System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting personal text and audio data; means for analyzing the text and audio data; means for generating a personal story based on the analysis; means for converting the generated story to audio; and means for storing and providing the converted audio in a form that can be played back by a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Currently, there is a lack of methods for systematically and emotionally recording personal memories and experiences. In particular, there is a lack of effective ways for older people to share their long lives with their families and for younger people to reflect on their growth. There are also limited methods for recording personal histories in audio format and easily playing them back. This makes it difficult to strengthen family ties and preserve and utilize individual historical anecdotes in a valuable way. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system that includes the following means: means for collecting personal text data and audio data, means for analyzing the text data and audio data, means for generating a personal narrative based on the analysis results, means for converting the generated narrative into audio, and means for saving the converted audio and providing it in a form that can be played by a user. The means for analyzing the text data and audio data uses natural language processing technology and speech recognition technology, and the means for converting into audio uses text-to-speech technology. This provides a system that generates moving, personal narratives that can be easily saved and played back, allowing for the effective recording and sharing of individual historical anecdotes.

[0006] "Text data" refers to data recorded in the form of text created by an individual, including diary entries, emails, messages, and the like.

[0007] "Audio data" refers to data containing audio information recorded by an individual, including voice memos and recordings of conversations.

[0008] "Means for collection" refers to a hardware or software interface for inputting and storing text and voice data.

[0009] "Means for analysis" refers to data processing techniques for analyzing collected text and audio data and extracting important information and emotions.

[0010] "Generative means" refers to a program or algorithm that creates a personal narrative based on the analysis results.

[0011] "Convert-to-speech means" refers to text-to-speech (TTS) technology for converting the generated text-based narrative into audio format.

[0012] "Means for storing" refers to a mechanism for storing the generated audio data in a storage device for later access and playback by a user.

[0013] "Means for providing in a reproducible form" refers to a system for providing audio data via a playback link or dedicated application so that users can easily play it.

[0014] "Natural language processing technology" refers to computer science and artificial intelligence technology that allows for understanding and analyzing text data in the same way as humans do.

[0015] "Speech recognition technology" refers to technology for converting voice data into text format, enabling the content of the voice to be mechanically understood and analyzed.

[0016] "Text-to-speech technology" refers to technology for generating natural-sounding speech from text data, enabling programmed speech synthesis. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention is a system that collects and analyzes an individual's past text data and voice data to generate a personal story and provide it in voice format. Hereinafter, a specific embodiment of the present invention will be described.

[0039] Data collection

[0040] Users: Users collect their own past text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings) through dedicated applications or web interfaces. Users can select and upload this data to the platform.

[0041] Device: The user's device uploads the collected data to the server in the appropriate format. The data is then formatted and compressed as necessary and securely sent to the server.

[0042] Data analysis

[0043] Server: The server analyzes the text and voice data received from the user. The text data is analyzed using natural language processing technology to extract important episodes and emotions. The voice data is converted into text using speech recognition technology. This allows for an integrated analysis of the text and voice data to extract information needed to form a moving personal story.

[0044] Story Generation

[0045] Server: Based on the analysis results, the server generates a personal narrative. The narrative generation is performed using an AI model to create a moving story that reflects the user's past experiences and emotions. This brings the user's past memories back to life in a new way, providing a visual and emotional experience.

[0046] Audio conversion

[0047] Server: The generated story is converted into audio format using text-to-speech technology, which allows the story to be delivered to the user in a natural voice. The technology used for speech conversion achieves high-quality speech synthesis, producing speech that is easily understandable to the user.

[0048] Data storage and playback

[0049] Server: The converted voice data is stored on the server and managed for user access. It identifies which data belongs to which user and sets appropriate access permissions.

[0050] Device: The user's device provides an interface for playing the generated audio data. The user can easily play the generated story through the provided link or application. By clicking the play button, the audio will start and the user can relive the past memory.

[0051] Specific examples

[0052] For example, user A wants to share his or her past growth records with his or her family. A uploads his or her diary and voice memos to a dedicated app. The app formats the data and sends it to a server. The server analyzes the data and extracts A's important episodes and emotions. Based on the analysis results, an AI model generates a personal story and converts it into audio using text-to-speech technology. The audio data is then stored on the server, and A can play back and share the generated story with his or her family via a provided link. This system brings A's memories back to life in a moving way, deepening the bond between the family.

[0053] In this way, the system of the present invention can provide a new experience to the user by reproducing personal memories and experiences as an audio story.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] User: Using a dedicated application or web interface, the user collects their own text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings). The user selects this data and clicks the upload button to send it to the system.

[0057] Step 2:

[0058] Device: The user's device formats the collected text and audio data and converts it into the appropriate format. This data is compressed if necessary and prepared for transmission to the server. The destination server URL is set and the data is uploaded to the server.

[0059] Step 3:

[0060] Server: The server receives the uploaded data and stores it in storage. Text data is stored in a database, and audio data is stored in an appropriate directory. This allows data to be managed consistently within the system.

[0061] Step 4:

[0062] Server: Applying natural language processing (NLP) technology to the received text data, the server analyzes the context and sentiment. Important episodes and sentiments are tagged and the analysis results are generated. Audio data is converted into text using speech recognition technology and analyzed in the same way.

[0063] Step 5:

[0064] Server: Based on the analyzed data, the AI ​​model generates a personal narrative. The AI ​​model combines episodes and emotions extracted from the text data and converted audio data to create a moving and personal story. The text of the generated narrative is generated.

[0065] Step 6:

[0066] Server: The generated story text is converted into audio format using Text-to-Speech (TTS) technology, which synthesizes the story text into natural voice. The generated audio file is stored on the server.

[0067] Step 7:

[0068] Server: The converted voice data is managed in a form that can be accessed by users. Voice data is identified for each user and appropriate access rights are set. Users can access the generated voice data through an individual link.

[0069] Step 8:

[0070] Device: The user's device provides a playback interface for the generated audio data. The user can click the provided link to play the audio data through a browser or dedicated app. The playback interface should be designed to be intuitive and easy to use.

[0071] Step 9:

[0072] Users: Users can play the generated audio data and listen to their personal stories, which can bring back memories and experiences, providing new emotional experiences. Users can also share this audio data with their family and friends.

[0073] Example 1

[0074] 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."

[0075] Conventional story generation systems that use personal data have insufficient data collection and analysis, making it difficult to generate high-quality stories that reflect the user's individual experiences and emotions. Furthermore, when converting the generated stories into speech, there is a lack of means to provide natural, high-quality speech. Furthermore, insufficient management of data storage and playback makes it difficult for users to easily play and experience the generated stories.

[0076] 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.

[0077] In this invention, the server includes means for collecting personal text data and voice data, means for analyzing the text data and voice data, means for generating a personal story based on the analysis results, means for converting the generated story into voice, and means for saving the converted voice and providing it in a form that can be played back by the user. This enables the generation of high-quality stories that reflect the individual experiences and emotions of the user and the conversion of voice into natural, easy-to-listen-to voice, allowing the user to easily play back and experience the generated story.

[0078] "Personal text data" refers to data in written form generated by the user, such as diary entries, emails, and memos.

[0079] "Audio data" refers to data based on user-generated voice, such as voice memos, recordings, and call logs.

[0080] "Means of collection" refers to the technical means of collecting and uploading personal text and voice data to the platform through a dedicated application or web interface.

[0081] The "analyzing means" refers to a technical means that uses natural language processing technology and speech recognition technology to analyze the user's text data and voice data and extract important episodes and emotional information.

[0082] "Means for generating personal stories" refers to technical means that use an AI model based on the analysis results to create a story that reflects the user's past experiences and emotions.

[0083] "Convert-to-speech means" means a technical means for converting the generated text-based narrative into a natural, audible audio format using text-to-speech technology.

[0084] "Means of storing and providing in a form that can be played by users" refers to the technical means of managing and providing the converted audio data by storing it on a server and setting appropriate access rights so that users can play the audio data through a link or application.

[0085] "Natural language processing technology" is a technology for analyzing text data, and includes morphological analysis, sentiment analysis, etc.

[0086] "Speech recognition technology" is a technology for converting voice data into text format.

[0087] A "generative AI model" is an artificial intelligence technique used to generate a personal narrative based on analyzed data.

[0088] "Text-to-speech technology" is technology that converts text into speech.

[0089] "Formatting and compression means" refers to the technical means by which collected data is converted into a standardized format and compressed to increase the efficiency of data transmission.

[0090] The present invention is a system that collects and analyzes an individual's past text data and voice data to generate a personal story and provide it in voice format. Hereinafter, a specific embodiment of the present invention will be described.

[0091] Data collection

[0092] User: Using a dedicated application or web interface, users collect their own past text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings). This data is collected in one place through the application and uploaded to the platform.

[0093] Terminal: The user's terminal formats the collected data into a standardized format (e.g., UTF-8 for text data, WAV for audio data), and compresses it (e.g., ZIP format) to improve data transmission efficiency. The formatted and compressed data is then securely sent to the server.

[0094] Data analysis

[0095] Server: The server decompresses and analyzes the text and audio data received from the user. The text data is analyzed using natural language processing technology, which includes morphological analysis and sentiment analysis, and the specific software used is "NLPPipeline." The audio data is converted into text format using speech recognition technology, which uses "SpeechToTextConverter." The converted text data is also analyzed in the same way, and important episodes and emotional information are extracted.

[0096] Story Generation

[0097] Server: Based on the analysis results, the server generates a personal story using the AI ​​model "StoryGeneratorAI." This AI model creates a moving story that reflects the user's past experiences and emotions. The generated story is designed to bring back the user's past memories in a new form.

[0098] Audio conversion

[0099] Server: The generated story is converted into audio format using the text-to-speech technology "TextToSpeechSynthesizer." This technology converts the story into natural, high-quality audio that is easy for users to understand. During the speech conversion process, the tone, intonation, rhythm, etc. are adjusted to produce a voice that sounds like a natural conversation.

[0100] Data storage and playback

[0101] Server: The converted voice data is stored on the server. When stored, the data is identified for each user and appropriate access rights are set. The voice data stored in the database is managed to protect it from unauthorized access.

[0102] Device: The user's device provides a playback interface for the generated audio data using the application "StoryPlaybackApp." The user can play the generated audio data through the provided link or application. By clicking the play button, the user can enjoy the story as audio.

[0103] Specific examples

[0104] For example, suppose user A wants to share his or her past growth records with his or her family. User A uploads his or her diary and voice memos to a dedicated app. The app converts this data into a standard format and sends it to the server. The server then analyzes the data using "NLPPipeline" and "SpeechToTextConverter" to extract important episodes and emotional information. Based on the analysis results, the AI ​​model "StoryGeneratorAI" generates a personal story and converts it into audio using "TextToSpeechSynthesizer." The audio data is then stored on the server, and User A can play back and share the generated story with his or her family through "StoryPlaybackApp."

[0105] This system brings Mr. A's memories back to life in a moving way and deepens his bond with his family.

[0106] Prompt Sentence Examples

[0107] User A has uploaded his / her past diary entries and voice memos to the system. Explain the process of generating a moving personal story from these, converting it into audio, and playing it back.

[0108] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0109] Step 1:

[0110] User: Using a dedicated application or web interface, users upload their past text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings) as input. Data collection begins when the user clicks the upload button within the application. This data is then saved on the device via the application.

[0111] Input: Text data, audio data

[0112] Output: Text and audio data stored on the device

[0113] Step 2:

[0114] Terminal: The terminal formats the collected text and audio data into a standardized format (e.g., UTF-8 format for text data and WAV format for audio data). After formatting, the data is compressed (e.g., ZIP format) to improve transmission efficiency. The formatted and compressed data is sent to the server using a secure protocol (e.g., HTTPS).

[0115] Input: Text and voice data stored on the device

[0116] Output: Formatted and compressed data file sent to the server

[0117] Step 3:

[0118] Server: The server decompresses the received compressed file and obtains the text and audio data. The text data is then analyzed using the natural language processing technology "NLPPipeline." Morphological analysis and sentiment analysis are performed to extract important episodes and emotional information. Meanwhile, the audio data is converted to text format using the speech recognition technology "SpeechToTextConverter," and then analyzed in the same way.

[0119] Input: Compressed file received on the server

[0120] Output: Text data with important episodes and emotional information extracted

[0121] Step 4:

[0122] Server: Based on the analyzed data, the server uses the AI ​​model "StoryGeneratorAI" to generate a personal story. The AI ​​model takes into account the user's past experiences and emotions and outputs a moving story in chronological order. The generation process uses neural networks and generative probabilities.

[0123] Input: Text data with important episodes and emotional information extracted

[0124] Output: Generated personal narrative (text format)

[0125] Step 5:

[0126] Server: The generated story is converted into audio using the TextToSpeechSynthesizer, which adjusts the sound quality and intonation to produce a natural, easy-to-listen-to voice.

[0127] Input: Generated personal story (text format)

[0128] Output: The story converted into audio format

[0129] Step 6:

[0130] Server: The converted voice data is stored on the server. Each user's data is identified and appropriate access rights are set. The stored voice data is managed using server-side security features to protect it from unauthorized access.

[0131] Input: A story converted into audio format

[0132] Output: Audio data stored on the server

[0133] Step 7:

[0134] Device: The user's device provides a playback interface for the generated audio data using the "StoryPlaybackApp". The user can access the generated audio data through the provided link or application and play the story by clicking the play button.

[0135] Input: Audio data stored on the server

[0136] Output: Audio data provided through a playback interface

[0137] The above is the specific processing flow of the program for this system. Each step works closely together to provide a moving story that reflects the individual's past experiences and emotions, bringing a new experience to the user.

[0138] (Application example 1)

[0139] 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."

[0140] Existing virtual stores have difficulty providing personalized services and product recommendations based on customers' past purchase history and feedback. This prevents them from providing services tailored to each individual customer, hindering customer satisfaction. Furthermore, conventional systems have difficulty effectively analyzing collected past data to extract meaningful information and provide it to customers in real time.

[0141] 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.

[0142] In this invention, the server includes means for collecting personal text data and voice data, means for analyzing the text data and voice data, means for generating a personal story based on the analysis results, means for converting the generated story into voice, means for saving the converted voice and providing it in a form that can be played by the user, means for collecting and analyzing personal data such as the customer's past purchase history, feedback, chat logs, etc., means for generating personalized recommended products and services based on the analyzed data, and means for announcing the generated recommended products and services in voice format. This makes it possible to effectively utilize the customer's past data and provide personalized services and product recommendations in real time.

[0143] - "Personal text data" refers to text data generated or owned by a user, including diaries, emails, chat logs, etc.

[0144] "Audio data" refers to data containing user voice information, including voice memos, recording files, call history, and the like.

[0145] The "analyzing means" refers to a device or software program for analyzing collected text data and audio data using digital signal processing or natural language processing techniques.

[0146] A "means for generating a personal story" is a device or software program that uses an artificial intelligence model to generate a story that reflects the user's past information and emotions based on the analysis results.

[0147] A "narrative-to-audio means" is a device or software program that converts the generated narrative into audio form using text-to-speech technology.

[0148] The "means for storing the converted audio and providing it in a form that can be played by a user" is a device or software program that stores the audio data and makes it playable through a user-accessible interface.

[0149] "Purchase history" is data that includes information about products that a user has purchased in the past.

[0150] "Feedback" refers to data including user evaluations and impressions of products and services.

[0151] A "chat log" is a conversation history that a user has had through a chat application.

[0152] "Personalized recommended products and services" are those that recommend products and services that are most suitable for a user based on the user's past data.

[0153] The "means for providing generated recommended products and services in the form of voice" is a device or software program for notifying the user of personalized recommended products and services by voice.

[0154] This invention is a system that collects and analyzes personal text and voice data to generate a personal story and deliver it in audio format. It also includes a function to provide personalized audio recommendations for products and services based on data such as the customer's past purchase history, feedback, and chat logs.

[0155] Data collection

[0156] User:

[0157] Users collect their past text data (diaries, emails, chat logs) and audio data (voice memos, recordings) through a dedicated application or web interface. Users can select and upload this data to the platform. The history of users' past purchases and feedback are also collected.

[0158] Device:

[0159] The user's device uploads the collected data to the server in the appropriate format. The data is then reshaped and compressed as necessary and sent securely to the server.

[0160] Data analysis

[0161] server:

[0162] The server analyzes the text and voice data received from the user. This is done using natural language processing and voice recognition technologies. The text data is analyzed using a Transformer-based natural language processing model (e.g., BERT or GPT-3) to extract important episodes and emotions. The voice data is converted into text format using voice recognition technology. This allows for an integrated analysis of the text and voice data, and meaningful information is extracted from the customer's behavioral history.

[0163] Narrative and recommendation generation

[0164] server:

[0165] Based on the analysis results, a personal narrative is generated using an AI model (e.g., GPT-2 or GPT-3). This creates an inspiring story that reflects the user's past experiences and emotions. Furthermore, personalized product and service recommendations are also generated using the AI ​​model based on the analyzed personal data.

[0166] Voice conversion and guidance

[0167] server:

[0168] The generated story and recommendations are converted into audio format using text-to-speech technology (e.g., gTTS), which allows the story and product recommendations to be delivered to the user in a natural voice. The technology used for speech conversion achieves high-quality speech synthesis, producing speech that is easily understandable to the user.

[0169] Data storage and playback

[0170] server:

[0171] The converted voice data is stored on a server and managed so that users can access it. The data is identified as belonging to each user, and appropriate access rights are set.

[0172] Device:

[0173] The user's device provides a playback interface for the generated audio data, and the user can easily play the generated story and recommended products through the provided links or applications, allowing the user to experience past memories in a new way or check out new products and services.

[0174] Specific examples

[0175] For example, user A wants to share his or her past growth records with his or her family. A uploads his or her diary and voice memos to a dedicated app. The app formats the data and sends it to a server. The server analyzes the data and extracts A's important episodes and emotions. Based on the analysis results, an AI model generates a personal story and converts it into audio using text-to-speech technology. The audio data is then stored on the server, and A can play back and share the story with his or her family via a provided link.

[0176] Also, when entering a virtual store, the application analyzes reviews of products the user has previously purchased and, if it is recommending new products based on those reviews, provides a prompt like this to the generative AI model:

[0177] "Product reviewed: XYZ Camera. Review: The image quality is excellent and I am very satisfied. I would buy it again. Suggestion: New product."

[0178] Based on this, a generative AI model (e.g., GPT-2) can suggest new cameras and related accessories and vocalize the suggestions to provide personalized recommendations to the user.

[0179] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0180] Step 1: Data collection

[0181] Users collect their own past text data (diaries, emails, chat logs) and voice data (voice memos, recordings) through a dedicated application or web interface. Users can select this data and upload it to the platform. The history of products purchased by users and their feedback are also collected. The input is the user's text data and voice data, and the output is that this data is formatted and sent to the server.

[0182] Step 2: Send data

[0183] The terminals then format the collected data, compress it, and upload it to the server. The data is then formatted into a format that can be understood within the system and sent to the server using a secure communication protocol (e.g., HTTPS). The input is the formatted and compressed user text and voice data, and the output is that this data is securely sent to the server.

[0184] Step 3: Data analysis

[0185] The server analyzes the text and voice data received from the user. Natural language processing technology (e.g., BERT or GPT-3) is used to analyze the text data and extract important episodes and emotions. The voice data is converted into text format using speech recognition technology (e.g., Google Speech-to-Text). This allows for integrated analysis of the text and voice data and extracts meaningful information. The input is the user's text and voice data, and the output is analyzed episodes and emotional information.

[0186] Step 4: Narrative generation

[0187] The server uses an AI model (e.g., GPT-2 or GPT-3) based on the analysis results to generate a personal narrative. This narrative is an inspiring story that reflects the user's past experiences and emotions. Furthermore, personalized recommended products and services based on the analyzed personal data are also generated using an AI model. The input is the analysis results, and the output is the generated narrative and recommended products and services.

[0188] Step 5: Audio conversion

[0189] The server converts the generated story and recommendations into audio format using text-to-speech technology (e.g., gTTS). This allows the story and recommended products to be delivered to the user in a natural voice. The technology used for speech conversion achieves high-quality speech synthesis, producing speech that is easily understandable to the user. The input is the generated text, and the output is audio data.

[0190] Step 6: Data storage and playback

[0191] The server stores the converted voice data on the server and manages it so that users can access it. It identifies which data belongs to which user and sets appropriate access rights. The input is voice data, and the output is the stored voice data.

[0192] The device provides a playback interface for the generated audio data. The user can easily play the generated story and recommended products through the provided link or application. The input is the saved audio data, and the output is the played audio data. This allows the user to experience past memories in a new way or check out new products and services.

[0193] 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.

[0194] The present invention is a system that collects an individual's past text data and voice data, analyzes them to generate a personal story, and provides it in voice format. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, a more moving and personal story can be generated. Below, a specific description of an embodiment of the present invention is given.

[0195] Data collection

[0196] User: Using a dedicated application or web interface, the user collects their own text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings). The user selects this data and clicks the upload button to send it to the system.

[0197] Device: The user's device formats the collected text and audio data and converts it into the appropriate format. This data is compressed if necessary and prepared for transmission to the server. The destination server URL is set and the data is uploaded to the server.

[0198] Data analysis and emotion recognition

[0199] Server: The server receives the uploaded data and stores it in storage. Text data is stored in a database, and audio data is stored in an appropriate directory. This allows data to be managed consistently within the system.

[0200] Server: The server applies natural language processing (NLP) techniques to the received text data to analyze the context and sentiment. The emotion engine participates in this analysis process, recognizing and tagging the user's sentiment from the text and voice data. Important episodes and sentiments are tagged, and the analysis results are generated.

[0201] Server: The voice data is converted into text using speech recognition technology and analyzed by the emotion engine. The emotion engine recognizes the emotion in the voice data and reflects it in the analysis results.

[0202] Story Generation

[0203] Server: Based on the analysis results, the AI ​​model generates a personal narrative. The AI ​​model combines episodes and emotions extracted from the text data and converted audio data to create a moving and personal story based especially on emotion tagging. The text of the generated narrative is generated.

[0204] Audio conversion

[0205] Server: The generated story text is converted into audio format using Text-to-Speech (TTS) technology, which provides the story text to the user as a natural voice. The generated audio file is stored on the server.

[0206] Data storage and playback

[0207] Server: The converted voice data is managed in a form that can be accessed by users. Voice data is identified for each user and appropriate access rights are set. Users can access the generated voice data through an individual link.

[0208] Device: The user's device provides a playback interface for the generated audio data. The user can click the provided link to play the audio data through a browser or dedicated app. The playback interface should be designed to be intuitive and easy to use.

[0209] Specific examples

[0210] For example, user B wants to share his / her past growth record with his / her family. He / she uploads his / her diary and voice memos to a dedicated app. The app formats this data and sends it to the server.

[0211] The server analyzes the data and recognizes important episodes and emotions of Mr. B. The emotion engine tags emotions that should be emphasized, and the AI ​​model generates a story based on that. This story is converted into audio using text-to-speech technology and stored on the server as audio data.

[0212] Mr. B can replay the generated story through the provided link and share it with his family. This system brings back Mr. B's memories in a moving way and deepens the bond between him and his family.

[0213] In this way, by combining emotion recognition, the system of the present invention can recreate personal memories and experiences as more moving and personal stories, providing users with a new experience.

[0214] The processing flow will be explained below.

[0215] Step 1:

[0216] User: Using a dedicated application or web interface, the user collects their own text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings). The user selects this data and clicks the upload button to send it to the system.

[0217] Step 2:

[0218] Device: The user's device formats the collected text and audio data and converts it into the appropriate format. This data is compressed if necessary and prepared for transmission to the server. The destination server URL is set and the data is uploaded to the server.

[0219] Step 3:

[0220] Server: The server receives the uploaded data and stores it in storage. Text data is stored in a database, and audio data is stored in an appropriate directory. This allows data to be managed consistently within the system.

[0221] Step 4:

[0222] Server: Applying natural language processing (NLP) techniques to the received text data to analyze context and sentiment. The emotion engine participates in the analysis process, recognizing and tagging the user's sentiment from the text and voice data. Emotion tagging identifies important episodes and sentiments, and generates analysis results.

[0223] Step 5:

[0224] Server: The voice data is converted into text using voice recognition technology and analyzed by the emotion engine. The emotion engine recognizes the emotion in the voice data and reflects it in the analysis results.

[0225] Step 6:

[0226] Server: Based on the analyzed data, the AI ​​model generates a personal narrative. The AI ​​model combines episodes and emotions extracted from the text data and converted audio data to create a moving and personal story based especially on emotion tagging. The text of the generated narrative is generated.

[0227] Step 7:

[0228] Server: The generated story text is converted into audio format using Text-to-Speech (TTS) technology, which provides the story text to the user as a natural voice. The generated audio file is stored on the server.

[0229] Step 8:

[0230] Server: The converted voice data is managed in a form that can be accessed by users. Voice data is identified for each user and appropriate access rights are set. Users can access the generated voice data through an individual link.

[0231] Step 9:

[0232] Device: The user's device provides a playback interface for the generated audio data. The user can click the provided link to play the audio data through a browser or dedicated app. The playback interface should be designed to be intuitive and easy to use.

[0233] Step 10:

[0234] Users: Users can play the generated audio data and listen to their personal stories, which can bring back memories and experiences, providing new emotional experiences. Users can also share this audio data with their family and friends.

[0235] Example 2

[0236] 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."

[0237] Conventional systems have struggled to effectively collect and analyze personal text and audio data to generate moving, personal stories. Furthermore, when providing the stories in audio format, they lack natural emotional expression. Therefore, there is a need for a method to more fully reproduce and share users' emotions and experiences.

[0238] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0239] In this invention, the server includes means for collecting personal data, means for formatting the data and transmitting it to the server, means for analyzing the data on the server and recognizing emotions, means for generating a story based on the analysis results, means for converting the generated story into audio, and means for saving the converted audio and providing it in a format that can be played by the user. This makes it possible to effectively analyze the user's emotions and experiences, generate a moving and personal story, and provide it in a natural audio format.

[0240] "Personal data" refers to information generated or collected by an individual, including, among other things, text data and voice data.

[0241] "Means of collection" refers to a mechanism that provides users with the ability to select and upload personal data using a dedicated application or web interface.

[0242] "Formatting" refers to the process of converting collected data into a standard format, compressing or converting it as needed, and preparing it for transmission to a server.

[0243] "Means for sending to a server" refers to a function for uploading formatted data to a specified server URL using the HTTP protocol.

[0244] "Means of analysis" refers to the process of receiving data on a server and analyzing the context and sentiment of the data using natural language processing technology and voice recognition technology.

[0245] "Means for recognizing emotions" refers to the function of using an emotion engine during the analysis process to recognize and tag user emotions from text and voice data.

[0246] "Means for generating stories" refers to the process of using an AI model based on the analysis results to generate personal and moving stories that take into account emotional tags.

[0247] "Converting to speech means" refers to the process of converting the generated narrative text into audio format using text-to-speech technology.

[0248] "Means for providing the converted audio data in a form that can be saved and played back" refers to the function of managing the converted audio data so that it can be accessed by users, and providing it by setting appropriate access rights.

[0249] The system of the present invention collects and analyzes an individual's text and voice data, generates a personal narrative based on the collected data, and presents it in audio format. The operation of this system is described in detail below.

[0250] Data collection

[0251] Users: Using a dedicated application or web interface, users upload their own text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings) to the system. Uploading can be done simply by clicking a button.

[0252] Data Formatting and Transmission

[0253] Terminal: The user's terminal converts the collected data into a standard format (e.g., a text file in UTF-8 format, or an audio file in WAV or MP3 format). The audio data is compressed if necessary, and the converted data is sent to the server. The transmission is performed using the HTTP protocol, and the server URL is set.

[0254] Data analysis and emotion recognition

[0255] Server: The server receives the data sent by the user, stores the text data in a database, and the voice data in an appropriate directory. It uses natural language processing (NLP) techniques to analyze the context and content of the text data. During this analysis, the emotion recognition engine tags emotions from the text and extracts specific episodes.

[0256] Server: The voice data is converted to text using speech recognition technology (e.g., Google Cloud Speech-to-Text API). This text is also analyzed using emotion recognition technology and tagged with an emotion.

[0257] Story Generation

[0258] Server: Based on the analysis results, a generative AI model (e.g., GPT-3, GPT-4) is used to generate a personal story. This model combines episodes and emotions extracted from text data and converted audio data, taking into account emotion tags in particular, to create a moving and personal story.

[0259] Audio conversion

[0260] Server: The generated story text is converted into audio format using Text-to-Speech (TTS) technology (e.g., Microsoft Azure Cognitive Services TTS API). The generated audio file (e.g., MP3 format) is stored on the server.

[0261] Data storage and playback

[0262] Server: The converted audio data is managed in association with the user's ID, and appropriate access rights are set. A separate folder is created for each user to protect the data.

[0263] User: The user can access the generated audio data by clicking the provided link. Clicking the link launches a browser or dedicated app, and an interface for playing the generated story is displayed. The user can easily listen to the audio data by clicking the play button.

[0264] Specific examples

[0265] For example, if user B wants to share his or her growth record with his or her family, B uploads his or her diary and voice memos to a dedicated app. The device formats the collected data and sends it to a server. The server analyzes the data and recognizes B's important episodes and emotions. Based on the emotional information tagged by the emotion engine, a generative AI model generates a personal story. The generated story is converted into audio using TTS technology and saved on the server. B can play the generated story via the provided link and share it with his or her family.

[0266] Prompt Sentence Examples

[0267] "Extract the most emotional parts from Mr. B's diary and generate a moving story."

[0268] "Convert the contents of the user's voice memo into text and create a story based on the results analyzed by the emotion engine."

[0269] "Based on the following text data, please have your AI model generate a story that combines emotional episodes."

[0270] This invention allows personal memories and experiences to be recreated as more moving and personal stories, providing users with a new experience.

[0271] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0272] Step 1: Data collection

[0273] User: Using a dedicated application or web interface, the user selects their own text data (e.g., diary entries, emails) or audio data (e.g., voice memos, recordings) and clicks the upload button.

[0274] Input: User text and voice data

[0275] Output: raw data saved to the device

[0276] Step 2: Format and send data

[0277] Device: The user's device converts the collected text data into a standard format (e.g., a UTF-8 text file) and the audio data into an appropriate format (e.g., WAV or MP3). The audio data is compressed if necessary. The formatted data is then sent to the server using the HTTP protocol.

[0278] Input: Collected raw text and audio data

[0279] Output: Formatted data sent to the server

[0280] Step 3: Receiving and storing data

[0281] Server: The server receives the HTTP request, stores the text data in a database, and stores the audio data in the appropriate directory.

[0282] Input: Formatted data sent from the terminal

[0283] Output: Text data stored in a database and audio data stored in a directory

[0284] Step 4: Text data analysis and emotion recognition

[0285] Server: The server applies natural language processing (NLP) techniques to the stored text data to analyze its context and content. During this analysis, the emotion engine tags the text with emotions and extracts specific episodes.

[0286] Input: Saved text data

[0287] Output: Text analysis results with sentiment tags

[0288] Step 5: Convert speech data to text and recognize emotions

[0289] Server: The server converts the voice data into text using speech recognition technology (e.g., Google Cloud Speech-to-Text API). It also applies emotion recognition technology to the converted text data and attaches emotion tags to it.

[0290] Input: Stored audio data

[0291] Output: Text data with emotion tags

[0292] Step 6: Narrative generation

[0293] Server: Based on the analysis results, constructs prompts for a generative AI model (e.g., GPT-3, GPT-4) to generate a personal story. The prompts include emotion tags and extracted episodes. The generative AI model then generates a story based on these prompts.

[0294] Input: Sentiment-tagged text analysis results and converted text data

[0295] Output: Generated personal narrative text

[0296] Step 7: Transcribing the story

[0297] Server: The generated story text is converted into audio format using Text-to-Speech (TTS) technology (e.g., Microsoft Azure Cognitive Services TTS API). The generated audio file (e.g., MP3 format) is stored on the server.

[0298] Input: Generated personal narrative text

[0299] Output: The generated audio file

[0300] Step 8: Save and provide access to your audio files

[0301] Server: The converted voice data is managed in association with the user's ID, and appropriate access rights are set. A separate folder is created for each user to protect the data. The user can access the generated voice data via the provided link.

[0302] Input: Generated audio file

[0303] Output: Audio data managed in a user-accessible form

[0304] Step 9: Playing back audio data

[0305] User: The user clicks on the provided link to open an interface that plays the generated story via a browser or dedicated app. By clicking the play button, the user can listen to the generated audio data.

[0306] Input: The link the user visits

[0307] Output: Audio data played in a browser or dedicated app

[0308] (Application example 2)

[0309] 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."

[0310] In today's world, individual users generate large amounts of digital data on a daily basis, but there are few opportunities to reuse that data as personal, moving stories. Furthermore, in electronic payment services, simply compiling and analyzing users' purchase and interaction histories makes it difficult to provide emotional value to users. This can prevent users from emotionally reflecting on their purchasing experiences and memories, potentially resulting in a decrease in satisfaction with purchasing activities.

[0311] 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 collecting text data and voice data of the user, means for analyzing the text data and voice data, means for generating a personal story based on the analysis results, means for converting the generated story into voice, means for saving the converted voice and providing it in a form that the user can play back, means for collecting and analyzing the user's purchase history and interaction history and recognizing emotions, and means for generating a story that includes emotions at the time of purchase and playing it back as voice. This makes it possible for the user to look back on their purchasing experiences and memories as moving stories.

[0312] "Text data" refers to digital information in the form of text, such as diary entries, emails, and messages created by users.

[0313] "Voice data" refers to digital voice information such as recorded voice memos, dialogues, and telephone conversations generated by the user.

[0314] "Analysis means" refers to a method of analyzing collected text data and audio data using natural language processing technology and speech recognition technology to understand context and emotions.

[0315] A "story generation means" is an algorithm or software that generates a story that includes the user's personal experiences and emotions based on the analysis results.

[0316] "Convert to speech" is the process of converting the generated narrative into audio format using text-to-speech technology.

[0317] "Storage means" refers to a mechanism for storing the converted audio data in a digital format and managing it in a manner that allows access by the user.

[0318] "Providing in a reproducible form" means providing an interface or platform that allows users to easily access and play the generated audio data.

[0319] "Purchase history" is data that records detailed information about products that a user has purchased using an electronic payment service.

[0320] "Dialogue history" is a log of messages and conversations exchanged between a user and the system in an electronic payment service.

[0321] "Emotion recognition means" is a technology that analyzes and understands the emotional elements contained in a user's purchasing history and interaction history.

[0322] "Narrative generation" involves the process of constructing a story that reflects the user's emotions and experiences at the time of purchase.

[0323] "Means for playing as audio" refers to a technique for converting the generated story into audio format using TTS technology and providing it to the user in a reproducible format.

[0324] This invention is a system that collects and analyzes a user's past text data and voice data, generates a personal story, and provides it in voice format. Furthermore, it aims to recognize emotions in purchase history and conversation history, and generate and provide an inspiring story based on this.

[0325] Data collection

[0326] Users use a dedicated application or web interface to collect their own text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings). The user's purchase history and interaction history are also collected. They select this data and click the upload button to send it to the system. The user's device then formats the collected data and converts it into an appropriate format. This data is then compressed and sent to the server.

[0327] Data analysis and emotion recognition

[0328] The server receives the uploaded data and stores it in storage. The data is converted into an appropriate format and saved in a database. Natural language processing (NLP) and speech recognition technologies are used for analysis. The emotion engine recognizes and tags the user's emotions from text, voice data, purchase history, and conversation history. Important episodes and emotions are tagged in the analysis results.

[0329] Story Generation

[0330] The AI ​​model on the server generates a personal narrative based on the analysis results. The AI ​​model is a generative AI model that combines episodes and emotions extracted from the text data and converted audio data to create a moving and personal story based on emotion tagging in particular.

[0331] Audio conversion

[0332] The generated story text is converted into audio format using Text-to-Speech (TTS) technology, which provides the story text to the user as natural-sounding audio, and the generated audio file is stored on the server.

[0333] Data storage and playback

[0334] The converted voice data is managed in a form that can be accessed by users. Voice data is identified for each user and appropriate access rights are set. Users can access the generated voice data through individual links. The user's device provides a playback interface for the generated voice data. The playback interface has an intuitive and easy-to-use design.

[0335] Specific examples

[0336] For example, if user A wants to share his or her past purchase history as an inspiring story with his or her family, he or she uploads his or her purchase history and voice memos to a dedicated app. The app formats this data and sends it to a server. The server analyzes the data and recognizes A's important purchase episodes and emotions. An emotion engine tags emotions that should be particularly emphasized, and an AI model generates a story based on this. This story is converted into audio using text-to-speech technology and saved as audio data on the server. A can play the generated story through the provided link and share it with his or her family.

[0337] Prompt Sentence Examples

[0338] "Data entered: List of products that made you feel excited at the moment you purchased them"

[0339] "Prompt for generative AI model: Generate a story with emotions about a product the user has purchased in the past. Here is the data: [data content]"

[0340] This allows users to vividly relive their past purchasing experiences and share them with family and friends in an emotional way.

[0341] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0342] Step 1:

[0343] The user uses a dedicated application or web interface to collect their own text data (e.g., diary entries, emails) and voice data (e.g., voice memos, recordings). The user also collects purchase history and interaction history. They select this data and click the send button to send it to the system. The input to this step is various types of user data, and the output to the server is this data set.

[0344] Step 2:

[0345] The user's device formats the collected data, converts text and audio data into the appropriate format, compresses the data if necessary, sets the URL of the upload server, and then sends the data to the server. The input for this step is the various data provided by the user, and the output is the formatted data.

[0346] Step 3:

[0347] The server receives the uploaded data, stores the text data in a database, and the audio data in the appropriate directory. This step updates the database. The input is the formatted data, and the output is the stored data.

[0348] Step 4:

[0349] The server applies natural language processing (NLP) techniques to the received text data to analyze the context and sentiment. An emotion engine also participates in this analysis, recognizing and tagging the user's sentiment from the text and voice data. This stage primarily involves data analysis and tagging. The input is the stored data, and the output is the analysis results.

[0350] Step 5:

[0351] The server applies speech recognition technology to the voice data to convert it into text, which is then analyzed using an emotion engine. Emotions are also recognized from the converted text data and tagged. The input is the voice data, and the output is the converted and analyzed text data and emotion tags.

[0352] Step 6:

[0353] Based on the analyzed text data, emotion tags, and purchase history data, an AI model on the server generates a personal narrative for the user. This AI model is a generative AI model, creating a story based specifically on emotion tagging. The inputs for this step are the analysis results and purchase history data, and the output is the generated narrative text.

[0354] Step 7:

[0355] The server converts the generated story text into audio format using Text-to-Speech (TTS) technology. The converted audio file is stored on the server. The input is the generated story text, and the output is the converted audio file.

[0356] Step 8:

[0357] The server manages the converted audio data in a form that allows users to access it. The audio data is stored in a folder identified for each user, and appropriate access rights are set. The input of this step is the converted audio file, and the output is audio data stored in an accessible form.

[0358] Step 9:

[0359] The user's device provides a playback interface for the generated audio data. The user accesses and plays the audio data through the provided link. The playback interface is designed to be intuitive and easy to use. The input of this step is the access link to the audio data, and the output is the playback of the audio data.

[0360] In this way, a moving story can be generated using the user's past data and provided in audio format, which can bring back the user's purchasing experiences and memories in an emotional way.

[0361] 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.

[0362] 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.

[0363] 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.

[0364] [Second embodiment]

[0365] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0366] 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.

[0367] 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).

[0368] 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.

[0369] 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.

[0370] 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).

[0371] 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.

[0372] 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.

[0373] 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.

[0374] 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.

[0375] In the smart glasses 214, 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.

[0376] 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."

[0377] The present invention is a system that collects and analyzes an individual's past text data and voice data to generate a personal story and provide it in voice format. Hereinafter, a specific embodiment of the present invention will be described.

[0378] Data collection

[0379] Users: Users collect their own past text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings) through dedicated applications or web interfaces. Users can select and upload this data to the platform.

[0380] Device: The user's device uploads the collected data to the server in the appropriate format. The data is then formatted and compressed as necessary and securely sent to the server.

[0381] Data analysis

[0382] Server: The server analyzes the text and voice data received from the user. The text data is analyzed using natural language processing technology to extract important episodes and emotions. The voice data is converted into text using speech recognition technology. This allows for an integrated analysis of the text and voice data to extract information needed to form a moving personal story.

[0383] Story Generation

[0384] Server: Based on the analysis results, the server generates a personal narrative. The narrative generation is performed using an AI model to create a moving story that reflects the user's past experiences and emotions. This brings the user's past memories back to life in a new way, providing a visual and emotional experience.

[0385] Audio conversion

[0386] Server: The generated story is converted into audio format using text-to-speech technology, which allows the story to be delivered to the user in a natural voice. The technology used for speech conversion achieves high-quality speech synthesis, producing speech that is easily understandable to the user.

[0387] Data storage and playback

[0388] Server: The converted voice data is stored on the server and managed for user access. It identifies which data belongs to which user and sets appropriate access permissions.

[0389] Device: The user's device provides an interface for playing the generated audio data. The user can easily play the generated story through the provided link or application. By clicking the play button, the audio will start and the user can relive the past memory.

[0390] Specific examples

[0391] For example, user A wants to share his or her past growth records with his or her family. A uploads his or her diary and voice memos to a dedicated app. The app formats the data and sends it to a server. The server analyzes the data and extracts A's important episodes and emotions. Based on the analysis results, an AI model generates a personal story and converts it into audio using text-to-speech technology. The audio data is then stored on the server, and A can play back and share the generated story with his or her family via a provided link. This system brings A's memories back to life in a moving way, deepening the bond between the family.

[0392] In this way, the system of the present invention can provide a new experience to the user by reproducing personal memories and experiences as an audio story.

[0393] The processing flow will be explained below.

[0394] Step 1:

[0395] User: Using a dedicated application or web interface, the user collects their own text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings). The user selects this data and clicks the upload button to send it to the system.

[0396] Step 2:

[0397] Device: The user's device formats the collected text and audio data and converts it into the appropriate format. This data is compressed if necessary and prepared for transmission to the server. The destination server URL is set and the data is uploaded to the server.

[0398] Step 3:

[0399] Server: The server receives the uploaded data and stores it in storage. Text data is stored in a database, and audio data is stored in an appropriate directory. This allows data to be managed consistently within the system.

[0400] Step 4:

[0401] Server: Applying natural language processing (NLP) technology to the received text data, the server analyzes the context and sentiment. Important episodes and sentiments are tagged and the analysis results are generated. Audio data is converted into text using speech recognition technology and analyzed in the same way.

[0402] Step 5:

[0403] Server: Based on the analyzed data, the AI ​​model generates a personal narrative. The AI ​​model combines episodes and emotions extracted from the text data and converted audio data to create a moving and personal story. The text of the generated narrative is generated.

[0404] Step 6:

[0405] Server: The generated story text is converted into audio format using Text-to-Speech (TTS) technology, which synthesizes the story text into natural voice. The generated audio file is stored on the server.

[0406] Step 7:

[0407] Server: The converted voice data is managed in a form that can be accessed by users. Voice data is identified for each user and appropriate access rights are set. Users can access the generated voice data through an individual link.

[0408] Step 8:

[0409] Device: The user's device provides a playback interface for the generated audio data. The user can click the provided link to play the audio data through a browser or dedicated app. The playback interface should be designed to be intuitive and easy to use.

[0410] Step 9:

[0411] Users: Users can play the generated audio data and listen to their personal stories, which can bring back memories and experiences, providing new emotional experiences. Users can also share this audio data with their family and friends.

[0412] Example 1

[0413] 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."

[0414] Conventional story generation systems that use personal data have insufficient data collection and analysis, making it difficult to generate high-quality stories that reflect the user's individual experiences and emotions. Furthermore, when converting the generated stories into speech, there is a lack of means to provide natural, high-quality speech. Furthermore, insufficient management of data storage and playback makes it difficult for users to easily play and experience the generated stories.

[0415] 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.

[0416] In this invention, the server includes means for collecting personal text data and voice data, means for analyzing the text data and voice data, means for generating a personal story based on the analysis results, means for converting the generated story into voice, and means for saving the converted voice and providing it in a form that can be played back by the user. This enables the generation of high-quality stories that reflect the individual experiences and emotions of the user and the conversion of voice into natural, easy-to-listen-to voice, allowing the user to easily play back and experience the generated story.

[0417] "Personal text data" refers to data in written form generated by the user, such as diary entries, emails, and memos.

[0418] "Audio data" refers to data based on user-generated voice, such as voice memos, recordings, and call logs.

[0419] "Means of collection" refers to the technical means of collecting and uploading personal text and voice data to the platform through a dedicated application or web interface.

[0420] The "analyzing means" refers to a technical means that uses natural language processing technology and speech recognition technology to analyze the user's text data and voice data and extract important episodes and emotional information.

[0421] "Means for generating personal stories" refers to technical means that use an AI model based on the analysis results to create a story that reflects the user's past experiences and emotions.

[0422] "Convert-to-speech means" means a technical means for converting the generated text-based narrative into a natural, audible audio format using text-to-speech technology.

[0423] "Means of storing and providing in a form that can be played by users" refers to the technical means of managing and providing the converted audio data by storing it on a server and setting appropriate access rights so that users can play the audio data through a link or application.

[0424] "Natural language processing technology" is a technology for analyzing text data, and includes morphological analysis, sentiment analysis, etc.

[0425] "Speech recognition technology" is a technology for converting voice data into text format.

[0426] A "generative AI model" is an artificial intelligence technique used to generate a personal narrative based on analyzed data.

[0427] "Text-to-speech technology" is technology that converts text into speech.

[0428] "Formatting and compression means" refers to the technical means by which collected data is converted into a standardized format and compressed to increase the efficiency of data transmission.

[0429] The present invention is a system that collects and analyzes an individual's past text data and voice data to generate a personal story and provide it in voice format. Hereinafter, a specific embodiment of the present invention will be described.

[0430] Data collection

[0431] User: Using a dedicated application or web interface, users collect their own past text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings). This data is collected in one place through the application and uploaded to the platform.

[0432] Terminal: The user's terminal formats the collected data into a standardized format (e.g., UTF-8 for text data, WAV for audio data), and compresses it (e.g., ZIP format) to improve data transmission efficiency. The formatted and compressed data is then securely sent to the server.

[0433] Data analysis

[0434] Server: The server decompresses and analyzes the text and audio data received from the user. The text data is analyzed using natural language processing technology, which includes morphological analysis and sentiment analysis, and the specific software used is "NLPPipeline." The audio data is converted into text format using speech recognition technology, which uses "SpeechToTextConverter." The converted text data is also analyzed in the same way, and important episodes and emotional information are extracted.

[0435] Story Generation

[0436] Server: Based on the analysis results, the server generates a personal story using the AI ​​model "StoryGeneratorAI." This AI model creates a moving story that reflects the user's past experiences and emotions. The generated story is designed to bring back the user's past memories in a new form.

[0437] Audio conversion

[0438] Server: The generated story is converted into audio format using the text-to-speech technology "TextToSpeechSynthesizer." This technology converts the story into natural, high-quality audio that is easy for users to understand. During the speech conversion process, the tone, intonation, rhythm, etc. are adjusted to produce a voice that sounds like a natural conversation.

[0439] Data storage and playback

[0440] Server: The converted voice data is stored on the server. When stored, the data is identified for each user and appropriate access rights are set. The voice data stored in the database is managed to protect it from unauthorized access.

[0441] Device: The user's device provides a playback interface for the generated audio data using the application "StoryPlaybackApp." The user can play the generated audio data through the provided link or application. By clicking the play button, the user can enjoy the story as audio.

[0442] Specific examples

[0443] For example, suppose user A wants to share his or her past growth records with his or her family. User A uploads his or her diary and voice memos to a dedicated app. The app converts this data into a standard format and sends it to the server. The server then analyzes the data using "NLPPipeline" and "SpeechToTextConverter" to extract important episodes and emotional information. Based on the analysis results, the AI ​​model "StoryGeneratorAI" generates a personal story and converts it into audio using "TextToSpeechSynthesizer." The audio data is then stored on the server, and User A can play back and share the generated story with his or her family through "StoryPlaybackApp."

[0444] This system brings Mr. A's memories back to life in a moving way and deepens his bond with his family.

[0445] Prompt Sentence Examples

[0446] User A has uploaded his / her past diary entries and voice memos to the system. Explain the process of generating a moving personal story from these, converting it into audio, and playing it back.

[0447] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0448] Step 1:

[0449] User: Using a dedicated application or web interface, users upload their past text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings) as input. Data collection begins when the user clicks the upload button within the application. This data is then saved on the device via the application.

[0450] Input: Text data, audio data

[0451] Output: Text and audio data stored on the device

[0452] Step 2:

[0453] Terminal: The terminal formats the collected text and audio data into a standardized format (e.g., UTF-8 format for text data and WAV format for audio data). After formatting, the data is compressed (e.g., ZIP format) to improve transmission efficiency. The formatted and compressed data is sent to the server using a secure protocol (e.g., HTTPS).

[0454] Input: Text and voice data stored on the device

[0455] Output: Formatted and compressed data file sent to the server

[0456] Step 3:

[0457] Server: The server decompresses the received compressed file and obtains the text and audio data. The text data is then analyzed using the natural language processing technology "NLPPipeline." Morphological analysis and sentiment analysis are performed to extract important episodes and emotional information. Meanwhile, the audio data is converted to text format using the speech recognition technology "SpeechToTextConverter," and then analyzed in the same way.

[0458] Input: Compressed file received on the server

[0459] Output: Text data with important episodes and emotional information extracted

[0460] Step 4:

[0461] Server: Based on the analyzed data, the server uses the AI ​​model "StoryGeneratorAI" to generate a personal story. The AI ​​model takes into account the user's past experiences and emotions and outputs a moving story in chronological order. The generation process uses neural networks and generative probabilities.

[0462] Input: Text data with important episodes and emotional information extracted

[0463] Output: Generated personal narrative (text format)

[0464] Step 5:

[0465] Server: The generated story is converted into audio using the TextToSpeechSynthesizer, which adjusts the sound quality and intonation to produce a natural, easy-to-listen-to voice.

[0466] Input: Generated personal story (text format)

[0467] Output: The story converted into audio format

[0468] Step 6:

[0469] Server: The converted voice data is stored on the server. Each user's data is identified and appropriate access rights are set. The stored voice data is managed using server-side security features to protect it from unauthorized access.

[0470] Input: A story converted into audio format

[0471] Output: Audio data stored on the server

[0472] Step 7:

[0473] Device: The user's device provides a playback interface for the generated audio data using the "StoryPlaybackApp". The user can access the generated audio data through the provided link or application and play the story by clicking the play button.

[0474] Input: Audio data stored on the server

[0475] Output: Audio data provided through a playback interface

[0476] The above is the specific processing flow of the program for this system. Each step works closely together to provide a moving story that reflects the individual's past experiences and emotions, bringing a new experience to the user.

[0477] (Application example 1)

[0478] 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."

[0479] Existing virtual stores have difficulty providing personalized services and product recommendations based on customers' past purchase history and feedback. This prevents them from providing services tailored to each individual customer, hindering customer satisfaction. Furthermore, conventional systems have difficulty effectively analyzing collected past data to extract meaningful information and provide it to customers in real time.

[0480] 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.

[0481] In this invention, the server includes means for collecting personal text data and voice data, means for analyzing the text data and voice data, means for generating a personal story based on the analysis results, means for converting the generated story into voice, means for saving the converted voice and providing it in a form that can be played by the user, means for collecting and analyzing personal data such as the customer's past purchase history, feedback, chat logs, etc., means for generating personalized recommended products and services based on the analyzed data, and means for announcing the generated recommended products and services in voice format. This makes it possible to effectively utilize the customer's past data and provide personalized services and product recommendations in real time.

[0482] - "Personal text data" refers to text data generated or owned by a user, including diaries, emails, chat logs, etc.

[0483] "Audio data" refers to data containing user voice information, including voice memos, recording files, call history, and the like.

[0484] The "analyzing means" refers to a device or software program for analyzing collected text data and audio data using digital signal processing or natural language processing techniques.

[0485] A "means for generating a personal story" is a device or software program that uses an artificial intelligence model to generate a story that reflects the user's past information and emotions based on the analysis results.

[0486] A "narrative-to-audio means" is a device or software program that converts the generated narrative into audio form using text-to-speech technology.

[0487] The "means for storing the converted audio and providing it in a form that can be played by a user" is a device or software program that stores the audio data and makes it playable through a user-accessible interface.

[0488] "Purchase history" is data that includes information about products that a user has purchased in the past.

[0489] "Feedback" refers to data including user evaluations and impressions of products and services.

[0490] A "chat log" is a conversation history that a user has had through a chat application.

[0491] "Personalized recommended products and services" are those that recommend products and services that are most suitable for a user based on the user's past data.

[0492] The "means for providing generated recommended products and services in the form of voice" is a device or software program for notifying the user of personalized recommended products and services by voice.

[0493] This invention is a system that collects and analyzes personal text and voice data to generate a personal story and deliver it in audio format. It also includes a function to provide personalized audio recommendations for products and services based on data such as the customer's past purchase history, feedback, and chat logs.

[0494] Data collection

[0495] User:

[0496] Users collect their past text data (diaries, emails, chat logs) and audio data (voice memos, recordings) through a dedicated application or web interface. Users can select and upload this data to the platform. The history of users' past purchases and feedback are also collected.

[0497] Device:

[0498] The user's device uploads the collected data to the server in the appropriate format. The data is then reshaped and compressed as necessary and sent securely to the server.

[0499] Data analysis

[0500] server:

[0501] The server analyzes the text and voice data received from the user. This is done using natural language processing and voice recognition technologies. The text data is analyzed using a Transformer-based natural language processing model (e.g., BERT or GPT-3) to extract important episodes and emotions. The voice data is converted into text format using voice recognition technology. This allows for an integrated analysis of the text and voice data, and meaningful information is extracted from the customer's behavioral history.

[0502] Narrative and recommendation generation

[0503] server:

[0504] Based on the analysis results, a personal narrative is generated using an AI model (e.g., GPT-2 or GPT-3). This creates an inspiring story that reflects the user's past experiences and emotions. Furthermore, personalized product and service recommendations are also generated using the AI ​​model based on the analyzed personal data.

[0505] Voice conversion and guidance

[0506] server:

[0507] The generated story and recommendations are converted into audio format using text-to-speech technology (e.g., gTTS), which allows the story and product recommendations to be delivered to the user in a natural voice. The technology used for speech conversion achieves high-quality speech synthesis, producing speech that is easily understandable to the user.

[0508] Data storage and playback

[0509] server:

[0510] The converted voice data is stored on a server and managed so that users can access it. The data is identified as belonging to each user, and appropriate access rights are set.

[0511] Device:

[0512] The user's device provides a playback interface for the generated audio data, and the user can easily play the generated story and recommended products through the provided links or applications, allowing the user to experience past memories in a new way or check out new products and services.

[0513] Specific examples

[0514] For example, user A wants to share his or her past growth records with his or her family. A uploads his or her diary and voice memos to a dedicated app. The app formats the data and sends it to a server. The server analyzes the data and extracts A's important episodes and emotions. Based on the analysis results, an AI model generates a personal story and converts it into audio using text-to-speech technology. The audio data is then stored on the server, and A can play back and share the story with his or her family via a provided link.

[0515] Also, when entering a virtual store, the application analyzes reviews of products the user has previously purchased and, if it is recommending new products based on those reviews, provides a prompt like this to the generative AI model:

[0516] "Product reviewed: XYZ Camera. Review: The image quality is excellent and I am very satisfied. I would buy it again. Suggestion: New product."

[0517] Based on this, a generative AI model (e.g., GPT-2) can suggest new cameras and related accessories and vocalize the suggestions to provide personalized recommendations to the user.

[0518] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0519] Step 1: Data collection

[0520] Users collect their own past text data (diaries, emails, chat logs) and voice data (voice memos, recordings) through a dedicated application or web interface. Users can select this data and upload it to the platform. The history of products purchased by users and their feedback are also collected. The input is the user's text data and voice data, and the output is that this data is formatted and sent to the server.

[0521] Step 2: Send data

[0522] The terminals then format the collected data, compress it, and upload it to the server. The data is then formatted into a format that can be understood within the system and sent to the server using a secure communication protocol (e.g., HTTPS). The input is the formatted and compressed user text and voice data, and the output is that this data is securely sent to the server.

[0523] Step 3: Data analysis

[0524] The server analyzes the text and voice data received from the user. Natural language processing technology (e.g., BERT or GPT-3) is used to analyze the text data and extract important episodes and emotions. The voice data is converted into text format using speech recognition technology (e.g., Google Speech-to-Text). This allows for integrated analysis of the text and voice data and extracts meaningful information. The input is the user's text and voice data, and the output is analyzed episodes and emotional information.

[0525] Step 4: Narrative generation

[0526] The server uses an AI model (e.g., GPT-2 or GPT-3) based on the analysis results to generate a personal narrative. This narrative is an inspiring story that reflects the user's past experiences and emotions. Furthermore, personalized recommended products and services based on the analyzed personal data are also generated using an AI model. The input is the analysis results, and the output is the generated narrative and recommended products and services.

[0527] Step 5: Audio conversion

[0528] The server converts the generated story and recommendations into audio format using text-to-speech technology (e.g., gTTS). This allows the story and recommended products to be delivered to the user in a natural voice. The technology used for speech conversion achieves high-quality speech synthesis, producing speech that is easily understandable to the user. The input is the generated text, and the output is audio data.

[0529] Step 6: Data storage and playback

[0530] The server stores the converted voice data on the server and manages it so that users can access it. It identifies which data belongs to which user and sets appropriate access rights. The input is voice data, and the output is the stored voice data.

[0531] The device provides a playback interface for the generated audio data. The user can easily play the generated story and recommended products through the provided link or application. The input is the saved audio data, and the output is the played audio data. This allows the user to experience past memories in a new way or check out new products and services.

[0532] 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.

[0533] The present invention is a system that collects an individual's past text data and voice data, analyzes them to generate a personal story, and provides it in voice format. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, a more moving and personal story can be generated. Below, a specific description of an embodiment of the present invention is given.

[0534] Data collection

[0535] User: Using a dedicated application or web interface, the user collects their own text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings). The user selects this data and clicks the upload button to send it to the system.

[0536] Device: The user's device formats the collected text and audio data and converts it into the appropriate format. This data is compressed if necessary and prepared for transmission to the server. The destination server URL is set and the data is uploaded to the server.

[0537] Data analysis and emotion recognition

[0538] Server: The server receives the uploaded data and stores it in storage. Text data is stored in a database, and audio data is stored in an appropriate directory. This allows data to be managed consistently within the system.

[0539] Server: The server applies natural language processing (NLP) techniques to the received text data to analyze the context and sentiment. The emotion engine participates in this analysis process, recognizing and tagging the user's sentiment from the text and voice data. Important episodes and sentiments are tagged, and the analysis results are generated.

[0540] Server: The voice data is converted into text using speech recognition technology and analyzed by the emotion engine. The emotion engine recognizes the emotion in the voice data and reflects it in the analysis results.

[0541] Story Generation

[0542] Server: Based on the analysis results, the AI ​​model generates a personal narrative. The AI ​​model combines episodes and emotions extracted from the text data and converted audio data to create a moving and personal story based especially on emotion tagging. The text of the generated narrative is generated.

[0543] Audio conversion

[0544] Server: The generated story text is converted into audio format using Text-to-Speech (TTS) technology, which provides the story text to the user as a natural voice. The generated audio file is stored on the server.

[0545] Data storage and playback

[0546] Server: The converted voice data is managed in a form that can be accessed by users. Voice data is identified for each user and appropriate access rights are set. Users can access the generated voice data through an individual link.

[0547] Device: The user's device provides a playback interface for the generated audio data. The user can click the provided link to play the audio data through a browser or dedicated app. The playback interface should be designed to be intuitive and easy to use.

[0548] Specific examples

[0549] For example, user B wants to share his / her past growth record with his / her family. He / she uploads his / her diary and voice memos to a dedicated app. The app formats this data and sends it to the server.

[0550] The server analyzes the data and recognizes important episodes and emotions of Mr. B. The emotion engine tags emotions that should be emphasized, and the AI ​​model generates a story based on that. This story is converted into audio using text-to-speech technology and stored on the server as audio data.

[0551] Mr. B can replay the generated story through the provided link and share it with his family. This system brings back Mr. B's memories in a moving way and deepens the bond between him and his family.

[0552] In this way, by combining emotion recognition, the system of the present invention can recreate personal memories and experiences as more moving and personal stories, providing users with a new experience.

[0553] The processing flow will be explained below.

[0554] Step 1:

[0555] User: Using a dedicated application or web interface, the user collects their own text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings). The user selects this data and clicks the upload button to send it to the system.

[0556] Step 2:

[0557] Device: The user's device formats the collected text and audio data and converts it into the appropriate format. This data is compressed if necessary and prepared for transmission to the server. The destination server URL is set and the data is uploaded to the server.

[0558] Step 3:

[0559] Server: The server receives the uploaded data and stores it in storage. Text data is stored in a database, and audio data is stored in an appropriate directory. This allows data to be managed consistently within the system.

[0560] Step 4:

[0561] Server: Applying natural language processing (NLP) techniques to the received text data to analyze context and sentiment. The emotion engine participates in the analysis process, recognizing and tagging the user's sentiment from the text and voice data. Emotion tagging identifies important episodes and sentiments, and generates analysis results.

[0562] Step 5:

[0563] Server: The voice data is converted into text using voice recognition technology and analyzed by the emotion engine. The emotion engine recognizes the emotion in the voice data and reflects it in the analysis results.

[0564] Step 6:

[0565] Server: Based on the analyzed data, the AI ​​model generates a personal narrative. The AI ​​model combines episodes and emotions extracted from the text data and converted audio data to create a moving and personal story based especially on emotion tagging. The text of the generated narrative is generated.

[0566] Step 7:

[0567] Server: The generated story text is converted into audio format using Text-to-Speech (TTS) technology, which provides the story text to the user as a natural voice. The generated audio file is stored on the server.

[0568] Step 8:

[0569] Server: The converted voice data is managed in a form that can be accessed by users. Voice data is identified for each user and appropriate access rights are set. Users can access the generated voice data through an individual link.

[0570] Step 9:

[0571] Device: The user's device provides a playback interface for the generated audio data. The user can click the provided link to play the audio data through a browser or dedicated app. The playback interface should be designed to be intuitive and easy to use.

[0572] Step 10:

[0573] Users: Users can play the generated audio data and listen to their personal stories, which can bring back memories and experiences, providing new emotional experiences. Users can also share this audio data with their family and friends.

[0574] Example 2

[0575] 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."

[0576] Conventional systems have struggled to effectively collect and analyze personal text and audio data to generate moving, personal stories. Furthermore, when providing the stories in audio format, they lack natural emotional expression. Therefore, there is a need for a method to more fully reproduce and share users' emotions and experiences.

[0577] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0578] In this invention, the server includes means for collecting personal data, means for formatting the data and transmitting it to the server, means for analyzing the data on the server and recognizing emotions, means for generating a story based on the analysis results, means for converting the generated story into audio, and means for saving the converted audio and providing it in a format that can be played by the user. This makes it possible to effectively analyze the user's emotions and experiences, generate a moving and personal story, and provide it in a natural audio format.

[0579] "Personal data" refers to information generated or collected by an individual, including, among other things, text data and voice data.

[0580] "Means of collection" refers to a mechanism that provides users with the ability to select and upload personal data using a dedicated application or web interface.

[0581] "Formatting" refers to the process of converting collected data into a standard format, compressing or converting it as needed, and preparing it for transmission to a server.

[0582] "Means for sending to a server" refers to a function for uploading formatted data to a specified server URL using the HTTP protocol.

[0583] "Means of analysis" refers to the process of receiving data on a server and analyzing the context and sentiment of the data using natural language processing technology and voice recognition technology.

[0584] "Means for recognizing emotions" refers to the function of using an emotion engine during the analysis process to recognize and tag user emotions from text and voice data.

[0585] "Means for generating stories" refers to the process of using an AI model based on the analysis results to generate personal and moving stories that take into account emotional tags.

[0586] "Converting to speech means" refers to the process of converting the generated narrative text into audio format using text-to-speech technology.

[0587] "Means for providing the converted audio data in a form that can be saved and played back" refers to the function of managing the converted audio data so that it can be accessed by users, and providing it by setting appropriate access rights.

[0588] The system of the present invention collects and analyzes an individual's text and voice data, generates a personal narrative based on the collected data, and presents it in audio format. The operation of this system is described in detail below.

[0589] Data collection

[0590] Users: Using a dedicated application or web interface, users upload their own text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings) to the system. Uploading can be done simply by clicking a button.

[0591] Data Formatting and Transmission

[0592] Terminal: The user's terminal converts the collected data into a standard format (e.g., a text file in UTF-8 format, or an audio file in WAV or MP3 format). The audio data is compressed if necessary, and the converted data is sent to the server. The transmission is performed using the HTTP protocol, and the server URL is set.

[0593] Data analysis and emotion recognition

[0594] Server: The server receives the data sent by the user, stores the text data in a database, and the voice data in an appropriate directory. It uses natural language processing (NLP) techniques to analyze the context and content of the text data. During this analysis, the emotion recognition engine tags emotions from the text and extracts specific episodes.

[0595] Server: The voice data is converted to text using speech recognition technology (e.g., Google Cloud Speech-to-Text API). This text is also analyzed using emotion recognition technology and tagged with an emotion.

[0596] Story Generation

[0597] Server: Based on the analysis results, a generative AI model (e.g., GPT-3, GPT-4) is used to generate a personal story. This model combines episodes and emotions extracted from text data and converted audio data, taking into account emotion tags in particular, to create a moving and personal story.

[0598] Audio conversion

[0599] Server: The generated story text is converted into audio format using Text-to-Speech (TTS) technology (e.g., Microsoft Azure Cognitive Services TTS API). The generated audio file (e.g., MP3 format) is stored on the server.

[0600] Data storage and playback

[0601] Server: The converted audio data is managed in association with the user's ID, and appropriate access rights are set. A separate folder is created for each user to protect the data.

[0602] User: The user can access the generated audio data by clicking the provided link. Clicking the link launches a browser or dedicated app, and an interface for playing the generated story is displayed. The user can easily listen to the audio data by clicking the play button.

[0603] Specific examples

[0604] For example, if user B wants to share his or her growth record with his or her family, B uploads his or her diary and voice memos to a dedicated app. The device formats the collected data and sends it to a server. The server analyzes the data and recognizes B's important episodes and emotions. Based on the emotional information tagged by the emotion engine, a generative AI model generates a personal story. The generated story is converted into audio using TTS technology and saved on the server. B can play the generated story via the provided link and share it with his or her family.

[0605] Prompt Sentence Examples

[0606] "Extract the most emotional parts from Mr. B's diary and generate a moving story."

[0607] "Convert the contents of the user's voice memo into text and create a story based on the results analyzed by the emotion engine."

[0608] "Based on the following text data, please have your AI model generate a story that combines emotional episodes."

[0609] This invention allows personal memories and experiences to be recreated as more moving and personal stories, providing users with a new experience.

[0610] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0611] Step 1: Data collection

[0612] User: Using a dedicated application or web interface, the user selects their own text data (e.g., diary entries, emails) or audio data (e.g., voice memos, recordings) and clicks the upload button.

[0613] Input: User text and voice data

[0614] Output: raw data saved to the device

[0615] Step 2: Format and send data

[0616] Device: The user's device converts the collected text data into a standard format (e.g., a UTF-8 text file) and the audio data into an appropriate format (e.g., WAV or MP3). The audio data is compressed if necessary. The formatted data is then sent to the server using the HTTP protocol.

[0617] Input: Collected raw text and audio data

[0618] Output: Formatted data sent to the server

[0619] Step 3: Receiving and storing data

[0620] Server: The server receives the HTTP request, stores the text data in a database, and stores the audio data in the appropriate directory.

[0621] Input: Formatted data sent from the terminal

[0622] Output: Text data stored in a database and audio data stored in a directory

[0623] Step 4: Text data analysis and emotion recognition

[0624] Server: The server applies natural language processing (NLP) techniques to the stored text data to analyze its context and content. During this analysis, the emotion engine tags the text with emotions and extracts specific episodes.

[0625] Input: Saved text data

[0626] Output: Text analysis results with sentiment tags

[0627] Step 5: Convert speech data to text and recognize emotions

[0628] Server: The server converts the voice data into text using speech recognition technology (e.g., Google Cloud Speech-to-Text API). It also applies emotion recognition technology to the converted text data and attaches emotion tags to it.

[0629] Input: Stored audio data

[0630] Output: Text data with emotion tags

[0631] Step 6: Narrative generation

[0632] Server: Based on the analysis results, constructs prompts for a generative AI model (e.g., GPT-3, GPT-4) to generate a personal story. The prompts include emotion tags and extracted episodes. The generative AI model then generates a story based on these prompts.

[0633] Input: Sentiment-tagged text analysis results and converted text data

[0634] Output: Generated personal narrative text

[0635] Step 7: Transcribing the story

[0636] Server: The generated story text is converted into audio format using Text-to-Speech (TTS) technology (e.g., Microsoft Azure Cognitive Services TTS API). The generated audio file (e.g., MP3 format) is stored on the server.

[0637] Input: Generated personal narrative text

[0638] Output: The generated audio file

[0639] Step 8: Save and provide access to your audio files

[0640] Server: The converted voice data is managed in association with the user's ID, and appropriate access rights are set. A separate folder is created for each user to protect the data. The user can access the generated voice data via the provided link.

[0641] Input: Generated audio file

[0642] Output: Audio data managed in a user-accessible form

[0643] Step 9: Playing back audio data

[0644] User: The user clicks on the provided link to open an interface that plays the generated story via a browser or dedicated app. By clicking the play button, the user can listen to the generated audio data.

[0645] Input: The link the user visits

[0646] Output: Audio data played in a browser or dedicated app

[0647] (Application example 2)

[0648] 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."

[0649] In today's world, individual users generate large amounts of digital data on a daily basis, but there are few opportunities to reuse that data as personal, moving stories. Furthermore, in electronic payment services, simply compiling and analyzing users' purchase and interaction histories makes it difficult to provide emotional value to users. This can prevent users from emotionally reflecting on their purchasing experiences and memories, potentially resulting in a decrease in satisfaction with purchasing activities.

[0650] 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 collecting text data and voice data of the user, means for analyzing the text data and voice data, means for generating a personal story based on the analysis results, means for converting the generated story into voice, means for saving the converted voice and providing it in a form that the user can play back, means for collecting and analyzing the user's purchase history and interaction history and recognizing emotions, and means for generating a story that includes emotions at the time of purchase and playing it back as voice. This makes it possible for the user to look back on their purchasing experiences and memories as moving stories.

[0651] "Text data" refers to digital information in the form of text, such as diary entries, emails, and messages created by users.

[0652] "Voice data" refers to digital voice information such as recorded voice memos, dialogues, and telephone conversations generated by the user.

[0653] "Analysis means" refers to a method of analyzing collected text data and audio data using natural language processing technology and speech recognition technology to understand context and emotions.

[0654] A "story generation means" is an algorithm or software that generates a story that includes the user's personal experiences and emotions based on the analysis results.

[0655] "Convert to speech" is the process of converting the generated narrative into audio format using text-to-speech technology.

[0656] "Storage means" refers to a mechanism for storing the converted audio data in a digital format and managing it in a manner that allows access by the user.

[0657] "Providing in a reproducible form" means providing an interface or platform that allows users to easily access and play the generated audio data.

[0658] "Purchase history" is data that records detailed information about products that a user has purchased using an electronic payment service.

[0659] "Dialogue history" is a log of messages and conversations exchanged between a user and the system in an electronic payment service.

[0660] "Emotion recognition means" is a technology that analyzes and understands the emotional elements contained in a user's purchasing history and interaction history.

[0661] "Narrative generation" involves the process of constructing a story that reflects the user's emotions and experiences at the time of purchase.

[0662] "Means for playing as audio" refers to a technique for converting the generated story into audio format using TTS technology and providing it to the user in a reproducible format.

[0663] This invention is a system that collects and analyzes a user's past text data and voice data, generates a personal story, and provides it in voice format. Furthermore, it aims to recognize emotions in purchase history and conversation history, and generate and provide an inspiring story based on this.

[0664] Data collection

[0665] Users use a dedicated application or web interface to collect their own text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings). The user's purchase history and interaction history are also collected. They select this data and click the upload button to send it to the system. The user's device then formats the collected data and converts it into an appropriate format. This data is then compressed and sent to the server.

[0666] Data analysis and emotion recognition

[0667] The server receives the uploaded data and stores it in storage. The data is converted into an appropriate format and saved in a database. Natural language processing (NLP) and speech recognition technologies are used for analysis. The emotion engine recognizes and tags the user's emotions from text, voice data, purchase history, and conversation history. Important episodes and emotions are tagged in the analysis results.

[0668] Story Generation

[0669] The AI ​​model on the server generates a personal narrative based on the analysis results. The AI ​​model is a generative AI model that combines episodes and emotions extracted from the text data and converted audio data to create a moving and personal story based on emotion tagging in particular.

[0670] Audio conversion

[0671] The generated story text is converted into audio format using Text-to-Speech (TTS) technology, which provides the story text to the user as natural-sounding audio, and the generated audio file is stored on the server.

[0672] Data storage and playback

[0673] The converted voice data is managed in a form that can be accessed by users. Voice data is identified for each user and appropriate access rights are set. Users can access the generated voice data through individual links. The user's device provides a playback interface for the generated voice data. The playback interface has an intuitive and easy-to-use design.

[0674] Specific examples

[0675] For example, if user A wants to share his or her past purchase history as an inspiring story with his or her family, he or she uploads his or her purchase history and voice memos to a dedicated app. The app formats this data and sends it to a server. The server analyzes the data and recognizes A's important purchase episodes and emotions. An emotion engine tags emotions that should be particularly emphasized, and an AI model generates a story based on this. This story is converted into audio using text-to-speech technology and saved as audio data on the server. A can play the generated story through the provided link and share it with his or her family.

[0676] Prompt Sentence Examples

[0677] "Data entered: List of products that made you feel excited at the moment you purchased them"

[0678] "Prompt for generative AI model: Generate a story with emotions about a product the user has purchased in the past. Here is the data: [data content]"

[0679] This allows users to vividly relive their past purchasing experiences and share them with family and friends in an emotional way.

[0680] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0681] Step 1:

[0682] The user uses a dedicated application or web interface to collect their own text data (e.g., diary entries, emails) and voice data (e.g., voice memos, recordings). The user also collects purchase history and interaction history. They select this data and click the send button to send it to the system. The input to this step is various types of user data, and the output to the server is this data set.

[0683] Step 2:

[0684] The user's device formats the collected data, converts text and audio data into the appropriate format, compresses the data if necessary, sets the URL of the upload server, and then sends the data to the server. The input for this step is the various data provided by the user, and the output is the formatted data.

[0685] Step 3:

[0686] The server receives the uploaded data, stores the text data in a database, and the audio data in the appropriate directory. This step updates the database. The input is the formatted data, and the output is the stored data.

[0687] Step 4:

[0688] The server applies natural language processing (NLP) techniques to the received text data to analyze the context and sentiment. An emotion engine also participates in this analysis, recognizing and tagging the user's sentiment from the text and voice data. This stage primarily involves data analysis and tagging. The input is the stored data, and the output is the analysis results.

[0689] Step 5:

[0690] The server applies speech recognition technology to the voice data to convert it into text, which is then analyzed using an emotion engine. Emotions are also recognized from the converted text data and tagged. The input is the voice data, and the output is the converted and analyzed text data and emotion tags.

[0691] Step 6:

[0692] Based on the analyzed text data, emotion tags, and purchase history data, an AI model on the server generates a personal narrative for the user. This AI model is a generative AI model, creating a story based specifically on emotion tagging. The inputs for this step are the analysis results and purchase history data, and the output is the generated narrative text.

[0693] Step 7:

[0694] The server converts the generated story text into audio format using Text-to-Speech (TTS) technology. The converted audio file is stored on the server. The input is the generated story text, and the output is the converted audio file.

[0695] Step 8:

[0696] The server manages the converted audio data in a form that allows users to access it. The audio data is stored in a folder identified for each user, and appropriate access rights are set. The input of this step is the converted audio file, and the output is audio data stored in an accessible form.

[0697] Step 9:

[0698] The user's device provides a playback interface for the generated audio data. The user accesses and plays the audio data through the provided link. The playback interface is designed to be intuitive and easy to use. The input of this step is the access link to the audio data, and the output is the playback of the audio data.

[0699] In this way, a moving story can be generated using the user's past data and provided in audio format, which can bring back the user's purchasing experiences and memories in an emotional way.

[0700] 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.

[0701] 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.

[0702] 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.

[0703] [Third embodiment]

[0704] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0705] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0706] 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).

[0707] 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.

[0708] 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.

[0709] 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).

[0710] 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.

[0711] 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.

[0712] 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.

[0713] 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.

[0714] 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.

[0715] 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."

[0716] The present invention is a system that collects and analyzes an individual's past text data and voice data to generate a personal story and provide it in voice format. Hereinafter, a specific embodiment of the present invention will be described.

[0717] Data collection

[0718] Users: Users collect their own past text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings) through dedicated applications or web interfaces. Users can select and upload this data to the platform.

[0719] Device: The user's device uploads the collected data to the server in the appropriate format. The data is then formatted and compressed as necessary and securely sent to the server.

[0720] Data analysis

[0721] Server: The server analyzes the text and voice data received from the user. The text data is analyzed using natural language processing technology to extract important episodes and emotions. The voice data is converted into text using speech recognition technology. This allows for an integrated analysis of the text and voice data to extract information needed to form a moving personal story.

[0722] Story Generation

[0723] Server: Based on the analysis results, the server generates a personal narrative. The narrative generation is performed using an AI model to create a moving story that reflects the user's past experiences and emotions. This brings the user's past memories back to life in a new way, providing a visual and emotional experience.

[0724] Audio conversion

[0725] Server: The generated story is converted into audio format using text-to-speech technology, which allows the story to be delivered to the user in a natural voice. The technology used for speech conversion achieves high-quality speech synthesis, producing speech that is easily understandable to the user.

[0726] Data storage and playback

[0727] Server: The converted voice data is stored on the server and managed for user access. It identifies which data belongs to which user and sets appropriate access permissions.

[0728] Device: The user's device provides an interface for playing the generated audio data. The user can easily play the generated story through the provided link or application. By clicking the play button, the audio will start and the user can relive the past memory.

[0729] Specific examples

[0730] For example, user A wants to share his or her past growth records with his or her family. A uploads his or her diary and voice memos to a dedicated app. The app formats the data and sends it to a server. The server analyzes the data and extracts A's important episodes and emotions. Based on the analysis results, an AI model generates a personal story and converts it into audio using text-to-speech technology. The audio data is then stored on the server, and A can play back and share the generated story with his or her family via a provided link. This system brings A's memories back to life in a moving way, deepening the bond between the family.

[0731] In this way, the system of the present invention can provide a new experience to the user by reproducing personal memories and experiences as an audio story.

[0732] The processing flow will be explained below.

[0733] Step 1:

[0734] User: Using a dedicated application or web interface, the user collects their own text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings). The user selects this data and clicks the upload button to send it to the system.

[0735] Step 2:

[0736] Device: The user's device formats the collected text and audio data and converts it into the appropriate format. This data is compressed if necessary and prepared for transmission to the server. The destination server URL is set and the data is uploaded to the server.

[0737] Step 3:

[0738] Server: The server receives the uploaded data and stores it in storage. Text data is stored in a database, and audio data is stored in an appropriate directory. This allows data to be managed consistently within the system.

[0739] Step 4:

[0740] Server: Applying natural language processing (NLP) technology to the received text data, the server analyzes the context and sentiment. Important episodes and sentiments are tagged and the analysis results are generated. Audio data is converted into text using speech recognition technology and analyzed in the same way.

[0741] Step 5:

[0742] Server: Based on the analyzed data, the AI ​​model generates a personal narrative. The AI ​​model combines episodes and emotions extracted from the text data and converted audio data to create a moving and personal story. The text of the generated narrative is generated.

[0743] Step 6:

[0744] Server: The generated story text is converted into audio format using Text-to-Speech (TTS) technology, which synthesizes the story text into natural voice. The generated audio file is stored on the server.

[0745] Step 7:

[0746] Server: The converted voice data is managed in a form that can be accessed by users. Voice data is identified for each user and appropriate access rights are set. Users can access the generated voice data through an individual link.

[0747] Step 8:

[0748] Device: The user's device provides a playback interface for the generated audio data. The user can click the provided link to play the audio data through a browser or dedicated app. The playback interface should be designed to be intuitive and easy to use.

[0749] Step 9:

[0750] Users: Users can play the generated audio data and listen to their personal stories, which can bring back memories and experiences, providing new emotional experiences. Users can also share this audio data with their family and friends.

[0751] Example 1

[0752] 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."

[0753] Conventional story generation systems that use personal data have insufficient data collection and analysis, making it difficult to generate high-quality stories that reflect the user's individual experiences and emotions. Furthermore, when converting the generated stories into speech, there is a lack of means to provide natural, high-quality speech. Furthermore, insufficient management of data storage and playback makes it difficult for users to easily play and experience the generated stories.

[0754] 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.

[0755] In this invention, the server includes means for collecting personal text data and voice data, means for analyzing the text data and voice data, means for generating a personal story based on the analysis results, means for converting the generated story into voice, and means for saving the converted voice and providing it in a form that can be played back by the user. This enables the generation of high-quality stories that reflect the individual experiences and emotions of the user and the conversion of voice into natural, easy-to-listen-to voice, allowing the user to easily play back and experience the generated story.

[0756] "Personal text data" refers to data in written form generated by the user, such as diary entries, emails, and memos.

[0757] "Audio data" refers to data based on user-generated voice, such as voice memos, recordings, and call logs.

[0758] "Means of collection" refers to the technical means of collecting and uploading personal text and voice data to the platform through a dedicated application or web interface.

[0759] The "analyzing means" refers to a technical means that uses natural language processing technology and speech recognition technology to analyze the user's text data and voice data and extract important episodes and emotional information.

[0760] "Means for generating personal stories" refers to technical means that use an AI model based on the analysis results to create a story that reflects the user's past experiences and emotions.

[0761] "Convert-to-speech means" means a technical means for converting the generated text-based narrative into a natural, audible audio format using text-to-speech technology.

[0762] "Means of storing and providing in a form that can be played by users" refers to the technical means of managing and providing the converted audio data by storing it on a server and setting appropriate access rights so that users can play the audio data through a link or application.

[0763] "Natural language processing technology" is a technology for analyzing text data, and includes morphological analysis, sentiment analysis, etc.

[0764] "Speech recognition technology" is a technology for converting voice data into text format.

[0765] A "generative AI model" is an artificial intelligence technique used to generate a personal narrative based on analyzed data.

[0766] "Text-to-speech technology" is technology that converts text into speech.

[0767] "Formatting and compression means" refers to the technical means by which collected data is converted into a standardized format and compressed to increase the efficiency of data transmission.

[0768] The present invention is a system that collects and analyzes an individual's past text data and voice data to generate a personal story and provide it in voice format. Hereinafter, a specific embodiment of the present invention will be described.

[0769] Data collection

[0770] User: Using a dedicated application or web interface, users collect their own past text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings). This data is collected in one place through the application and uploaded to the platform.

[0771] Terminal: The user's terminal formats the collected data into a standardized format (e.g., UTF-8 for text data, WAV for audio data), and compresses it (e.g., ZIP format) to improve data transmission efficiency. The formatted and compressed data is then securely sent to the server.

[0772] Data analysis

[0773] Server: The server decompresses and analyzes the text and audio data received from the user. The text data is analyzed using natural language processing technology, which includes morphological analysis and sentiment analysis, and the specific software used is "NLPPipeline." The audio data is converted into text format using speech recognition technology, which uses "SpeechToTextConverter." The converted text data is also analyzed in the same way, and important episodes and emotional information are extracted.

[0774] Story Generation

[0775] Server: Based on the analysis results, the server generates a personal story using the AI ​​model "StoryGeneratorAI." This AI model creates a moving story that reflects the user's past experiences and emotions. The generated story is designed to bring back the user's past memories in a new form.

[0776] Audio conversion

[0777] Server: The generated story is converted into audio format using the text-to-speech technology "TextToSpeechSynthesizer." This technology converts the story into natural, high-quality audio that is easy for users to understand. During the speech conversion process, the tone, intonation, rhythm, etc. are adjusted to produce a voice that sounds like a natural conversation.

[0778] Data storage and playback

[0779] Server: The converted voice data is stored on the server. When stored, the data is identified for each user and appropriate access rights are set. The voice data stored in the database is managed to protect it from unauthorized access.

[0780] Device: The user's device provides a playback interface for the generated audio data using the application "StoryPlaybackApp." The user can play the generated audio data through the provided link or application. By clicking the play button, the user can enjoy the story as audio.

[0781] Specific examples

[0782] For example, suppose user A wants to share his or her past growth records with his or her family. User A uploads his or her diary and voice memos to a dedicated app. The app converts this data into a standard format and sends it to the server. The server then analyzes the data using "NLPPipeline" and "SpeechToTextConverter" to extract important episodes and emotional information. Based on the analysis results, the AI ​​model "StoryGeneratorAI" generates a personal story and converts it into audio using "TextToSpeechSynthesizer." The audio data is then stored on the server, and User A can play back and share the generated story with his or her family through "StoryPlaybackApp."

[0783] This system brings Mr. A's memories back to life in a moving way and deepens his bond with his family.

[0784] Prompt Sentence Examples

[0785] User A has uploaded his / her past diary entries and voice memos to the system. Explain the process of generating a moving personal story from these, converting it into audio, and playing it back.

[0786] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0787] Step 1:

[0788] User: Using a dedicated application or web interface, users upload their past text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings) as input. Data collection begins when the user clicks the upload button within the application. This data is then saved on the device via the application.

[0789] Input: Text data, audio data

[0790] Output: Text and audio data stored on the device

[0791] Step 2:

[0792] Terminal: The terminal formats the collected text and audio data into a standardized format (e.g., UTF-8 format for text data and WAV format for audio data). After formatting, the data is compressed (e.g., ZIP format) to improve transmission efficiency. The formatted and compressed data is sent to the server using a secure protocol (e.g., HTTPS).

[0793] Input: Text and voice data stored on the device

[0794] Output: Formatted and compressed data file sent to the server

[0795] Step 3:

[0796] Server: The server decompresses the received compressed file and obtains the text and audio data. The text data is then analyzed using the natural language processing technology "NLPPipeline." Morphological analysis and sentiment analysis are performed to extract important episodes and emotional information. Meanwhile, the audio data is converted to text format using the speech recognition technology "SpeechToTextConverter," and then analyzed in the same way.

[0797] Input: Compressed file received on the server

[0798] Output: Text data with important episodes and emotional information extracted

[0799] Step 4:

[0800] Server: Based on the analyzed data, the server uses the AI ​​model "StoryGeneratorAI" to generate a personal story. The AI ​​model takes into account the user's past experiences and emotions and outputs a moving story in chronological order. The generation process uses neural networks and generative probabilities.

[0801] Input: Text data with important episodes and emotional information extracted

[0802] Output: Generated personal narrative (text format)

[0803] Step 5:

[0804] Server: The generated story is converted into audio using the TextToSpeechSynthesizer, which adjusts the sound quality and intonation to produce a natural, easy-to-listen-to voice.

[0805] Input: Generated personal story (text format)

[0806] Output: The story converted into audio format

[0807] Step 6:

[0808] Server: The converted voice data is stored on the server. Each user's data is identified and appropriate access rights are set. The stored voice data is managed using server-side security features to protect it from unauthorized access.

[0809] Input: A story converted into audio format

[0810] Output: Audio data stored on the server

[0811] Step 7:

[0812] Device: The user's device provides a playback interface for the generated audio data using the "StoryPlaybackApp". The user can access the generated audio data through the provided link or application and play the story by clicking the play button.

[0813] Input: Audio data stored on the server

[0814] Output: Audio data provided through a playback interface

[0815] The above is the specific processing flow of the program for this system. Each step works closely together to provide a moving story that reflects the individual's past experiences and emotions, bringing a new experience to the user.

[0816] (Application example 1)

[0817] 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."

[0818] Existing virtual stores have difficulty providing personalized services and product recommendations based on customers' past purchase history and feedback. This prevents them from providing services tailored to each individual customer, hindering customer satisfaction. Furthermore, conventional systems have difficulty effectively analyzing collected past data to extract meaningful information and provide it to customers in real time.

[0819] 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.

[0820] In this invention, the server includes means for collecting personal text data and voice data, means for analyzing the text data and voice data, means for generating a personal story based on the analysis results, means for converting the generated story into voice, means for saving the converted voice and providing it in a form that can be played by the user, means for collecting and analyzing personal data such as the customer's past purchase history, feedback, chat logs, etc., means for generating personalized recommended products and services based on the analyzed data, and means for announcing the generated recommended products and services in voice format. This makes it possible to effectively utilize the customer's past data and provide personalized services and product recommendations in real time.

[0821] - "Personal text data" refers to text data generated or owned by a user, including diaries, emails, chat logs, etc.

[0822] "Audio data" refers to data containing user voice information, including voice memos, recording files, call history, and the like.

[0823] The "analyzing means" refers to a device or software program for analyzing collected text data and audio data using digital signal processing or natural language processing techniques.

[0824] A "means for generating a personal story" is a device or software program that uses an artificial intelligence model to generate a story that reflects the user's past information and emotions based on the analysis results.

[0825] A "narrative-to-audio means" is a device or software program that converts the generated narrative into audio form using text-to-speech technology.

[0826] The "means for storing the converted audio and providing it in a form that can be played by a user" is a device or software program that stores the audio data and makes it playable through a user-accessible interface.

[0827] "Purchase history" is data that includes information about products that a user has purchased in the past.

[0828] "Feedback" refers to data including user evaluations and impressions of products and services.

[0829] A "chat log" is a conversation history that a user has had through a chat application.

[0830] "Personalized recommended products and services" are those that recommend products and services that are most suitable for a user based on the user's past data.

[0831] The "means for providing generated recommended products and services in the form of voice" is a device or software program for notifying the user of personalized recommended products and services by voice.

[0832] This invention is a system that collects and analyzes personal text and voice data to generate a personal story and deliver it in audio format. It also includes a function to provide personalized audio recommendations for products and services based on data such as the customer's past purchase history, feedback, and chat logs.

[0833] Data collection

[0834] User:

[0835] Users collect their past text data (diaries, emails, chat logs) and audio data (voice memos, recordings) through a dedicated application or web interface. Users can select and upload this data to the platform. The history of users' past purchases and feedback are also collected.

[0836] Device:

[0837] The user's device uploads the collected data to the server in the appropriate format. The data is then reshaped and compressed as necessary and sent securely to the server.

[0838] Data analysis

[0839] server:

[0840] The server analyzes the text and voice data received from the user. This is done using natural language processing and voice recognition technologies. The text data is analyzed using a Transformer-based natural language processing model (e.g., BERT or GPT-3) to extract important episodes and emotions. The voice data is converted into text format using voice recognition technology. This allows for an integrated analysis of the text and voice data, and meaningful information is extracted from the customer's behavioral history.

[0841] Narrative and recommendation generation

[0842] server:

[0843] Based on the analysis results, a personal narrative is generated using an AI model (e.g., GPT-2 or GPT-3). This creates an inspiring story that reflects the user's past experiences and emotions. Furthermore, personalized product and service recommendations are also generated using the AI ​​model based on the analyzed personal data.

[0844] Voice conversion and guidance

[0845] server:

[0846] The generated story and recommendations are converted into audio format using text-to-speech technology (e.g., gTTS), which allows the story and product recommendations to be delivered to the user in a natural voice. The technology used for speech conversion achieves high-quality speech synthesis, producing speech that is easily understandable to the user.

[0847] Data storage and playback

[0848] server:

[0849] The converted voice data is stored on a server and managed so that users can access it. The data is identified as belonging to each user, and appropriate access rights are set.

[0850] Device:

[0851] The user's device provides a playback interface for the generated audio data, and the user can easily play the generated story and recommended products through the provided links or applications, allowing the user to experience past memories in a new way or check out new products and services.

[0852] Specific examples

[0853] For example, user A wants to share his or her past growth records with his or her family. A uploads his or her diary and voice memos to a dedicated app. The app formats the data and sends it to a server. The server analyzes the data and extracts A's important episodes and emotions. Based on the analysis results, an AI model generates a personal story and converts it into audio using text-to-speech technology. The audio data is then stored on the server, and A can play back and share the story with his or her family via a provided link.

[0854] Also, when entering a virtual store, the application analyzes reviews of products the user has previously purchased and, if it is recommending new products based on those reviews, provides a prompt like this to the generative AI model:

[0855] "Product reviewed: XYZ Camera. Review: The image quality is excellent and I am very satisfied. I would buy it again. Suggestion: New product."

[0856] Based on this, a generative AI model (e.g., GPT-2) can suggest new cameras and related accessories and vocalize the suggestions to provide personalized recommendations to the user.

[0857] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0858] Step 1: Data collection

[0859] Users collect their own past text data (diaries, emails, chat logs) and voice data (voice memos, recordings) through a dedicated application or web interface. Users can select this data and upload it to the platform. The history of products purchased by users and their feedback are also collected. The input is the user's text data and voice data, and the output is that this data is formatted and sent to the server.

[0860] Step 2: Send data

[0861] The terminals then format the collected data, compress it, and upload it to the server. The data is then formatted into a format that can be understood within the system and sent to the server using a secure communication protocol (e.g., HTTPS). The input is the formatted and compressed user text and voice data, and the output is that this data is securely sent to the server.

[0862] Step 3: Data analysis

[0863] The server analyzes the text and voice data received from the user. Natural language processing technology (e.g., BERT or GPT-3) is used to analyze the text data and extract important episodes and emotions. The voice data is converted into text format using speech recognition technology (e.g., Google Speech-to-Text). This allows for integrated analysis of the text and voice data and extracts meaningful information. The input is the user's text and voice data, and the output is analyzed episodes and emotional information.

[0864] Step 4: Narrative generation

[0865] The server uses an AI model (e.g., GPT-2 or GPT-3) based on the analysis results to generate a personal narrative. This narrative is an inspiring story that reflects the user's past experiences and emotions. Furthermore, personalized recommended products and services based on the analyzed personal data are also generated using an AI model. The input is the analysis results, and the output is the generated narrative and recommended products and services.

[0866] Step 5: Audio conversion

[0867] The server converts the generated story and recommendations into audio format using text-to-speech technology (e.g., gTTS). This allows the story and recommended products to be delivered to the user in a natural voice. The technology used for speech conversion achieves high-quality speech synthesis, producing speech that is easily understandable to the user. The input is the generated text, and the output is audio data.

[0868] Step 6: Data storage and playback

[0869] The server stores the converted voice data on the server and manages it so that users can access it. It identifies which data belongs to which user and sets appropriate access rights. The input is voice data, and the output is the stored voice data.

[0870] The device provides a playback interface for the generated audio data. The user can easily play the generated story and recommended products through the provided link or application. The input is the saved audio data, and the output is the played audio data. This allows the user to experience past memories in a new way or check out new products and services.

[0871] 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.

[0872] The present invention is a system that collects an individual's past text data and voice data, analyzes them to generate a personal story, and provides it in voice format. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, a more moving and personal story can be generated. Below, a specific description of an embodiment of the present invention is given.

[0873] Data collection

[0874] User: Using a dedicated application or web interface, the user collects their own text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings). The user selects this data and clicks the upload button to send it to the system.

[0875] Device: The user's device formats the collected text and audio data and converts it into the appropriate format. This data is compressed if necessary and prepared for transmission to the server. The destination server URL is set and the data is uploaded to the server.

[0876] Data analysis and emotion recognition

[0877] Server: The server receives the uploaded data and stores it in storage. Text data is stored in a database, and audio data is stored in an appropriate directory. This allows data to be managed consistently within the system.

[0878] Server: The server applies natural language processing (NLP) techniques to the received text data to analyze the context and sentiment. The emotion engine participates in this analysis process, recognizing and tagging the user's sentiment from the text and voice data. Important episodes and sentiments are tagged, and the analysis results are generated.

[0879] Server: The voice data is converted into text using speech recognition technology and analyzed by the emotion engine. The emotion engine recognizes the emotion in the voice data and reflects it in the analysis results.

[0880] Story Generation

[0881] Server: Based on the analysis results, the AI ​​model generates a personal narrative. The AI ​​model combines episodes and emotions extracted from the text data and converted audio data to create a moving and personal story based especially on emotion tagging. The text of the generated narrative is generated.

[0882] Audio conversion

[0883] Server: The generated story text is converted into audio format using Text-to-Speech (TTS) technology, which provides the story text to the user as a natural voice. The generated audio file is stored on the server.

[0884] Data storage and playback

[0885] Server: The converted voice data is managed in a form that can be accessed by users. Voice data is identified for each user and appropriate access rights are set. Users can access the generated voice data through an individual link.

[0886] Device: The user's device provides a playback interface for the generated audio data. The user can click the provided link to play the audio data through a browser or dedicated app. The playback interface should be designed to be intuitive and easy to use.

[0887] Specific examples

[0888] For example, user B wants to share his / her past growth record with his / her family. He / she uploads his / her diary and voice memos to a dedicated app. The app formats this data and sends it to the server.

[0889] The server analyzes the data and recognizes important episodes and emotions of Mr. B. The emotion engine tags emotions that should be emphasized, and the AI ​​model generates a story based on that. This story is converted into audio using text-to-speech technology and stored on the server as audio data.

[0890] Mr. B can replay the generated story through the provided link and share it with his family. This system brings back Mr. B's memories in a moving way and deepens the bond between him and his family.

[0891] In this way, by combining emotion recognition, the system of the present invention can recreate personal memories and experiences as more moving and personal stories, providing users with a new experience.

[0892] The processing flow will be explained below.

[0893] Step 1:

[0894] User: Using a dedicated application or web interface, the user collects their own text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings). The user selects this data and clicks the upload button to send it to the system.

[0895] Step 2:

[0896] Device: The user's device formats the collected text and audio data and converts it into the appropriate format. This data is compressed if necessary and prepared for transmission to the server. The destination server URL is set and the data is uploaded to the server.

[0897] Step 3:

[0898] Server: The server receives the uploaded data and stores it in storage. Text data is stored in a database, and audio data is stored in an appropriate directory. This allows data to be managed consistently within the system.

[0899] Step 4:

[0900] Server: Applying natural language processing (NLP) techniques to the received text data to analyze context and sentiment. The emotion engine participates in the analysis process, recognizing and tagging the user's sentiment from the text and voice data. Emotion tagging identifies important episodes and sentiments, and generates analysis results.

[0901] Step 5:

[0902] Server: The voice data is converted into text using voice recognition technology and analyzed by the emotion engine. The emotion engine recognizes the emotion in the voice data and reflects it in the analysis results.

[0903] Step 6:

[0904] Server: Based on the analyzed data, the AI ​​model generates a personal narrative. The AI ​​model combines episodes and emotions extracted from the text data and converted audio data to create a moving and personal story based especially on emotion tagging. The text of the generated narrative is generated.

[0905] Step 7:

[0906] Server: The generated story text is converted into audio format using Text-to-Speech (TTS) technology, which provides the story text to the user as a natural voice. The generated audio file is stored on the server.

[0907] Step 8:

[0908] Server: The converted voice data is managed in a form that can be accessed by users. Voice data is identified for each user and appropriate access rights are set. Users can access the generated voice data through an individual link.

[0909] Step 9:

[0910] Device: The user's device provides a playback interface for the generated audio data. The user can click the provided link to play the audio data through a browser or dedicated app. The playback interface should be designed to be intuitive and easy to use.

[0911] Step 10:

[0912] Users: Users can play the generated audio data and listen to their personal stories, which can bring back memories and experiences, providing new emotional experiences. Users can also share this audio data with their family and friends.

[0913] Example 2

[0914] 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."

[0915] Conventional systems have struggled to effectively collect and analyze personal text and audio data to generate moving, personal stories. Furthermore, when providing the stories in audio format, they lack natural emotional expression. Therefore, there is a need for a method to more fully reproduce and share users' emotions and experiences.

[0916] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0917] In this invention, the server includes means for collecting personal data, means for formatting the data and transmitting it to the server, means for analyzing the data on the server and recognizing emotions, means for generating a story based on the analysis results, means for converting the generated story into audio, and means for saving the converted audio and providing it in a format that can be played by the user. This makes it possible to effectively analyze the user's emotions and experiences, generate a moving and personal story, and provide it in a natural audio format.

[0918] "Personal data" refers to information generated or collected by an individual, including, among other things, text data and voice data.

[0919] "Means of collection" refers to a mechanism that provides users with the ability to select and upload personal data using a dedicated application or web interface.

[0920] "Formatting" refers to the process of converting collected data into a standard format, compressing or converting it as needed, and preparing it for transmission to a server.

[0921] "Means for sending to a server" refers to a function for uploading formatted data to a specified server URL using the HTTP protocol.

[0922] "Means of analysis" refers to the process of receiving data on a server and analyzing the context and sentiment of the data using natural language processing technology and voice recognition technology.

[0923] "Means for recognizing emotions" refers to the function of using an emotion engine during the analysis process to recognize and tag user emotions from text and voice data.

[0924] "Means for generating stories" refers to the process of using an AI model based on the analysis results to generate personal and moving stories that take into account emotional tags.

[0925] "Converting to speech means" refers to the process of converting the generated narrative text into audio format using text-to-speech technology.

[0926] "Means for providing the converted audio data in a form that can be saved and played back" refers to the function of managing the converted audio data so that it can be accessed by users, and providing it by setting appropriate access rights.

[0927] The system of the present invention collects and analyzes an individual's text and voice data, generates a personal narrative based on the collected data, and presents it in audio format. The operation of this system is described in detail below.

[0928] Data collection

[0929] Users: Using a dedicated application or web interface, users upload their own text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings) to the system. Uploading can be done simply by clicking a button.

[0930] Data Formatting and Transmission

[0931] Terminal: The user's terminal converts the collected data into a standard format (e.g., a text file in UTF-8 format, or an audio file in WAV or MP3 format). The audio data is compressed if necessary, and the converted data is sent to the server. The transmission is performed using the HTTP protocol, and the server URL is set.

[0932] Data analysis and emotion recognition

[0933] Server: The server receives the data sent by the user, stores the text data in a database, and the voice data in an appropriate directory. It uses natural language processing (NLP) techniques to analyze the context and content of the text data. During this analysis, the emotion recognition engine tags emotions from the text and extracts specific episodes.

[0934] Server: The voice data is converted to text using speech recognition technology (e.g., Google Cloud Speech-to-Text API). This text is also analyzed using emotion recognition technology and tagged with an emotion.

[0935] Story Generation

[0936] Server: Based on the analysis results, a generative AI model (e.g., GPT-3, GPT-4) is used to generate a personal story. This model combines episodes and emotions extracted from text data and converted audio data, taking into account emotion tags in particular, to create a moving and personal story.

[0937] Audio conversion

[0938] Server: The generated story text is converted into audio format using Text-to-Speech (TTS) technology (e.g., Microsoft Azure Cognitive Services TTS API). The generated audio file (e.g., MP3 format) is stored on the server.

[0939] Data storage and playback

[0940] Server: The converted audio data is managed in association with the user's ID, and appropriate access rights are set. A separate folder is created for each user to protect the data.

[0941] User: The user can access the generated audio data by clicking the provided link. Clicking the link launches a browser or dedicated app, and an interface for playing the generated story is displayed. The user can easily listen to the audio data by clicking the play button.

[0942] Specific examples

[0943] For example, if user B wants to share his or her growth record with his or her family, B uploads his or her diary and voice memos to a dedicated app. The device formats the collected data and sends it to a server. The server analyzes the data and recognizes B's important episodes and emotions. Based on the emotional information tagged by the emotion engine, a generative AI model generates a personal story. The generated story is converted into audio using TTS technology and saved on the server. B can play the generated story via the provided link and share it with his or her family.

[0944] Prompt Sentence Examples

[0945] "Extract the most emotional parts from Mr. B's diary and generate a moving story."

[0946] "Convert the contents of the user's voice memo into text and create a story based on the results analyzed by the emotion engine."

[0947] "Based on the following text data, please have your AI model generate a story that combines emotional episodes."

[0948] This invention allows personal memories and experiences to be recreated as more moving and personal stories, providing users with a new experience.

[0949] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0950] Step 1: Data collection

[0951] User: Using a dedicated application or web interface, the user selects their own text data (e.g., diary entries, emails) or audio data (e.g., voice memos, recordings) and clicks the upload button.

[0952] Input: User text and voice data

[0953] Output: raw data saved to the device

[0954] Step 2: Format and send data

[0955] Device: The user's device converts the collected text data into a standard format (e.g., a UTF-8 text file) and the audio data into an appropriate format (e.g., WAV or MP3). The audio data is compressed if necessary. The formatted data is then sent to the server using the HTTP protocol.

[0956] Input: Collected raw text and audio data

[0957] Output: Formatted data sent to the server

[0958] Step 3: Receiving and storing data

[0959] Server: The server receives the HTTP request, stores the text data in a database, and stores the audio data in the appropriate directory.

[0960] Input: Formatted data sent from the terminal

[0961] Output: Text data stored in a database and audio data stored in a directory

[0962] Step 4: Text data analysis and emotion recognition

[0963] Server: The server applies natural language processing (NLP) techniques to the stored text data to analyze its context and content. During this analysis, the emotion engine tags the text with emotions and extracts specific episodes.

[0964] Input: Saved text data

[0965] Output: Text analysis results with sentiment tags

[0966] Step 5: Convert speech data to text and recognize emotions

[0967] Server: The server converts the voice data into text using speech recognition technology (e.g., Google Cloud Speech-to-Text API). It also applies emotion recognition technology to the converted text data and attaches emotion tags to it.

[0968] Input: Stored audio data

[0969] Output: Text data with emotion tags

[0970] Step 6: Narrative generation

[0971] Server: Based on the analysis results, constructs prompts for a generative AI model (e.g., GPT-3, GPT-4) to generate a personal story. The prompts include emotion tags and extracted episodes. The generative AI model then generates a story based on these prompts.

[0972] Input: Sentiment-tagged text analysis results and converted text data

[0973] Output: Generated personal narrative text

[0974] Step 7: Transcribing the story

[0975] Server: The generated story text is converted into audio format using Text-to-Speech (TTS) technology (e.g., Microsoft Azure Cognitive Services TTS API). The generated audio file (e.g., MP3 format) is stored on the server.

[0976] Input: Generated personal narrative text

[0977] Output: The generated audio file

[0978] Step 8: Save and provide access to your audio files

[0979] Server: The converted voice data is managed in association with the user's ID, and appropriate access rights are set. A separate folder is created for each user to protect the data. The user can access the generated voice data via the provided link.

[0980] Input: Generated audio file

[0981] Output: Audio data managed in a user-accessible form

[0982] Step 9: Playing back audio data

[0983] User: The user clicks on the provided link to open an interface that plays the generated story via a browser or dedicated app. By clicking the play button, the user can listen to the generated audio data.

[0984] Input: The link the user visits

[0985] Output: Audio data played in a browser or dedicated app

[0986] (Application example 2)

[0987] 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."

[0988] In today's world, individual users generate large amounts of digital data on a daily basis, but there are few opportunities to reuse that data as personal, moving stories. Furthermore, in electronic payment services, simply compiling and analyzing users' purchase and interaction histories makes it difficult to provide emotional value to users. This can prevent users from emotionally reflecting on their purchasing experiences and memories, potentially resulting in a decrease in satisfaction with purchasing activities.

[0989] 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 collecting text data and voice data of the user, means for analyzing the text data and voice data, means for generating a personal story based on the analysis results, means for converting the generated story into voice, means for saving the converted voice and providing it in a form that the user can play back, means for collecting and analyzing the user's purchase history and interaction history and recognizing emotions, and means for generating a story that includes emotions at the time of purchase and playing it back as voice. This makes it possible for the user to look back on their purchasing experiences and memories as moving stories.

[0990] "Text data" refers to digital information in the form of text, such as diary entries, emails, and messages created by users.

[0991] "Voice data" refers to digital voice information such as recorded voice memos, dialogues, and telephone conversations generated by the user.

[0992] "Analysis means" refers to a method of analyzing collected text data and audio data using natural language processing technology and speech recognition technology to understand context and emotions.

[0993] A "story generation means" is an algorithm or software that generates a story that includes the user's personal experiences and emotions based on the analysis results.

[0994] "Convert to speech" is the process of converting the generated narrative into audio format using text-to-speech technology.

[0995] "Storage means" refers to a mechanism for storing the converted audio data in a digital format and managing it in a manner that allows access by the user.

[0996] "Providing in a reproducible form" means providing an interface or platform that allows users to easily access and play the generated audio data.

[0997] "Purchase history" is data that records detailed information about products that a user has purchased using an electronic payment service.

[0998] "Dialogue history" is a log of messages and conversations exchanged between a user and the system in an electronic payment service.

[0999] "Emotion recognition means" is a technology that analyzes and understands the emotional elements contained in a user's purchasing history and interaction history.

[1000] "Narrative generation" involves the process of constructing a story that reflects the user's emotions and experiences at the time of purchase.

[1001] "Means for playing as audio" refers to a technique for converting the generated story into audio format using TTS technology and providing it to the user in a reproducible format.

[1002] This invention is a system that collects and analyzes a user's past text data and voice data, generates a personal story, and provides it in voice format. Furthermore, it aims to recognize emotions in purchase history and conversation history, and generate and provide an inspiring story based on this.

[1003] Data collection

[1004] Users use a dedicated application or web interface to collect their own text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings). The user's purchase history and interaction history are also collected. They select this data and click the upload button to send it to the system. The user's device then formats the collected data and converts it into an appropriate format. This data is then compressed and sent to the server.

[1005] Data analysis and emotion recognition

[1006] The server receives the uploaded data and stores it in storage. The data is converted into an appropriate format and saved in a database. Natural language processing (NLP) and speech recognition technologies are used for analysis. The emotion engine recognizes and tags the user's emotions from text, voice data, purchase history, and conversation history. Important episodes and emotions are tagged in the analysis results.

[1007] Story Generation

[1008] The AI ​​model on the server generates a personal narrative based on the analysis results. The AI ​​model is a generative AI model that combines episodes and emotions extracted from the text data and converted audio data to create a moving and personal story based on emotion tagging in particular.

[1009] Audio conversion

[1010] The generated story text is converted into audio format using Text-to-Speech (TTS) technology, which provides the story text to the user as natural-sounding audio, and the generated audio file is stored on the server.

[1011] Data storage and playback

[1012] The converted voice data is managed in a form that can be accessed by users. Voice data is identified for each user and appropriate access rights are set. Users can access the generated voice data through individual links. The user's device provides a playback interface for the generated voice data. The playback interface has an intuitive and easy-to-use design.

[1013] Specific examples

[1014] For example, if user A wants to share his or her past purchase history as an inspiring story with his or her family, he or she uploads his or her purchase history and voice memos to a dedicated app. The app formats this data and sends it to a server. The server analyzes the data and recognizes A's important purchase episodes and emotions. An emotion engine tags emotions that should be particularly emphasized, and an AI model generates a story based on this. This story is converted into audio using text-to-speech technology and saved as audio data on the server. A can play the generated story through the provided link and share it with his or her family.

[1015] Prompt Sentence Examples

[1016] "Data entered: List of products that made you feel excited at the moment you purchased them"

[1017] "Prompt for generative AI model: Generate a story with emotions about a product the user has purchased in the past. Here is the data: [data content]"

[1018] This allows users to vividly relive their past purchasing experiences and share them with family and friends in an emotional way.

[1019] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1020] Step 1:

[1021] The user uses a dedicated application or web interface to collect their own text data (e.g., diary entries, emails) and voice data (e.g., voice memos, recordings). The user also collects purchase history and interaction history. They select this data and click the send button to send it to the system. The input to this step is various types of user data, and the output to the server is this data set.

[1022] Step 2:

[1023] The user's device formats the collected data, converts text and audio data into the appropriate format, compresses the data if necessary, sets the URL of the upload server, and then sends the data to the server. The input for this step is the various data provided by the user, and the output is the formatted data.

[1024] Step 3:

[1025] The server receives the uploaded data, stores the text data in a database, and the audio data in the appropriate directory. This step updates the database. The input is the formatted data, and the output is the stored data.

[1026] Step 4:

[1027] The server applies natural language processing (NLP) techniques to the received text data to analyze the context and sentiment. An emotion engine also participates in this analysis, recognizing and tagging the user's sentiment from the text and voice data. This stage primarily involves data analysis and tagging. The input is the stored data, and the output is the analysis results.

[1028] Step 5:

[1029] The server applies speech recognition technology to the voice data to convert it into text, which is then analyzed using an emotion engine. Emotions are also recognized from the converted text data and tagged. The input is the voice data, and the output is the converted and analyzed text data and emotion tags.

[1030] Step 6:

[1031] Based on the analyzed text data, emotion tags, and purchase history data, an AI model on the server generates a personal narrative for the user. This AI model is a generative AI model, creating a story based specifically on emotion tagging. The inputs for this step are the analysis results and purchase history data, and the output is the generated narrative text.

[1032] Step 7:

[1033] The server converts the generated story text into audio format using Text-to-Speech (TTS) technology. The converted audio file is stored on the server. The input is the generated story text, and the output is the converted audio file.

[1034] Step 8:

[1035] The server manages the converted audio data in a form that allows users to access it. The audio data is stored in a folder identified for each user, and appropriate access rights are set. The input of this step is the converted audio file, and the output is audio data stored in an accessible form.

[1036] Step 9:

[1037] The user's device provides a playback interface for the generated audio data. The user accesses and plays the audio data through the provided link. The playback interface is designed to be intuitive and easy to use. The input of this step is the access link to the audio data, and the output is the playback of the audio data.

[1038] In this way, a moving story can be generated using the user's past data and provided in audio format, which can bring back the user's purchasing experiences and memories in an emotional way.

[1039] 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.

[1040] 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.

[1041] 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.

[1042] [Fourth embodiment]

[1043] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1044] 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.

[1045] 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).

[1046] 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.

[1047] 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.

[1048] 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).

[1049] 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.

[1050] 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.

[1051] 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.

[1052] 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.

[1053] 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.

[1054] 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.

[1055] 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."

[1056] The present invention is a system that collects and analyzes an individual's past text data and voice data to generate a personal story and provide it in voice format. Hereinafter, a specific embodiment of the present invention will be described.

[1057] Data collection

[1058] Users: Users collect their own past text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings) through dedicated applications or web interfaces. Users can select and upload this data to the platform.

[1059] Device: The user's device uploads the collected data to the server in the appropriate format. The data is then formatted and compressed as necessary and securely sent to the server.

[1060] Data analysis

[1061] Server: The server analyzes the text and voice data received from the user. The text data is analyzed using natural language processing technology to extract important episodes and emotions. The voice data is converted into text using speech recognition technology. This allows for an integrated analysis of the text and voice data to extract information needed to form a moving personal story.

[1062] Story Generation

[1063] Server: Based on the analysis results, the server generates a personal narrative. The narrative generation is performed using an AI model to create a moving story that reflects the user's past experiences and emotions. This brings the user's past memories back to life in a new way, providing a visual and emotional experience.

[1064] Audio conversion

[1065] Server: The generated story is converted into audio format using text-to-speech technology, which allows the story to be delivered to the user in a natural voice. The technology used for speech conversion achieves high-quality speech synthesis, producing speech that is easily understandable to the user.

[1066] Data storage and playback

[1067] Server: The converted voice data is stored on the server and managed for user access. It identifies which data belongs to which user and sets appropriate access permissions.

[1068] Device: The user's device provides an interface for playing the generated audio data. The user can easily play the generated story through the provided link or application. By clicking the play button, the audio will start and the user can relive the past memory.

[1069] Specific examples

[1070] For example, user A wants to share his or her past growth records with his or her family. A uploads his or her diary and voice memos to a dedicated app. The app formats the data and sends it to a server. The server analyzes the data and extracts A's important episodes and emotions. Based on the analysis results, an AI model generates a personal story and converts it into audio using text-to-speech technology. The audio data is then stored on the server, and A can play back and share the generated story with his or her family via a provided link. This system brings A's memories back to life in a moving way, deepening the bond between the family.

[1071] In this way, the system of the present invention can provide a new experience to the user by reproducing personal memories and experiences as an audio story.

[1072] The processing flow will be explained below.

[1073] Step 1:

[1074] User: Using a dedicated application or web interface, the user collects their own text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings). The user selects this data and clicks the upload button to send it to the system.

[1075] Step 2:

[1076] Device: The user's device formats the collected text and audio data and converts it into the appropriate format. This data is compressed if necessary and prepared for transmission to the server. The destination server URL is set and the data is uploaded to the server.

[1077] Step 3:

[1078] Server: The server receives the uploaded data and stores it in storage. Text data is stored in a database, and audio data is stored in an appropriate directory. This allows data to be managed consistently within the system.

[1079] Step 4:

[1080] Server: Applying natural language processing (NLP) technology to the received text data, the server analyzes the context and sentiment. Important episodes and sentiments are tagged and the analysis results are generated. Audio data is converted into text using speech recognition technology and analyzed in the same way.

[1081] Step 5:

[1082] Server: Based on the analyzed data, the AI ​​model generates a personal narrative. The AI ​​model combines episodes and emotions extracted from the text data and converted audio data to create a moving and personal story. The text of the generated narrative is generated.

[1083] Step 6:

[1084] Server: The generated story text is converted into audio format using Text-to-Speech (TTS) technology, which synthesizes the story text into natural voice. The generated audio file is stored on the server.

[1085] Step 7:

[1086] Server: The converted voice data is managed in a form that can be accessed by users. Voice data is identified for each user and appropriate access rights are set. Users can access the generated voice data through an individual link.

[1087] Step 8:

[1088] Device: The user's device provides a playback interface for the generated audio data. The user can click the provided link to play the audio data through a browser or dedicated app. The playback interface should be designed to be intuitive and easy to use.

[1089] Step 9:

[1090] Users: Users can play the generated audio data and listen to their personal stories, which can bring back memories and experiences, providing new emotional experiences. Users can also share this audio data with their family and friends.

[1091] Example 1

[1092] 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."

[1093] Conventional story generation systems that use personal data have insufficient data collection and analysis, making it difficult to generate high-quality stories that reflect the user's individual experiences and emotions. Furthermore, when converting the generated stories into speech, there is a lack of means to provide natural, high-quality speech. Furthermore, insufficient management of data storage and playback makes it difficult for users to easily play and experience the generated stories.

[1094] 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.

[1095] In this invention, the server includes means for collecting personal text data and voice data, means for analyzing the text data and voice data, means for generating a personal story based on the analysis results, means for converting the generated story into voice, and means for saving the converted voice and providing it in a form that can be played back by the user. This enables the generation of high-quality stories that reflect the individual experiences and emotions of the user and the conversion of voice into natural, easy-to-listen-to voice, allowing the user to easily play back and experience the generated story.

[1096] "Personal text data" refers to data in written form generated by the user, such as diary entries, emails, and memos.

[1097] "Audio data" refers to data based on user-generated voice, such as voice memos, recordings, and call logs.

[1098] "Means of collection" refers to the technical means of collecting and uploading personal text and voice data to the platform through a dedicated application or web interface.

[1099] The "analyzing means" refers to a technical means that uses natural language processing technology and speech recognition technology to analyze the user's text data and voice data and extract important episodes and emotional information.

[1100] "Means for generating personal stories" refers to technical means that use an AI model based on the analysis results to create a story that reflects the user's past experiences and emotions.

[1101] "Convert-to-speech means" means a technical means for converting the generated text-based narrative into a natural, audible audio format using text-to-speech technology.

[1102] "Means of storing and providing in a form that can be played by users" refers to the technical means of managing and providing the converted audio data by storing it on a server and setting appropriate access rights so that users can play the audio data through a link or application.

[1103] "Natural language processing technology" is a technology for analyzing text data, and includes morphological analysis, sentiment analysis, etc.

[1104] "Speech recognition technology" is a technology for converting voice data into text format.

[1105] A "generative AI model" is an artificial intelligence technique used to generate a personal narrative based on analyzed data.

[1106] "Text-to-speech technology" is technology that converts text into speech.

[1107] "Formatting and compression means" refers to the technical means by which collected data is converted into a standardized format and compressed to increase the efficiency of data transmission.

[1108] The present invention is a system that collects and analyzes an individual's past text data and voice data to generate a personal story and provide it in voice format. Hereinafter, a specific embodiment of the present invention will be described.

[1109] Data collection

[1110] User: Using a dedicated application or web interface, users collect their own past text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings). This data is collected in one place through the application and uploaded to the platform.

[1111] Terminal: The user's terminal formats the collected data into a standardized format (e.g., UTF-8 for text data, WAV for audio data), and compresses it (e.g., ZIP format) to improve data transmission efficiency. The formatted and compressed data is then securely sent to the server.

[1112] Data analysis

[1113] Server: The server decompresses and analyzes the text and audio data received from the user. The text data is analyzed using natural language processing technology, which includes morphological analysis and sentiment analysis, and the specific software used is "NLPPipeline." The audio data is converted into text format using speech recognition technology, which uses "SpeechToTextConverter." The converted text data is also analyzed in the same way, and important episodes and emotional information are extracted.

[1114] Story Generation

[1115] Server: Based on the analysis results, the server generates a personal story using the AI ​​model "StoryGeneratorAI." This AI model creates a moving story that reflects the user's past experiences and emotions. The generated story is designed to bring back the user's past memories in a new form.

[1116] Audio conversion

[1117] Server: The generated story is converted into audio format using the text-to-speech technology "TextToSpeechSynthesizer." This technology converts the story into natural, high-quality audio that is easy for users to understand. During the speech conversion process, the tone, intonation, rhythm, etc. are adjusted to produce a voice that sounds like a natural conversation.

[1118] Data storage and playback

[1119] Server: The converted voice data is stored on the server. When stored, the data is identified for each user and appropriate access rights are set. The voice data stored in the database is managed to protect it from unauthorized access.

[1120] Device: The user's device provides a playback interface for the generated audio data using the application "StoryPlaybackApp." The user can play the generated audio data through the provided link or application. By clicking the play button, the user can enjoy the story as audio.

[1121] Specific examples

[1122] For example, suppose user A wants to share his or her past growth records with his or her family. User A uploads his or her diary and voice memos to a dedicated app. The app converts this data into a standard format and sends it to the server. The server then analyzes the data using "NLPPipeline" and "SpeechToTextConverter" to extract important episodes and emotional information. Based on the analysis results, the AI ​​model "StoryGeneratorAI" generates a personal story and converts it into audio using "TextToSpeechSynthesizer." The audio data is then stored on the server, and User A can play back and share the generated story with his or her family through "StoryPlaybackApp."

[1123] This system brings Mr. A's memories back to life in a moving way and deepens his bond with his family.

[1124] Prompt Sentence Examples

[1125] User A has uploaded his / her past diary entries and voice memos to the system. Explain the process of generating a moving personal story from these, converting it into audio, and playing it back.

[1126] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1127] Step 1:

[1128] User: Using a dedicated application or web interface, users upload their past text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings) as input. Data collection begins when the user clicks the upload button within the application. This data is then saved on the device via the application.

[1129] Input: Text data, audio data

[1130] Output: Text and audio data stored on the device

[1131] Step 2:

[1132] Terminal: The terminal formats the collected text and audio data into a standardized format (e.g., UTF-8 format for text data and WAV format for audio data). After formatting, the data is compressed (e.g., ZIP format) to improve transmission efficiency. The formatted and compressed data is sent to the server using a secure protocol (e.g., HTTPS).

[1133] Input: Text and voice data stored on the device

[1134] Output: Formatted and compressed data file sent to the server

[1135] Step 3:

[1136] Server: The server decompresses the received compressed file and obtains the text and audio data. The text data is then analyzed using the natural language processing technology "NLPPipeline." Morphological analysis and sentiment analysis are performed to extract important episodes and emotional information. Meanwhile, the audio data is converted to text format using the speech recognition technology "SpeechToTextConverter," and then analyzed in the same way.

[1137] Input: Compressed file received on the server

[1138] Output: Text data with important episodes and emotional information extracted

[1139] Step 4:

[1140] Server: Based on the analyzed data, the server uses the AI ​​model "StoryGeneratorAI" to generate a personal story. The AI ​​model takes into account the user's past experiences and emotions and outputs a moving story in chronological order. The generation process uses neural networks and generative probabilities.

[1141] Input: Text data with important episodes and emotional information extracted

[1142] Output: Generated personal narrative (text format)

[1143] Step 5:

[1144] Server: The generated story is converted into audio using the TextToSpeechSynthesizer, which adjusts the sound quality and intonation to produce a natural, easy-to-listen-to voice.

[1145] Input: Generated personal story (text format)

[1146] Output: The story converted into audio format

[1147] Step 6:

[1148] Server: The converted voice data is stored on the server. Each user's data is identified and appropriate access rights are set. The stored voice data is managed using server-side security features to protect it from unauthorized access.

[1149] Input: A story converted into audio format

[1150] Output: Audio data stored on the server

[1151] Step 7:

[1152] Device: The user's device provides a playback interface for the generated audio data using the "StoryPlaybackApp". The user can access the generated audio data through the provided link or application and play the story by clicking the play button.

[1153] Input: Audio data stored on the server

[1154] Output: Audio data provided through a playback interface

[1155] The above is the specific processing flow of the program for this system. Each step works closely together to provide a moving story that reflects the individual's past experiences and emotions, bringing a new experience to the user.

[1156] (Application example 1)

[1157] 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."

[1158] Existing virtual stores have difficulty providing personalized services and product recommendations based on customers' past purchase history and feedback. This prevents them from providing services tailored to each individual customer, hindering customer satisfaction. Furthermore, conventional systems have difficulty effectively analyzing collected past data to extract meaningful information and provide it to customers in real time.

[1159] 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.

[1160] In this invention, the server includes means for collecting personal text data and voice data, means for analyzing the text data and voice data, means for generating a personal story based on the analysis results, means for converting the generated story into voice, means for saving the converted voice and providing it in a form that can be played by the user, means for collecting and analyzing personal data such as the customer's past purchase history, feedback, chat logs, etc., means for generating personalized recommended products and services based on the analyzed data, and means for announcing the generated recommended products and services in voice format. This makes it possible to effectively utilize the customer's past data and provide personalized services and product recommendations in real time.

[1161] - "Personal text data" refers to text data generated or owned by a user, including diaries, emails, chat logs, etc.

[1162] "Audio data" refers to data containing user voice information, including voice memos, recording files, call history, and the like.

[1163] The "analyzing means" refers to a device or software program for analyzing collected text data and audio data using digital signal processing or natural language processing techniques.

[1164] A "means for generating a personal story" is a device or software program that uses an artificial intelligence model to generate a story that reflects the user's past information and emotions based on the analysis results.

[1165] A "narrative-to-audio means" is a device or software program that converts the generated narrative into audio form using text-to-speech technology.

[1166] The "means for storing the converted audio and providing it in a form that can be played by a user" is a device or software program that stores the audio data and makes it playable through a user-accessible interface.

[1167] "Purchase history" is data that includes information about products that a user has purchased in the past.

[1168] "Feedback" refers to data including user evaluations and impressions of products and services.

[1169] A "chat log" is a conversation history that a user has had through a chat application.

[1170] "Personalized recommended products and services" are those that recommend products and services that are most suitable for a user based on the user's past data.

[1171] The "means for providing generated recommended products and services in the form of voice" is a device or software program for notifying the user of personalized recommended products and services by voice.

[1172] This invention is a system that collects and analyzes personal text and voice data to generate a personal story and deliver it in audio format. It also includes a function to provide personalized audio recommendations for products and services based on data such as the customer's past purchase history, feedback, and chat logs.

[1173] Data collection

[1174] User:

[1175] Users collect their past text data (diaries, emails, chat logs) and audio data (voice memos, recordings) through a dedicated application or web interface. Users can select and upload this data to the platform. The history of users' past purchases and feedback are also collected.

[1176] Device:

[1177] The user's device uploads the collected data to the server in the appropriate format. The data is then reshaped and compressed as necessary and sent securely to the server.

[1178] Data analysis

[1179] server:

[1180] The server analyzes the text and voice data received from the user. This is done using natural language processing and voice recognition technologies. The text data is analyzed using a Transformer-based natural language processing model (e.g., BERT or GPT-3) to extract important episodes and emotions. The voice data is converted into text format using voice recognition technology. This allows for an integrated analysis of the text and voice data, and meaningful information is extracted from the customer's behavioral history.

[1181] Narrative and recommendation generation

[1182] server:

[1183] Based on the analysis results, a personal narrative is generated using an AI model (e.g., GPT-2 or GPT-3). This creates an inspiring story that reflects the user's past experiences and emotions. Furthermore, personalized product and service recommendations are also generated using the AI ​​model based on the analyzed personal data.

[1184] Voice conversion and guidance

[1185] server:

[1186] The generated story and recommendations are converted into audio format using text-to-speech technology (e.g., gTTS), which allows the story and product recommendations to be delivered to the user in a natural voice. The technology used for speech conversion achieves high-quality speech synthesis, producing speech that is easily understandable to the user.

[1187] Data storage and playback

[1188] server:

[1189] The converted voice data is stored on a server and managed so that users can access it. The data is identified as belonging to each user, and appropriate access rights are set.

[1190] Device:

[1191] The user's device provides a playback interface for the generated audio data, and the user can easily play the generated story and recommended products through the provided links or applications, allowing the user to experience past memories in a new way or check out new products and services.

[1192] Specific examples

[1193] For example, user A wants to share his or her past growth records with his or her family. A uploads his or her diary and voice memos to a dedicated app. The app formats the data and sends it to a server. The server analyzes the data and extracts A's important episodes and emotions. Based on the analysis results, an AI model generates a personal story and converts it into audio using text-to-speech technology. The audio data is then stored on the server, and A can play back and share the story with his or her family via a provided link.

[1194] Also, when entering a virtual store, the application analyzes reviews of products the user has previously purchased and, if it is recommending new products based on those reviews, provides a prompt like this to the generative AI model:

[1195] "Product reviewed: XYZ Camera. Review: The image quality is excellent and I am very satisfied. I would buy it again. Suggestion: New product."

[1196] Based on this, a generative AI model (e.g., GPT-2) can suggest new cameras and related accessories and vocalize the suggestions to provide personalized recommendations to the user.

[1197] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1198] Step 1: Data collection

[1199] Users collect their own past text data (diaries, emails, chat logs) and voice data (voice memos, recordings) through a dedicated application or web interface. Users can select this data and upload it to the platform. The history of products purchased by users and their feedback are also collected. The input is the user's text data and voice data, and the output is that this data is formatted and sent to the server.

[1200] Step 2: Send data

[1201] The terminals then format the collected data, compress it, and upload it to the server. The data is then formatted into a format that can be understood within the system and sent to the server using a secure communication protocol (e.g., HTTPS). The input is the formatted and compressed user text and voice data, and the output is that this data is securely sent to the server.

[1202] Step 3: Data analysis

[1203] The server analyzes the text and voice data received from the user. Natural language processing technology (e.g., BERT or GPT-3) is used to analyze the text data and extract important episodes and emotions. The voice data is converted into text format using speech recognition technology (e.g., Google Speech-to-Text). This allows for integrated analysis of the text and voice data and extracts meaningful information. The input is the user's text and voice data, and the output is analyzed episodes and emotional information.

[1204] Step 4: Narrative generation

[1205] The server uses an AI model (e.g., GPT-2 or GPT-3) based on the analysis results to generate a personal narrative. This narrative is an inspiring story that reflects the user's past experiences and emotions. Furthermore, personalized recommended products and services based on the analyzed personal data are also generated using an AI model. The input is the analysis results, and the output is the generated narrative and recommended products and services.

[1206] Step 5: Audio conversion

[1207] The server converts the generated story and recommendations into audio format using text-to-speech technology (e.g., gTTS). This allows the story and recommended products to be delivered to the user in a natural voice. The technology used for speech conversion achieves high-quality speech synthesis, producing speech that is easily understandable to the user. The input is the generated text, and the output is audio data.

[1208] Step 6: Data storage and playback

[1209] The server stores the converted voice data on the server and manages it so that users can access it. It identifies which data belongs to which user and sets appropriate access rights. The input is voice data, and the output is the stored voice data.

[1210] The device provides a playback interface for the generated audio data. The user can easily play the generated story and recommended products through the provided link or application. The input is the saved audio data, and the output is the played audio data. This allows the user to experience past memories in a new way or check out new products and services.

[1211] 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.

[1212] The present invention is a system that collects an individual's past text data and voice data, analyzes them to generate a personal story, and provides it in voice format. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, a more moving and personal story can be generated. Below, a specific description of an embodiment of the present invention is given.

[1213] Data collection

[1214] User: Using a dedicated application or web interface, the user collects their own text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings). The user selects this data and clicks the upload button to send it to the system.

[1215] Device: The user's device formats the collected text and audio data and converts it into the appropriate format. This data is compressed if necessary and prepared for transmission to the server. The destination server URL is set and the data is uploaded to the server.

[1216] Data analysis and emotion recognition

[1217] Server: The server receives the uploaded data and stores it in storage. Text data is stored in a database, and audio data is stored in an appropriate directory. This allows data to be managed consistently within the system.

[1218] Server: The server applies natural language processing (NLP) techniques to the received text data to analyze the context and sentiment. The emotion engine participates in this analysis process, recognizing and tagging the user's sentiment from the text and voice data. Important episodes and sentiments are tagged, and the analysis results are generated.

[1219] Server: The voice data is converted into text using speech recognition technology and analyzed by the emotion engine. The emotion engine recognizes the emotion in the voice data and reflects it in the analysis results.

[1220] Story Generation

[1221] Server: Based on the analysis results, the AI ​​model generates a personal narrative. The AI ​​model combines episodes and emotions extracted from the text data and converted audio data to create a moving and personal story based especially on emotion tagging. The text of the generated narrative is generated.

[1222] Audio conversion

[1223] Server: The generated story text is converted into audio format using Text-to-Speech (TTS) technology, which provides the story text to the user as a natural voice. The generated audio file is stored on the server.

[1224] Data storage and playback

[1225] Server: The converted voice data is managed in a form that can be accessed by users. Voice data is identified for each user and appropriate access rights are set. Users can access the generated voice data through an individual link.

[1226] Device: The user's device provides a playback interface for the generated audio data. The user can click the provided link to play the audio data through a browser or dedicated app. The playback interface should be designed to be intuitive and easy to use.

[1227] Specific examples

[1228] For example, user B wants to share his / her past growth record with his / her family. He / she uploads his / her diary and voice memos to a dedicated app. The app formats this data and sends it to the server.

[1229] The server analyzes the data and recognizes important episodes and emotions of Mr. B. The emotion engine tags emotions that should be emphasized, and the AI ​​model generates a story based on that. This story is converted into audio using text-to-speech technology and stored on the server as audio data.

[1230] Mr. B can replay the generated story through the provided link and share it with his family. This system brings back Mr. B's memories in a moving way and deepens the bond between him and his family.

[1231] In this way, by combining emotion recognition, the system of the present invention can recreate personal memories and experiences as more moving and personal stories, providing users with a new experience.

[1232] The processing flow will be explained below.

[1233] Step 1:

[1234] User: Using a dedicated application or web interface, the user collects their own text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings). The user selects this data and clicks the upload button to send it to the system.

[1235] Step 2:

[1236] Device: The user's device formats the collected text and audio data and converts it into the appropriate format. This data is compressed if necessary and prepared for transmission to the server. The destination server URL is set and the data is uploaded to the server.

[1237] Step 3:

[1238] Server: The server receives the uploaded data and stores it in storage. Text data is stored in a database, and audio data is stored in an appropriate directory. This allows data to be managed consistently within the system.

[1239] Step 4:

[1240] Server: Applying natural language processing (NLP) techniques to the received text data to analyze context and sentiment. The emotion engine participates in the analysis process, recognizing and tagging the user's sentiment from the text and voice data. Emotion tagging identifies important episodes and sentiments, and generates analysis results.

[1241] Step 5:

[1242] Server: The voice data is converted into text using voice recognition technology and analyzed by the emotion engine. The emotion engine recognizes the emotion in the voice data and reflects it in the analysis results.

[1243] Step 6:

[1244] Server: Based on the analyzed data, the AI ​​model generates a personal narrative. The AI ​​model combines episodes and emotions extracted from the text data and converted audio data to create a moving and personal story based especially on emotion tagging. The text of the generated narrative is generated.

[1245] Step 7:

[1246] Server: The generated story text is converted into audio format using Text-to-Speech (TTS) technology, which provides the story text to the user as a natural voice. The generated audio file is stored on the server.

[1247] Step 8:

[1248] Server: The converted voice data is managed in a form that can be accessed by users. Voice data is identified for each user and appropriate access rights are set. Users can access the generated voice data through an individual link.

[1249] Step 9:

[1250] Device: The user's device provides a playback interface for the generated audio data. The user can click the provided link to play the audio data through a browser or dedicated app. The playback interface should be designed to be intuitive and easy to use.

[1251] Step 10:

[1252] Users: Users can play the generated audio data and listen to their personal stories, which can bring back memories and experiences, providing new emotional experiences. Users can also share this audio data with their family and friends.

[1253] Example 2

[1254] 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."

[1255] Conventional systems have struggled to effectively collect and analyze personal text and audio data to generate moving, personal stories. Furthermore, when providing the stories in audio format, they lack natural emotional expression. Therefore, there is a need for a method to more fully reproduce and share users' emotions and experiences.

[1256] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1257] In this invention, the server includes means for collecting personal data, means for formatting the data and transmitting it to the server, means for analyzing the data on the server and recognizing emotions, means for generating a story based on the analysis results, means for converting the generated story into audio, and means for saving the converted audio and providing it in a format that can be played by the user. This makes it possible to effectively analyze the user's emotions and experiences, generate a moving and personal story, and provide it in a natural audio format.

[1258] "Personal data" refers to information generated or collected by an individual, including, among other things, text data and voice data.

[1259] "Means of collection" refers to a mechanism that provides users with the ability to select and upload personal data using a dedicated application or web interface.

[1260] "Formatting" refers to the process of converting collected data into a standard format, compressing or converting it as needed, and preparing it for transmission to a server.

[1261] "Means for sending to a server" refers to a function for uploading formatted data to a specified server URL using the HTTP protocol.

[1262] "Means of analysis" refers to the process of receiving data on a server and analyzing the context and sentiment of the data using natural language processing technology and voice recognition technology.

[1263] "Means for recognizing emotions" refers to the function of using an emotion engine during the analysis process to recognize and tag user emotions from text and voice data.

[1264] "Means for generating stories" refers to the process of using an AI model based on the analysis results to generate personal and moving stories that take into account emotional tags.

[1265] "Converting to speech means" refers to the process of converting the generated narrative text into audio format using text-to-speech technology.

[1266] "Means for providing the converted audio data in a form that can be saved and played back" refers to the function of managing the converted audio data so that it can be accessed by users, and providing it by setting appropriate access rights.

[1267] The system of the present invention collects and analyzes an individual's text and voice data, generates a personal narrative based on the collected data, and presents it in audio format. The operation of this system is described in detail below.

[1268] Data collection

[1269] Users: Using a dedicated application or web interface, users upload their own text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings) to the system. Uploading can be done simply by clicking a button.

[1270] Data Formatting and Transmission

[1271] Terminal: The user's terminal converts the collected data into a standard format (e.g., a text file in UTF-8 format, or an audio file in WAV or MP3 format). The audio data is compressed if necessary, and the converted data is sent to the server. The transmission is performed using the HTTP protocol, and the server URL is set.

[1272] Data analysis and emotion recognition

[1273] Server: The server receives the data sent by the user, stores the text data in a database, and the voice data in an appropriate directory. It uses natural language processing (NLP) techniques to analyze the context and content of the text data. During this analysis, the emotion recognition engine tags emotions from the text and extracts specific episodes.

[1274] Server: The voice data is converted to text using speech recognition technology (e.g., Google Cloud Speech-to-Text API). This text is also analyzed using emotion recognition technology and tagged with an emotion.

[1275] Story Generation

[1276] Server: Based on the analysis results, a generative AI model (e.g., GPT-3, GPT-4) is used to generate a personal story. This model combines episodes and emotions extracted from text data and converted audio data, taking into account emotion tags in particular, to create a moving and personal story.

[1277] Audio conversion

[1278] Server: The generated story text is converted into audio format using Text-to-Speech (TTS) technology (e.g., Microsoft Azure Cognitive Services TTS API). The generated audio file (e.g., MP3 format) is stored on the server.

[1279] Data storage and playback

[1280] Server: The converted audio data is managed in association with the user's ID, and appropriate access rights are set. A separate folder is created for each user to protect the data.

[1281] User: The user can access the generated audio data by clicking the provided link. Clicking the link launches a browser or dedicated app, and an interface for playing the generated story is displayed. The user can easily listen to the audio data by clicking the play button.

[1282] Specific examples

[1283] For example, if user B wants to share his or her growth record with his or her family, B uploads his or her diary and voice memos to a dedicated app. The device formats the collected data and sends it to a server. The server analyzes the data and recognizes B's important episodes and emotions. Based on the emotional information tagged by the emotion engine, a generative AI model generates a personal story. The generated story is converted into audio using TTS technology and saved on the server. B can play the generated story via the provided link and share it with his or her family.

[1284] Prompt Sentence Examples

[1285] "Extract the most emotional parts from Mr. B's diary and generate a moving story."

[1286] "Convert the contents of the user's voice memo into text and create a story based on the results analyzed by the emotion engine."

[1287] "Based on the following text data, please have your AI model generate a story that combines emotional episodes."

[1288] This invention allows personal memories and experiences to be recreated as more moving and personal stories, providing users with a new experience.

[1289] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1290] Step 1: Data collection

[1291] User: Using a dedicated application or web interface, the user selects their own text data (e.g., diary entries, emails) or audio data (e.g., voice memos, recordings) and clicks the upload button.

[1292] Input: User text and voice data

[1293] Output: raw data saved to the device

[1294] Step 2: Format and send data

[1295] Device: The user's device converts the collected text data into a standard format (e.g., a UTF-8 text file) and the audio data into an appropriate format (e.g., WAV or MP3). The audio data is compressed if necessary. The formatted data is then sent to the server using the HTTP protocol.

[1296] Input: Collected raw text and audio data

[1297] Output: Formatted data sent to the server

[1298] Step 3: Receiving and storing data

[1299] Server: The server receives the HTTP request, stores the text data in a database, and stores the audio data in the appropriate directory.

[1300] Input: Formatted data sent from the terminal

[1301] Output: Text data stored in a database and audio data stored in a directory

[1302] Step 4: Text data analysis and emotion recognition

[1303] Server: The server applies natural language processing (NLP) techniques to the stored text data to analyze its context and content. During this analysis, the emotion engine tags the text with emotions and extracts specific episodes.

[1304] Input: Saved text data

[1305] Output: Text analysis results with sentiment tags

[1306] Step 5: Convert speech data to text and recognize emotions

[1307] Server: The server converts the voice data into text using speech recognition technology (e.g., Google Cloud Speech-to-Text API). It also applies emotion recognition technology to the converted text data and attaches emotion tags to it.

[1308] Input: Stored audio data

[1309] Output: Text data with emotion tags

[1310] Step 6: Narrative generation

[1311] Server: Based on the analysis results, constructs prompts for a generative AI model (e.g., GPT-3, GPT-4) to generate a personal story. The prompts include emotion tags and extracted episodes. The generative AI model then generates a story based on these prompts.

[1312] Input: Sentiment-tagged text analysis results and converted text data

[1313] Output: Generated personal narrative text

[1314] Step 7: Transcribing the story

[1315] Server: The generated story text is converted into audio format using Text-to-Speech (TTS) technology (e.g., Microsoft Azure Cognitive Services TTS API). The generated audio file (e.g., MP3 format) is stored on the server.

[1316] Input: Generated personal narrative text

[1317] Output: The generated audio file

[1318] Step 8: Save and provide access to your audio files

[1319] Server: The converted voice data is managed in association with the user's ID, and appropriate access rights are set. A separate folder is created for each user to protect the data. The user can access the generated voice data via the provided link.

[1320] Input: Generated audio file

[1321] Output: Audio data managed in a user-accessible form

[1322] Step 9: Playing back audio data

[1323] User: The user clicks on the provided link to open an interface that plays the generated story via a browser or dedicated app. By clicking the play button, the user can listen to the generated audio data.

[1324] Input: The link the user visits

[1325] Output: Audio data played in a browser or dedicated app

[1326] (Application example 2)

[1327] 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."

[1328] In today's world, individual users generate large amounts of digital data on a daily basis, but there are few opportunities to reuse that data as personal, moving stories. Furthermore, in electronic payment services, simply compiling and analyzing users' purchase and interaction histories makes it difficult to provide emotional value to users. This can prevent users from emotionally reflecting on their purchasing experiences and memories, potentially resulting in a decrease in satisfaction with purchasing activities.

[1329] 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 collecting text data and voice data of the user, means for analyzing the text data and voice data, means for generating a personal story based on the analysis results, means for converting the generated story into voice, means for saving the converted voice and providing it in a form that the user can play back, means for collecting and analyzing the user's purchase history and interaction history and recognizing emotions, and means for generating a story that includes emotions at the time of purchase and playing it back as voice. This makes it possible for the user to look back on their purchasing experiences and memories as moving stories.

[1330] "Text data" refers to digital information in the form of text, such as diary entries, emails, and messages created by users.

[1331] "Voice data" refers to digital voice information such as recorded voice memos, dialogues, and telephone conversations generated by the user.

[1332] "Analysis means" refers to a method of analyzing collected text data and audio data using natural language processing technology and speech recognition technology to understand context and emotions.

[1333] A "story generation means" is an algorithm or software that generates a story that includes the user's personal experiences and emotions based on the analysis results.

[1334] "Convert to speech" is the process of converting the generated narrative into audio format using text-to-speech technology.

[1335] "Storage means" refers to a mechanism for storing the converted audio data in a digital format and managing it in a manner that allows access by the user.

[1336] "Providing in a reproducible form" means providing an interface or platform that allows users to easily access and play the generated audio data.

[1337] "Purchase history" is data that records detailed information about products that a user has purchased using an electronic payment service.

[1338] "Dialogue history" is a log of messages and conversations exchanged between a user and the system in an electronic payment service.

[1339] "Emotion recognition means" is a technology that analyzes and understands the emotional elements contained in a user's purchasing history and interaction history.

[1340] "Narrative generation" involves the process of constructing a story that reflects the user's emotions and experiences at the time of purchase.

[1341] "Means for playing as audio" refers to a technique for converting the generated story into audio format using TTS technology and providing it to the user in a reproducible format.

[1342] This invention is a system that collects and analyzes a user's past text data and voice data, generates a personal story, and provides it in voice format. Furthermore, it aims to recognize emotions in purchase history and conversation history, and generate and provide an inspiring story based on this.

[1343] Data collection

[1344] Users use a dedicated application or web interface to collect their own text data (e.g., diary entries, emails) and audio data (e.g., voice memos, recordings). The user's purchase history and interaction history are also collected. They select this data and click the upload button to send it to the system. The user's device then formats the collected data and converts it into an appropriate format. This data is then compressed and sent to the server.

[1345] Data analysis and emotion recognition

[1346] The server receives the uploaded data and stores it in storage. The data is converted into an appropriate format and saved in a database. Natural language processing (NLP) and speech recognition technologies are used for analysis. The emotion engine recognizes and tags the user's emotions from text, voice data, purchase history, and conversation history. Important episodes and emotions are tagged in the analysis results.

[1347] Story Generation

[1348] The AI ​​model on the server generates a personal narrative based on the analysis results. The AI ​​model is a generative AI model that combines episodes and emotions extracted from the text data and converted audio data to create a moving and personal story based on emotion tagging in particular.

[1349] Audio conversion

[1350] The generated story text is converted into audio format using Text-to-Speech (TTS) technology, which provides the story text to the user as natural-sounding audio, and the generated audio file is stored on the server.

[1351] Data storage and playback

[1352] The converted voice data is managed in a form that can be accessed by users. Voice data is identified for each user and appropriate access rights are set. Users can access the generated voice data through individual links. The user's device provides a playback interface for the generated voice data. The playback interface has an intuitive and easy-to-use design.

[1353] Specific examples

[1354] For example, if user A wants to share his or her past purchase history as an inspiring story with his or her family, he or she uploads his or her purchase history and voice memos to a dedicated app. The app formats this data and sends it to a server. The server analyzes the data and recognizes A's important purchase episodes and emotions. An emotion engine tags emotions that should be particularly emphasized, and an AI model generates a story based on this. This story is converted into audio using text-to-speech technology and saved as audio data on the server. A can play the generated story through the provided link and share it with his or her family.

[1355] Prompt Sentence Examples

[1356] "Data entered: List of products that made you feel excited at the moment you purchased them"

[1357] "Prompt for generative AI model: Generate a story with emotions about a product the user has purchased in the past. Here is the data: [data content]"

[1358] This allows users to vividly relive their past purchasing experiences and share them with family and friends in an emotional way.

[1359] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1360] Step 1:

[1361] The user uses a dedicated application or web interface to collect their own text data (e.g., diary entries, emails) and voice data (e.g., voice memos, recordings). The user also collects purchase history and interaction history. They select this data and click the send button to send it to the system. The input to this step is various types of user data, and the output to the server is this data set.

[1362] Step 2:

[1363] The user's device formats the collected data, converts text and audio data into the appropriate format, compresses the data if necessary, sets the URL of the upload server, and then sends the data to the server. The input for this step is the various data provided by the user, and the output is the formatted data.

[1364] Step 3:

[1365] The server receives the uploaded data, stores the text data in a database, and the audio data in the appropriate directory. This step updates the database. The input is the formatted data, and the output is the stored data.

[1366] Step 4:

[1367] The server applies natural language processing (NLP) techniques to the received text data to analyze the context and sentiment. An emotion engine also participates in this analysis, recognizing and tagging the user's sentiment from the text and voice data. This stage primarily involves data analysis and tagging. The input is the stored data, and the output is the analysis results.

[1368] Step 5:

[1369] The server applies speech recognition technology to the voice data to convert it into text, which is then analyzed using an emotion engine. Emotions are also recognized from the converted text data and tagged. The input is the voice data, and the output is the converted and analyzed text data and emotion tags.

[1370] Step 6:

[1371] Based on the analyzed text data, emotion tags, and purchase history data, an AI model on the server generates a personal narrative for the user. This AI model is a generative AI model, creating a story based specifically on emotion tagging. The inputs for this step are the analysis results and purchase history data, and the output is the generated narrative text.

[1372] Step 7:

[1373] The server converts the generated story text into audio format using Text-to-Speech (TTS) technology. The converted audio file is stored on the server. The input is the generated story text, and the output is the converted audio file.

[1374] Step 8:

[1375] The server manages the converted audio data in a form that allows users to access it. The audio data is stored in a folder identified for each user, and appropriate access rights are set. The input of this step is the converted audio file, and the output is audio data stored in an accessible form.

[1376] Step 9:

[1377] The user's device provides a playback interface for the generated audio data. The user accesses and plays the audio data through the provided link. The playback interface is designed to be intuitive and easy to use. The input of this step is the access link to the audio data, and the output is the playback of the audio data.

[1378] In this way, a moving story can be generated using the user's past data and provided in audio format, which can bring back the user's purchasing experiences and memories in an emotional way.

[1379] 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.

[1380] 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.

[1381] 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.

[1382] 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.

[1383] FIG. 9 is a diagram illustrating 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 actions 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.

[1384] 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.

[1385] 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).

[1386] 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.

[1387] 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."

[1388] 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.

[1389] 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).

[1390] 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.

[1391] 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.

[1392] 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.

[1393] 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.

[1394] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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 example of a 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.

[1395] 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.

[1396] 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.

[1397] 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.

[1398] 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.

[1399] 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.

[1400] The following is further disclosed regarding the above embodiment.

[1401] (Claim 1)

[1402] means for collecting personal text data and voice data;

[1403] means for analyzing the text data and audio data;

[1404] A means of generating a personal narrative based on the analysis results;

[1405] a means for converting the generated narrative into audio;

[1406] a means for storing the converted audio and providing it to a user in a form that can be played back;

[1407] A system including:

[1408] (Claim 2)

[1409] 2. The system according to claim 1, wherein the means for analyzing the text data and the voice data uses natural language processing technology and voice recognition technology.

[1410] (Claim 3)

[1411] 10. The system of claim 1, wherein the means for converting to speech uses text-to-speech technology.

[1412] "Example 1"

[1413] (Claim 1)

[1414] means for collecting personal text data and voice data;

[1415] means for analyzing the text data and audio data;

[1416] A means of generating a personal narrative based on the analysis results;

[1417] a means for converting the generated narrative into audio;

[1418] a means for storing the converted audio and providing it to a user in a form that can be played back;

[1419] A system including:

[1420] (Claim 2)

[1421] 2. The system according to claim 1, wherein the means for analyzing the text data and the voice data uses natural language processing technology and voice recognition technology, and further comprises an analysis means for extracting individual experiences and emotions of the user.

[1422] (Claim 3)

[1423] 10. The system of claim 1, wherein the means for generating a personal narrative uses a generative AI model.

[1424] (Claim 4)

[1425] 2. The system of claim 1, wherein the means for converting to speech uses text-to-speech technology.

[1426] (Claim 5)

[1427] 10. The system of claim 1, further comprising: means for managing the stored audio data in a manner accessible to a user; and means for providing an interface for a user to play the audio data.

[1428] (Claim 6)

[1429] 10. The system of claim 1, further comprising means for formatting and compressing the text data and audio data into a specified format and transmitting the data to a server.

[1430] "Application Example 1"

[1431] (Claim 1)

[1432] means for collecting personal text data and voice data;

[1433] means for analyzing the text data and audio data;

[1434] A means of generating a personal narrative based on the analysis results;

[1435] a means for converting the generated narrative into audio;

[1436] a means for storing the converted audio and providing it to a user in a form that can be played back;

[1437] A means of collecting and analyzing personal data such as customers' past purchase history, feedback, chat logs, etc.;

[1438] means for generating personalized product and service recommendations based on the analyzed data;

[1439] A means for providing the generated recommended products and services in an audio format;

[1440] A system including:

[1441] (Claim 2)

[1442] 2. The system according to claim 1, wherein the means for analyzing the text data and the voice data uses natural language processing technology and voice recognition technology.

[1443] (Claim 3)

[1444] 10. The system of claim 1, wherein the means for converting to speech uses text-to-speech technology.

[1445] "Example 2: Combining Emotion Engines"

[1446] (Claim 1)

[1447] The means by which personal data is collected;

[1448] means for formatting said data and transmitting it to a server;

[1449] A means of analyzing data on the server and recognizing emotions,

[1450] A means for generating a story based on the analysis results;

[1451] a means for converting the generated narrative into audio;

[1452] a means for storing the converted audio and providing it to a user in a form that can be played back;

[1453] A system including:

[1454] (Claim 2)

[1455] 2. The system according to claim 1, wherein the data analysis means uses natural language processing technology and emotion recognition technology.

[1456] (Claim 3)

[1457] 10. The system of claim 1, wherein the means for converting to speech uses text-to-speech technology.

[1458] "Application example 2 when combining emotion engines"

[1459] (Claim 1)

[1460] means for collecting personal text data and voice data;

[1461] means for analyzing the text data and audio data;

[1462] A means of generating a personal narrative based on the analysis results;

[1463] a means for converting the generated narrative into audio;

[1464] a means for storing the converted audio and providing it to a user in a form that can be played back;

[1465] A means of collecting and analyzing users' purchase history and interaction history to recognize their emotions,

[1466] A means to generate a story that includes emotions at the time of purchase and play it back as audio,

[1467] A system including:

[1468] (Claim 2)

[1469] 2. The system according to claim 1, wherein the means for analyzing the text data and the voice data uses natural language processing technology and voice recognition technology.

[1470] (Claim 3)

[1471] 10. The system of claim 1, wherein the means for converting to speech uses text-to-speech technology. [Explanation of symbols]

[1472] 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. means for collecting personal text data and voice data; means for analyzing the text data and audio data; A means of generating a personal narrative based on the analysis results; a means for converting the generated narrative into audio; a means for storing the converted audio and providing it to a user in a form that can be played back; A system including:

2. 2. The system according to claim 1, wherein the means for analyzing the text data and the voice data uses natural language processing technology and voice recognition technology.

3. 2. The system of claim 1, wherein said means for converting to speech uses text-to-speech technology.

Images