if there are no problems, requests that it be saved. The server saves the final version of the story in a database so that the user can access it in the future (input: final confirmed data, output: saved data). In addition, the server generates a download link in PDF format if desired (input: saved data, output: download link).

[0549] (Application example 1)

[0550] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0551] In recent years, there has been a demand for improving customer experiences in brick-and-mortar stores, but it is difficult to provide personalized product recommendations and services to each individual customer. Furthermore, technologies for effectively leveraging customers' past experiences and preferences to provide specific and engaging customer experiences are still in their infancy. Furthermore, there is a need for flexible systems that can adapt based on customer feedback.

[0552] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0553] In this invention, the server includes means for inputting basic information from a user, means for generating questions based on the input basic information, means for presenting the generated questions to the user, means for collecting user responses, means for analyzing the collected responses and extracting keywords and episodes, means for constructing a story based on the extracted keywords and episodes, means for generating questions to convert the user's negative memories into a positive perspective, means for integrating all episodes and generating a story, means for saving the final version of the story, means for analyzing the user's preferences and past experiences from the collected responses and generating a personalized recommended product list, and means for presenting the generated recommended product list. This makes it possible to significantly improve customer experience by providing personalized recommended product lists in physical stores.

[0554] "Basic information" is information for identifying an individual, such as the user's name, date of birth, gender, occupation, and hobbies.

[0555] "Means for generating questions" refers to technology that automatically creates a list of appropriate questions based on the user's basic information and past answers.

[0556] A "means for presenting a question" is an interface or device that displays the generated question to the user.

[0557] The "means for collecting answers" refers to a technique for collecting answers entered by users as data.

[0558] "Means of analyzing and extracting keywords and episodes" refers to a technique that uses natural language processing technology to identify important words and events from the collected responses.

[0559] "Means of constructing a story" refers to the technique of creating a coherent story based on extracted keywords and episodes.

[0560] The "means for generating questions to convert a user's negative memories into a positive perspective" is a technique for creating additional questions to help the user view negative memories in a positive light.

[0561] "Narrative generation" is the technique of integrating all collected episodes to create a final story.

[0562] The "means for storing the final story" is a technique for storing the generated story in a database so that users can access it later.

[0563] The "means for generating an individualized recommended product list" is a technology for creating a product list optimized for a specific user based on the user's preferences and past experiences.

[0564] The "means for presenting a recommended product list" refers to an interface or device that displays the generated recommended product list to the user.

[0565] The process of entering basic information from the user is carried out using a device such as a smartphone or PC. After the user accesses the system, an interface for entering basic information such as name, date of birth, gender, occupation, and hobbies is displayed on the device. Once the user enters the basic information, the device sends the information to the server.

[0566] The server generates an appropriate list of questions based on the received basic information. The questions may relate to themes such as the user's memories from their school days, their occupation, or their hobbies. The generated questions are then sent back to the device, which then presents them to the user. An example of the initial question displayed is, "Who was the teacher who had the greatest influence on you during your school days?"

[0567] The user inputs answers to the questions through the device, such as "My math teacher had the biggest impact on me," and the device sends the answers to the server in real time.

[0568] The server analyzes the collected responses and uses natural language processing (NLP) technology to extract keywords and important anecdotes. For example, keywords such as "math teacher," "influenced me," and "fun classes" can be extracted from user responses.

[0569] The server then constructs part of a story based on these keywords and episodes. For example, it generates a sentence about "My encounter with my math teacher and its impact" and presents it to the user. The user can check the draft of this story through their device and provide corrections or additional information as needed. User feedback is then sent back to the server via their device.

[0570] For negative memories, the server generates new questions and executes a process to transform the user's negative experiences into positive ones. For example, a question such as "How has your painful experience helped you?" is generated. The user can then respond with "That experience helped me develop perseverance," thereby integrating the positive episode into the story.

[0571] Finally, the server combines all episodes and responses to generate a coherent story. This story highlights the user's growth and learning, and is then edited into a final version. The user can review the final version of the story through their device and, if satisfied, request that it be saved. The server saves this final version of the story in a database for future access by the user. A link is also provided to download the story in PDF format if desired.

[0572] As an application example, we will explain the case where the present invention is applied to a "personal shopping assistant" application in a physical store. When a user visiting a store accesses the system using smart glasses or a smartphone, a personalized list of recommended products is displayed based on the user's past shopping experiences and answers. This allows the user to enjoy shopping according to their preferences.

[0573] As an example of how to use a generative AI model, we will use the following prompt:

[0574] "Generate a story based on the user's experiences. Keywords: math teacher, impact, fun class"

[0575] In this way, the present invention allows users to generate personalized stories and recommended product lists based on their experiences, enhancing their experience in brick-and-mortar stores and other settings.

[0576] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0577] Step 1:

[0578] A user accesses the system through a terminal and inputs basic information (such as name, date of birth, gender, occupation, hobbies, etc.). The input basic information is sent from the terminal to the server. The input of this step is the basic information entered by the user, and the output is the basic information sent from the terminal to the server.

[0579] Step 2:

[0580] The server generates an appropriate list of questions based on the received basic information. Here, the server analyzes the basic information and automatically generates questions related to the user's memories from school, occupation, hobbies, etc. The input for this step is the basic information, and the output is the generated list of questions.

[0581] Step 3:

[0582] The generated question list is sent to the terminal, which then presents it to the user. The user confirms the questions on the terminal. The input of this step is the question list sent from the server, and the output is the questions presented to the user.

[0583] Step 4:

[0584] The user inputs an answer to the question through the terminal. For example, they input a specific answer such as "My math teacher had the biggest impact on me." The input of this step is the user's answer, and the output is the answer sent from the terminal to the server.

[0585] Step 5:

[0586] The server analyzes the collected answers and extracts keywords and important episodes using natural language processing (NLP) technology. For example, keywords such as "math teacher," "influenced me," and "fun class" can be extracted from the answers. The input for this step is the user's answer, and the output is the extracted keywords and episodes.

[0587] Step 6:

[0588] The server generates part of a story based on the extracted keywords and episodes. Using the generative AI model, it creates a sentence about, for example, "My encounter with my math teacher and its impact" and presents it to the user. The input for this step is keywords and episodes, and the output is the generated part of the story.

[0589] Step 7:

[0590] The user checks the generated draft of the story through the terminal and provides corrections or additional information as necessary. The user's feedback is sent back to the server through the terminal. The input of this step is the user's feedback, and the output is a part of the revised story.

[0591] Step 8:

[0592] The server generates questions to convert the user's negative answers into positive ones. For example, it provides new questions such as, "How has your painful experience helped you?" The input of this step is the negative answer, and the output is a newly generated question.

[0593] Step 9:

[0594] The user inputs a positive answer through the terminal and sends it to the server from the terminal. For example, the user provides an answer such as "That experience helped me develop perseverance." The input of this step is the user's positive answer, and the output is the positive answer sent from the terminal to the server.

[0595] Step 10:

[0596] The server integrates all episodes and responses to generate the final story, building a coherent narrative that highlights the user's growth and learning. The input for this step is all episodes and responses, and the output is the final story.

[0597] Step 11:

[0598] The server stores the final story, allowing users to access and review it via their devices, and also generates a link to download it in PDF format if desired. The input to this step is the final story, and the output is the stored story and a download link.

[0599] As a concrete example, a personal shopping assistant in a brick-and-mortar store might show a list of recommended products based on past shopping experiences and individual preferences via smart glasses or a smartphone when the user enters the store, using, for example, the following prompt:

[0600] "Generate stories based on user experiences. Keywords: math teacher, influenced, fun lessons."

[0601] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0602] The embodiments for carrying out the present invention will be described in detail below.

[0603] Users access the system using their own terminals. Once accessed, an interface for entering basic information (such as name and date of birth) is displayed on the terminal. The user enters the basic information, and the terminal sends the information to the server.

[0604] Based on the received basic information, the server generates a list of questions suited to the user. This list of questions is related to the user's past experiences and interests. The generated list of questions is sent to the terminal, which then presents it to the user. For example, the user might be asked a question such as, "Who was the teacher who had the greatest influence on you when you were a student?"

[0605] The user inputs answers to questions posed through the device. For example, they input a specific answer such as, "Professor Tanaka had the greatest influence on me." The device collects these answers and sends them to the server in real time.

[0606] The server stores the received responses in a database and uses NLP (natural language processing) technology to extract keywords and important episodes. For example, keywords such as "Mr. Tanaka," "influenced me," and "I enjoyed the math class" are extracted. The server is also equipped with an emotion engine that recognizes the user's emotions when analyzing the collected responses. This emotion recognition makes it possible to grasp the emotional tone and nuances of the responses.

[0607] Next, the server constructs part of a story based on the extracted keywords, episodes, and the user's emotions. Specifically, it generates a sentence about "My encounter with Professor Tanaka and its impact" and presents it to the user. The user can review this intermediate result through their device and provide corrections or additional information as necessary. The device then collects feedback from the user and sends it back to the server.

[0608] For negative memories, the server generates new questions and executes a process to convert them into a positive perspective. This process utilizes the emotion engine again to present appropriate questions based on the user's emotions. For example, a question such as "How do you think your painful experiences have helped you today?" is generated. The user enters a positive answer such as "My painful experiences have made me more patient," which is then collected by the device and sent to the server.

[0609] Finally, the server integrates all episodes, responses, and emotional information recognized by the emotion engine to generate a coherent story. This story emphasizes the user's growth and learning. The final version of the story is sent from the server to the user's device for review. The user can review this final version of the story and, if there are no problems, request to save it. The server saves the final version of the story in a database for the user to access in the future. If desired, a link to download it in PDF format is also generated.

[0610] As a concrete example, consider the case where a user makes a request to the system saying, "I want to talk about my memories from my school days." The user enters basic information and responds, "Professor Tanaka was the person who had the greatest influence on me." The server then analyzes "my meeting with Professor Tanaka and the impact he had on me" and constructs it as part of a story. Furthermore, in response to the question, "How has your difficult experience helped you today?" the user responds, "My difficult experience taught me perseverance," which is also integrated into the story. The emotion engine recognizes the user's positive emotions and emphasizes feedback and episodes accordingly.

[0611] In this way, the present invention provides a system that generates stories that allow users to look back on their own lives and positively perceive their self-growth and learning. By combining it with an emotion engine, it has the effect of gaining deeper understanding and empathy.

[0612] The processing flow will be explained below.

[0613] Step 1:

[0614] User: Accesses the system using a terminal and enters basic information such as name and date of birth into the interface.

[0615] Step 2:

[0616] Terminal: Sends the entered basic information to the server.

[0617] Step 3:

[0618] Server: Generates a list of questions appropriate for the user based on the received basic information.

[0619] Step 4:

[0620] Server: Sends the generated question list to the terminal.

[0621] Step 5:

[0622] Terminal: Presents the list of questions to the user.

[0623] Step 6:

[0624] User: Enters answers to the questions presented.

[0625] Step 7:

[0626] Terminal: Collects user responses and sends them to the server in real time.

[0627] Step 8:

[0628] Server: Stores the received answers in a database and extracts keywords and episodes using NLP (natural language processing) technology.

[0629] Step 9:

[0630] Server: Uses an emotion engine to recognize the user's emotions from the collected answers. For example, text analysis determines whether the user has a positive emotion such as "It was fun."

[0631] Step 10:

[0632] Server: Constructs parts of a story based on extracted keywords and episodes, and recognized user emotions.

[0633] Step 11:

[0634] Server: Sends the intermediate products of the constructed story to the terminal.

[0635] Step 12:

[0636] Terminal: Presents the intermediate product to the user.

[0637] Step 13:

[0638] User: Review the intermediate results and provide corrections or additional information as needed.

[0639] Step 14:

[0640] Terminal: Collects feedback from users and sends it to the server.

[0641] Step 15:

[0642] Server: Uses the emotion engine to generate new questions to turn negative episodes into positive ones, such as, "What did you learn from that painful experience?"

[0643] Step 16:

[0644] Server: Sends the generated question to the terminal.

[0645] Step 17:

[0646] Terminal: Presents the new question to the user.

[0647] Step 18:

[0648] User: Enter an answer to a new question.

[0649] Step 19:

[0650] Terminal: Collects the user's answers and sends them to the server.

[0651] Step 20:

[0652] Server: Uses an emotion engine to analyze the positive responses entered by users and integrate them into the story.

[0653] Step 21:

[0654] Server: Integrates all episodes, responses, and recognized emotional information to generate a coherent story.

[0655] Step 22:

[0656] Server: Sends the final story to the device.

[0657] Step 23:

[0658] Terminal: Presents the final story to the user.

[0659] Step 24:

[0660] User: Review the final version of the story and, if there are no problems, request to save it.

[0661] Step 25:

[0662] Server: Stores the final story in a database for future access by users.

[0663] Step 26:

[0664] Server: If desired, generate a link to download the PDF.

[0665] Example 2

[0666] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0667] Previously, story generation systems based on users' life experiences had a linear process from inputting information to generating a story, which meant that they were unable to fully reflect the user's emotions and individual experiences. Furthermore, they lacked the ability to appropriately respond to negative memories and complex analysis to maintain the consistency of the story.

[0668] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting basic information from a user, a means for generating questions based on the input basic information, a means for presenting the generated questions to the user, a means for collecting user answers, a means for analyzing the collected answers and extracting keywords and topics, a means for constructing a story based on the extracted keywords and topics, a means for generating questions to convert the user's negative memories into a positive perspective, a means for integrating all topics and generating a story, a means for saving the final version of the story, a means for analyzing answers using NLP technology, and a means including an emotion engine for recognizing and analyzing the user's emotions. This makes it possible to generate a story that reflects the user's emotions and individual experiences in detail and provide a consistent story.

[0669] A "user" is an individual who uses the system to enter basic information and answers to questions, providing the data necessary to generate a story.

[0670] "Basic information" refers to basic personal information such as the user's name, date of birth, and email address.

[0671] A "question" is a question that is generated based on the user's basic information and presented to the user.

[0672] An "answer" is information that a user enters in response to a question.

[0673] "Collection" refers to the process by which a terminal or server compiles information entered by a user.

[0674] "Analysis" refers to the process in which the server analyzes the collected responses and extracts keywords and topics.

[0675] "Keywords" are important words or phrases extracted from the user's responses.

[0676] "Topics" refer to important episodes or themes extracted from users' responses.

[0677] A "story" is a series of sentences or descriptions constructed based on extracted keywords and topics.

[0678] "Negative memories" are memories associated with unpleasant events or emotions experienced by the user.

[0679] A "positive perspective" is a perspective that reevaluates negative memories from a positive perspective.

[0680] "Synthesis" refers to the process by which the server combines multiple episodes or topics into a single coherent story.

[0681] The "final story" is the story in its completed form, incorporating all episodes and feedback.

[0682] "Preservation" refers to the process of recording the final story in a database and making it available for reuse as needed.

[0683] "NLP technology" refers to natural language processing technology, which is used to analyze user responses and extract keywords and topics.

[0684] An "emotion engine" is a technology that recognizes emotions in the analysis of collected responses and assists in generating stories based on those emotions.

[0685] MODE FOR CARRYING OUT THE INVENTION

[0686] The present invention is a story generation system based on a user's life experiences, and aims to generate a story that reflects the user's emotions and individual experiences from a positive perspective. This system uses various hardware and software to collect and analyze information provided by the user and generate a story that takes emotions into consideration based on that information. Specific embodiments for implementing the present invention will be described below.

[0687] Hardware Configuration

[0688] User's device: A device that can connect to the internet, such as a PC, smartphone, or tablet.

[0689] Server: A high-performance server equipped with a database, natural language processing (NLP) technology, and an emotion engine

[0690] Software Configuration

[0691] Web interface: Provides an interface for users to enter basic information and answers.

[0692] Database: A database such as MongoDB that stores user information and responses.

[0693] NLP technology: Software such as SpaCy and NLTK that extracts keywords and topics from user responses

[0694] Emotion engine: An engine that recognizes and analyzes user emotions, such as IBM Watson or Azure Cognitive Services

[0695] System processing overview

[0696] When a user accesses the system using their own terminal, an interface for entering basic information is displayed on the terminal. The user enters basic information (such as name and date of birth), and the terminal sends this information to the server. Based on the received basic information, the server generates a list of questions appropriate for the user. This list of questions is related to the user's past experiences and interests. The generated list of questions is sent to the terminal and presented to the user through the terminal.

[0697] The user enters answers to the questions posed, and the device collects and sends these answers to the server. The server stores the received answers in a database and uses NLP technology to analyze the answers and extract keywords and topics. For example, keywords such as "Mr. Tanaka," "I was influenced by him," and "I enjoyed the math class" are extracted. The emotion engine also recognizes and analyzes the user's emotions and generates a story based on those emotions.

[0698] The server then constructs a part of the story based on the extracted keywords, topics, and the user's emotions and presents it to the user. When the user provides feedback, the device collects it and sends it back to the server, which then modifies and reconstructs the story based on the feedback.

[0699] Even for negative memories, the server generates new questions and executes a process to convert them into a positive perspective. For example, a question might be posed: "How do you think your painful experiences have helped you today?" The user can then input a positive answer, such as "My painful experiences have made me more resilient," which is then collected by the device and sent to the server.

[0700] Finally, the server integrates all episodes, responses, and emotional information to generate a coherent story. The final story is presented to the user via their device, and if the user is satisfied with the content, the server stores the story in its database. If desired, a download link in PDF format is also provided.

[0701] Specific examples

[0702] As a concrete example, consider the case where a user makes a request to the system saying, "I want to talk about my memories from my school days." The user enters basic information and responds, "Professor Tanaka was the person who had the greatest influence on me." The server then analyzes "my meeting with Professor Tanaka and the impact he had on me" and constructs it as part of a story. Furthermore, in response to the question, "How has your difficult experience helped you today?" the user responds, "My difficult experience taught me perseverance," which is also integrated into the story. The emotion engine recognizes the user's positive emotions and emphasizes feedback and episodes accordingly.

[0703] An example of this prompt is:

[0704] Example prompt:

[0705] Please enter basic information such as your name and date of birth.

[0706] Who was the teacher who had the greatest influence on you when you were a student?

[0707] "How do you think your painful experiences have helped you become who you are today?"

[0708] In this way, the present invention provides a system that generates stories that allow users to look back on their own lives and positively perceive their self-growth and learning. By combining an emotion engine with NLP technology, it has the effect of gaining deeper understanding and empathy.

[0709] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0710] Program processing flow and detailed explanation of each processing step

[0711] Step 1:

[0712] User access and basic information entry

[0713] Input: The user enters the URL when accessing.

[0714] How it works: A user opens a browser and enters the system's URL to access the system. The terminal displays an interface for entering the user's basic information (name, date of birth, etc.).

[0715] Data processing: The user enters basic information into the form and clicks the submit button.

[0716] Output: The basic information entered is formed and sent from the terminal to the server.

[0717] Step 2:

[0718] Submit basic information and generate a questionnaire

[0719] Input: The device sends the user's basic information to the server.

[0720] How it works: When you press the submit button on the form, the device sends an HTTP POST request to the server, which receives the request, stores the information in a database, and creates a user profile.

[0721] Data processing: The server analyzes the received basic information and generates a list of relevant questions.

[0722] Output: The list of questions is sent from the server to the terminal.

[0723] Step 3:

[0724] Present a list of questions and enter answers

[0725] Input: A list of questions is presented to the user via a terminal.

[0726] How it works: The device displays a list of questions received from the server to the user, such as "Who was your teacher who had the greatest influence on you during your school days?"

[0727] Data processing: The user enters answers to questions.

[0728] Output: The user's answer is entered into the terminal.

[0729] Step 4:

[0730] Submit and analyze responses

[0731] Input: The user's answer is sent from the device to the server.

[0732] How it works: The device sends the answers entered by the user to the server as an HTTP POST request. The server receives the answers and stores them in a database.

[0733] Data processing: The server applies NLP models to the stored answers to extract keywords and topics.

[0734] Output: Keywords and topics are generated on the server.

[0735] Step 5:

[0736] Story generation and presentation to users

[0737] Input: Extracted keywords and topics are available on the server.

[0738] Operation: The server generates a story about "Meeting with Professor Tanaka and its influence" and sends it to the device.

[0739] Data processing: The server uses a generative AI model to create a story based on extracted keywords, topics, and emotional information.

[0740] Output: A portion of the story is generated and sent to the device.

[0741] Step 6:

[0742] User feedback and rebuild

[0743] Input: User feedback is entered into the terminal.

[0744] What it does: The terminal displays an input field that says "Please enter additional information." The user enters the additional information: "Mr. Tanaka taught math classes."

[0745] Data processing: The device sends the input feedback to the server.

[0746] Output: Feedback is sent to the server and used to reconstruct the story.

[0747] Step 7:

[0748] Dealing with negative memories

[0749] Input: The user's negative answer is available on the server.

[0750] How it works: The server uses the emotion engine to generate a question: "How has your painful experience helped you today?" and sends it to the device.

[0751] Data manipulation: The user enters a positive answer to a new question.

[0752] Output: A positive response is sent from the device to the server.

[0753] Step 8:

[0754] Generate and save the final story

[0755] Input: All episodes, responses, and emotional information are integrated into the server.

[0756] How it works: The server uses that information to generate a final story that emphasizes the user's growth and learning.

[0757] Data processing: The server organizes the data collected at each step and generates a coherent narrative.

[0758] Output: The final story is generated and sent to the device for the user to review, after which it is stored in a database and a download link in PDF format is provided if desired.

[0759] (Application example 2)

[0760] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0761] Conventional story generation systems have difficulty effectively generating personalized stories based on a user's personal information and emotions. Furthermore, they lack the functionality to easily share the generated stories or output prompts. Therefore, a system that allows users to share their experiences in a rich way is needed.

[0762] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting basic information from a user, means for generating questions based on the input basic information, means for presenting the generated questions to the user, means for collecting user answers, means for analyzing the collected answers and extracting keywords and episodes, means for constructing a story based on the extracted keywords and episodes, means for generating questions to convert the user's negative memories into a positive perspective, means for integrating all episodes and generating a story, means for saving the final version of the story, means for the user to view and share the generated story on social media, and means for outputting the generated story as a prompt sentence. This makes it possible to generate and share personalized stories based on the user's individual experiences.

[0763] "Means for users to input basic information" refers to a mechanism that provides an interface for users to input personal information such as their name and date of birth.

[0764] The "means for generating questions" is a system that automatically generates questions appropriate for the user based on the basic information entered.

[0765] The "means for presenting a question to a user" is a function for displaying the generated question on the user's terminal so that the user can confirm it.

[0766] "Means for collecting user answers" refers to the process in which a user inputs answers to questions presented to them and the system collects those answers.

[0767] "Means for analyzing responses and extracting keywords and episodes" refers to a method for analyzing collected responses using natural language processing technology and extracting important keywords and episodes.

[0768] The "means of constructing a story" is a mechanism for generating a coherent story based on extracted keywords and episodes.

[0769] The "means for generating questions to convert a user's negative memories into positive ones" is a system that automatically creates appropriate questions to convert a user's negative memories into positive ones.

[0770] "Means of integrating all episodes and generating a narrative" refers to the process of integrating various episodes into a single story and ultimately completing a coherent narrative.

[0771] The "means for storing the final story" is the mechanism by which the final story, as reviewed and revised by the user, is stored in the database.

[0772] "Means for users to view stories and share them on social media" refers to a feature that allows users to view the generated stories within the application and share them on social media platforms.

[0773] "Means for outputting a story as a prompt sentence" is a mechanism for outputting the generated story in the form of a prompt sentence to be input into a generative AI model.

[0774] The present invention relates to a system that allows users to create and share personalized stories based on their personal experiences and emotions. The system includes a series of processes that collect basic information from users, present the users with customized questions based on the information, and analyze the users' answers to generate a story.

[0775] The entire system is mainly composed of a server, a user terminal, and a network environment. Each step will be explained in detail below.

[0776] Enter and collect basic information

[0777] First, the user enters basic information using a device. This basic information includes personal information such as name and date of birth. The user provides this information through an input interface on the user device and sends it to the server. The user can use a mobile device such as a smartphone or tablet to do this.

[0778] Question generation and presentation

[0779] Based on the received basic information, the server generates a list of questions suited to the user. This list of questions is related to the user's past experiences and interests. The generated list of questions is then sent back to the terminal and presented to the user. The user then enters answers to the questions.

[0780] Response collection and analysis

[0781] The user's answers are then sent in real time from the device to a server, which analyzes the collected answers and extracts important keywords and anecdotes using natural language processing (NLP) techniques. For example, the NLP model can use the Hugging Face Transformers library.

[0782] Story Building

[0783] The server constructs part of a story based on the extracted keywords and episodes. Furthermore, if negative memories are found in the user's answers, it generates additional questions to convert them into a positive perspective. This process uses an emotion engine to accurately recognize and analyze the user's emotions.

[0784] Creating and saving the final story

[0785] All episodes are integrated into a single coherent story. Users can review this final version of the story through their devices and provide corrections or additional information as needed. The final version of the story is stored on the server, allowing users to access it in the future.

[0786] Sharing and publishing stories

[0787] The generated stories can be viewed and reviewed by users, and can also be shared on social media.The system also has a function to output the stories as prompts to be input into generative AI models.

[0788] Specific examples

[0789] For example, if a user requests the system to "talk about memories from my school days," the user might respond by saying, "Professor Tanaka was the person who had the greatest influence on me." The server then analyzes "my meeting with Professor Tanaka and the impact he had on me" and constructs it as part of a story. Furthermore, in response to the question, "How have your difficult experiences helped you today?" the user might respond, "My difficult experiences have given me perseverance," and this would also be integrated into the story.

[0790] This system allows users to generate and share personalized stories based on their individual experiences. The generated stories are also output as prompts, such as:

[0791] A user requested to talk about his memories from his school days. He answered that Professor Tanaka had the greatest influence on him, and that his difficult experiences had given him perseverance.

[0792] This paragraph will be used as a prompt when inputting the user's story into the generative AI model.

[0793] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0794] Step 1:

[0795] A user accesses the system using their own terminal and enters basic information, such as name and date of birth. The terminal sends this information to the server. Input is done using a text field, and transmission is via an HTTP POST request. The output is the transfer of the basic information to the server.

[0796] Step 2:

[0797] The server generates a list of questions appropriate for the user based on the received basic information. During this process, the basic information is analyzed and questions related to the user's interests and past experiences are selected. Data processing involves querying the database for relevant questions and organizing them in a list format. The output is the generated list of questions.

[0798] Step 3:

[0799] The server sends the generated question list to the user's terminal. The user terminal displays an interface to present the question list to the user. This interface displays each question as text and provides a text box for entering an answer. The output is a question list displayed on the user's terminal.

[0800] Step 4:

[0801] The user inputs answers to the questions posed, and the terminal collects these answers. The collected answers are sent to the server in real time. The input is the user's text input, and the output is the transfer of the answer data to the server.

[0802] Step 5:

[0803] The server analyzes the received answers and extracts important keywords and episodes. This analysis uses natural language processing (NLP) techniques. Specifically, it processes the answer data using Hugging Face's Transformers library to perform entity recognition and sentiment analysis. The input is the user's answer data, and the output is the extracted keywords and episodes.

[0804] Step 6:

[0805] The server constructs part of a story based on the extracted keywords and episodes. If negative memories are detected during this process, it generates follow-up questions to convert them into positive perspectives. The output is an initial story proposal and, if necessary, follow-up questions.

[0806] Step 7:

[0807] The server sends the generated story idea to the user's device, where the user can review and modify it. Any additional information or modifications provided by the user are also sent to the server in real time. The input is the story idea sent from the server, and the output is the user's feedback.

[0808] Step 8:

[0809] The server generates the final version of the story based on user feedback. This involves data processing to integrate all episodes into a single coherent story. The generated story is stored in a database. The input is user feedback and the output is the final version of the story.

[0810] Step 9:

[0811] The server sends the final story to the user's device for viewing and sharing. Users can share the generated story on social media. The story is also output in the form of a prompt, which is used as input to the generative AI model. The input is the final story, and the output is the story share and prompt.

[0812] 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.

[0813] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0814] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0815] [Third embodiment]

[0816] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0817] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0818] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0819] 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.

[0820] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0821] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0822] 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.

[0823] 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.

[0824] 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 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.

[0825] 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.

[0826] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0827] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0828] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The embodiments for carrying out the present invention will be described in detail below.

[0829] First, the user accesses the system using their own terminal. After accessing, an interface for entering basic information (such as name and date of birth) is displayed on the terminal. The user enters this basic information, and the terminal sends it to the server.

[0830] The server generates an appropriate list of questions based on the received basic information. This list of questions may relate to themes such as the user's memories from school, occupation, and hobbies. The generated questions are sent to the terminal, which then presents them to the user. The first question displayed may be, "Who was the teacher who had the greatest influence on you during your school days?"

[0831] The user inputs answers to the questions through the device. For example, they input a specific answer such as, "Professor Tanaka had the greatest influence on me." The device collects these answers and sends them to the server in real time.

[0832] The server stores the collected responses in a database and uses NLP (natural language processing) technology to extract keywords and important episodes. For example, keywords such as "Professor Tanaka," "I was influenced by him," and "I enjoyed the math class" can be extracted from the user's responses.

[0833] The server then constructs part of a story based on these keywords and episodes. Specifically, it generates a sentence about "My encounter with Professor Tanaka and its influence" and presents it to the user. The user can review this intermediate result through their device and provide corrections or additional information as needed. The device then collects feedback from the user and sends that information back to the server.

[0834] For negative memories, the server generates new questions and executes a process to convert them into a positive perspective. For example, a question such as, "How do you think your painful experiences have helped you today?" is generated. The user inputs a positive answer such as, "My painful experiences have made me more resilient," which is then collected by the device and sent to the server.

[0835] Finally, the server combines all episodes and responses into a coherent story that highlights the user's growth and learning. The final story is sent from the server to the device for review by the user. The user can review the final story and, if there are no problems, request that it be saved. The server saves the final story in its database for future access by the user. A link to download it as a PDF is also generated, if desired.

[0836] As a concrete example, consider the case where a user requests the system to "talk about memories from their school days." The user enters basic information and responds, "Professor Tanaka was the person who had the greatest influence on me." The server then analyzes "your encounter with Professor Tanaka and the impact he had on you" and constructs it as part of a story. Furthermore, in response to the question, "How have your difficult experiences helped you today?" the user responds, "My difficult experiences have given me perseverance," which is also integrated into the story.

[0837] In this way, the present invention provides a system for generating a story that allows users to look back on their own lives and view their personal growth and learning in a positive light.

[0838] The processing flow will be explained below.

[0839] Step 1:

[0840] User: Accesses the system using a terminal and enters basic information (such as name and date of birth) into the interface.

[0841] Step 2:

[0842] Terminal: Sends the entered basic information to the server.

[0843] Step 3:

[0844] Server: Generates a list of questions appropriate for the user based on the received basic information.

[0845] Step 4:

[0846] Server: Sends the generated question list to the terminal.

[0847] Step 5:

[0848] Terminal: Presents the list of questions to the user.

[0849] Step 6:

[0850] User: Enters answers to the questions presented.

[0851] Step 7:

[0852] Terminal: Collects user responses and sends them to the server in real time.

[0853] Step 8:

[0854] Server: Stores the received answers in a database and extracts keywords and episodes using NLP (natural language processing) technology.

[0855] Step 9:

[0856] Server: Constructs parts of a story based on extracted keywords and episodes.

[0857] Step 10:

[0858] Server: Sends the intermediate products of the constructed story to the terminal.

[0859] Step 11:

[0860] Terminal: Presents the intermediate product to the user.

[0861] Step 12:

[0862] User: Review the intermediate results and provide corrections or additional information as needed.

[0863] Step 13:

[0864] Terminal: Collects feedback from users and sends it to the server.

[0865] Step 14:

[0866] Server: Generates new questions for negative memories.

[0867] Step 15:

[0868] Server: Sends the generated question to the terminal.

[0869] Step 16:

[0870] Terminal: Presents the new question to the user.

[0871] Step 17:

[0872] User: Enter an answer to a new question.

[0873] Step 18:

[0874] Terminal: Collects the user's answers and sends them to the server.

[0875] Step 19:

[0876] Server: Analyze positive responses and integrate them into the narrative.

[0877] Step 20:

[0878] Server: Integrates all episodes and responses and generates a coherent story.

[0879] Step 21:

[0880] Server: Sends the final story to the device.

[0881] Step 22:

[0882] Terminal: Presents the final story to the user.

[0883] Step 23:

[0884] User: Review the final version of the story and, if there are no problems, request to save it.

[0885] Step 24:

[0886] Server: Stores the final story in a database for future access by users.

[0887] Step 25:

[0888] Server: If desired, generate a link to download the PDF.

[0889] Example 1

[0890] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0891] Conventional story generation systems have had difficulty generating a coherent story by appropriately utilizing the user's basic information and episodes. They also lacked the ability to provide a positive perspective on negative memories, making it difficult to incorporate user feedback and revise the story. Furthermore, there were limited ways for users to access and download the generated story.

[0892] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0893] In this invention, the server includes means for inputting basic information from a user, means for generating questions based on the input basic information, means for generating questions using a generative AI model, means for presenting the generated questions to the user, means for collecting user responses, means for analyzing the collected responses and extracting keywords and episodes, means for generating a story based on the extracted keywords and episodes, means for generating questions to convert the user's negative memories into a positive perspective, means for integrating all episodes and generating a story, means for regenerating the story based on feedback provided by the user, means for generating sentences based on the keywords and episodes using the generative AI model, means for accessing and downloading the generated story, and means for saving the final version of the story. This provides a system that utilizes the user's basic information and episodes to generate a consistent story, convert negative memories into positive ones, and further modifies the story based on user feedback, making it accessible and downloadable.

[0894] "Basic information" refers to information related to the user's profile, such as the user's name, date of birth, occupation, and hobbies.

[0895] A "question" is an inquiry related to a specific topic that is generated by the server based on the user's basic information.

[0896] A "generative AI model" refers to an algorithm that generates text using natural language processing technology, and includes, for example, GPT-4.

[0897] "User's response" is the reply entered by the user in response to the question posed by the server.

[0898] "Keywords" are important words or phrases extracted from the user's answers.

[0899] An "episode" is a detail of a particular experience or event extracted from a user's responses.

[0900] A "story" is a coherent written story generated based on the user's basic information, keywords, and episodes.

[0901] "Negative memories" refer to unpleasant events or emotions that a user has experienced in the past.

[0902] A "positive perspective" is a way of finding a positive interpretation or impact on negative memories.

[0903] "Feedback" refers to corrections or additional information that users provide to the generated story.

[0904] "Regeneration" is the process of regenerating an existing story based on user feedback.

[0905] The present invention provides a system that allows users to have a positive perspective on their lives and generate a coherent narrative. This system operates in cooperation with users, terminals, and a server.

[0906] Hardware and software configuration

[0907] User terminal: Uses devices such as PCs, smartphones, tablets, etc. Accesses the system through browser software (e.g., Google Chrome, Safari).

[0908] Server: Built using Python frameworks (Django, Flask). Databases use MySQL or PostgreSQL, and libraries such as NLTK and Spacy are used for natural language processing. GPT-4 is used as the generative AI model.

[0909] System Operation

[0910] First, the user accesses the system using a browser. The terminal displays an online interface and prompts the user to enter basic information (such as name, date of birth, etc.). After the user enters the required information, the terminal sends the information to the server in JSON format.

[0911] Next, the server uses a Python algorithm to analyze the user's basic information. Based on that, it automatically generates a list of questions appropriate to the user's profile. For example, questions related to "memories of school days," "occupation," and "hobbies" are generated. The server then sends this list of questions to the terminal in JSON format.

[0912] The device displays the received question to the user. The user enters an answer to the question (e.g., "Who was the teacher who had the greatest influence on you during your school days?" and answers "Professor Tanaka was the most influential"), which the device then sends to the server in real time.

[0913] The server stores the answers in a database and then uses natural language processing technology to extract keywords and important anecdotes from the answers. Based on the extracted information, a generative AI model (GPT-4) is used to generate parts of a story. The generated text is then presented to the user via their device.

[0914] The user checks the story generated on the device and enters corrections or additional information as necessary. The device then sends the user's feedback to the server and regenerates the story.

[0915] For negative memories, the server generates a new question (e.g., "How do you think your painful experiences have helped you today?") and executes a process to convert the memory into a positive perspective. The user inputs an answer such as "My painful experiences have made me more resilient," and the device sends this answer to the server.

[0916] Finally, the server integrates all episodes and responses into a single, coherent story that emphasizes the user's growth and learning. The user can review the final version of the story on their device and, if there are no problems, request that it be saved. The server saves the final version of the story in its database and, if desired, generates a download link in PDF format.

[0917] Examples of concrete examples and prompts

[0918] As a concrete example, consider the case where a user requests the system to "talk about memories from their school days." The user enters basic information and responds, "Professor Tanaka was the person who had the greatest influence on me." The server then analyzes "my encounter with Professor Tanaka and the impact he had on me" and constructs it as part of a story. Furthermore, in response to the question, "How have your difficult experiences helped you today?" the user responds, "My difficult experiences have given me perseverance," and this is also integrated into the story.

[0919] An example of a prompt is:

[0920] "Tell us about the teacher who had the greatest influence on you as a student. How did they influence you?"

[0921] "How have your painful experiences helped you become who you are today?"

[0922] Examples include:

[0923] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0924] Step 1:

[0925] A user accesses the system using their own terminal. When the user opens a browser and accesses the specified URL, the system's web page is displayed. The terminal provides the user with an interface for entering basic information. This information includes name, date of birth, occupation, hobbies, etc. When the user enters the basic information and clicks the send button, the terminal sends the information to the server in JSON format (input: basic information, output: transmitted data).

[0926] Step 2:

[0927] The server analyzes the basic information received from the user. Specifically, it uses a Python algorithm to analyze the information and create a user profile (input: basic information, output: user profile). The server then uses a generative AI model to generate a customized list of questions based on the user profile (input: user profile, output: list of questions). The generated questions are based on a theme, such as "memories from school" or "hobbies."

[0928] Step 3:

[0929] The server sends the generated question list to the terminal in JSON format (input: question list, output: transmitted data). The terminal displays the received question list to the user. The user enters answers to the displayed questions. For example, in response to the question "Who was the teacher who had the greatest influence on you during your student days?" the user might answer "Professor Tanaka had the greatest influence on me." When the user enters the answer and clicks the send button, the terminal collects the answer and transmits it to the server in real time (input: answer, output: transmitted data).

[0930] Step 4:

[0931] The server receives the user's answers and stores them in a database, typically a MySQL or PostgreSQL database (input: answers, output: saved data). The server then uses natural language processing techniques to analyze the saved answers. It uses libraries such as Python's NLTK and Spacy to extract important keywords and episodes from the answers (input: answer data, output: keywords, episodes).

[0932] Step 5:

[0933] The server generates part of a story based on the extracted keywords and episodes. Using a generative AI model (e.g., GPT-4), it generates part of a story about "Meeting with Professor Tanaka and its impact" (input: keywords, episodes, output: generated part of the story). The generated text is sent to the device.

[0934] Step 6:

[0935] The device displays part of the generated story to the user. The user checks the content and enters corrections or additional information as needed. For example, the user may add details such as "Professor Tanaka's class was particularly interesting in math" (Input: User feedback, Output: Corrected data). The device collects the user's feedback and sends it back to the server (Input: Corrected data, Output: Sent data).

[0936] Step 7:

[0937] The server generates a new question to convert the user's negative memories into a positive one. For example, it generates a question such as, "How do you think your painful experiences have helped you today?" (Input: User's negative answer, Output: New question). The user responds to this question by saying, "My painful experiences have given me more patience," and the device collects this answer and sends it to the server (Input: Positive answer, Output: Transmitted data).

[0938] Step 8:

[0939] The server integrates all episodes and responses to generate a coherent story. It uses a generative AI model to generate text that highlights the user's growth and learning (input: episodes and responses, output: final story). The final story is sent to the device for the user to review.

[0940] Step 9:

[0941] The terminal displays the final version of the story to the user. The user checks the final version of the story and, if there are no problems, requests that it be saved. The server saves the final version of the story in a database so that the user can access it in the future (input: final confirmed data, output: saved data). In addition, the server generates a download link in PDF format if desired (input: saved data, output: download link).

[0942] (Application example 1)

[0943] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0944] In recent years, there has been a demand for improving customer experiences in brick-and-mortar stores, but it is difficult to provide personalized product recommendations and services to each individual customer. Furthermore, technologies for effectively leveraging customers' past experiences and preferences to provide specific and engaging customer experiences are still in their infancy. Furthermore, there is a need for flexible systems that can adapt based on customer feedback.

[0945] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0946] In this invention, the server includes means for inputting basic information from a user, means for generating questions based on the input basic information, means for presenting the generated questions to the user, means for collecting user responses, means for analyzing the collected responses and extracting keywords and episodes, means for constructing a story based on the extracted keywords and episodes, means for generating questions to convert the user's negative memories into a positive perspective, means for integrating all episodes and generating a story, means for saving the final version of the story, means for analyzing the user's preferences and past experiences from the collected responses and generating a personalized recommended product list, and means for presenting the generated recommended product list. This makes it possible to significantly improve customer experience by providing personalized recommended product lists in physical stores.

[0947] "Basic information" is information for identifying an individual, such as the user's name, date of birth, gender, occupation, and hobbies.

[0948] "Means for generating questions" refers to technology that automatically creates a list of appropriate questions based on the user's basic information and past answers.

[0949] A "means for presenting a question" is an interface or device that displays the generated question to the user.

[0950] The "means for collecting answers" refers to a technique for collecting answers entered by users as data.

[0951] "Means of analyzing and extracting keywords and episodes" refers to a technique that uses natural language processing technology to identify important words and events from the collected responses.

[0952] "Means of constructing a story" refers to the technique of creating a coherent story based on extracted keywords and episodes.

[0953] The "means for generating questions to convert a user's negative memories into a positive perspective" is a technique for creating additional questions to help the user view negative memories in a positive light.

[0954] "Narrative generation" is the technique of integrating all collected episodes to create a final story.

[0955] The "means for storing the final story" is a technique for storing the generated story in a database so that users can access it later.

[0956] The "means for generating an individualized recommended product list" is a technology for creating a product list optimized for a specific user based on the user's preferences and past experiences.

[0957] The "means for presenting a recommended product list" refers to an interface or device that displays the generated recommended product list to the user.

[0958] The process of entering basic information from the user is carried out using a device such as a smartphone or PC. After the user accesses the system, an interface for entering basic information such as name, date of birth, gender, occupation, and hobbies is displayed on the device. Once the user enters the basic information, the device sends the information to the server.

[0959] The server generates an appropriate list of questions based on the received basic information. The questions may relate to themes such as the user's memories from their school days, their occupation, or their hobbies. The generated questions are then sent back to the device, which then presents them to the user. An example of the initial question displayed is, "Who was the teacher who had the greatest influence on you during your school days?"

[0960] The user inputs answers to the questions through the device, such as "My math teacher had the biggest impact on me," and the device sends the answers to the server in real time.

[0961] The server analyzes the collected responses and uses natural language processing (NLP) technology to extract keywords and important anecdotes. For example, keywords such as "math teacher," "influenced me," and "fun classes" can be extracted from user responses.

[0962] The server then constructs part of a story based on these keywords and episodes. For example, it generates a sentence about "My encounter with my math teacher and its impact" and presents it to the user. The user can check the draft of this story through their device and provide corrections or additional information as needed. User feedback is then sent back to the server via their device.

[0963] For negative memories, the server generates new questions and executes a process to transform the user's negative experiences into positive ones. For example, a question such as "How has your painful experience helped you?" is generated. The user can then respond with "That experience helped me develop perseverance," thereby integrating the positive episode into the story.

[0964] Finally, the server combines all episodes and responses to generate a coherent story. This story highlights the user's growth and learning, and is then edited into a final version. The user can review the final version of the story through their device and, if satisfied, request that it be saved. The server saves this final version of the story in a database for future access by the user. A link is also provided to download the story in PDF format if desired.

[0965] As an application example, we will explain the case where the present invention is applied to a "personal shopping assistant" application in a physical store. When a user visiting a store accesses the system using smart glasses or a smartphone, a personalized list of recommended products is displayed based on the user's past shopping experiences and answers. This allows the user to enjoy shopping according to their preferences.

[0966] As an example of how to use a generative AI model, we will use the following prompt:

[0967] "Generate a story based on the user's experiences. Keywords: math teacher, impact, fun class"

[0968] In this way, the present invention allows users to generate personalized stories and recommended product lists based on their experiences, enhancing their experience in brick-and-mortar stores and other settings.

[0969] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0970] Step 1:

[0971] A user accesses the system through a terminal and inputs basic information (such as name, date of birth, gender, occupation, hobbies, etc.). The input basic information is sent from the terminal to the server. The input of this step is the basic information entered by the user, and the output is the basic information sent from the terminal to the server.

[0972] Step 2:

[0973] The server generates an appropriate list of questions based on the received basic information. Here, the server analyzes the basic information and automatically generates questions related to the user's memories from school, occupation, hobbies, etc. The input for this step is the basic information, and the output is the generated list of questions.

[0974] Step 3:

[0975] The generated question list is sent to the terminal, which then presents it to the user. The user confirms the questions on the terminal. The input of this step is the question list sent from the server, and the output is the questions presented to the user.

[0976] Step 4:

[0977] The user inputs an answer to the question through the terminal. For example, they input a specific answer such as "My math teacher had the biggest impact on me." The input of this step is the user's answer, and the output is the answer sent from the terminal to the server.

[0978] Step 5:

[0979] The server analyzes the collected answers and extracts keywords and important episodes using natural language processing (NLP) technology. For example, keywords such as "math teacher," "influenced me," and "fun class" can be extracted from the answers. The input for this step is the user's answer, and the output is the extracted keywords and episodes.

[0980] Step 6:

[0981] The server generates part of a story based on the extracted keywords and episodes. Using the generative AI model, it creates a sentence about, for example, "My encounter with my math teacher and its impact" and presents it to the user. The input for this step is keywords and episodes, and the output is the generated part of the story.

[0982] Step 7:

[0983] The user checks the generated draft of the story through the terminal and provides corrections or additional information as necessary. The user's feedback is sent back to the server through the terminal. The input of this step is the user's feedback, and the output is a part of the revised story.

[0984] Step 8:

[0985] The server generates questions to convert the user's negative answers into positive ones. For example, it provides new questions such as, "How has your painful experience helped you?" The input of this step is the negative answer, and the output is a newly generated question.

[0986] Step 9:

[0987] The user inputs a positive answer through the terminal and sends it to the server from the terminal. For example, the user provides an answer such as "That experience helped me develop perseverance." The input of this step is the user's positive answer, and the output is the positive answer sent from the terminal to the server.

[0988] Step 10:

[0989] The server integrates all episodes and responses to generate the final story, building a coherent narrative that highlights the user's growth and learning. The input for this step is all episodes and responses, and the output is the final story.

[0990] Step 11:

[0991] The server stores the final story, allowing users to access and review it via their devices, and also generates a link to download it in PDF format if desired. The input to this step is the final story, and the output is the stored story and a download link.

[0992] As a concrete example, a personal shopping assistant in a brick-and-mortar store might show a list of recommended products based on past shopping experiences and individual preferences via smart glasses or a smartphone when the user enters the store, using, for example, the following prompt:

[0993] "Generate stories based on user experiences. Keywords: math teacher, influenced, fun lessons."

[0994] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0995] The embodiments for carrying out the present invention will be described in detail below.

[0996] Users access the system using their own terminals. Once accessed, an interface for entering basic information (such as name and date of birth) is displayed on the terminal. The user enters the basic information, and the terminal sends the information to the server.

[0997] Based on the received basic information, the server generates a list of questions suited to the user. This list of questions is related to the user's past experiences and interests. The generated list of questions is sent to the terminal, which then presents it to the user. For example, the user might be asked a question such as, "Who was the teacher who had the greatest influence on you when you were a student?"

[0998] The user inputs answers to questions posed through the device. For example, they input a specific answer such as, "Professor Tanaka had the greatest influence on me." The device collects these answers and sends them to the server in real time.

[0999] The server stores the received responses in a database and uses NLP (natural language processing) technology to extract keywords and important episodes. For example, keywords such as "Mr. Tanaka," "influenced me," and "I enjoyed the math class" are extracted. The server is also equipped with an emotion engine that recognizes the user's emotions when analyzing the collected responses. This emotion recognition makes it possible to grasp the emotional tone and nuances of the responses.

[1000] Next, the server constructs part of a story based on the extracted keywords, episodes, and the user's emotions. Specifically, it generates a sentence about "My encounter with Professor Tanaka and its impact" and presents it to the user. The user can review this intermediate result through their device and provide corrections or additional information as necessary. The device then collects feedback from the user and sends it back to the server.

[1001] For negative memories, the server generates new questions and executes a process to convert them into a positive perspective. This process utilizes the emotion engine again to present appropriate questions based on the user's emotions. For example, a question such as "How do you think your painful experiences have helped you today?" is generated. The user enters a positive answer such as "My painful experiences have made me more patient," which is then collected by the device and sent to the server.

[1002] Finally, the server integrates all episodes, responses, and emotional information recognized by the emotion engine to generate a coherent story. This story emphasizes the user's growth and learning. The final version of the story is sent from the server to the user's device for review. The user can review this final version of the story and, if there are no problems, request to save it. The server saves the final version of the story in a database for the user to access in the future. If desired, a link to download it in PDF format is also generated.

[1003] As a concrete example, consider the case where a user makes a request to the system saying, "I want to talk about my memories from my school days." The user enters basic information and responds, "Professor Tanaka was the person who had the greatest influence on me." The server then analyzes "my meeting with Professor Tanaka and the impact he had on me" and constructs it as part of a story. Furthermore, in response to the question, "How has your difficult experience helped you today?" the user responds, "My difficult experience taught me perseverance," which is also integrated into the story. The emotion engine recognizes the user's positive emotions and emphasizes feedback and episodes accordingly.

[1004] In this way, the present invention provides a system that generates stories that allow users to look back on their own lives and positively perceive their self-growth and learning. By combining it with an emotion engine, it has the effect of gaining deeper understanding and empathy.

[1005] The processing flow will be explained below.

[1006] Step 1:

[1007] User: Accesses the system using a terminal and enters basic information such as name and date of birth into the interface.

[1008] Step 2:

[1009] Terminal: Sends the entered basic information to the server.

[1010] Step 3:

[1011] Server: Generates a list of questions appropriate for the user based on the received basic information.

[1012] Step 4:

[1013] Server: Sends the generated question list to the terminal.

[1014] Step 5:

[1015] Terminal: Presents the list of questions to the user.

[1016] Step 6:

[1017] User: Enters answers to the questions presented.

[1018] Step 7:

[1019] Terminal: Collects user responses and sends them to the server in real time.

[1020] Step 8:

[1021] Server: Stores the received answers in a database and extracts keywords and episodes using NLP (natural language processing) technology.

[1022] Step 9:

[1023] Server: Uses an emotion engine to recognize the user's emotions from the collected answers. For example, text analysis determines whether the user has a positive emotion such as "It was fun."

[1024] Step 10:

[1025] Server: Constructs parts of a story based on extracted keywords and episodes, and recognized user emotions.

[1026] Step 11:

[1027] Server: Sends the intermediate products of the constructed story to the terminal.

[1028] Step 12:

[1029] Terminal: Presents the intermediate product to the user.

[1030] Step 13:

[1031] User: Review the intermediate results and provide corrections or additional information as needed.

[1032] Step 14:

[1033] Terminal: Collects feedback from users and sends it to the server.

[1034] Step 15:

[1035] Server: Uses the emotion engine to generate new questions to turn negative episodes into positive ones, such as, "What did you learn from that painful experience?"

[1036] Step 16:

[1037] Server: Sends the generated question to the terminal.

[1038] Step 17:

[1039] Terminal: Presents the new question to the user.

[1040] Step 18:

[1041] User: Enter an answer to a new question.

[1042] Step 19:

[1043] Terminal: Collects the user's answers and sends them to the server.

[1044] Step 20:

[1045] Server: Uses an emotion engine to analyze the positive responses entered by users and integrate them into the story.

[1046] Step 21:

[1047] Server: Integrates all episodes, responses, and recognized emotional information to generate a coherent story.

[1048] Step 22:

[1049] Server: Sends the final story to the device.

[1050] Step 23:

[1051] Terminal: Presents the final story to the user.

[1052] Step 24:

[1053] User: Review the final version of the story and, if there are no problems, request to save it.

[1054] Step 25:

[1055] Server: Stores the final story in a database for future access by users.

[1056] Step 26:

[1057] Server: If desired, generate a link to download the PDF.

[1058] Example 2

[1059] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1060] Previously, story generation systems based on users' life experiences had a linear process from inputting information to generating a story, which meant that they were unable to fully reflect the user's emotions and individual experiences. Furthermore, they lacked the ability to appropriately respond to negative memories and complex analysis to maintain the consistency of the story.

[1061] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting basic information from a user, a means for generating questions based on the input basic information, a means for presenting the generated questions to the user, a means for collecting user answers, a means for analyzing the collected answers and extracting keywords and topics, a means for constructing a story based on the extracted keywords and topics, a means for generating questions to convert the user's negative memories into a positive perspective, a means for integrating all topics and generating a story, a means for saving the final version of the story, a means for analyzing answers using NLP technology, and a means including an emotion engine for recognizing and analyzing the user's emotions. This makes it possible to generate a story that reflects the user's emotions and individual experiences in detail and provide a consistent story.

[1062] A "user" is an individual who uses the system to enter basic information and answers to questions, providing the data necessary to generate a story.

[1063] "Basic information" refers to basic personal information such as the user's name, date of birth, and email address.

[1064] A "question" is a question that is generated based on the user's basic information and presented to the user.

[1065] An "answer" is information that a user enters in response to a question.

[1066] "Collection" refers to the process by which a terminal or server compiles information entered by a user.

[1067] "Analysis" refers to the process in which the server analyzes the collected responses and extracts keywords and topics.

[1068] "Keywords" are important words or phrases extracted from the user's responses.

[1069] "Topics" refer to important episodes or themes extracted from users' responses.

[1070] A "story" is a series of sentences or descriptions constructed based on extracted keywords and topics.

[1071] "Negative memories" are memories associated with unpleasant events or emotions experienced by the user.

[1072] A "positive perspective" is a perspective that reevaluates negative memories from a positive perspective.

[1073] "Synthesis" refers to the process by which the server combines multiple episodes or topics into a single coherent story.

[1074] The "final story" is the story in its completed form, incorporating all episodes and feedback.

[1075] "Preservation" refers to the process of recording the final story in a database and making it available for reuse as needed.

[1076] "NLP technology" refers to natural language processing technology, which is used to analyze user responses and extract keywords and topics.

[1077] An "emotion engine" is a technology that recognizes emotions in the analysis of collected responses and assists in generating stories based on those emotions.

[1078] MODE FOR CARRYING OUT THE INVENTION

[1079] The present invention is a story generation system based on a user's life experiences, and aims to generate a story that reflects the user's emotions and individual experiences from a positive perspective. This system uses various hardware and software to collect and analyze information provided by the user and generate a story that takes emotions into consideration based on that information. Specific embodiments for implementing the present invention will be described below.

[1080] Hardware Configuration

[1081] User's device: A device that can connect to the internet, such as a PC, smartphone, or tablet.

[1082] Server: A high-performance server equipped with a database, natural language processing (NLP) technology, and an emotion engine

[1083] Software Configuration

[1084] Web interface: Provides an interface for users to enter basic information and answers.

[1085] Database: A database such as MongoDB that stores user information and responses.

[1086] NLP technology: Software such as SpaCy and NLTK that extracts keywords and topics from user responses

[1087] Emotion engine: An engine that recognizes and analyzes user emotions, such as IBM Watson or Azure Cognitive Services

[1088] System processing overview

[1089] When a user accesses the system using their own terminal, an interface for entering basic information is displayed on the terminal. The user enters basic information (such as name and date of birth), and the terminal sends this information to the server. Based on the received basic information, the server generates a list of questions appropriate for the user. This list of questions is related to the user's past experiences and interests. The generated list of questions is sent to the terminal and presented to the user through the terminal.

[1090] The user enters answers to the questions posed, and the device collects and sends these answers to the server. The server stores the received answers in a database and uses NLP technology to analyze the answers and extract keywords and topics. For example, keywords such as "Mr. Tanaka," "I was influenced by him," and "I enjoyed the math class" are extracted. The emotion engine also recognizes and analyzes the user's emotions and generates a story based on those emotions.

[1091] The server then constructs a part of the story based on the extracted keywords, topics, and the user's emotions and presents it to the user. When the user provides feedback, the device collects it and sends it back to the server, which then modifies and reconstructs the story based on the feedback.

[1092] Even for negative memories, the server generates new questions and executes a process to convert them into a positive perspective. For example, a question might be posed: "How do you think your painful experiences have helped you today?" The user can then input a positive answer, such as "My painful experiences have made me more resilient," which is then collected by the device and sent to the server.

[1093] Finally, the server integrates all episodes, responses, and emotional information to generate a coherent story. The final story is presented to the user via their device, and if the user is satisfied with the content, the server stores the story in its database. If desired, a download link in PDF format is also provided.

[1094] Specific examples

[1095] As a concrete example, consider the case where a user makes a request to the system saying, "I want to talk about my memories from my school days." The user enters basic information and responds, "Professor Tanaka was the person who had the greatest influence on me." The server then analyzes "my meeting with Professor Tanaka and the impact he had on me" and constructs it as part of a story. Furthermore, in response to the question, "How has your difficult experience helped you today?" the user responds, "My difficult experience taught me perseverance," which is also integrated into the story. The emotion engine recognizes the user's positive emotions and emphasizes feedback and episodes accordingly.

[1096] An example of this prompt is:

[1097] Example prompt:

[1098] Please enter basic information such as your name and date of birth.

[1099] Who was the teacher who had the greatest influence on you when you were a student?

[1100] "How do you think your painful experiences have helped you become who you are today?"

[1101] In this way, the present invention provides a system that generates stories that allow users to look back on their own lives and positively perceive their self-growth and learning. By combining an emotion engine with NLP technology, it has the effect of gaining deeper understanding and empathy.

[1102] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1103] Program processing flow and detailed explanation of each processing step

[1104] Step 1:

[1105] User access and basic information entry

[1106] Input: The user enters the URL when accessing.

[1107] How it works: A user opens a browser and enters the system's URL to access the system. The terminal displays an interface for entering the user's basic information (name, date of birth, etc.).

[1108] Data processing: The user enters basic information into the form and clicks the submit button.

[1109] Output: The basic information entered is formed and sent from the terminal to the server.

[1110] Step 2:

[1111] Submit basic information and generate a questionnaire

[1112] Input: The device sends the user's basic information to the server.

[1113] How it works: When you press the submit button on the form, the device sends an HTTP POST request to the server, which receives the request, stores the information in a database, and creates a user profile.

[1114] Data processing: The server analyzes the received basic information and generates a list of relevant questions.

[1115] Output: The list of questions is sent from the server to the terminal.

[1116] Step 3:

[1117] Present a list of questions and enter answers

[1118] Input: A list of questions is presented to the user via a terminal.

[1119] How it works: The device displays a list of questions received from the server to the user, such as "Who was your teacher who had the greatest influence on you during your school days?"

[1120] Data processing: The user enters answers to questions.

[1121] Output: The user's answer is entered into the terminal.

[1122] Step 4:

[1123] Submit and analyze responses

[1124] Input: The user's answer is sent from the device to the server.

[1125] How it works: The device sends the answers entered by the user to the server as an HTTP POST request. The server receives the answers and stores them in a database.

[1126] Data processing: The server applies NLP models to the stored answers to extract keywords and topics.

[1127] Output: Keywords and topics are generated on the server.

[1128] Step 5:

[1129] Story generation and presentation to users

[1130] Input: Extracted keywords and topics are available on the server.

[1131] Operation: The server generates a story about "Meeting with Professor Tanaka and its influence" and sends it to the device.

[1132] Data processing: The server uses a generative AI model to create a story based on extracted keywords, topics, and emotional information.

[1133] Output: A portion of the story is generated and sent to the device.

[1134] Step 6:

[1135] User feedback and rebuild

[1136] Input: User feedback is entered into the terminal.

[1137] What it does: The terminal displays an input field that says "Please enter additional information." The user enters the additional information: "Mr. Tanaka taught math classes."

[1138] Data processing: The device sends the input feedback to the server.

[1139] Output: Feedback is sent to the server and used to reconstruct the story.

[1140] Step 7:

[1141] Dealing with negative memories

[1142] Input: The user's negative answer is available on the server.

[1143] How it works: The server uses the emotion engine to generate a question: "How has your painful experience helped you today?" and sends it to the device.

[1144] Data manipulation: The user enters a positive answer to a new question.

[1145] Output: A positive response is sent from the device to the server.

[1146] Step 8:

[1147] Generate and save the final story

[1148] Input: All episodes, responses, and emotional information are integrated into the server.

[1149] How it works: The server uses that information to generate a final story that emphasizes the user's growth and learning.

[1150] Data processing: The server organizes the data collected at each step and generates a coherent narrative.

[1151] Output: The final story is generated and sent to the device for the user to review, after which it is stored in a database and a download link in PDF format is provided if desired.

[1152] (Application example 2)

[1153] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1154] Conventional story generation systems have difficulty effectively generating personalized stories based on a user's personal information and emotions. Furthermore, they lack the functionality to easily share the generated stories or output prompts. Therefore, a system that allows users to share their experiences in a rich way is needed.

[1155] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting basic information from a user, means for generating questions based on the input basic information, means for presenting the generated questions to the user, means for collecting user answers, means for analyzing the collected answers and extracting keywords and episodes, means for constructing a story based on the extracted keywords and episodes, means for generating questions to convert the user's negative memories into a positive perspective, means for integrating all episodes and generating a story, means for saving the final version of the story, means for the user to view and share the generated story on social media, and means for outputting the generated story as a prompt sentence. This makes it possible to generate and share personalized stories based on the user's individual experiences.

[1156] "Means for users to input basic information" refers to a mechanism that provides an interface for users to input personal information such as their name and date of birth.

[1157] The "means for generating questions" is a system that automatically generates questions appropriate for the user based on the basic information entered.

[1158] The "means for presenting a question to a user" is a function for displaying the generated question on the user's terminal so that the user can confirm it.

[1159] "Means for collecting user answers" refers to the process in which a user inputs answers to questions presented to them and the system collects those answers.

[1160] "Means for analyzing responses and extracting keywords and episodes" refers to a method for analyzing collected responses using natural language processing technology and extracting important keywords and episodes.

[1161] The "means of constructing a story" is a mechanism for generating a coherent story based on extracted keywords and episodes.

[1162] The "means for generating questions to convert a user's negative memories into positive ones" is a system that automatically creates appropriate questions to convert a user's negative memories into positive ones.

[1163] "Means of integrating all episodes and generating a narrative" refers to the process of integrating various episodes into a single story and ultimately completing a coherent narrative.

[1164] The "means for storing the final story" is the mechanism by which the final story, as reviewed and revised by the user, is stored in the database.

[1165] "Means for users to view stories and share them on social media" refers to a feature that allows users to view the generated stories within the application and share them on social media platforms.

[1166] "Means for outputting a story as a prompt sentence" is a mechanism for outputting the generated story in the form of a prompt sentence to be input into a generative AI model.

[1167] The present invention relates to a system that allows users to create and share personalized stories based on their personal experiences and emotions. The system includes a series of processes that collect basic information from users, present the users with customized questions based on the information, and analyze the users' answers to generate a story.

[1168] The entire system is mainly composed of a server, a user terminal, and a network environment. Each step will be explained in detail below.

[1169] Enter and collect basic information

[1170] First, the user enters basic information using a device. This basic information includes personal information such as name and date of birth. The user provides this information through an input interface on the user device and sends it to the server. The user can use a mobile device such as a smartphone or tablet to do this.

[1171] Question generation and presentation

[1172] Based on the received basic information, the server generates a list of questions suited to the user. This list of questions is related to the user's past experiences and interests. The generated list of questions is then sent back to the terminal and presented to the user. The user then enters answers to the questions.

[1173] Response collection and analysis

[1174] The user's answers are then sent in real time from the device to a server, which analyzes the collected answers and extracts important keywords and anecdotes using natural language processing (NLP) techniques. For example, the NLP model can use the Hugging Face Transformers library.

[1175] Story Building

[1176] The server constructs part of a story based on the extracted keywords and episodes. Furthermore, if negative memories are found in the user's answers, it generates additional questions to convert them into a positive perspective. This process uses an emotion engine to accurately recognize and analyze the user's emotions.

[1177] Creating and saving the final story

[1178] All episodes are integrated into a single coherent story. Users can review this final version of the story through their devices and provide corrections or additional information as needed. The final version of the story is stored on the server, allowing users to access it in the future.

[1179] Sharing and publishing stories

[1180] The generated stories can be viewed and reviewed by users, and can also be shared on social media.The system also has a function to output the stories as prompts to be input into generative AI models.

[1181] Specific examples

[1182] For example, if a user requests the system to "talk about memories from my school days," the user might respond by saying, "Professor Tanaka was the person who had the greatest influence on me." The server then analyzes "my meeting with Professor Tanaka and the impact he had on me" and constructs it as part of a story. Furthermore, in response to the question, "How have your difficult experiences helped you today?" the user might respond, "My difficult experiences have given me perseverance," and this would also be integrated into the story.

[1183] This system allows users to generate and share personalized stories based on their individual experiences. The generated stories are also output as prompts, such as:

[1184] A user requested to talk about his memories from his school days. He answered that Professor Tanaka had the greatest influence on him, and that his difficult experiences had given him perseverance.

[1185] This paragraph will be used as a prompt when inputting the user's story into the generative AI model.

[1186] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1187] Step 1:

[1188] A user accesses the system using their own terminal and enters basic information, such as name and date of birth. The terminal sends this information to the server. Input is done using a text field, and transmission is via an HTTP POST request. The output is the transfer of the basic information to the server.

[1189] Step 2:

[1190] The server generates a list of questions appropriate for the user based on the received basic information. During this process, the basic information is analyzed and questions related to the user's interests and past experiences are selected. Data processing involves querying the database for relevant questions and organizing them in a list format. The output is the generated list of questions.

[1191] Step 3:

[1192] The server sends the generated question list to the user's terminal. The user terminal displays an interface to present the question list to the user. This interface displays each question as text and provides a text box for entering an answer. The output is a question list displayed on the user's terminal.

[1193] Step 4:

[1194] The user inputs answers to the questions posed, and the terminal collects these answers. The collected answers are sent to the server in real time. The input is the user's text input, and the output is the transfer of the answer data to the server.

[1195] Step 5:

[1196] The server analyzes the received answers and extracts important keywords and episodes. This analysis uses natural language processing (NLP) techniques. Specifically, it processes the answer data using Hugging Face's Transformers library to perform entity recognition and sentiment analysis. The input is the user's answer data, and the output is the extracted keywords and episodes.

[1197] Step 6:

[1198] The server constructs part of a story based on the extracted keywords and episodes. If negative memories are detected during this process, it generates follow-up questions to convert them into positive perspectives. The output is an initial story proposal and, if necessary, follow-up questions.

[1199] Step 7:

[1200] The server sends the generated story idea to the user's device, where the user can review and modify it. Any additional information or modifications provided by the user are also sent to the server in real time. The input is the story idea sent from the server, and the output is the user's feedback.

[1201] Step 8:

[1202] The server generates the final version of the story based on user feedback. This involves data processing to integrate all episodes into a single coherent story. The generated story is stored in a database. The input is user feedback and the output is the final version of the story.

[1203] Step 9:

[1204] The server sends the final story to the user's device for viewing and sharing. Users can share the generated story on social media. The story is also output in the form of a prompt, which is used as input to the generative AI model. The input is the final story, and the output is the story share and prompt.

[1205] 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.

[1206] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1207] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1208] [Fourth embodiment]

[1209] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1210] 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.

[1211] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[1212] 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.

[1213] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1214] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1215] 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.

[1216] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1217] 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.

[1218] 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 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.

[1219] 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.

[1220] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1221] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1222] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The embodiments for carrying out the present invention will be described in detail below.

[1223] First, the user accesses the system using their own terminal. After accessing, an interface for entering basic information (such as name and date of birth) is displayed on the terminal. The user enters this basic information, and the terminal sends it to the server.

[1224] The server generates an appropriate list of questions based on the received basic information. This list of questions may relate to themes such as the user's memories from school, occupation, and hobbies. The generated questions are sent to the terminal, which then presents them to the user. The first question displayed may be, "Who was the teacher who had the greatest influence on you during your school days?"

[1225] The user inputs answers to the questions through the device. For example, they input a specific answer such as, "Professor Tanaka had the greatest influence on me." The device collects these answers and sends them to the server in real time.

[1226] The server stores the collected responses in a database and uses NLP (natural language processing) technology to extract keywords and important episodes. For example, keywords such as "Professor Tanaka," "I was influenced by him," and "I enjoyed the math class" can be extracted from the user's responses.

[1227] The server then constructs part of a story based on these keywords and episodes. Specifically, it generates a sentence about "My encounter with Professor Tanaka and its influence" and presents it to the user. The user can review this intermediate result through their device and provide corrections or additional information as needed. The device then collects feedback from the user and sends that information back to the server.

[1228] For negative memories, the server generates new questions and executes a process to convert them into a positive perspective. For example, a question such as, "How do you think your painful experiences have helped you today?" is generated. The user inputs a positive answer such as, "My painful experiences have made me more resilient," which is then collected by the device and sent to the server.

[1229] Finally, the server combines all episodes and responses into a coherent story that highlights the user's growth and learning. The final story is sent from the server to the device for review by the user. The user can review the final story and, if there are no problems, request that it be saved. The server saves the final story in its database for future access by the user. A link to download it as a PDF is also generated, if desired.

[1230] As a concrete example, consider the case where a user requests the system to "talk about memories from their school days." The user enters basic information and responds, "Professor Tanaka was the person who had the greatest influence on me." The server then analyzes "your encounter with Professor Tanaka and the impact he had on you" and constructs it as part of a story. Furthermore, in response to the question, "How have your difficult experiences helped you today?" the user responds, "My difficult experiences have given me perseverance," which is also integrated into the story.

[1231] In this way, the present invention provides a system for generating a story that allows users to look back on their own lives and view their personal growth and learning in a positive light.

[1232] The processing flow will be explained below.

[1233] Step 1:

[1234] User: Accesses the system using a terminal and enters basic information (such as name and date of birth) into the interface.

[1235] Step 2:

[1236] Terminal: Sends the entered basic information to the server.

[1237] Step 3:

[1238] Server: Generates a list of questions appropriate for the user based on the received basic information.

[1239] Step 4:

[1240] Server: Sends the generated question list to the terminal.

[1241] Step 5:

[1242] Terminal: Presents the list of questions to the user.

[1243] Step 6:

[1244] User: Enters answers to the questions presented.

[1245] Step 7:

[1246] Terminal: Collects user responses and sends them to the server in real time.

[1247] Step 8:

[1248] Server: Stores the received answers in a database and extracts keywords and episodes using NLP (natural language processing) technology.

[1249] Step 9:

[1250] Server: Constructs parts of a story based on extracted keywords and episodes.

[1251] Step 10:

[1252] Server: Sends the intermediate products of the constructed story to the terminal.

[1253] Step 11:

[1254] Terminal: Presents the intermediate product to the user.

[1255] Step 12:

[1256] User: Review the intermediate results and provide corrections or additional information as needed.

[1257] Step 13:

[1258] Terminal: Collects feedback from users and sends it to the server.

[1259] Step 14:

[1260] Server: Generates new questions for negative memories.

[1261] Step 15:

[1262] Server: Sends the generated question to the terminal.

[1263] Step 16:

[1264] Terminal: Presents the new question to the user.

[1265] Step 17:

[1266] User: Enter an answer to a new question.

[1267] Step 18:

[1268] Terminal: Collects the user's answers and sends them to the server.

[1269] Step 19:

[1270] Server: Analyze positive responses and integrate them into the narrative.

[1271] Step 20:

[1272] Server: Integrates all episodes and responses and generates a coherent story.

[1273] Step 21:

[1274] Server: Sends the final story to the device.

[1275] Step 22:

[1276] Terminal: Presents the final story to the user.

[1277] Step 23:

[1278] User: Review the final version of the story and, if there are no problems, request to save it.

[1279] Step 24:

[1280] Server: Stores the final story in a database for future access by users.

[1281] Step 25:

[1282] Server: If desired, generate a link to download the PDF.

[1283] Example 1

[1284] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1285] Conventional story generation systems have had difficulty generating a coherent story by appropriately utilizing the user's basic information and episodes. They also lacked the ability to provide a positive perspective on negative memories, making it difficult to incorporate user feedback and revise the story. Furthermore, there were limited ways for users to access and download the generated story.

[1286] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1287] In this invention, the server includes means for inputting basic information from a user, means for generating questions based on the input basic information, means for generating questions using a generative AI model, means for presenting the generated questions to the user, means for collecting user responses, means for analyzing the collected responses and extracting keywords and episodes, means for generating a story based on the extracted keywords and episodes, means for generating questions to convert the user's negative memories into a positive perspective, means for integrating all episodes and generating a story, means for regenerating the story based on feedback provided by the user, means for generating sentences based on the keywords and episodes using the generative AI model, means for accessing and downloading the generated story, and means for saving the final version of the story. This provides a system that utilizes the user's basic information and episodes to generate a consistent story, convert negative memories into positive ones, and further modifies the story based on user feedback, making it accessible and downloadable.

[1288] "Basic information" refers to information related to the user's profile, such as the user's name, date of birth, occupation, and hobbies.

[1289] A "question" is an inquiry related to a specific topic that is generated by the server based on the user's basic information.

[1290] A "generative AI model" refers to an algorithm that generates text using natural language processing technology, and includes, for example, GPT-4.

[1291] "User's response" is the reply entered by the user in response to the question posed by the server.

[1292] "Keywords" are important words or phrases extracted from the user's answers.

[1293] An "episode" is a detail of a particular experience or event extracted from a user's responses.

[1294] A "story" is a coherent written story generated based on the user's basic information, keywords, and episodes.

[1295] "Negative memories" refer to unpleasant events or emotions that a user has experienced in the past.

[1296] A "positive perspective" is a way of finding a positive interpretation or impact on negative memories.

[1297] "Feedback" refers to corrections or additional information that users provide to the generated story.

[1298] "Regeneration" is the process of regenerating an existing story based on user feedback.

[1299] The present invention provides a system that allows users to have a positive perspective on their lives and generate a coherent narrative. This system operates in cooperation with users, terminals, and a server.

[1300] Hardware and software configuration

[1301] User terminal: Uses devices such as PCs, smartphones, tablets, etc. Accesses the system through browser software (e.g., Google Chrome, Safari).

[1302] Server: Built using Python frameworks (Django, Flask). Databases use MySQL or PostgreSQL, and libraries such as NLTK and Spacy are used for natural language processing. GPT-4 is used as the generative AI model.

[1303] System Operation

[1304] First, the user accesses the system using a browser. The terminal displays an online interface and prompts the user to enter basic information (such as name, date of birth, etc.). After the user enters the required information, the terminal sends the information to the server in JSON format.

[1305] Next, the server uses a Python algorithm to analyze the user's basic information. Based on that, it automatically generates a list of questions appropriate to the user's profile. For example, questions related to "memories of school days," "occupation," and "hobbies" are generated. The server then sends this list of questions to the terminal in JSON format.

[1306] The device displays the received question to the user. The user enters an answer to the question (e.g., "Who was the teacher who had the greatest influence on you during your school days?" and answers "Professor Tanaka was the most influential"), which the device then sends to the server in real time.

[1307] The server stores the answers in a database and then uses natural language processing technology to extract keywords and important anecdotes from the answers. Based on the extracted information, a generative AI model (GPT-4) is used to generate parts of a story. The generated text is then presented to the user via their device.

[1308] The user checks the story generated on the device and enters corrections or additional information as necessary. The device then sends the user's feedback to the server and regenerates the story.

[1309] For negative memories, the server generates a new question (e.g., "How do you think your painful experiences have helped you today?") and executes a process to convert the memory into a positive perspective. The user inputs an answer such as "My painful experiences have made me more resilient," and the device sends this answer to the server.

[1310] Finally, the server integrates all episodes and responses into a single, coherent story that emphasizes the user's growth and learning. The user can review the final version of the story on their device and, if there are no problems, request that it be saved. The server saves the final version of the story in its database and, if desired, generates a download link in PDF format.

[1311] Examples of concrete examples and prompts

[1312] As a concrete example, consider the case where a user requests the system to "talk about memories from their school days." The user enters basic information and responds, "Professor Tanaka was the person who had the greatest influence on me." The server then analyzes "my encounter with Professor Tanaka and the impact he had on me" and constructs it as part of a story. Furthermore, in response to the question, "How have your difficult experiences helped you today?" the user responds, "My difficult experiences have given me perseverance," and this is also integrated into the story.

[1313] An example of a prompt is:

[1314] "Tell us about the teacher who had the greatest influence on you as a student. How did they influence you?"

[1315] "How have your painful experiences helped you become who you are today?"

[1316] Examples include:

[1317] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1318] Step 1:

[1319] A user accesses the system using their own terminal. When the user opens a browser and accesses the specified URL, the system's web page is displayed. The terminal provides the user with an interface for entering basic information. This information includes name, date of birth, occupation, hobbies, etc. When the user enters the basic information and clicks the send button, the terminal sends the information to the server in JSON format (input: basic information, output: transmitted data).

[1320] Step 2:

[1321] The server analyzes the basic information received from the user. Specifically, it uses a Python algorithm to analyze the information and create a user profile (input: basic information, output: user profile). The server then uses a generative AI model to generate a customized list of questions based on the user profile (input: user profile, output: list of questions). The generated questions are based on a theme, such as "memories from school" or "hobbies."

[1322] Step 3:

[1323] The server sends the generated question list to the terminal in JSON format (input: question list, output: transmitted data). The terminal displays the received question list to the user. The user enters answers to the displayed questions. For example, in response to the question "Who was the teacher who had the greatest influence on you during your student days?" the user might answer "Professor Tanaka had the greatest influence on me." When the user enters the answer and clicks the send button, the terminal collects the answer and transmits it to the server in real time (input: answer, output: transmitted data).

[1324] Step 4:

[1325] The server receives the user's answers and stores them in a database, typically a MySQL or PostgreSQL database (input: answers, output: saved data). The server then uses natural language processing techniques to analyze the saved answers. It uses libraries such as Python's NLTK and Spacy to extract important keywords and episodes from the answers (input: answer data, output: keywords, episodes).

[1326] Step 5:

[1327] The server generates part of a story based on the extracted keywords and episodes. Using a generative AI model (e.g., GPT-4), it generates part of a story about "Meeting with Professor Tanaka and its impact" (input: keywords, episodes, output: generated part of the story). The generated text is sent to the device.

[1328] Step 6:

[1329] The device displays part of the generated story to the user. The user checks the content and enters corrections or additional information as needed. For example, the user may add details such as "Professor Tanaka's class was particularly interesting in math" (Input: User feedback, Output: Corrected data). The device collects the user's feedback and sends it back to the server (Input: Corrected data, Output: Sent data).

[1330] Step 7:

[1331] The server generates a new question to convert the user's negative memories into a positive one. For example, it generates a question such as, "How do you think your painful experiences have helped you today?" (Input: User's negative answer, Output: New question). The user responds to this question by saying, "My painful experiences have given me more patience," and the device collects this answer and sends it to the server (Input: Positive answer, Output: Transmitted data).

[1332] Step 8:

[1333] The server integrates all episodes and responses to generate a coherent story. It uses a generative AI model to generate text that highlights the user's growth and learning (input: episodes and responses, output: final story). The final story is sent to the device for the user to review.

[1334] Step 9:

[1335] The terminal displays the final version of the story to the user. The user checks the final version of the story and, if there are no problems, requests that it be saved. The server saves the final version of the story in a database so that the user can access it in the future (input: final confirmed data, output: saved data). In addition, the server generates a download link in PDF format if desired (input: saved data, output: download link).

[1336] (Application example 1)

[1337] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1338] In recent years, there has been a demand for improving customer experiences in brick-and-mortar stores, but it is difficult to provide personalized product recommendations and services to each individual customer. Furthermore, technologies for effectively leveraging customers' past experiences and preferences to provide specific and engaging customer experiences are still in their infancy. Furthermore, there is a need for flexible systems that can adapt based on customer feedback.

[1339] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1340] In this invention, the server includes means for inputting basic information from a user, means for generating questions based on the input basic information, means for presenting the generated questions to the user, means for collecting user responses, means for analyzing the collected responses and extracting keywords and episodes, means for constructing a story based on the extracted keywords and episodes, means for generating questions to convert the user's negative memories into a positive perspective, means for integrating all episodes and generating a story, means for saving the final version of the story, means for analyzing the user's preferences and past experiences from the collected responses and generating a personalized recommended product list, and means for presenting the generated recommended product list. This makes it possible to significantly improve customer experience by providing personalized recommended product lists in physical stores.

[1341] "Basic information" is information for identifying an individual, such as the user's name, date of birth, gender, occupation, and hobbies.

[1342] "Means for generating questions" refers to technology that automatically creates a list of appropriate questions based on the user's basic information and past answers.

[1343] A "means for presenting a question" is an interface or device that displays the generated question to the user.

[1344] The "means for collecting answers" refers to a technique for collecting answers entered by users as data.

[1345] "Means of analyzing and extracting keywords and episodes" refers to a technique that uses natural language processing technology to identify important words and events from the collected responses.

[1346] "Means of constructing a story" refers to the technique of creating a coherent story based on extracted keywords and episodes.

[1347] The "means for generating questions to convert a user's negative memories into a positive perspective" is a technique for creating additional questions to help the user view negative memories in a positive light.

[1348] "Narrative generation" is the technique of integrating all collected episodes to create a final story.

[1349] The "means for storing the final story" is a technique for storing the generated story in a database so that users can access it later.

[1350] The "means for generating an individualized recommended product list" is a technology for creating a product list optimized for a specific user based on the user's preferences and past experiences.

[1351] The "means for presenting a recommended product list" refers to an interface or device that displays the generated recommended product list to the user.

[1352] The process of entering basic information from the user is carried out using a device such as a smartphone or PC. After the user accesses the system, an interface for entering basic information such as name, date of birth, gender, occupation, and hobbies is displayed on the device. Once the user enters the basic information, the device sends the information to the server.

[1353] The server generates an appropriate list of questions based on the received basic information. The questions may relate to themes such as the user's memories from their school days, their occupation, or their hobbies. The generated questions are then sent back to the device, which then presents them to the user. An example of the initial question displayed is, "Who was the teacher who had the greatest influence on you during your school days?"

[1354] The user inputs answers to the questions through the device, such as "My math teacher had the biggest impact on me," and the device sends the answers to the server in real time.

[1355] The server analyzes the collected responses and uses natural language processing (NLP) technology to extract keywords and important anecdotes. For example, keywords such as "math teacher," "influenced me," and "fun classes" can be extracted from user responses.

[1356] The server then constructs part of a story based on these keywords and episodes. For example, it generates a sentence about "My encounter with my math teacher and its impact" and presents it to the user. The user can check the draft of this story through their device and provide corrections or additional information as needed. User feedback is then sent back to the server via their device.

[1357] For negative memories, the server generates new questions and executes a process to transform the user's negative experiences into positive ones. For example, a question such as "How has your painful experience helped you?" is generated. The user can then respond with "That experience helped me develop perseverance," thereby integrating the positive episode into the story.

[1358] Finally, the server combines all episodes and responses to generate a coherent story. This story highlights the user's growth and learning, and is then edited into a final version. The user can review the final version of the story through their device and, if satisfied, request that it be saved. The server saves this final version of the story in a database for future access by the user. A link is also provided to download the story in PDF format if desired.

[1359] As an application example, we will explain the case where the present invention is applied to a "personal shopping assistant" application in a physical store. When a user visiting a store accesses the system using smart glasses or a smartphone, a personalized list of recommended products is displayed based on the user's past shopping experiences and answers. This allows the user to enjoy shopping according to their preferences.

[1360] As an example of how to use a generative AI model, we will use the following prompt:

[1361] "Generate a story based on the user's experiences. Keywords: math teacher, impact, fun class"

[1362] In this way, the present invention allows users to generate personalized stories and recommended product lists based on their experiences, enhancing their experience in brick-and-mortar stores and other settings.

[1363] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1364] Step 1:

[1365] A user accesses the system through a terminal and inputs basic information (such as name, date of birth, gender, occupation, hobbies, etc.). The input basic information is sent from the terminal to the server. The input of this step is the basic information entered by the user, and the output is the basic information sent from the terminal to the server.

[1366] Step 2:

[1367] The server generates an appropriate list of questions based on the received basic information. Here, the server analyzes the basic information and automatically generates questions related to the user's memories from school, occupation, hobbies, etc. The input for this step is the basic information, and the output is the generated list of questions.

[1368] Step 3:

[1369] The generated question list is sent to the terminal, which then presents it to the user. The user confirms the questions on the terminal. The input of this step is the question list sent from the server, and the output is the questions presented to the user.

[1370] Step 4:

[1371] The user inputs an answer to the question through the terminal. For example, they input a specific answer such as "My math teacher had the biggest impact on me." The input of this step is the user's answer, and the output is the answer sent from the terminal to the server.

[1372] Step 5:

[1373] The server analyzes the collected answers and extracts keywords and important episodes using natural language processing (NLP) technology. For example, keywords such as "math teacher," "influenced me," and "fun class" can be extracted from the answers. The input for this step is the user's answer, and the output is the extracted keywords and episodes.

[1374] Step 6:

[1375] The server generates part of a story based on the extracted keywords and episodes. Using the generative AI model, it creates a sentence about, for example, "My encounter with my math teacher and its impact" and presents it to the user. The input for this step is keywords and episodes, and the output is the generated part of the story.

[1376] Step 7:

[1377] The user checks the generated draft of the story through the terminal and provides corrections or additional information as necessary. The user's feedback is sent back to the server through the terminal. The input of this step is the user's feedback, and the output is a part of the revised story.

[1378] Step 8:

[1379] The server generates questions to convert the user's negative answers into positive ones. For example, it provides new questions such as, "How has your painful experience helped you?" The input of this step is the negative answer, and the output is a newly generated question.

[1380] Step 9:

[1381] The user inputs a positive answer through the terminal and sends it to the server from the terminal. For example, the user provides an answer such as "That experience helped me develop perseverance." The input of this step is the user's positive answer, and the output is the positive answer sent from the terminal to the server.

[1382] Step 10:

[1383] The server integrates all episodes and responses to generate the final story, building a coherent narrative that highlights the user's growth and learning. The input for this step is all episodes and responses, and the output is the final story.

[1384] Step 11:

[1385] The server stores the final story, allowing users to access and review it via their devices, and also generates a link to download it in PDF format if desired. The input to this step is the final story, and the output is the stored story and a download link.

[1386] As a concrete example, a personal shopping assistant in a brick-and-mortar store might show a list of recommended products based on past shopping experiences and individual preferences via smart glasses or a smartphone when the user enters the store, using, for example, the following prompt:

[1387] "Generate stories based on user experiences. Keywords: math teacher, influenced, fun lessons."

[1388] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1389] The embodiments for carrying out the present invention will be described in detail below.

[1390] Users access the system using their own terminals. Once accessed, an interface for entering basic information (such as name and date of birth) is displayed on the terminal. The user enters the basic information, and the terminal sends the information to the server.

[1391] Based on the received basic information, the server generates a list of questions suited to the user. This list of questions is related to the user's past experiences and interests. The generated list of questions is sent to the terminal, which then presents it to the user. For example, the user might be asked a question such as, "Who was the teacher who had the greatest influence on you when you were a student?"

[1392] The user inputs answers to questions posed through the device. For example, they input a specific answer such as, "Professor Tanaka had the greatest influence on me." The device collects these answers and sends them to the server in real time.

[1393] The server stores the received responses in a database and uses NLP (natural language processing) technology to extract keywords and important episodes. For example, keywords such as "Mr. Tanaka," "influenced me," and "I enjoyed the math class" are extracted. The server is also equipped with an emotion engine that recognizes the user's emotions when analyzing the collected responses. This emotion recognition makes it possible to grasp the emotional tone and nuances of the responses.

[1394] Next, the server constructs part of a story based on the extracted keywords, episodes, and the user's emotions. Specifically, it generates a sentence about "My encounter with Professor Tanaka and its impact" and presents it to the user. The user can review this intermediate result through their device and provide corrections or additional information as necessary. The device then collects feedback from the user and sends it back to the server.

[1395] For negative memories, the server generates new questions and executes a process to convert them into a positive perspective. This process utilizes the emotion engine again to present appropriate questions based on the user's emotions. For example, a question such as "How do you think your painful experiences have helped you today?" is generated. The user enters a positive answer such as "My painful experiences have made me more patient," which is then collected by the device and sent to the server.

[1396] Finally, the server integrates all episodes, responses, and emotional information recognized by the emotion engine to generate a coherent story. This story emphasizes the user's growth and learning. The final version of the story is sent from the server to the user's device for review. The user can review this final version of the story and, if there are no problems, request to save it. The server saves the final version of the story in a database for the user to access in the future. If desired, a link to download it in PDF format is also generated.

[1397] As a concrete example, consider the case where a user makes a request to the system saying, "I want to talk about my memories from my school days." The user enters basic information and responds, "Professor Tanaka was the person who had the greatest influence on me." The server then analyzes "my meeting with Professor Tanaka and the impact he had on me" and constructs it as part of a story. Furthermore, in response to the question, "How has your difficult experience helped you today?" the user responds, "My difficult experience taught me perseverance," which is also integrated into the story. The emotion engine recognizes the user's positive emotions and emphasizes feedback and episodes accordingly.

[1398] In this way, the present invention provides a system that generates stories that allow users to look back on their own lives and positively perceive their self-growth and learning. By combining it with an emotion engine, it has the effect of gaining deeper understanding and empathy.

[1399] The processing flow will be explained below.

[1400] Step 1:

[1401] User: Accesses the system using a terminal and enters basic information such as name and date of birth into the interface.

[1402] Step 2:

[1403] Terminal: Sends the entered basic information to the server.

[1404] Step 3:

[1405] Server: Generates a list of questions appropriate for the user based on the received basic information.

[1406] Step 4:

[1407] Server: Sends the generated question list to the terminal.

[1408] Step 5:

[1409] Terminal: Presents the list of questions to the user.

[1410] Step 6:

[1411] User: Enters answers to the questions presented.

[1412] Step 7:

[1413] Terminal: Collects user responses and sends them to the server in real time.

[1414] Step 8:

[1415] Server: Stores the received answers in a database and extracts keywords and episodes using NLP (natural language processing) technology.

[1416] Step 9:

[1417] Server: Uses an emotion engine to recognize the user's emotions from the collected answers. For example, text analysis determines whether the user has a positive emotion such as "It was fun."

[1418] Step 10:

[1419] Server: Constructs parts of a story based on extracted keywords and episodes, and recognized user emotions.

[1420] Step 11:

[1421] Server: Sends the intermediate products of the constructed story to the terminal.

[1422] Step 12:

[1423] Terminal: Presents the intermediate product to the user.

[1424] Step 13:

[1425] User: Review the intermediate results and provide corrections or additional information as needed.

[1426] Step 14:

[1427] Terminal: Collects feedback from users and sends it to the server.

[1428] Step 15:

[1429] Server: Uses the emotion engine to generate new questions to turn negative episodes into positive ones, such as, "What did you learn from that painful experience?"

[1430] Step 16:

[1431] Server: Sends the generated question to the terminal.

[1432] Step 17:

[1433] Terminal: Presents the new question to the user.

[1434] Step 18:

[1435] User: Enter an answer to a new question.

[1436] Step 19:

[1437] Terminal: Collects the user's answers and sends them to the server.

[1438] Step 20:

[1439] Server: Uses an emotion engine to analyze the positive responses entered by users and integrate them into the story.

[1440] Step 21:

[1441] Server: Integrates all episodes, responses, and recognized emotional information to generate a coherent story.

[1442] Step 22:

[1443] Server: Sends the final story to the device.

[1444] Step 23:

[1445] Terminal: Presents the final story to the user.

[1446] Step 24:

[1447] User: Review the final version of the story and, if there are no problems, request to save it.

[1448] Step 25:

[1449] Server: Stores the final story in a database for future access by users.

[1450] Step 26:

[1451] Server: If desired, generate a link to download the PDF.

[1452] Example 2

[1453] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1454] Previously, story generation systems based on users' life experiences had a linear process from inputting information to generating a story, which meant that they were unable to fully reflect the user's emotions and individual experiences. Furthermore, they lacked the ability to appropriately respond to negative memories and complex analysis to maintain the consistency of the story.

[1455] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting basic information from a user, a means for generating questions based on the input basic information, a means for presenting the generated questions to the user, a means for collecting user answers, a means for analyzing the collected answers and extracting keywords and topics, a means for constructing a story based on the extracted keywords and topics, a means for generating questions to convert the user's negative memories into a positive perspective, a means for integrating all topics and generating a story, a means for saving the final version of the story, a means for analyzing answers using NLP technology, and a means including an emotion engine for recognizing and analyzing the user's emotions. This makes it possible to generate a story that reflects the user's emotions and individual experiences in detail and provide a consistent story.

[1456] A "user" is an individual who uses the system to enter basic information and answers to questions, providing the data necessary to generate a story.

[1457] "Basic information" refers to basic personal information such as the user's name, date of birth, and email address.

[1458] A "question" is a question that is generated based on the user's basic information and presented to the user.

[1459] An "answer" is information that a user enters in response to a question.

[1460] "Collection" refers to the process by which a terminal or server compiles information entered by a user.

[1461] "Analysis" refers to the process in which the server analyzes the collected responses and extracts keywords and topics.

[1462] "Keywords" are important words or phrases extracted from the user's responses.

[1463] "Topics" refer to important episodes or themes extracted from users' responses.

[1464] A "story" is a series of sentences or descriptions constructed based on extracted keywords and topics.

[1465] "Negative memories" are memories associated with unpleasant events or emotions experienced by the user.

[1466] A "positive perspective" is a perspective that reevaluates negative memories from a positive perspective.

[1467] "Synthesis" refers to the process by which the server combines multiple episodes or topics into a single coherent story.

[1468] The "final story" is the story in its completed form, incorporating all episodes and feedback.

[1469] "Preservation" refers to the process of recording the final story in a database and making it available for reuse as needed.

[1470] "NLP technology" refers to natural language processing technology, which is used to analyze user responses and extract keywords and topics.

[1471] An "emotion engine" is a technology that recognizes emotions in the analysis of collected responses and assists in generating stories based on those emotions.

[1472] MODE FOR CARRYING OUT THE INVENTION

[1473] The present invention is a story generation system based on a user's life experiences, and aims to generate a story that reflects the user's emotions and individual experiences from a positive perspective. This system uses various hardware and software to collect and analyze information provided by the user and generate a story that takes emotions into consideration based on that information. Specific embodiments for implementing the present invention will be described below.

[1474] Hardware Configuration

[1475] User's device: A device that can connect to the internet, such as a PC, smartphone, or tablet.

[1476] Server: A high-performance server equipped with a database, natural language processing (NLP) technology, and an emotion engine

[1477] Software Configuration

[1478] Web interface: Provides an interface for users to enter basic information and answers.

[1479] Database: A database such as MongoDB that stores user information and responses.

[1480] NLP technology: Software such as SpaCy and NLTK that extracts keywords and topics from user responses

[1481] Emotion engine: An engine that recognizes and analyzes user emotions, such as IBM Watson or Azure Cognitive Services

[1482] System processing overview

[1483] When a user accesses the system using their own terminal, an interface for entering basic information is displayed on the terminal. The user enters basic information (such as name and date of birth), and the terminal sends this information to the server. Based on the received basic information, the server generates a list of questions appropriate for the user. This list of questions is related to the user's past experiences and interests. The generated list of questions is sent to the terminal and presented to the user through the terminal.

[1484] The user enters answers to the questions posed, and the device collects and sends these answers to the server. The server stores the received answers in a database and uses NLP technology to analyze the answers and extract keywords and topics. For example, keywords such as "Mr. Tanaka," "I was influenced by him," and "I enjoyed the math class" are extracted. The emotion engine also recognizes and analyzes the user's emotions and generates a story based on those emotions.

[1485] The server then constructs a part of the story based on the extracted keywords, topics, and the user's emotions and presents it to the user. When the user provides feedback, the device collects it and sends it back to the server, which then modifies and reconstructs the story based on the feedback.

[1486] Even for negative memories, the server generates new questions and executes a process to convert them into a positive perspective. For example, a question might be posed: "How do you think your painful experiences have helped you today?" The user can then input a positive answer, such as "My painful experiences have made me more resilient," which is then collected by the device and sent to the server.

[1487] Finally, the server integrates all episodes, responses, and emotional information to generate a coherent story. The final story is presented to the user via their device, and if the user is satisfied with the content, the server stores the story in its database. If desired, a download link in PDF format is also provided.

[1488] Specific examples

[1489] As a concrete example, consider the case where a user makes a request to the system saying, "I want to talk about my memories from my school days." The user enters basic information and responds, "Professor Tanaka was the person who had the greatest influence on me." The server then analyzes "my meeting with Professor Tanaka and the impact he had on me" and constructs it as part of a story. Furthermore, in response to the question, "How has your difficult experience helped you today?" the user responds, "My difficult experience taught me perseverance," which is also integrated into the story. The emotion engine recognizes the user's positive emotions and emphasizes feedback and episodes accordingly.

[1490] An example of this prompt is:

[1491] Example prompt:

[1492] Please enter basic information such as your name and date of birth.

[1493] Who was the teacher who had the greatest influence on you when you were a student?

[1494] "How do you think your painful experiences have helped you become who you are today?"

[1495] In this way, the present invention provides a system that generates stories that allow users to look back on their own lives and positively perceive their self-growth and learning. By combining an emotion engine with NLP technology, it has the effect of gaining deeper understanding and empathy.

[1496] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1497] Program processing flow and detailed explanation of each processing step

[1498] Step 1:

[1499] User access and basic information entry

[1500] Input: The user enters the URL when accessing.

[1501] How it works: A user opens a browser and enters the system's URL to access the system. The terminal displays an interface for entering the user's basic information (name, date of birth, etc.).

[1502] Data processing: The user enters basic information into the form and clicks the submit button.

[1503] Output: The basic information entered is formed and sent from the terminal to the server.

[1504] Step 2:

[1505] Submit basic information and generate a questionnaire

[1506] Input: The device sends the user's basic information to the server.

[1507] How it works: When you press the submit button on the form, the device sends an HTTP POST request to the server, which receives the request, stores the information in a database, and creates a user profile.

[1508] Data processing: The server analyzes the received basic information and generates a list of relevant questions.

[1509] Output: The list of questions is sent from the server to the terminal.

[1510] Step 3:

[1511] Present a list of questions and enter answers

[1512] Input: A list of questions is presented to the user via a terminal.

[1513] How it works: The device displays a list of questions received from the server to the user, such as "Who was your teacher who had the greatest influence on you during your school days?"

[1514] Data processing: The user enters answers to questions.

[1515] Output: The user's answer is entered into the terminal.

[1516] Step 4:

[1517] Submit and analyze responses

[1518] Input: The user's answer is sent from the device to the server.

[1519] How it works: The device sends the answers entered by the user to the server as an HTTP POST request. The server receives the answers and stores them in a database.

[1520] Data processing: The server applies NLP models to the stored answers to extract keywords and topics.

[1521] Output: Keywords and topics are generated on the server.

[1522] Step 5:

[1523] Story generation and presentation to users

[1524] Input: Extracted keywords and topics are available on the server.

[1525] Operation: The server generates a story about "Meeting with Professor Tanaka and its influence" and sends it to the device.

[1526] Data processing: The server uses a generative AI model to create a story based on extracted keywords, topics, and emotional information.

[1527] Output: A portion of the story is generated and sent to the device.

[1528] Step 6:

[1529] User feedback and rebuild

[1530] Input: User feedback is entered into the terminal.

[1531] What it does: The terminal displays an input field that says "Please enter additional information." The user enters the additional information: "Mr. Tanaka taught math classes."

[1532] Data processing: The device sends the input feedback to the server.

[1533] Output: Feedback is sent to the server and used to reconstruct the story.

[1534] Step 7:

[1535] Dealing with negative memories

[1536] Input: The user's negative answer is available on the server.

[1537] How it works: The server uses the emotion engine to generate a question: "How has your painful experience helped you today?" and sends it to the device.

[1538] Data manipulation: The user enters a positive answer to a new question.

[1539] Output: A positive response is sent from the device to the server.

[1540] Step 8:

[1541] Generate and save the final story

[1542] Input: All episodes, responses, and emotional information are integrated into the server.

[1543] How it works: The server uses that information to generate a final story that emphasizes the user's growth and learning.

[1544] Data processing: The server organizes the data collected at each step and generates a coherent narrative.

[1545] Output: The final story is generated and sent to the device for the user to review, after which it is stored in a database and a download link in PDF format is provided if desired.

[1546] (Application example 2)

[1547] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1548] Conventional story generation systems have difficulty effectively generating personalized stories based on a user's personal information and emotions. Furthermore, they lack the functionality to easily share the generated stories or output prompts. Therefore, a system that allows users to share their experiences in a rich way is needed.

[1549] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting basic information from a user, means for generating questions based on the input basic information, means for presenting the generated questions to the user, means for collecting user answers, means for analyzing the collected answers and extracting keywords and episodes, means for constructing a story based on the extracted keywords and episodes, means for generating questions to convert the user's negative memories into a positive perspective, means for integrating all episodes and generating a story, means for saving the final version of the story, means for the user to view and share the generated story on social media, and means for outputting the generated story as a prompt sentence. This makes it possible to generate and share personalized stories based on the user's individual experiences.

[1550] "Means for users to input basic information" refers to a mechanism that provides an interface for users to input personal information such as their name and date of birth.

[1551] The "means for generating questions" is a system that automatically generates questions appropriate for the user based on the basic information entered.

[1552] The "means for presenting a question to a user" is a function for displaying the generated question on the user's terminal so that the user can confirm it.

[1553] "Means for collecting user answers" refers to the process in which a user inputs answers to questions presented to them and the system collects those answers.

[1554] "Means for analyzing responses and extracting keywords and episodes" refers to a method for analyzing collected responses using natural language processing technology and extracting important keywords and episodes.

[1555] The "means of constructing a story" is a mechanism for generating a coherent story based on extracted keywords and episodes.

[1556] The "means for generating questions to convert a user's negative memories into positive ones" is a system that automatically creates appropriate questions to convert a user's negative memories into positive ones.

[1557] "Means of integrating all episodes and generating a narrative" refers to the process of integrating various episodes into a single story and ultimately completing a coherent narrative.

[1558] The "means for storing the final story" is the mechanism by which the final story, as reviewed and revised by the user, is stored in the database.

[1559] "Means for users to view stories and share them on social media" refers to a feature that allows users to view the generated stories within the application and share them on social media platforms.

[1560] "Means for outputting a story as a prompt sentence" is a mechanism for outputting the generated story in the form of a prompt sentence to be input into a generative AI model.

[1561] The present invention relates to a system that allows users to create and share personalized stories based on their personal experiences and emotions. The system includes a series of processes that collect basic information from users, present the users with customized questions based on the information, and analyze the users' answers to generate a story.

[1562] The entire system is mainly composed of a server, a user terminal, and a network environment. Each step will be explained in detail below.

[1563] Enter and collect basic information

[1564] First, the user enters basic information using a device. This basic information includes personal information such as name and date of birth. The user provides this information through an input interface on the user device and sends it to the server. The user can use a mobile device such as a smartphone or tablet to do this.

[1565] Question generation and presentation

[1566] Based on the received basic information, the server generates a list of questions suited to the user. This list of questions is related to the user's past experiences and interests. The generated list of questions is then sent back to the terminal and presented to the user. The user then enters answers to the questions.

[1567] Response collection and analysis

[1568] The user's answers are then sent in real time from the device to a server, which analyzes the collected answers and extracts important keywords and anecdotes using natural language processing (NLP) techniques. For example, the NLP model can use the Hugging Face Transformers library.

[1569] Story Building

[1570] The server constructs part of a story based on the extracted keywords and episodes. Furthermore, if negative memories are found in the user's answers, it generates additional questions to convert them into a positive perspective. This process uses an emotion engine to accurately recognize and analyze the user's emotions.

[1571] Creating and saving the final story

[1572] All episodes are integrated into a single coherent story. Users can review this final version of the story through their devices and provide corrections or additional information as needed. The final version of the story is stored on the server, allowing users to access it in the future.

[1573] Sharing and publishing stories

[1574] The generated stories can be viewed and reviewed by users, and can also be shared on social media.The system also has a function to output the stories as prompts to be input into generative AI models.

[1575] Specific examples

[1576] For example, if a user requests the system to "talk about memories from my school days," the user might respond by saying, "Professor Tanaka was the person who had the greatest influence on me." The server then analyzes "my meeting with Professor Tanaka and the impact he had on me" and constructs it as part of a story. Furthermore, in response to the question, "How have your difficult experiences helped you today?" the user might respond, "My difficult experiences have given me perseverance," and this would also be integrated into the story.

[1577] This system allows users to generate and share personalized stories based on their individual experiences. The generated stories are also output as prompts, such as:

[1578] A user requested to talk about his memories from his school days. He answered that Professor Tanaka had the greatest influence on him, and that his difficult experiences had given him perseverance.

[1579] This paragraph will be used as a prompt when inputting the user's story into the generative AI model.

[1580] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1581] Step 1:

[1582] A user accesses the system using their own terminal and enters basic information, such as name and date of birth. The terminal sends this information to the server. Input is done using a text field, and transmission is via an HTTP POST request. The output is the transfer of the basic information to the server.

[1583] Step 2:

[1584] The server generates a list of questions appropriate for the user based on the received basic information. During this process, the basic information is analyzed and questions related to the user's interests and past experiences are selected. Data processing involves querying the database for relevant questions and organizing them in a list format. The output is the generated list of questions.

[1585] Step 3:

[1586] The server sends the generated question list to the user's terminal. The user terminal displays an interface to present the question list to the user. This interface displays each question as text and provides a text box for entering an answer. The output is a question list displayed on the user's terminal.

[1587] Step 4:

[1588] The user inputs answers to the questions posed, and the terminal collects these answers. The collected answers are sent to the server in real time. The input is the user's text input, and the output is the transfer of the answer data to the server.

[1589] Step 5:

[1590] The server analyzes the received answers and extracts important keywords and episodes. This analysis uses natural language processing (NLP) techniques. Specifically, it processes the answer data using Hugging Face's Transformers library to perform entity recognition and sentiment analysis. The input is the user's answer data, and the output is the extracted keywords and episodes.

[1591] Step 6:

[1592] The server constructs part of a story based on the extracted keywords and episodes. If negative memories are detected during this process, it generates follow-up questions to convert them into positive perspectives. The output is an initial story proposal and, if necessary, follow-up questions.

[1593] Step 7:

[1594] The server sends the generated story idea to the user's device, where the user can review and modify it. Any additional information or modifications provided by the user are also sent to the server in real time. The input is the story idea sent from the server, and the output is the user's feedback.

[1595] Step 8:

[1596] The server generates the final version of the story based on user feedback. This involves data processing to integrate all episodes into a single coherent story. The generated story is stored in a database. The input is user feedback and the output is the final version of the story.

[1597] Step 9:

[1598] The server sends the final story to the user's device for viewing and sharing. Users can share the generated story on social media. The story is also output in the form of a prompt, which is used as input to the generative AI model. The input is the final story, and the output is the story share and prompt.

[1599] 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.

[1600] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1601] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1602] 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.

[1603] 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 includes both affect 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.

[1604] 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.

[1605] 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).

[1606] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, 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.

[1607] 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."

[1608] 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.

[1609] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1610] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1611] 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.

[1612] 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.

[1613] 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.

[1614] 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. Another processor is 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.

[1615] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1616] 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.

[1617] 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.

[1618] 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.

[1619] 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.

[1620] The following is further disclosed regarding the above embodiment.

[1621] (Claim 1)

[1622] a means for inputting basic information from a user;

[1623] A means for generating questions based on the input basic information;

[1624] means for presenting the generated question to a user;

[1625] a means for collecting user responses;

[1626] A means of analyzing the collected responses and extracting keywords and episodes,

[1627] A method for constructing a story based on extracted keywords and episodes,

[1628] A means for generating questions to convert a user's negative memories into a positive perspective;

[1629] A way to integrate all the episodes and generate a story,

[1630] A system that includes a means to preserve the final version of the story.

[1631] (Claim 2)

[1632] 10. The system of claim 1, further comprising means for modifying the story based on feedback provided by the user.

[1633] (Claim 3)

[1634] 10. The system of claim 1, further comprising means for allowing a user to access and download the generated stories.

[1635] "Example 1"

[1636] (Claim 1)

[1637] a means for inputting basic information from a user;

[1638] A means for generating questions based on the input basic information;

[1639] means for presenting the generated question to a user;

[1640] a means for collecting user responses;

[1641] A means of analyzing the collected responses and extracting keywords and episodes,

[1642] A method for constructing a story based on extracted keywords and episodes,

[1643] A means for generating questions to convert a user's negative memories into a positive perspective;

[1644] A way to integrate all the episodes and generate a story,

[1645] A means for users to review the generated stories and provide any necessary corrections or additional information;

[1646] A means to access and download the generated stories;

[1647] A system that includes a means to preserve the final version of the story.

[1648] (Claim 2)

[1649] 10. The system of claim 1, further comprising means for regenerating the story based on feedback provided by the user.

[1650] (Claim 3)

[1651] 2. The system of claim 1, further comprising means for generating sentences based on keywords and episodes using a generative AI model.

[1652] "Application Example 1"

[1653] (Claim 1)

[1654] a means for inputting basic information from a user;

[1655] A means for generating questions based on the input basic information;

[1656] means for presenting the generated question to a user;

[1657] a means for collecting user responses;

[1658] A means of analyzing the collected responses and extracting keywords and episodes,

[1659] A method for constructing a story based on extracted keywords and episodes,

[1660] A means for generating questions to convert a user's negative memories into a positive perspective;

[1661] A way to integrate all the episodes and generate a story,

[1662] A means of preserving the final version of the story;

[1663] A means for analyzing the user's preferences and past experiences from the collected answers and generating a personalized list of recommended products;

[1664] A means for presenting the generated recommended product list;

[1665] A system including:

[1666] (Claim 2)

[1667] 10. The system of claim 1, further comprising means for modifying the story and the recommended product list based on feedback provided by the user.

[1668] (Claim 3)

[1669] 10. The system of claim 1, further comprising means for allowing a user to access and download the generated stories and recommended product lists.

[1670] "Example 2: Combining Emotion Engines"

[1671] (Claim 1)

[1672] a means for inputting basic information from a user;

[1673] A means for generating questions based on the input basic information;

[1674] means for presenting the generated question to a user;

[1675] a means for collecting user responses;

[1676] A means of analyzing the collected responses and extracting keywords and topics,

[1677] A means of constructing a story based on extracted keywords and topics,

[1678] A means for generating questions to convert a user's negative memories into positive perspectives;

[1679] A way to integrate all topics and generate a story,

[1680] A means of preserving the final version of the story;

[1681] A means of analyzing the responses using NLP techniques;

[1682] A system including means including an emotion engine for recognizing and analyzing user emotions.

[1683] (Claim 2)

[1684] 10. The system of claim 1, further comprising means for modifying the story based on feedback provided by the user.

[1685] (Claim 3)

[1686] 10. The system of claim 1, further comprising means for allowing a user to access and download the generated stories.

[1687] "Application example 2 when combining emotion engines"

[1688] (Claim 1)

[1689] a means for inputting basic information from a user;

[1690] A means for generating questions based on the input basic information;

[1691] means for presenting the generated question to a user;

[1692] a means for collecting user responses;

[1693] A means of analyzing the collected responses and extracting keywords and episodes,

[1694] A method for constructing a story based on extracted keywords and episodes,

[1695] A means for generating questions to convert a user's negative memories into a positive perspective;

[1696] A way to integrate all the episodes and generate a story,

[1697] A means of preserving the final version of the story;

[1698] A way for users to view and share the generated stories on social media;

[1699] a means for outputting the generated story as a prompt sentence;

[1700] A system including:

[1701] (Claim 2)

[1702] 10. The system of claim 1, further comprising means for modifying the story based on feedback provided by the user.

[1703] (Claim 3)

[1704] 10. The system of claim 1, further comprising means for allowing a user to access and download the generated stories. [Explanation of symbols]

[1705] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for inputting basic information from a user; A means for generating questions based on the input basic information; means for presenting the generated question to a user; a means for collecting user responses; A means of analyzing the collected responses and extracting keywords and episodes, A method for constructing a story based on extracted keywords and episodes, A means for generating questions to convert a user's negative memories into a positive perspective; A way to integrate all the episodes and generate a story, and a means for saving the final version of the story.

2. 10. The system of claim 1, further comprising means for modifying the story based on feedback provided by the user.

3. 10. The system of claim 1, further comprising means for allowing a user to access and download the generated stories.

Images