system

Abstract

Provide a system. A means for a user to input a diary or memo in text format; means for transmitting input text data to a server; A server receives the text data and performs natural language analysis; A means of generating feedback and advice based on the analysis results; means for transmitting the generated feedback and advice to a user terminal; a means for displaying feedback and advice on the user terminal; A system including:

Description

[Technical Field]

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

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

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

[0004] In recent years, the importance of self-improvement through daily introspection has been recognized, but it is difficult to efficiently engage in introspection amidst busy daily schedules. Furthermore, improving the quality of self-reflection requires not only recording events but also reflecting on serious emotions and specific improvement measures. However, this requires a considerable amount of time and effort, which can lead to difficulties in continuing self-reflection. Therefore, the objective of this invention is to provide a system that utilizes AI to efficiently and effectively support users' daily introspection and promote continuous self-improvement. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including: a means for a user to input a diary or memo in text format; a means for transmitting the input text data to a server; a means for the server to receive the text data and perform natural language analysis; a means for generating feedback and advice based on the analysis results; a means for transmitting the generated feedback and advice to a user terminal; and a means for the user terminal to display the feedback and advice. The system further includes a means for generating a message prompting the user to self-reflect in the form of a question based on the analysis results, and a means for performing sentiment analysis, keyword extraction, and theme extraction on the input text using natural language processing technology, thereby supporting the user in engaging in deeper self-reflection.

[0006] "User" refers to a person who uses the system to enter diary entries and memos and engage in self-reflection activities.

[0007] "Text data" refers to the contents of a diary or memo entered by a user, and is information expressed in character string format.

[0008] "Server" refers to a central computing device that receives text data, performs natural language analysis, and generates feedback and advice.

[0009] "Natural language analysis" refers to the technology that allows computers to understand text data and perform sentiment analysis, keyword extraction, and theme extraction.

[0010] "Feedback" refers to evaluations and advice for users that are generated based on the analysis results.

[0011] "Advice" refers to specific suggestions or recommendations provided to support the user's self-reflection activities.

[0012] "Question-style messages that encourage self-reflection" refers to feedback in the form of questions that encourage users to think for themselves.

[0013] "Natural language processing technology" refers to technology that enables computers to understand and analyze human language.

[0014] "Sentiment analysis" refers to the technology of classifying emotions contained in text data into categories such as "positive," "negative," and "neutral."

[0015] "Keyword extraction" refers to the technique of identifying important words and phrases from text data.

[0016] "Theme extraction" refers to the technique of extracting major topics and arguments from text data. [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] This invention is an AI support system that allows users to record their daily events and emotions and use that information to engage in effective self-reflection. Users enter their diary entries and notes, and the system analyzes their content and provides appropriate feedback and advice to promote self-improvement.

[0039] Explaining program processing in natural language

[0040] The system mainly consists of the following elements:

[0041] 1. User Input

[0042] Using a dedicated application, users can input the events, feelings, and thoughts of the day in text format. For example, they might input, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[0043] 2. Data Transmission

[0044] The device sends the entered text data to the server, using a secure protocol (e.g., HTTPS) to protect the user's privacy.

[0045] 3. Receiving and analyzing text data

[0046] The server receives the text data and passes it to a natural language analysis engine, which analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction.

[0047] For example, sentiment analysis classifies the message as "negative," and keyword extraction extracts "meeting," "boss," and "lack of preparation."

[0048] 4. Generating feedback and advice

[0049] Based on the analysis results, the server generates appropriate feedback and advice for the user, particularly suggesting areas for improvement and specific actions to take next time.

[0050] For example, the generated feedback is, "Let's think about what preparations you need to make to make the next meeting a success."

[0051] 5. Sending feedback and advice

[0052] The server generates feedback and advice and sends it to the device, using a secure protocol for data transmission.

[0053] 6. Viewing feedback and advice

[0054] The device displays feedback and advice to the user, who can then open the application and review the feedback provided.

[0055] 7. User Reflection

[0056] The user reflects on the feedback and creates a plan of action for the next time. For example, they might take specific steps to better prepare for the next meeting and improve communication with their boss.

[0057] Specific examples

[0058] Example 1: Work reflection

[0059] The user types, "My presentation today didn't go well. Maybe I didn't practice enough."

[0060] The terminal transmits this text data to the server.

[0061] The server receives the text data and passes it to a natural language analysis engine.

[0062] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "presentation" and "lack of practice."

[0063] The server generates feedback such as, "Let's think about what practice you need to do to make your next presentation a success."

[0064] The server sends the feedback to the terminal, which displays it to the user.

[0065] Users read the feedback and create their own practice plan.

[0066] Example 2: Private introspection

[0067] The user types, "I had lunch with a friend and something I said or did made things awkward. How can I improve in the future?"

[0068] The terminal transmits this text data to the server.

[0069] The server receives the text data and passes it to a natural language analysis engine.

[0070] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "friends," "lunch," and "awkward."

[0071] The server generates feedback such as, "Let's think specifically about what kind of behavior you should be careful about at your next lunch."

[0072] The server sends the feedback to the terminal, which displays it to the user.

[0073] Users read the feedback, reflect on their own actions, and think about specific improvements.

[0074] This concludes the description of the preferred embodiment of the present invention, which provides users with an effective tool for self-reflection, learning from everyday occurrences, and promoting self-improvement.

[0075] The processing flow will be explained below.

[0076] Step 1:

[0077] The user opens a dedicated application and enters the events, feelings, and thoughts of the day in text format. For example, they might enter, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[0078] Step 2:

[0079] The device sends the entered text data to the server. Data transmission is performed using a secure protocol (e.g., HTTPS) to protect the user's privacy.

[0080] Step 3:

[0081] The server receives the text data and passes it to a natural language analysis engine, which analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction.

[0082] Step 4:

[0083] A natural language analysis engine analyzes text data. First, it performs sentiment analysis to determine whether the input text is classified as "positive," "negative," or "neutral." Next, it performs keyword extraction to identify important words and phrases. Finally, it performs theme extraction to extract key topics and arguments.

[0084] Step 5:

[0085] The server generates appropriate feedback and advice for the user based on the analysis results. For example, if the result of the sentiment analysis is determined to be "negative" and the keywords "meeting," "boss," and "lack of preparation" are extracted, the server generates the following feedback: "Let's think about what preparations you need to make to ensure the success of your next meeting."

[0086] Step 6:

[0087] The server generates feedback and advice and sends it to the device using a secure protocol.

[0088] Step 7:

[0089] The device displays the received feedback and advice to the user. When the user opens the application, the provided feedback and advice is displayed on the screen.

[0090] Step 8:

[0091] Users can review the displayed feedback and advice and reflect on it in their own self-reflection activities, such as "I will prepare thoroughly for the next meeting and think of concrete steps to improve communication with my boss."

[0092] Through these steps, the system efficiently and effectively supports the user's self-reflection activities and helps the user to continue self-improvement.

[0093] Example 1

[0094] 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."

[0095] Previously, systems that allowed users to record their daily events and emotions and use them for self-reflection had problems such as insufficient analysis of the input information and vague feedback provided. Furthermore, there were also cases where user privacy was not adequately protected. This resulted in issues such as ineffective self-reflection and difficulty in promoting self-improvement.

[0096] 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.

[0097] In this invention, the server includes means for performing sentiment analysis, keyword extraction, and theme extraction of input text using a natural language analysis engine, means for generating feedback and advice using a generative AI model, and means for transmitting and receiving the input text data and the generated feedback and advice using a secure protocol. This allows users to receive specific feedback and advice based on detailed analysis results, enabling them to engage in self-reflection activities with peace of mind.

[0098] A "user" is a person who uses this system to enter diary entries and memos.

[0099] "Text data" is data in which diary entries and memos entered by the user are expressed in text format.

[0100] The "server" is a device that receives text data sent by users, analyzes it, and generates and sends feedback and advice.

[0101] A "natural language analysis engine" is a program or device that analyzes text data and extracts the emotions, keywords, and themes contained therein.

[0102] A "generative AI model" is an artificial intelligence model that generates feedback and advice based on analysis results.

[0103] "Feedback and advice" refers to specific instructions and advice provided to the user based on the analysis results.

[0104] A "secure protocol" is a set of rules for encrypting communication content and sending and receiving data safely.

[0105] "Sentiment analysis" is a technology that identifies a user's emotions from the content of text data and determines whether those emotions are positive or negative.

[0106] "Keyword extraction" is a technique for identifying important words and phrases from text data.

[0107] "Theme extraction" is a technique for identifying major topics or themes within text data.

[0108] A "user terminal" is a device on which a user enters a diary or memo and checks feedback and advice. Examples of such devices include smartphones and tablets.

[0109] "Reflection" is an activity in which the user reflects on themselves based on the feedback and advice provided and makes a plan of action for the next time.

[0110] This invention is an AI support system that allows users to record their daily events and emotions and use that information to engage in effective self-reflection. Users use a dedicated application to enter their diary and notes, and the server analyzes the content and provides appropriate feedback and advice to promote self-improvement.

[0111] Hardware and Software Configuration

[0112] The system consists of the following elements:

[0113] User device: A device such as a smartphone or tablet where users input text data and receive feedback.

[0114] Server: A device that analyzes text data and generates and sends feedback and advice.

[0115] Natural language analysis engines, such as Google® Natural Language API and Amazon Comprehend, that perform sentiment analysis, keyword extraction, and theme extraction on text data.

[0116] Generative AI models: Artificial intelligence models for generating feedback and advice, such as OpenAI® GPT-3®

[0117] Secure protocols: Protocols for securely sending and receiving user data, such as HTTPS

[0118] Data processing and calculation

[0119] 1. User Input

[0120] Users open a dedicated application and enter the events and feelings of the day in text format. For example, they might enter, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[0121] 2. Data Transmission

[0122] The device sends the entered text data to the server using the HTTPS protocol, encrypting the data and protecting the user's privacy.

[0123] 3. Receiving and analyzing text data

[0124] The server receives the text data from the device and passes it to a natural language analysis engine for analysis. The analysis involves sentiment analysis, keyword extraction, and theme extraction. For example, sentiment analysis may determine a negative sentiment, while keyword extraction may identify "meeting," "boss," and "lack of preparation."

[0125] 4. Generating feedback and advice

[0126] Based on the analysis results, the server uses a generative AI model to generate appropriate feedback and advice. It is important that the generated feedback is specific and suggests actions that can be used next time. For example, feedback such as "Let's think about what preparations are needed to make the next meeting a success" could be generated.

[0127] 5. Sending feedback and advice

[0128] The generated feedback and advice is then sent to the user terminal, again using a secure protocol.

[0129] 6. Viewing feedback and advice

[0130] The user device displays the feedback and advice to the user, who then opens the application and checks the provided feedback.

[0131] 7. User Reflection

[0132] The user reflects on the feedback provided and creates a plan of action for the next time, for example, "I will be more prepared for the next meeting and think of concrete steps to improve communication with my boss."

[0133] Specific examples

[0134] Example 1: Work reflection

[0135] The user types, "My presentation didn't go well today. Maybe I didn't practice enough."

[0136] The terminal transmits this text data to the server.

[0137] The server receives the text data and passes it to a natural language analysis engine.

[0138] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "presentation" and "lack of practice."

[0139] The server generates feedback such as, "Let's think about what practice you need to do to make your next presentation a success."

[0140] The server sends the feedback to the terminal, which displays it to the user.

[0141] Users read the feedback and create their own practice plan.

[0142] Example 2: Private introspection

[0143] A user types, "I had lunch with a friend and something I said or did made things awkward. How can I improve in the future?"

[0144] The terminal transmits this text data to the server.

[0145] The server receives the text data and passes it to a natural language analysis engine.

[0146] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "friends," "lunch," and "awkward."

[0147] The server generates feedback such as, "Let's think specifically about what kind of behavior we should be careful about at our next lunch."

[0148] The server sends the feedback to the terminal, which displays it to the user.

[0149] Users read the feedback, reflect on their own actions, and think about specific improvements.

[0150] This concludes the description of the preferred embodiment of the present invention. The system provides an effective tool for users to engage in self-reflection, learn from everyday occurrences, and promote self-improvement.

[0151] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0152] Step 1:

[0153] The user provides input.

[0154] Input: The user opens a dedicated application and enters the events and feelings of the day in text format.

[0155] Specific behavior: The user launches the application, enters daily events and emotions into the text input field, and presses the "Send" button.

[0156] Output: The input text data.

[0157] Step 2:

[0158] The terminal transmits the input text data to the server.

[0159] Input: Text data entered by the user.

[0160] Specific operation: The device sends the user's input to the server using the HTTPS protocol.

[0161] Output: The encrypted text data sent to the server.

[0162] Step 3:

[0163] The server passes the received text data to a natural language analysis engine.

[0164] Input: Encrypted text data.

[0165] What happens: The server receives an HTTPS request and passes the text data to a natural language analysis engine.

[0166] Output: Text data passed to the natural language analysis engine.

[0167] Step 4:

[0168] A natural language analysis engine analyzes the text data.

[0169] Input: Text data passed to the natural language analysis engine.

[0170] Specific operation: The natural language analysis engine performs sentiment analysis, keyword extraction, and theme extraction. For example, sentiment analysis may determine a message as "negative," and keywords such as "meeting," "boss," and "lack of preparation" may be extracted.

[0171] Output: Analysis results (sentiment, keywords, themes).

[0172] Step 5:

[0173] The server generates feedback and advice using a generative AI model based on the analysis results.

[0174] Input: Analysis results (sentiments, keywords, themes).

[0175] Specific operation: The server inputs prompts into the generative AI model and generates appropriate feedback and advice. For example, feedback such as "Let's think about what preparations you need to make to ensure the success of your next meeting" is generated.

[0176] Output: Generated feedback and advice.

[0177] Step 6:

[0178] The server sends the generated feedback and advice to the device.

[0179] Input: Generated feedback and advice.

[0180] Specific operation: The server sends the feedback to the device using the HTTPS protocol.

[0181] Output: Feedback and advice sent to the device.

[0182] Step 7:

[0183] The device will provide feedback and advice.

[0184] Input: Feedback and advice sent to your device.

[0185] Specific behavior: The device decodes the received feedback and displays it in the application's user interface.

[0186] Output: Feedback and advice displayed to the user.

[0187] Step 8:

[0188] The user performs a self-reflection activity.

[0189] Input: Any feedback or advice you see.

[0190] Specific Action: The user reads the feedback and creates a plan of action for the next time. For example, the user may perform a self-reflection activity such as, "I will think of specific steps to be more prepared for the next meeting and improve communication with my boss."

[0191] Output: A concrete action plan.

[0192] (Application example 1)

[0193] 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."

[0194] Conventional AI support systems require users to input events and emotions in text format, and do not provide voice-based diary entries or visual feedback using wearable devices. This makes it difficult for users to easily engage in self-reflection, and there is a risk that small everyday events or emotions may be overlooked. Furthermore, conventional systems are limited to text input, which makes them lack immediacy and convenience.

[0195] 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.

[0196] In this invention, the server includes a means for a user to input a diary or memo in text format, a means for transmitting the input text data to the server, a means for performing natural language analysis, a means for recording a diary or memo by voice input, a means for converting the voice input into text format, and a means for visually displaying feedback via a wearable device such as smart glasses. This allows the user to easily record a diary or memo through voice input and immediately receive appropriate feedback from the analysis results. Furthermore, visual feedback via the wearable device promotes introspection.

[0197] "User" refers to a user who uses this system to enter diary entries and memos and receive feedback and advice.

[0198] "Text format" is a data format based on character information entered by the user.

[0199] A "server" is a computer system that receives data sent by a user, analyzes it, generates feedback, and sends it back to the user.

[0200] "Natural language analysis" is the act of analyzing text data entered by a user using natural language processing technology to perform sentiment analysis, keyword extraction, etc.

[0201] "Feedback" refers to advice and improvement suggestions provided to users based on the analysis results.

[0202] "Advice" refers to specific improvement measures or suggested actions provided to users based on the analysis results.

[0203] "User terminal" means a device used by a user to operate the system and receive feedback and advice.

[0204] "Voice input" is a method in which a user inputs information by speaking.

[0205] "Convert to text" is the process of converting speech input into text data.

[0206] "Smart glasses" are wearable devices worn by users to present visual information.

[0207] A "wearable device" is a computing device that can be worn by a user.

[0208] "Visually displaying" means showing information to the user through the device's display.

[0209] This invention is an advanced AI support system that records the user's daily events and emotions and encourages introspection based on that data. Specific embodiments for realizing this system are described below.

[0210] The system components are:

[0211] 1. User terminals: including wearable devices such as smart glasses and smartphones.

[0212] 2. Voice input system: This system allows users to input daily events and emotions through voice and converts them into text. Specifically, it uses voice recognition APIs such as Google Cloud Speech-to-Text and IBM Watson (registered trademark).

[0213] 3. Natural Language Analysis System: A system that analyzes text data. It performs processes such as sentiment analysis, keyword extraction, and theme extraction. Specifically, it uses a sentiment analysis model that utilizes the Transformers pipeline.

[0214] 4. Feedback generation system: A system that generates appropriate feedback and advice for users based on the analysis results.

[0215] 5. Server: This is the central processing unit that performs the analysis and generation mentioned above, and receives data from users and processes and analyzes it.

[0216] Program processing overview

[0217] 1. User Input:

[0218] Using a wearable device such as smart glasses, users can input their day's events and emotions by voice, for example, "Today was a bad day at work."

[0219] 2. Data transmission:

[0220] The user terminal converts the voice data into text and sends it to the server via a secure protocol (e.g., HTTPS).

[0221] 3. Receiving and analyzing text data:

[0222] The server receives the text data and passes it to a natural language analysis system, which performs sentiment analysis, determining, for example, that the sentiment is "negative," and extracts keywords such as "work" and "didn't go well."

[0223] 4. Generate feedback and advice:

[0224] The server generates feedback based on the analysis results, creating advice such as, "Think about what preparations you need to make the next time you work."

[0225] 5. Sending and Viewing Feedback:

[0226] The server transmits the generated feedback to the user's device, which visually displays the feedback through the smart glasses.

[0227] Specific examples

[0228] Work reflection

[0229] A user speaks, "Today's meeting went wrong. Maybe I wasn't prepared enough."

[0230] The user terminal converts the voice data into text and sends it to the server.

[0231] The server receives the text data and performs sentiment analysis and keyword extraction.

[0232] The server generates feedback saying, "Let's think about how to prepare for the meeting."

[0233] Feedback is displayed to the user through the smart glasses.

[0234] Private introspection

[0235] The user speaks, "I said too much at dinner with a friend. How can I improve?"

[0236] The user terminal converts the voice data into text and sends it to the server.

[0237] The server receives the text data and performs sentiment analysis and keyword extraction.

[0238] The server generates feedback such as, "Let's think about what we should pay attention to the next time we dine here."

[0239] Feedback is displayed to the user through the smart glasses.

[0240] Prompt Sentence Examples

[0241] Here are some example prompts to encourage users to speak:

[0242] "How was your day today?"

[0243] "Tell me what happened today and how you felt."

[0244] "Is there anything you'd like to improve?"

[0245] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0246] Step 1:

[0247] Using a wearable device such as smart glasses, users input their day's events and emotions through voice, for example, "Today was a bad day at work."

[0248] Step 2:

[0249] The device receives voice input and converts it to text using a speech recognition system (for example, Google Cloud Speech-to-Text or IBM Watson). The input is voice data, and the output is text data. This conversion processes the user's voice into text information.

[0250] Step 3:

[0251] The terminal sends the converted text data to the server via a secure protocol (e.g., HTTPS). The input is the text data, and the output is the transmission completion status. Encryption technology is used for data transmission for security.

[0252] Step 4:

[0253] The server receives the text data and passes it to the natural language analysis system. The input is text data, and the received data is subjected to analysis. Here, the received data is added to a queue to be analyzed.

[0254] Step 5:

[0255] The server's natural language analysis system performs sentiment analysis, keyword extraction, and theme extraction on the text data. This uses a sentiment analysis model that utilizes a Transformers pipeline. The input is text data, and the output is a sentiment determination result and a keyword list. At this stage, the data is sentiment-tagged and key keywords are extracted.

[0256] Step 6:

[0257] Based on the analysis results, the server generates appropriate feedback and advice. For example, it creates advice such as, "Think about what preparations you need to make the next time you work." The input is the emotion determination result and a list of keywords, and the output is a feedback message. In this process, a generative AI model is used to generate appropriate advice for the user.

[0258] Step 7:

[0259] The server securely transmits the generated feedback message to the user terminal. The input is the feedback message, and the output is a transmission completion status. The transmission means uses a secure protocol.

[0260] Step 8:

[0261] The terminal visually displays the received feedback message to the user via a wearable device such as smart glasses. The input is the feedback message, and the output is the feedback content displayed on the glasses' display. Here, the user can confirm which specific areas need improvement.

[0262] The above is the flow of program processing and specific operations of the system that realizes the application example.

[0263] 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.

[0264] This invention is an AI support system that allows users to record their daily events and emotions and use that information to engage in effective self-reflection. The system analyzes the contents of diary entries and notes, and provides appropriate feedback and advice to promote self-improvement. Furthermore, by incorporating an emotion engine, the system can recognize the user's emotions and provide more appropriate feedback.

[0265] Explaining program processing in natural language

[0266] The system mainly consists of the following elements:

[0267] 1. User Input

[0268] Using a dedicated application, users can input the events, feelings, and thoughts of the day in text format. For example, they might input, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[0269] 2. Data Transmission

[0270] The device sends the entered text data to the server. Data transmission is performed using a secure protocol (e.g., HTTPS) to protect the user's privacy.

[0271] 3. Receiving and analyzing text data

[0272] The server receives the text data and passes it to a natural language analysis engine, which analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction.

[0273] The emotion engine also analyzes text data and recognizes the user's emotions.

[0274] 4. Generating feedback and advice

[0275] The server generates appropriate feedback and advice for the user based on the analysis results. For example, if the result of the sentiment analysis is determined to be "negative" and the keywords "meeting," "boss," and "lack of preparation" are extracted, the server generates the following feedback: "Let's think about what preparations you need to make to ensure the success of your next meeting."

[0276] The emotion engine may also tailor the feedback depending on the user's emotional state, for example, generating feedback such as "Think of concrete steps you can take to feel more positive about your upcoming meeting."

[0277] 5. Sending feedback and advice

[0278] The server generates feedback and advice and sends it to the device using a secure protocol.

[0279] 6. Viewing feedback and advice

[0280] The device displays feedback and advice to the user. When the user opens the application, the provided feedback and advice is displayed on the screen.

[0281] 7. User Reflection

[0282] Users can review the displayed feedback and advice and reflect on it in their own self-reflection activities, such as "I will prepare thoroughly for the next meeting and think of concrete steps to improve communication with my boss."

[0283] Specific examples

[0284] Example 1: Work reflection

[0285] The user types, "My presentation today didn't go well. Maybe I didn't practice enough."

[0286] The terminal transmits this text data to the server.

[0287] The server receives the text data and passes it to a natural language analysis engine.

[0288] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "presentation" and "lack of practice."

[0289] The emotion engine recognizes the user's emotional state and generates feedback accordingly.

[0290] The server generates feedback such as, "Let's think about what practice you need to do to make your next presentation a success."

[0291] The server sends the feedback to the terminal, which displays it to the user.

[0292] Users read the feedback and create their own practice plan.

[0293] Example 2: Private introspection

[0294] The user types, "I had lunch with a friend and something I said or did made things awkward. How can I improve in the future?"

[0295] The terminal transmits this text data to the server.

[0296] The server receives the text data and passes it to a natural language analysis engine.

[0297] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "friends," "lunch," and "awkward."

[0298] The emotion engine recognizes the user's emotional state and generates feedback accordingly.

[0299] The server generates feedback such as, "Let's think specifically about what kind of behavior you should be careful about at your next lunch."

[0300] The server sends the feedback to the terminal, which displays it to the user.

[0301] Users read the feedback, reflect on their own actions, and think about specific improvements.

[0302] The above is an embodiment of the present invention. This system is an efficient tool for users to engage in self-reflection, learn from everyday events, and promote self-improvement. In addition, by combining it with an emotion engine, it can provide appropriate support according to the user's emotions.

[0303] The processing flow will be explained below.

[0304] Step 1:

[0305] The user opens a dedicated application and enters the events, feelings, and thoughts of the day in text format. For example, they might enter, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[0306] Step 2:

[0307] The device sends the entered text data to the server. Data transmission is performed using a secure protocol (e.g., HTTPS) to protect the user's privacy.

[0308] Step 3:

[0309] The server receives the text data and passes it to a natural language analysis engine, which analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction.

[0310] Step 4:

[0311] A natural language analysis engine analyzes text data. First, it performs sentiment analysis to determine whether the input text is classified as "positive," "negative," or "neutral." Next, it performs keyword extraction to identify important words and phrases. Finally, it performs theme extraction to extract key topics and arguments.

[0312] Step 5:

[0313] The emotion engine analyzes the text data to recognize the user's emotional state. This analysis involves detailed classification of emotions sensed from the user's input text and identifies the current emotional state.

[0314] Step 6:

[0315] The server generates appropriate feedback and advice for the user based on the analysis results. For example, if the result of the sentiment analysis is determined to be "negative" and the keywords "meeting," "boss," and "lack of preparation" are extracted, the server generates feedback such as "Let's think about what kind of preparations you need to make to ensure the success of your next meeting." It also generates feedback such as "Let's think about specific steps to have a positive attitude toward future meetings," reflecting the results of the emotion engine.

[0316] Step 7:

[0317] The server generates feedback and advice and sends it to the device using a secure protocol.

[0318] Step 8:

[0319] The device displays the received feedback and advice to the user. When the user opens the application, the provided feedback and advice is displayed on the screen.

[0320] Step 9:

[0321] Users can review the displayed feedback and advice and reflect on it in their own self-reflection activities, such as "I will prepare thoroughly for the next meeting and think of concrete steps to improve communication with my boss."

[0322] Specific examples

[0323] Example 1: Work reflection

[0324] The user types, "My presentation today didn't go well. Maybe I didn't practice enough."

[0325] The terminal transmits this text data to the server.

[0326] The server receives the text data and passes it to a natural language analysis engine.

[0327] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "presentation" and "lack of practice."

[0328] The emotion engine recognizes the user's emotional state and generates feedback accordingly.

[0329] The server generates feedback such as, "Let's think about what practice you need to do to make your next presentation a success."

[0330] The server sends the feedback to the terminal, which displays it to the user.

[0331] Users read the feedback and create their own practice plan.

[0332] Example 2: Private introspection

[0333] The user types, "I had lunch with a friend and something I said or did made things awkward. How can I improve in the future?"

[0334] The terminal transmits this text data to the server.

[0335] The server receives the text data and passes it to a natural language analysis engine.

[0336] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "friends," "lunch," and "awkward."

[0337] The emotion engine recognizes the user's emotional state and generates feedback accordingly.

[0338] The server generates feedback such as, "Let's think specifically about what kind of behavior you should be careful about at your next lunch."

[0339] The server sends the feedback to the terminal, which displays it to the user.

[0340] Users read the feedback, reflect on their own actions, and think about specific improvements.

[0341] This allows users to self-reflect, learn from everyday events, and promote self-improvement. In addition, by combining it with an emotion engine, appropriate support is provided according to the user's emotions.

[0342] Example 2

[0343] 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."

[0344] While systems aimed at self-reflection and self-improvement exist, providing appropriate feedback based on the user's emotional state has been difficult. From a security perspective, ensuring the safety of data transmission has also been an issue. Furthermore, rather than simple feedback, users need messages that encourage specific actions and self-reflection steps, but there has been a lack of systems that address this need.

[0345] 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.

[0346] In this invention, the server includes means for a user to input a diary or memo in text format, means for transmitting the input text data to the server, means for the server to receive the text data and perform natural language analysis, means for generating feedback and advice based on the analysis results, means for transmitting the generated feedback and advice to the user terminal, means for the user terminal to display the feedback and advice, means for transmitting and receiving data using a secure protocol, means for analyzing the user's emotions from the input text using an emotion recognition engine, means for generating feedback and advice based on the analysis results using a generative AI model, and means for adjusting the generated feedback and advice according to the user's emotional state. This makes it possible to provide appropriate feedback according to the user's emotions, ensure secure transmission and reception of data, and further encourage the user to take specific introspection and action steps.

[0347] "User input" is a means by which system users use a dedicated application to input the events, feelings, and thoughts of the day in text format.

[0348] "Data transmission" refers to the act and means by which a terminal transmits input text data to a server, and data security is ensured by using a secure protocol such as HTTPS.

[0349] "Receiving text data" refers to the act and means by which the server receives text data sent from the terminal.

[0350] "Natural language analysis" refers to the techniques and means of analyzing input text data using techniques such as sentiment analysis, keyword extraction, and theme extraction.

[0351] An "emotion recognition engine" is a means of recognizing a user's emotional state from text data and analyzing emotions such as positive or negative.

[0352] "Generation of feedback and advice" is a means of generating specific feedback and advice for the user based on the results of natural language analysis and emotion recognition.

[0353] A "generative AI model" is an artificial intelligence model used to generate feedback and advice based on analytical results.

[0354] A "secure data transmission protocol" is a communication method used to encrypt and safely transmit and receive data, such as HTTPS.

[0355] "Feedback and advice display" refers to a means for visually displaying the feedback and advice received by the user terminal to the user.

[0356] "Reflection activities" are activities in which users review feedback and advice, review their own actions and thoughts, and consider ways to improve.

[0357] This invention is a system that allows users to record daily events and emotions and to use that information to effectively reflect on themselves. Specific steps for implementing this system are described below.

[0358] The system mainly consists of the following elements:

[0359] 1. User Input

[0360] Using a dedicated application, users can input the events, feelings, and thoughts of the day in text format. For example, they might input, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[0361] 2. Data Transmission

[0362] The device sends the entered text data to the server using a secure protocol such as HTTPS, which encrypts the data during the process and protects the user's privacy.

[0363] 3. Receiving and analyzing text data

[0364] Once the server receives the text data, it passes it to a natural language analysis engine (e.g., Google Cloud Natural Language API or IBM Watson NLU). The analysis engine analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction. Additionally, it uses an emotion recognition engine (e.g., the undisclosed TeNTEN emotion recognition API) to recognize the user's emotional state.

[0365] 4. Generating feedback and advice

[0366] The server generates appropriate feedback and advice for the user based on the analysis results. A trained generative AI model (e.g., GPT-3) is used here. For example, if the result of the sentiment analysis is determined to be "negative," the server generates feedback such as, "Let's think about what preparations you need to make to ensure the success of your next meeting." The emotion recognition engine further refines the feedback according to the user's emotional state, providing the user with the most appropriate advice.

[0367] 5. Sending feedback and advice

[0368] Server-generated feedback and advice is sent to the device using HTTPS.

[0369] 6. Viewing feedback and advice

[0370] The device displays feedback and advice to the user. When the user opens the application, the provided feedback and advice is displayed on the screen.

[0371] 7. User Reflection

[0372] Users can review the displayed feedback and advice and reflect on it in their own self-reflection activities, such as "I will prepare thoroughly for the next meeting and think of concrete steps to improve communication with my boss."

[0373] Specific examples

[0374] Example 1: Work reflection

[0375] The user types, "My presentation today didn't go well. Maybe I didn't practice enough."

[0376] The terminal transmits this text data to the server.

[0377] The server receives the text data and passes it to a natural language analysis engine.

[0378] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "presentation" and "lack of practice."

[0379] An emotion recognition engine recognizes the user's emotional state and generates feedback accordingly.

[0380] The server generates feedback such as, "Let's think about what practice you need to do to make your next presentation a success."

[0381] The server sends the feedback to the terminal, which displays it to the user.

[0382] Users read the feedback and create their own practice plan.

[0383] Example 2: Private introspection

[0384] The user types, "I had lunch with a friend and something I said or did made things awkward. How can I improve in the future?"

[0385] The terminal transmits this text data to the server.

[0386] The server receives the text data and passes it to a natural language analysis engine.

[0387] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "friends," "lunch," and "awkward."

[0388] An emotion recognition engine recognizes the user's emotional state and generates feedback accordingly.

[0389] The server generates feedback such as, "Let's think specifically about what kind of behavior you should be careful about at your next lunch."

[0390] The server sends the feedback to the terminal, which displays it to the user.

[0391] Users read the feedback, reflect on their own actions, and think about specific improvements.

[0392] This system is an efficient tool for users to engage in self-reflection, learn from everyday events, and promote self-improvement. It also combines an emotion engine to provide appropriate support based on the user's emotions. By using secure protocols, user privacy is also protected.

[0393] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0394] Step 1: User Input

[0395] Using a dedicated application, users input the events, feelings, and thoughts of the day in text format. This application runs on smartphones and PCs and provides an easily accessible user interface. For example, a user can input, "Today's meeting with my boss didn't go well. It may be because I wasn't prepared enough." This input data is saved in text format.

[0396] Step 2: Send data

[0397] The terminal sends the text data entered by the user to the server. A secure protocol such as HTTPS is used for transmission, and the data is encrypted before being sent. This protects the user's privacy. The input is the user's text data, and the output is a notification to the server that transmission has been completed.

[0398] Step 3: Receiving and parsing text data

[0399] The server decrypts the received text data and sends it to a natural language analysis engine. The natural language analysis engine analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction. For example, it uses Google Cloud Natural Language API or IBM Watson NLU. The input to the analysis engine is the text data sent by the user, and the output is the emotional state, extracted keywords, and themes. It also analyzes the user's emotions using an emotion recognition engine. Specifically, the emotion recognition API assigns emotional labels such as "positive" or "negative."

[0400] Step 4: Generate feedback and advice

[0401] The server generates appropriate feedback and advice based on the analysis results obtained from the natural language analysis engine and emotion recognition engine. A generative AI model (e.g., GPT-3) is used for this generation. The input is the analysis result, and the output is specific feedback or advice text. For example, if the result of the sentiment analysis is "negative," feedback such as "Let's think about what preparations are needed to make the next meeting a success" is generated. The generative AI model generates the feedback based on a prompt sentence. An example of a prompt sentence is, "What concerns does the user have about the meeting?"

[0402] Step 5: Send feedback and advice

[0403] The server sends the generated feedback and advice to the device. The data is encrypted and sent using a secure protocol (e.g., HTTPS). The input is the feedback or advice text, and the output is a notification to the device that transmission has been completed.

[0404] Step 6: View feedback and advice

[0405] The terminal decrypts the received feedback and advice and displays it to the user on the application. When the user opens the application, the provided feedback and advice is displayed on the screen. The input is the text of the feedback and advice received from the server, and the output is what is displayed to the user.

[0406] Step 7: User reflection

[0407] The user can review the displayed feedback and advice and reflect it in their own self-reflection activities. For example, they can take action such as writing down specific preparation items for the next meeting or taking notes on key points to communicate with their boss. The input is the text of the feedback and advice, and the output is the user's specific action plan. By inputting the results of this self-reflection activity back into the application at a later date, continuous self-improvement is possible.

[0408] (Application example 2)

[0409] 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."

[0410] Existing virtual stores do not adequately collect or analyze customer feedback, making it difficult to provide specific product suggestions or improvement proposals based on customer sentiment. They also lacked a function to recommend appropriate products that took past feedback history into account. For these reasons, a system was needed to improve customer satisfaction and achieve more effective marketing.

[0411] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0412] In this invention, the server includes: a means for a user to input diary entries and feedback in text format; a means for transmitting the input text data to the server; a means for the server to receive the text data and perform natural language analysis; a means for generating feedback and advice based on the analysis results; a means for transmitting the generated feedback and advice to the user terminal; and a means for the user terminal to display the feedback and advice and a means for recommending products appropriate to the emotional state of the user, taking into account past feedback history. This makes it possible to provide specific feedback based on the customer's emotions and improve customer satisfaction. Furthermore, the appropriate product recommendation function taking into account past feedback history enables more effective marketing.

[0413] A "user" is a person who uses the system and is the entity that inputs text data such as feedback and diaries.

[0414] The "text format" is a data format consisting of characters, and is used to represent input content such as diary entries and feedback.

[0415] "Feedback" refers to the textual input of a user's thoughts and opinions about their shopping experience in the virtual store and the products they purchased.

[0416] A "server" is a computer system that receives text data sent by a user, analyzes it, and generates feedback.

[0417] "Natural language analysis" is a technology that uses computers to process human language and understand its content, and includes sentiment analysis, keyword extraction, and theme extraction.

[0418] "Sentiment analysis" is a technique for determining the emotional state of text data, evaluating it as positive, negative, neutral, etc.

[0419] "Keyword extraction" is a technique for extracting important words and phrases from text data, which is useful for analyzing feedback.

[0420] "Theme extraction" is a technique for identifying major topics from text data and is used to understand the content of feedback.

[0421] "Advice" refers to specific suggestions or advice that the server provides to the user based on the analysis results.

[0422] "Past feedback history" is a record of feedback provided by the user to date, which is used for future analysis and product recommendations.

[0423] The "product recommendation function" is a function that suggests suitable products to the user, taking into account their past feedback history and current emotional state.

[0424] A "user terminal" is a device used by a user to input and display feedback and advice, and includes a smartphone, a personal computer, etc.

[0425] This invention is an AI support system for virtual stores that analyzes user input of diary entries and feedback, and provides appropriate feedback and product recommendations. This system mainly consists of the following elements:

[0426] 1. User Input

[0427] First, users access the virtual store using their smartphone, PC, or other device. They then enter text feedback about their shopping experience and the products they purchased. For example, they might enter feedback like, "I felt the battery life of my latest smartwatch was shorter than expected."

[0428] 2. Data Transmission

[0429] The device sends the entered text data to the server using a secure protocol such as HTTPS to protect the user's privacy.

[0430] 3. Receiving and analyzing text data

[0431] The server receives the text data and passes it to a natural language analysis engine, which analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction. Sentiment analysis is performed using libraries such as TextBlob.

[0432] 4. Generating feedback and advice

[0433] The server generates appropriate feedback and advice for the user based on the analysis results. For example, if the result of the sentiment analysis is determined to be "negative" and the keyword "battery life" is extracted, the server will generate the following feedback: "We apologize for the inconvenience. We will suggest a different model with a longer battery life."

[0434] 5. Sending feedback and advice

[0435] The server generates feedback and advice and sends it to the device using a secure protocol. The user device receives the feedback and advice and displays it to the user.

[0436] 6. Use of past feedback history

[0437] The server also takes into account past feedback history and provides a recommendation function to recommend products that suit the user's emotions and history, enabling more personalized advice and product suggestions.

[0438] By combining these elements, the system can provide specific feedback and appropriate product recommendations based on customer sentiment. In particular, natural language processing and sentiment analysis can be used to understand the content of user feedback and generate optimal advice.

[0439] Specific examples

[0440] Customer-provided feedback: "The battery life of my latest smartwatch is shorter than expected."

[0441] System feedback: Negative and says "We apologize for the inconvenience. We suggest you try another model with a longer battery life. Please check the link below."

[0442] Example prompt sentence:

[0443] "Customer provided feedback: The battery life of their latest smartwatch was shorter than expected. Generate a sentiment analysis and appropriate advice based on this feedback."

[0444] Thus, the present invention is directed to a system that efficiently analyzes user-entered feedback and provides specific suggestions for improving user satisfaction.

[0445] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0446] Step 1:

[0447] Users enter diary entries and feedback in text format.

[0448] Specifically, users access the virtual store's application using a smartphone or PC and enter their thoughts and opinions about their shopping experience and the products they purchased in text form, such as "I felt that the battery life of my latest smartwatch was shorter than expected."

[0449] Step 2:

[0450] The terminal transmits the input text data to the server.

[0451] Specifically, the terminal sends the user's feedback to the server using a secure protocol such as HTTPS, where the input is the text data entered by the user and the output is the data sent to the server.

[0452] Step 3:

[0453] The server receives the text data and passes it to a natural language analysis engine.

[0454] Specifically, the server receives data sent by the user and passes it to a natural language analysis engine (e.g., TextBlob library) for processing. The input is the text data sent by the user, and the output is the data passed to the analysis engine.

[0455] Step 4:

[0456] The natural language analysis engine analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction.

[0457] Specifically, the engine analyzes text data, determines whether the sentiment is positive or negative, and extracts key keywords and themes. The input is the text data passed from the server, and the output is the analysis results (e.g., sentiment analysis results, keywords, themes).

[0458] Step 5:

[0459] The server generates feedback and advice based on the analysis results.

[0460] Specifically, the server generates appropriate feedback and advice for the user based on the results of the sentiment analysis and the extracted keywords. For example, if the result is "negative" and the keyword "battery life" is extracted, the server generates the following feedback: "We apologize for the inconvenience. We will suggest a different model with a longer battery life." The input is the analysis result, and the output is the generated feedback and advice.

[0461] Step 6:

[0462] The server generates feedback and advice and sends it to the user's terminal.

[0463] Specifically, the server transmits the generated feedback and advice to the user terminal using a secure protocol. The input is the generated feedback and advice, and the output is transmission to the user terminal.

[0464] Step 7:

[0465] The user's device displays feedback and advice.

[0466] Specifically, the terminal displays the feedback and advice provided to the user so that the user can confirm the content. The input is the feedback and advice sent from the server, and the output is the display to the user.

[0467] Step 8:

[0468] The server takes into account past feedback history and recommends products that suit your emotional state.

[0469] Specifically, the server recommends products suitable for the user based on the user's past feedback history and current emotional state. For example, it proposes a "model with long battery life." The input is the past feedback history and the current analysis results, and the output is the recommended product information.

[0470] 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.

[0471] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0472] 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.

[0473] [Second embodiment]

[0474] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0475] 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.

[0476] 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).

[0477] 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.

[0478] 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.

[0479] 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).

[0480] 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.

[0481] 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.

[0482] 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.

[0483] 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.

[0484] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0485] 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."

[0486] This invention is an AI support system that allows users to record their daily events and emotions and use that information to engage in effective self-reflection. Users enter their diary entries and notes, and the system analyzes their content and provides appropriate feedback and advice to promote self-improvement.

[0487] Explaining program processing in natural language

[0488] The system mainly consists of the following elements:

[0489] 1. User Input

[0490] Using a dedicated application, users can input the events, feelings, and thoughts of the day in text format. For example, they might input, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[0491] 2. Data Transmission

[0492] The device sends the entered text data to the server, using a secure protocol (e.g., HTTPS) to protect the user's privacy.

[0493] 3. Receiving and analyzing text data

[0494] The server receives the text data and passes it to a natural language analysis engine, which analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction.

[0495] For example, sentiment analysis classifies the message as "negative," and keyword extraction extracts "meeting," "boss," and "lack of preparation."

[0496] 4. Generating feedback and advice

[0497] Based on the analysis results, the server generates appropriate feedback and advice for the user, particularly suggesting areas for improvement and specific actions to take next time.

[0498] For example, the generated feedback is, "Let's think about what preparations you need to make to make the next meeting a success."

[0499] 5. Sending feedback and advice

[0500] The server generates feedback and advice and sends it to the device, using a secure protocol for data transmission.

[0501] 6. Viewing feedback and advice

[0502] The device displays feedback and advice to the user, who can then open the application and review the feedback provided.

[0503] 7. User Reflection

[0504] The user reflects on the feedback and creates a plan of action for the next time. For example, they might take specific steps to better prepare for the next meeting and improve communication with their boss.

[0505] Specific examples

[0506] Example 1: Work reflection

[0507] The user types, "My presentation today didn't go well. Maybe I didn't practice enough."

[0508] The terminal transmits this text data to the server.

[0509] The server receives the text data and passes it to a natural language analysis engine.

[0510] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "presentation" and "lack of practice."

[0511] The server generates feedback such as, "Let's think about what practice you need to do to make your next presentation a success."

[0512] The server sends the feedback to the terminal, which displays it to the user.

[0513] Users read the feedback and create their own practice plan.

[0514] Example 2: Private introspection

[0515] The user types, "I had lunch with a friend and something I said or did made things awkward. How can I improve in the future?"

[0516] The terminal transmits this text data to the server.

[0517] The server receives the text data and passes it to a natural language analysis engine.

[0518] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "friends," "lunch," and "awkward."

[0519] The server generates feedback such as, "Let's think specifically about what kind of behavior you should be careful about at your next lunch."

[0520] The server sends the feedback to the terminal, which displays it to the user.

[0521] Users read the feedback, reflect on their own actions, and think about specific improvements.

[0522] This concludes the description of the preferred embodiment of the present invention, which provides users with an effective tool for self-reflection, learning from everyday occurrences, and promoting self-improvement.

[0523] The processing flow will be explained below.

[0524] Step 1:

[0525] The user opens a dedicated application and enters the events, feelings, and thoughts of the day in text format. For example, they might enter, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[0526] Step 2:

[0527] The device sends the entered text data to the server. Data transmission is performed using a secure protocol (e.g., HTTPS) to protect the user's privacy.

[0528] Step 3:

[0529] The server receives the text data and passes it to a natural language analysis engine, which analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction.

[0530] Step 4:

[0531] A natural language analysis engine analyzes text data. First, it performs sentiment analysis to determine whether the input text is classified as "positive," "negative," or "neutral." Next, it performs keyword extraction to identify important words and phrases. Finally, it performs theme extraction to extract key topics and arguments.

[0532] Step 5:

[0533] The server generates appropriate feedback and advice for the user based on the analysis results. For example, if the result of the sentiment analysis is determined to be "negative" and the keywords "meeting," "boss," and "lack of preparation" are extracted, the server generates the following feedback: "Let's think about what preparations you need to make to ensure the success of your next meeting."

[0534] Step 6:

[0535] The server generates feedback and advice and sends it to the device using a secure protocol.

[0536] Step 7:

[0537] The device displays the received feedback and advice to the user. When the user opens the application, the provided feedback and advice is displayed on the screen.

[0538] Step 8:

[0539] Users can review the displayed feedback and advice and reflect on it in their own self-reflection activities, such as "I will prepare thoroughly for the next meeting and think of concrete steps to improve communication with my boss."

[0540] Through these steps, the system efficiently and effectively supports the user's self-reflection activities and helps the user to continue self-improvement.

[0541] Example 1

[0542] 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."

[0543] Previously, systems that allowed users to record their daily events and emotions and use them for self-reflection had problems such as insufficient analysis of the input information and vague feedback provided. Furthermore, there were also cases where user privacy was not adequately protected. This resulted in issues such as ineffective self-reflection and difficulty in promoting self-improvement.

[0544] 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.

[0545] In this invention, the server includes means for performing sentiment analysis, keyword extraction, and theme extraction of input text using a natural language analysis engine, means for generating feedback and advice using a generative AI model, and means for transmitting and receiving the input text data and the generated feedback and advice using a secure protocol. This allows users to receive specific feedback and advice based on detailed analysis results, enabling them to engage in self-reflection activities with peace of mind.

[0546] A "user" is a person who uses this system to enter diary entries and memos.

[0547] "Text data" is data in which diary entries and memos entered by the user are expressed in text format.

[0548] The "server" is a device that receives text data sent by users, analyzes it, and generates and sends feedback and advice.

[0549] A "natural language analysis engine" is a program or device that analyzes text data and extracts the emotions, keywords, and themes contained therein.

[0550] A "generative AI model" is an artificial intelligence model that generates feedback and advice based on analysis results.

[0551] "Feedback and advice" refers to specific instructions and advice provided to the user based on the analysis results.

[0552] A "secure protocol" is a set of rules for encrypting communication content and sending and receiving data safely.

[0553] "Sentiment analysis" is a technology that identifies a user's emotions from the content of text data and determines whether those emotions are positive or negative.

[0554] "Keyword extraction" is a technique for identifying important words and phrases from text data.

[0555] "Theme extraction" is a technique for identifying major topics or themes within text data.

[0556] A "user terminal" is a device on which a user enters a diary or memo and checks feedback and advice. Examples of such devices include smartphones and tablets.

[0557] "Reflection" is an activity in which the user reflects on themselves based on the feedback and advice provided and makes a plan of action for the next time.

[0558] This invention is an AI support system that allows users to record their daily events and emotions and use that information to engage in effective self-reflection. Users use a dedicated application to enter their diary and notes, and the server analyzes the content and provides appropriate feedback and advice to promote self-improvement.

[0559] Hardware and Software Configuration

[0560] The system consists of the following elements:

[0561] User device: A device such as a smartphone or tablet where users input text data and receive feedback.

[0562] Server: A device that analyzes text data and generates and sends feedback and advice.

[0563] Natural language analysis engines: such as Google Natural Language API and Amazon Comprehend, which perform sentiment analysis, keyword extraction, and theme extraction on text data.

[0564] Generative AI models: Artificial intelligence models for generating feedback and advice, such as OpenAI GPT-3

[0565] Secure protocols: Protocols for securely sending and receiving user data, such as HTTPS

[0566] Data processing and calculation

[0567] 1. User Input

[0568] Users open a dedicated application and enter the events and feelings of the day in text format. For example, they might enter, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[0569] 2. Data Transmission

[0570] The device sends the entered text data to the server using the HTTPS protocol, encrypting the data and protecting the user's privacy.

[0571] 3. Receiving and analyzing text data

[0572] The server receives the text data from the device and passes it to a natural language analysis engine for analysis. The analysis involves sentiment analysis, keyword extraction, and theme extraction. For example, sentiment analysis may determine a negative sentiment, while keyword extraction may identify "meeting," "boss," and "lack of preparation."

[0573] 4. Generating feedback and advice

[0574] Based on the analysis results, the server uses a generative AI model to generate appropriate feedback and advice. It is important that the generated feedback is specific and suggests actions that can be used next time. For example, feedback such as "Let's think about what preparations are needed to make the next meeting a success" could be generated.

[0575] 5. Sending feedback and advice

[0576] The generated feedback and advice is then sent to the user terminal, again using a secure protocol.

[0577] 6. Viewing feedback and advice

[0578] The user device displays the feedback and advice to the user, who then opens the application and checks the provided feedback.

[0579] 7. User Reflection

[0580] The user reflects on the feedback provided and creates a plan of action for the next time, for example, "I will be more prepared for the next meeting and think of concrete steps to improve communication with my boss."

[0581] Specific examples

[0582] Example 1: Work reflection

[0583] The user types, "My presentation didn't go well today. Maybe I didn't practice enough."

[0584] The terminal transmits this text data to the server.

[0585] The server receives the text data and passes it to a natural language analysis engine.

[0586] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "presentation" and "lack of practice."

[0587] The server generates feedback such as, "Let's think about what practice you need to do to make your next presentation a success."

[0588] The server sends the feedback to the terminal, which displays it to the user.

[0589] Users read the feedback and create their own practice plan.

[0590] Example 2: Private introspection

[0591] A user types, "I had lunch with a friend and something I said or did made things awkward. How can I improve in the future?"

[0592] The terminal transmits this text data to the server.

[0593] The server receives the text data and passes it to a natural language analysis engine.

[0594] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "friends," "lunch," and "awkward."

[0595] The server generates feedback such as, "Let's think specifically about what kind of behavior we should be careful about at our next lunch."

[0596] The server sends the feedback to the terminal, which displays it to the user.

[0597] Users read the feedback, reflect on their own actions, and think about specific improvements.

[0598] This concludes the description of the preferred embodiment of the present invention. The system provides an effective tool for users to engage in self-reflection, learn from everyday occurrences, and promote self-improvement.

[0599] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0600] Step 1:

[0601] The user provides input.

[0602] Input: The user opens a dedicated application and enters the events and feelings of the day in text format.

[0603] Specific behavior: The user launches the application, enters daily events and emotions into the text input field, and presses the "Send" button.

[0604] Output: The input text data.

[0605] Step 2:

[0606] The terminal transmits the input text data to the server.

[0607] Input: Text data entered by the user.

[0608] Specific operation: The device sends the user's input to the server using the HTTPS protocol.

[0609] Output: The encrypted text data sent to the server.

[0610] Step 3:

[0611] The server passes the received text data to a natural language analysis engine.

[0612] Input: Encrypted text data.

[0613] What happens: The server receives an HTTPS request and passes the text data to a natural language analysis engine.

[0614] Output: Text data passed to the natural language analysis engine.

[0615] Step 4:

[0616] A natural language analysis engine analyzes the text data.

[0617] Input: Text data passed to the natural language analysis engine.

[0618] Specific operation: The natural language analysis engine performs sentiment analysis, keyword extraction, and theme extraction. For example, sentiment analysis may determine a message as "negative," and keywords such as "meeting," "boss," and "lack of preparation" may be extracted.

[0619] Output: Analysis results (sentiment, keywords, themes).

[0620] Step 5:

[0621] The server generates feedback and advice using a generative AI model based on the analysis results.

[0622] Input: Analysis results (sentiments, keywords, themes).

[0623] Specific operation: The server inputs prompts into the generative AI model and generates appropriate feedback and advice. For example, feedback such as "Let's think about what preparations you need to make to ensure the success of your next meeting" is generated.

[0624] Output: Generated feedback and advice.

[0625] Step 6:

[0626] The server sends the generated feedback and advice to the device.

[0627] Input: Generated feedback and advice.

[0628] Specific operation: The server sends the feedback to the device using the HTTPS protocol.

[0629] Output: Feedback and advice sent to the device.

[0630] Step 7:

[0631] The device will provide feedback and advice.

[0632] Input: Feedback and advice sent to your device.

[0633] Specific behavior: The device decodes the received feedback and displays it in the application's user interface.

[0634] Output: Feedback and advice displayed to the user.

[0635] Step 8:

[0636] The user performs a self-reflection activity.

[0637] Input: Any feedback or advice you see.

[0638] Specific Action: The user reads the feedback and creates a plan of action for the next time. For example, the user may perform a self-reflection activity such as, "I will think of specific steps to be more prepared for the next meeting and improve communication with my boss."

[0639] Output: A concrete action plan.

[0640] (Application example 1)

[0641] 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."

[0642] Conventional AI support systems require users to input events and emotions in text format, and do not provide voice-based diary entries or visual feedback using wearable devices. This makes it difficult for users to easily engage in self-reflection, and there is a risk that small everyday events or emotions may be overlooked. Furthermore, conventional systems are limited to text input, which makes them lack immediacy and convenience.

[0643] 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.

[0644] In this invention, the server includes a means for a user to input a diary or memo in text format, a means for transmitting the input text data to the server, a means for performing natural language analysis, a means for recording a diary or memo by voice input, a means for converting the voice input into text format, and a means for visually displaying feedback via a wearable device such as smart glasses. This allows the user to easily record a diary or memo through voice input and immediately receive appropriate feedback from the analysis results. Furthermore, visual feedback via the wearable device promotes introspection.

[0645] "User" refers to a user who uses this system to enter diary entries and memos and receive feedback and advice.

[0646] "Text format" is a data format based on character information entered by the user.

[0647] A "server" is a computer system that receives data sent by a user, analyzes it, generates feedback, and sends it back to the user.

[0648] "Natural language analysis" is the act of analyzing text data entered by a user using natural language processing technology to perform sentiment analysis, keyword extraction, etc.

[0649] "Feedback" refers to advice and improvement suggestions provided to users based on the analysis results.

[0650] "Advice" refers to specific improvement measures or suggested actions provided to users based on the analysis results.

[0651] "User terminal" means a device used by a user to operate the system and receive feedback and advice.

[0652] "Voice input" is a method in which a user inputs information by speaking.

[0653] "Convert to text" is the process of converting speech input into text data.

[0654] "Smart glasses" are wearable devices worn by users to present visual information.

[0655] A "wearable device" is a computing device that can be worn by a user.

[0656] "Visually displaying" means showing information to the user through the device's display.

[0657] This invention is an advanced AI support system that records the user's daily events and emotions and encourages introspection based on that data. Specific embodiments for realizing this system are described below.

[0658] The system components are:

[0659] 1. User terminals: including wearable devices such as smart glasses and smartphones.

[0660] 2. Voice input system: This system allows users to input daily events and emotions by voice and converts them into text. Specifically, it uses voice recognition APIs such as Google Cloud Speech-to-Text and IBM Watson.

[0661] 3. Natural Language Analysis System: A system that analyzes text data. It performs processes such as sentiment analysis, keyword extraction, and theme extraction. Specifically, it uses a sentiment analysis model that utilizes the Transformers pipeline.

[0662] 4. Feedback generation system: A system that generates appropriate feedback and advice for users based on the analysis results.

[0663] 5. Server: This is the central processing unit that performs the analysis and generation mentioned above, and receives data from users and processes and analyzes it.

[0664] Program processing overview

[0665] 1. User Input:

[0666] Using a wearable device such as smart glasses, users can input their day's events and emotions by voice, for example, "Today was a bad day at work."

[0667] 2. Data transmission:

[0668] The user terminal converts the voice data into text and sends it to the server via a secure protocol (e.g., HTTPS).

[0669] 3. Receiving and analyzing text data:

[0670] The server receives the text data and passes it to a natural language analysis system, which performs sentiment analysis, determining, for example, that the sentiment is "negative," and extracts keywords such as "work" and "didn't go well."

[0671] 4. Generate feedback and advice:

[0672] The server generates feedback based on the analysis results, creating advice such as, "Think about what preparations you need to make the next time you work."

[0673] 5. Sending and Viewing Feedback:

[0674] The server transmits the generated feedback to the user's device, which visually displays the feedback through the smart glasses.

[0675] Specific examples

[0676] Work reflection

[0677] A user speaks, "Today's meeting went wrong. Maybe I wasn't prepared enough."

[0678] The user terminal converts the voice data into text and sends it to the server.

[0679] The server receives the text data and performs sentiment analysis and keyword extraction.

[0680] The server generates feedback saying, "Let's think about how to prepare for the meeting."

[0681] Feedback is displayed to the user through the smart glasses.

[0682] Private introspection

[0683] The user speaks, "I said too much at dinner with a friend. How can I improve?"

[0684] The user terminal converts the voice data into text and sends it to the server.

[0685] The server receives the text data and performs sentiment analysis and keyword extraction.

[0686] The server generates feedback such as, "Let's think about what we should pay attention to the next time we dine here."

[0687] Feedback is displayed to the user through the smart glasses.

[0688] Prompt Sentence Examples

[0689] Here are some example prompts to encourage users to speak:

[0690] "How was your day today?"

[0691] "Tell me what happened today and how you felt."

[0692] "Is there anything you'd like to improve?"

[0693] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0694] Step 1:

[0695] Using a wearable device such as smart glasses, users input their day's events and emotions through voice, for example, "Today was a bad day at work."

[0696] Step 2:

[0697] The device receives voice input and converts it to text using a speech recognition system (for example, Google Cloud Speech-to-Text or IBM Watson). The input is voice data, and the output is text data. This conversion processes the user's voice into text information.

[0698] Step 3:

[0699] The terminal sends the converted text data to the server via a secure protocol (e.g., HTTPS). The input is the text data, and the output is the transmission completion status. Encryption technology is used for data transmission for security.

[0700] Step 4:

[0701] The server receives the text data and passes it to the natural language analysis system. The input is text data, and the received data is subjected to analysis. Here, the received data is added to a queue to be analyzed.

[0702] Step 5:

[0703] The server's natural language analysis system performs sentiment analysis, keyword extraction, and theme extraction on the text data. This uses a sentiment analysis model that utilizes a Transformers pipeline. The input is text data, and the output is a sentiment determination result and a keyword list. At this stage, the data is sentiment-tagged and key keywords are extracted.

[0704] Step 6:

[0705] Based on the analysis results, the server generates appropriate feedback and advice. For example, it creates advice such as, "Think about what preparations you need to make the next time you work." The input is the emotion determination result and a list of keywords, and the output is a feedback message. In this process, a generative AI model is used to generate appropriate advice for the user.

[0706] Step 7:

[0707] The server securely transmits the generated feedback message to the user terminal. The input is the feedback message, and the output is a transmission completion status. The transmission means uses a secure protocol.

[0708] Step 8:

[0709] The terminal visually displays the received feedback message to the user via a wearable device such as smart glasses. The input is the feedback message, and the output is the feedback content displayed on the glasses' display. Here, the user can confirm which specific areas need improvement.

[0710] The above is the flow of program processing and specific operations of the system that realizes the application example.

[0711] 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.

[0712] This invention is an AI support system that allows users to record their daily events and emotions and use that information to engage in effective self-reflection. The system analyzes the contents of diary entries and notes, and provides appropriate feedback and advice to promote self-improvement. Furthermore, by incorporating an emotion engine, the system can recognize the user's emotions and provide more appropriate feedback.

[0713] Explaining program processing in natural language

[0714] The system mainly consists of the following elements:

[0715] 1. User Input

[0716] Using a dedicated application, users can input the events, feelings, and thoughts of the day in text format. For example, they might input, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[0717] 2. Data Transmission

[0718] The device sends the entered text data to the server. Data transmission is performed using a secure protocol (e.g., HTTPS) to protect the user's privacy.

[0719] 3. Receiving and analyzing text data

[0720] The server receives the text data and passes it to a natural language analysis engine, which analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction.

[0721] The emotion engine also analyzes text data and recognizes the user's emotions.

[0722] 4. Generating feedback and advice

[0723] The server generates appropriate feedback and advice for the user based on the analysis results. For example, if the result of the sentiment analysis is determined to be "negative" and the keywords "meeting," "boss," and "lack of preparation" are extracted, the server generates the following feedback: "Let's think about what preparations you need to make to ensure the success of your next meeting."

[0724] The emotion engine may also tailor the feedback depending on the user's emotional state, for example, generating feedback such as "Think of concrete steps you can take to feel more positive about your upcoming meeting."

[0725] 5. Sending feedback and advice

[0726] The server generates feedback and advice and sends it to the device using a secure protocol.

[0727] 6. Viewing feedback and advice

[0728] The device displays feedback and advice to the user. When the user opens the application, the provided feedback and advice is displayed on the screen.

[0729] 7. User Reflection

[0730] Users can review the displayed feedback and advice and reflect on it in their own self-reflection activities, such as "I will prepare thoroughly for the next meeting and think of concrete steps to improve communication with my boss."

[0731] Specific examples

[0732] Example 1: Work reflection

[0733] The user types, "My presentation today didn't go well. Maybe I didn't practice enough."

[0734] The terminal transmits this text data to the server.

[0735] The server receives the text data and passes it to a natural language analysis engine.

[0736] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "presentation" and "lack of practice."

[0737] The emotion engine recognizes the user's emotional state and generates feedback accordingly.

[0738] The server generates feedback such as, "Let's think about what practice you need to do to make your next presentation a success."

[0739] The server sends the feedback to the terminal, which displays it to the user.

[0740] Users read the feedback and create their own practice plan.

[0741] Example 2: Private introspection

[0742] The user types, "I had lunch with a friend and something I said or did made things awkward. How can I improve in the future?"

[0743] The terminal transmits this text data to the server.

[0744] The server receives the text data and passes it to a natural language analysis engine.

[0745] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "friends," "lunch," and "awkward."

[0746] The emotion engine recognizes the user's emotional state and generates feedback accordingly.

[0747] The server generates feedback such as, "Let's think specifically about what kind of behavior you should be careful about at your next lunch."

[0748] The server sends the feedback to the terminal, which displays it to the user.

[0749] Users read the feedback, reflect on their own actions, and think about specific improvements.

[0750] The above is an embodiment of the present invention. This system is an efficient tool for users to engage in self-reflection, learn from everyday events, and promote self-improvement. In addition, by combining it with an emotion engine, it can provide appropriate support according to the user's emotions.

[0751] The processing flow will be explained below.

[0752] Step 1:

[0753] The user opens a dedicated application and enters the events, feelings, and thoughts of the day in text format. For example, they might enter, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[0754] Step 2:

[0755] The device sends the entered text data to the server. Data transmission is performed using a secure protocol (e.g., HTTPS) to protect the user's privacy.

[0756] Step 3:

[0757] The server receives the text data and passes it to a natural language analysis engine, which analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction.

[0758] Step 4:

[0759] A natural language analysis engine analyzes text data. First, it performs sentiment analysis to determine whether the input text is classified as "positive," "negative," or "neutral." Next, it performs keyword extraction to identify important words and phrases. Finally, it performs theme extraction to extract key topics and arguments.

[0760] Step 5:

[0761] The emotion engine analyzes the text data to recognize the user's emotional state. This analysis involves detailed classification of emotions sensed from the user's input text and identifies the current emotional state.

[0762] Step 6:

[0763] The server generates appropriate feedback and advice for the user based on the analysis results. For example, if the result of the sentiment analysis is determined to be "negative" and the keywords "meeting," "boss," and "lack of preparation" are extracted, the server generates feedback such as "Let's think about what kind of preparations you need to make to ensure the success of your next meeting." It also generates feedback such as "Let's think about specific steps to have a positive attitude toward future meetings," reflecting the results of the emotion engine.

[0764] Step 7:

[0765] The server generates feedback and advice and sends it to the device using a secure protocol.

[0766] Step 8:

[0767] The device displays the received feedback and advice to the user. When the user opens the application, the provided feedback and advice is displayed on the screen.

[0768] Step 9:

[0769] Users can review the displayed feedback and advice and reflect on it in their own self-reflection activities, such as "I will prepare thoroughly for the next meeting and think of concrete steps to improve communication with my boss."

[0770] Specific examples

[0771] Example 1: Work reflection

[0772] The user types, "My presentation today didn't go well. Maybe I didn't practice enough."

[0773] The terminal transmits this text data to the server.

[0774] The server receives the text data and passes it to a natural language analysis engine.

[0775] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "presentation" and "lack of practice."

[0776] The emotion engine recognizes the user's emotional state and generates feedback accordingly.

[0777] The server generates feedback such as, "Let's think about what practice you need to do to make your next presentation a success."

[0778] The server sends the feedback to the terminal, which displays it to the user.

[0779] Users read the feedback and create their own practice plan.

[0780] Example 2: Private introspection

[0781] The user types, "I had lunch with a friend and something I said or did made things awkward. How can I improve in the future?"

[0782] The terminal transmits this text data to the server.

[0783] The server receives the text data and passes it to a natural language analysis engine.

[0784] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "friends," "lunch," and "awkward."

[0785] The emotion engine recognizes the user's emotional state and generates feedback accordingly.

[0786] The server generates feedback such as, "Let's think specifically about what kind of behavior you should be careful about at your next lunch."

[0787] The server sends the feedback to the terminal, which displays it to the user.

[0788] Users read the feedback, reflect on their own actions, and think about specific improvements.

[0789] This allows users to self-reflect, learn from everyday events, and promote self-improvement. In addition, by combining it with an emotion engine, appropriate support is provided according to the user's emotions.

[0790] Example 2

[0791] 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."

[0792] While systems aimed at self-reflection and self-improvement exist, providing appropriate feedback based on the user's emotional state has been difficult. From a security perspective, ensuring the safety of data transmission has also been an issue. Furthermore, rather than simple feedback, users need messages that encourage specific actions and self-reflection steps, but there has been a lack of systems that address this need.

[0793] 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.

[0794] In this invention, the server includes means for a user to input a diary or memo in text format, means for transmitting the input text data to the server, means for the server to receive the text data and perform natural language analysis, means for generating feedback and advice based on the analysis results, means for transmitting the generated feedback and advice to the user terminal, means for the user terminal to display the feedback and advice, means for transmitting and receiving data using a secure protocol, means for analyzing the user's emotions from the input text using an emotion recognition engine, means for generating feedback and advice based on the analysis results using a generative AI model, and means for adjusting the generated feedback and advice according to the user's emotional state. This makes it possible to provide appropriate feedback according to the user's emotions, ensure secure transmission and reception of data, and further encourage the user to take specific introspection and action steps.

[0795] "User input" is a means by which system users use a dedicated application to input the events, feelings, and thoughts of the day in text format.

[0796] "Data transmission" refers to the act and means by which a terminal transmits input text data to a server, and data security is ensured by using a secure protocol such as HTTPS.

[0797] "Receiving text data" refers to the act and means by which the server receives text data sent from the terminal.

[0798] "Natural language analysis" refers to the techniques and means of analyzing input text data using techniques such as sentiment analysis, keyword extraction, and theme extraction.

[0799] An "emotion recognition engine" is a means of recognizing a user's emotional state from text data and analyzing emotions such as positive or negative.

[0800] "Generation of feedback and advice" is a means of generating specific feedback and advice for the user based on the results of natural language analysis and emotion recognition.

[0801] A "generative AI model" is an artificial intelligence model used to generate feedback and advice based on analytical results.

[0802] A "secure data transmission protocol" is a communication method used to encrypt and safely transmit and receive data, such as HTTPS.

[0803] "Feedback and advice display" refers to a means for visually displaying the feedback and advice received by the user terminal to the user.

[0804] "Reflection activities" are activities in which users review feedback and advice, review their own actions and thoughts, and consider ways to improve.

[0805] This invention is a system that allows users to record daily events and emotions and to use that information to effectively reflect on themselves. Specific steps for implementing this system are described below.

[0806] The system mainly consists of the following elements:

[0807] 1. User Input

[0808] Using a dedicated application, users can input the events, feelings, and thoughts of the day in text format. For example, they might input, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[0809] 2. Data Transmission

[0810] The device sends the entered text data to the server using a secure protocol such as HTTPS, which encrypts the data during the process and protects the user's privacy.

[0811] 3. Receiving and analyzing text data

[0812] Once the server receives the text data, it passes it to a natural language analysis engine (e.g., Google Cloud Natural Language API or IBM Watson NLU). The analysis engine analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction. Additionally, it uses an emotion recognition engine (e.g., the undisclosed TeNTEN emotion recognition API) to recognize the user's emotional state.

[0813] 4. Generating feedback and advice

[0814] The server generates appropriate feedback and advice for the user based on the analysis results. A trained generative AI model (e.g., GPT-3) is used here. For example, if the result of the sentiment analysis is determined to be "negative," the server generates feedback such as, "Let's think about what preparations you need to make to ensure the success of your next meeting." The emotion recognition engine further refines the feedback according to the user's emotional state, providing the user with the most appropriate advice.

[0815] 5. Sending feedback and advice

[0816] Server-generated feedback and advice is sent to the device using HTTPS.

[0817] 6. Viewing feedback and advice

[0818] The device displays feedback and advice to the user. When the user opens the application, the provided feedback and advice is displayed on the screen.

[0819] 7. User Reflection

[0820] Users can review the displayed feedback and advice and reflect on it in their own self-reflection activities, such as "I will prepare thoroughly for the next meeting and think of concrete steps to improve communication with my boss."

[0821] Specific examples

[0822] Example 1: Work reflection

[0823] The user types, "My presentation today didn't go well. Maybe I didn't practice enough."

[0824] The terminal transmits this text data to the server.

[0825] The server receives the text data and passes it to a natural language analysis engine.

[0826] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "presentation" and "lack of practice."

[0827] An emotion recognition engine recognizes the user's emotional state and generates feedback accordingly.

[0828] The server generates feedback such as, "Let's think about what practice you need to do to make your next presentation a success."

[0829] The server sends the feedback to the terminal, which displays it to the user.

[0830] Users read the feedback and create their own practice plan.

[0831] Example 2: Private introspection

[0832] The user types, "I had lunch with a friend and something I said or did made things awkward. How can I improve in the future?"

[0833] The terminal transmits this text data to the server.

[0834] The server receives the text data and passes it to a natural language analysis engine.

[0835] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "friends," "lunch," and "awkward."

[0836] An emotion recognition engine recognizes the user's emotional state and generates feedback accordingly.

[0837] The server generates feedback such as, "Let's think specifically about what kind of behavior you should be careful about at your next lunch."

[0838] The server sends the feedback to the terminal, which displays it to the user.

[0839] Users read the feedback, reflect on their own actions, and think about specific improvements.

[0840] This system is an efficient tool for users to engage in self-reflection, learn from everyday events, and promote self-improvement. It also combines an emotion engine to provide appropriate support based on the user's emotions. By using secure protocols, user privacy is also protected.

[0841] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0842] Step 1: User Input

[0843] Using a dedicated application, users input the events, feelings, and thoughts of the day in text format. This application runs on smartphones and PCs and provides an easily accessible user interface. For example, a user can input, "Today's meeting with my boss didn't go well. It may be because I wasn't prepared enough." This input data is saved in text format.

[0844] Step 2: Send data

[0845] The terminal sends the text data entered by the user to the server. A secure protocol such as HTTPS is used for transmission, and the data is encrypted before being sent. This protects the user's privacy. The input is the user's text data, and the output is a notification to the server that transmission has been completed.

[0846] Step 3: Receiving and parsing text data

[0847] The server decrypts the received text data and sends it to a natural language analysis engine. The natural language analysis engine analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction. For example, it uses Google Cloud Natural Language API or IBM Watson NLU. The input to the analysis engine is the text data sent by the user, and the output is the emotional state, extracted keywords, and themes. It also analyzes the user's emotions using an emotion recognition engine. Specifically, the emotion recognition API assigns emotional labels such as "positive" or "negative."

[0848] Step 4: Generate feedback and advice

[0849] The server generates appropriate feedback and advice based on the analysis results obtained from the natural language analysis engine and emotion recognition engine. A generative AI model (e.g., GPT-3) is used for this generation. The input is the analysis result, and the output is specific feedback or advice text. For example, if the result of the sentiment analysis is "negative," feedback such as "Let's think about what preparations are needed to make the next meeting a success" is generated. The generative AI model generates the feedback based on a prompt sentence. An example of a prompt sentence is, "What concerns does the user have about the meeting?"

[0850] Step 5: Send feedback and advice

[0851] The server sends the generated feedback and advice to the device. The data is encrypted and sent using a secure protocol (e.g., HTTPS). The input is the feedback or advice text, and the output is a notification to the device that transmission has been completed.

[0852] Step 6: View feedback and advice

[0853] The terminal decrypts the received feedback and advice and displays it to the user on the application. When the user opens the application, the provided feedback and advice is displayed on the screen. The input is the text of the feedback and advice received from the server, and the output is what is displayed to the user.

[0854] Step 7: User reflection

[0855] The user can review the displayed feedback and advice and reflect it in their own self-reflection activities. For example, they can take action such as writing down specific preparation items for the next meeting or taking notes on key points to communicate with their boss. The input is the text of the feedback and advice, and the output is the user's specific action plan. By inputting the results of this self-reflection activity back into the application at a later date, continuous self-improvement is possible.

[0856] (Application example 2)

[0857] 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."

[0858] Existing virtual stores do not adequately collect or analyze customer feedback, making it difficult to provide specific product suggestions or improvement proposals based on customer sentiment. They also lacked a function to recommend appropriate products that took past feedback history into account. For these reasons, a system was needed to improve customer satisfaction and achieve more effective marketing.

[0859] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0860] In this invention, the server includes: a means for a user to input diary entries and feedback in text format; a means for transmitting the input text data to the server; a means for the server to receive the text data and perform natural language analysis; a means for generating feedback and advice based on the analysis results; a means for transmitting the generated feedback and advice to the user terminal; and a means for the user terminal to display the feedback and advice and a means for recommending products appropriate to the emotional state of the user, taking into account past feedback history. This makes it possible to provide specific feedback based on the customer's emotions and improve customer satisfaction. Furthermore, the appropriate product recommendation function taking into account past feedback history enables more effective marketing.

[0861] A "user" is a person who uses the system and is the entity that inputs text data such as feedback and diaries.

[0862] The "text format" is a data format consisting of characters, and is used to represent input content such as diary entries and feedback.

[0863] "Feedback" refers to the textual input of a user's thoughts and opinions about their shopping experience in the virtual store and the products they purchased.

[0864] A "server" is a computer system that receives text data sent by a user, analyzes it, and generates feedback.

[0865] "Natural language analysis" is a technology that uses computers to process human language and understand its content, and includes sentiment analysis, keyword extraction, and theme extraction.

[0866] "Sentiment analysis" is a technique for determining the emotional state of text data, evaluating it as positive, negative, neutral, etc.

[0867] "Keyword extraction" is a technique for extracting important words and phrases from text data, which is useful for analyzing feedback.

[0868] "Theme extraction" is a technique for identifying major topics from text data and is used to understand the content of feedback.

[0869] "Advice" refers to specific suggestions or advice that the server provides to the user based on the analysis results.

[0870] "Past feedback history" is a record of feedback provided by the user to date, which is used for future analysis and product recommendations.

[0871] The "product recommendation function" is a function that suggests suitable products to the user, taking into account their past feedback history and current emotional state.

[0872] A "user terminal" is a device used by a user to input and display feedback and advice, and includes a smartphone, a personal computer, etc.

[0873] This invention is an AI support system for virtual stores that analyzes user input of diary entries and feedback, and provides appropriate feedback and product recommendations. This system mainly consists of the following elements:

[0874] 1. User Input

[0875] First, users access the virtual store using their smartphone, PC, or other device. They then enter text feedback about their shopping experience and the products they purchased. For example, they might enter feedback like, "I felt the battery life of my latest smartwatch was shorter than expected."

[0876] 2. Data Transmission

[0877] The device sends the entered text data to the server using a secure protocol such as HTTPS to protect the user's privacy.

[0878] 3. Receiving and analyzing text data

[0879] The server receives the text data and passes it to a natural language analysis engine, which analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction. Sentiment analysis is performed using libraries such as TextBlob.

[0880] 4. Generating feedback and advice

[0881] The server generates appropriate feedback and advice for the user based on the analysis results. For example, if the result of the sentiment analysis is determined to be "negative" and the keyword "battery life" is extracted, the server will generate the following feedback: "We apologize for the inconvenience. We will suggest a different model with a longer battery life."

[0882] 5. Sending feedback and advice

[0883] The server generates feedback and advice and sends it to the device using a secure protocol. The user device receives the feedback and advice and displays it to the user.

[0884] 6. Use of past feedback history

[0885] The server also takes into account past feedback history and provides a recommendation function to recommend products that suit the user's emotions and history, enabling more personalized advice and product suggestions.

[0886] By combining these elements, the system can provide specific feedback and appropriate product recommendations based on customer sentiment. In particular, natural language processing and sentiment analysis can be used to understand the content of user feedback and generate optimal advice.

[0887] Specific examples

[0888] Customer-provided feedback: "The battery life of my latest smartwatch is shorter than expected."

[0889] System feedback: Negative and says "We apologize for the inconvenience. We suggest you try another model with a longer battery life. Please check the link below."

[0890] Example prompt sentence:

[0891] "Customer provided feedback: The battery life of their latest smartwatch was shorter than expected. Generate a sentiment analysis and appropriate advice based on this feedback."

[0892] Thus, the present invention is directed to a system that efficiently analyzes user-entered feedback and provides specific suggestions for improving user satisfaction.

[0893] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0894] Step 1:

[0895] Users enter diary entries and feedback in text format.

[0896] Specifically, users access the virtual store's application using a smartphone or PC and enter their thoughts and opinions about their shopping experience and the products they purchased in text form, such as "I felt that the battery life of my latest smartwatch was shorter than expected."

[0897] Step 2:

[0898] The terminal transmits the input text data to the server.

[0899] Specifically, the terminal sends the user's feedback to the server using a secure protocol such as HTTPS, where the input is the text data entered by the user and the output is the data sent to the server.

[0900] Step 3:

[0901] The server receives the text data and passes it to a natural language analysis engine.

[0902] Specifically, the server receives data sent by the user and passes it to a natural language analysis engine (e.g., TextBlob library) for processing. The input is the text data sent by the user, and the output is the data passed to the analysis engine.

[0903] Step 4:

[0904] The natural language analysis engine analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction.

[0905] Specifically, the engine analyzes text data, determines whether the sentiment is positive or negative, and extracts key keywords and themes. The input is the text data passed from the server, and the output is the analysis results (e.g., sentiment analysis results, keywords, themes).

[0906] Step 5:

[0907] The server generates feedback and advice based on the analysis results.

[0908] Specifically, the server generates appropriate feedback and advice for the user based on the results of the sentiment analysis and the extracted keywords. For example, if the result is "negative" and the keyword "battery life" is extracted, the server generates the following feedback: "We apologize for the inconvenience. We will suggest a different model with a longer battery life." The input is the analysis result, and the output is the generated feedback and advice.

[0909] Step 6:

[0910] The server generates feedback and advice and sends it to the user's terminal.

[0911] Specifically, the server transmits the generated feedback and advice to the user terminal using a secure protocol. The input is the generated feedback and advice, and the output is transmission to the user terminal.

[0912] Step 7:

[0913] The user's device displays feedback and advice.

[0914] Specifically, the terminal displays the feedback and advice provided to the user so that the user can confirm the content. The input is the feedback and advice sent from the server, and the output is the display to the user.

[0915] Step 8:

[0916] The server takes into account past feedback history and recommends products that suit your emotional state.

[0917] Specifically, the server recommends products suitable for the user based on the user's past feedback history and current emotional state. For example, it proposes a "model with long battery life." The input is the past feedback history and the current analysis results, and the output is the recommended product information.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] [Third embodiment]

[0922] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0923] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0924] 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).

[0925] 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.

[0926] 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.

[0927] 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).

[0928] 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.

[0929] 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.

[0930] 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.

[0931] 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.

[0932] 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.

[0933] 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."

[0934] This invention is an AI support system that allows users to record their daily events and emotions and use that information to engage in effective self-reflection. Users enter their diary entries and notes, and the system analyzes their content and provides appropriate feedback and advice to promote self-improvement.

[0935] Explaining program processing in natural language

[0936] The system mainly consists of the following elements:

[0937] 1. User Input

[0938] Using a dedicated application, users can input the events, feelings, and thoughts of the day in text format. For example, they might input, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[0939] 2. Data Transmission

[0940] The device sends the entered text data to the server, using a secure protocol (e.g., HTTPS) to protect the user's privacy.

[0941] 3. Receiving and analyzing text data

[0942] The server receives the text data and passes it to a natural language analysis engine, which analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction.

[0943] For example, sentiment analysis classifies the message as "negative," and keyword extraction extracts "meeting," "boss," and "lack of preparation."

[0944] 4. Generating feedback and advice

[0945] Based on the analysis results, the server generates appropriate feedback and advice for the user, particularly suggesting areas for improvement and specific actions to take next time.

[0946] For example, the generated feedback is, "Let's think about what preparations you need to make to make the next meeting a success."

[0947] 5. Sending feedback and advice

[0948] The server generates feedback and advice and sends it to the device, using a secure protocol for data transmission.

[0949] 6. Viewing feedback and advice

[0950] The device displays feedback and advice to the user, who can then open the application and review the feedback provided.

[0951] 7. User Reflection

[0952] The user reflects on the feedback and creates a plan of action for the next time. For example, they might take specific steps to better prepare for the next meeting and improve communication with their boss.

[0953] Specific examples

[0954] Example 1: Work reflection

[0955] The user types, "My presentation today didn't go well. Maybe I didn't practice enough."

[0956] The terminal transmits this text data to the server.

[0957] The server receives the text data and passes it to a natural language analysis engine.

[0958] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "presentation" and "lack of practice."

[0959] The server generates feedback such as, "Let's think about what practice you need to do to make your next presentation a success."

[0960] The server sends the feedback to the terminal, which displays it to the user.

[0961] Users read the feedback and create their own practice plan.

[0962] Example 2: Private introspection

[0963] The user types, "I had lunch with a friend and something I said or did made things awkward. How can I improve in the future?"

[0964] The terminal transmits this text data to the server.

[0965] The server receives the text data and passes it to a natural language analysis engine.

[0966] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "friends," "lunch," and "awkward."

[0967] The server generates feedback such as, "Let's think specifically about what kind of behavior you should be careful about at your next lunch."

[0968] The server sends the feedback to the terminal, which displays it to the user.

[0969] Users read the feedback, reflect on their own actions, and think about specific improvements.

[0970] This concludes the description of the preferred embodiment of the present invention, which provides users with an effective tool for self-reflection, learning from everyday occurrences, and promoting self-improvement.

[0971] The processing flow will be explained below.

[0972] Step 1:

[0973] The user opens a dedicated application and enters the events, feelings, and thoughts of the day in text format. For example, they might enter, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[0974] Step 2:

[0975] The device sends the entered text data to the server. Data transmission is performed using a secure protocol (e.g., HTTPS) to protect the user's privacy.

[0976] Step 3:

[0977] The server receives the text data and passes it to a natural language analysis engine, which analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction.

[0978] Step 4:

[0979] A natural language analysis engine analyzes text data. First, it performs sentiment analysis to determine whether the input text is classified as "positive," "negative," or "neutral." Next, it performs keyword extraction to identify important words and phrases. Finally, it performs theme extraction to extract key topics and arguments.

[0980] Step 5:

[0981] The server generates appropriate feedback and advice for the user based on the analysis results. For example, if the result of the sentiment analysis is determined to be "negative" and the keywords "meeting," "boss," and "lack of preparation" are extracted, the server generates the following feedback: "Let's think about what preparations you need to make to ensure the success of your next meeting."

[0982] Step 6:

[0983] The server generates feedback and advice and sends it to the device using a secure protocol.

[0984] Step 7:

[0985] The device displays the received feedback and advice to the user. When the user opens the application, the provided feedback and advice is displayed on the screen.

[0986] Step 8:

[0987] Users can review the displayed feedback and advice and reflect on it in their own self-reflection activities, such as "I will prepare thoroughly for the next meeting and think of concrete steps to improve communication with my boss."

[0988] Through these steps, the system efficiently and effectively supports the user's self-reflection activities and helps the user to continue self-improvement.

[0989] Example 1

[0990] 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."

[0991] Previously, systems that allowed users to record their daily events and emotions and use them for self-reflection had problems such as insufficient analysis of the input information and vague feedback provided. Furthermore, there were also cases where user privacy was not adequately protected. This resulted in issues such as ineffective self-reflection and difficulty in promoting self-improvement.

[0992] 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.

[0993] In this invention, the server includes means for performing sentiment analysis, keyword extraction, and theme extraction of input text using a natural language analysis engine, means for generating feedback and advice using a generative AI model, and means for transmitting and receiving the input text data and the generated feedback and advice using a secure protocol. This allows users to receive specific feedback and advice based on detailed analysis results, enabling them to engage in self-reflection activities with peace of mind.

[0994] A "user" is a person who uses this system to enter diary entries and memos.

[0995] "Text data" is data in which diary entries and memos entered by the user are expressed in text format.

[0996] The "server" is a device that receives text data sent by users, analyzes it, and generates and sends feedback and advice.

[0997] A "natural language analysis engine" is a program or device that analyzes text data and extracts the emotions, keywords, and themes contained therein.

[0998] A "generative AI model" is an artificial intelligence model that generates feedback and advice based on analysis results.

[0999] "Feedback and advice" refers to specific instructions and advice provided to the user based on the analysis results.

[1000] A "secure protocol" is a set of rules for encrypting communication content and sending and receiving data safely.

[1001] "Sentiment analysis" is a technology that identifies a user's emotions from the content of text data and determines whether those emotions are positive or negative.

[1002] "Keyword extraction" is a technique for identifying important words and phrases from text data.

[1003] "Theme extraction" is a technique for identifying major topics or themes within text data.

[1004] A "user terminal" is a device on which a user enters a diary or memo and checks feedback and advice. Examples of such devices include smartphones and tablets.

[1005] "Reflection" is an activity in which the user reflects on themselves based on the feedback and advice provided and makes a plan of action for the next time.

[1006] This invention is an AI support system that allows users to record their daily events and emotions and use that information to engage in effective self-reflection. Users use a dedicated application to enter their diary and notes, and the server analyzes the content and provides appropriate feedback and advice to promote self-improvement.

[1007] Hardware and Software Configuration

[1008] The system consists of the following elements:

[1009] User device: A device such as a smartphone or tablet where users input text data and receive feedback.

[1010] Server: A device that analyzes text data and generates and sends feedback and advice.

[1011] Natural language analysis engines: such as Google Natural Language API and Amazon Comprehend, which perform sentiment analysis, keyword extraction, and theme extraction on text data.

[1012] Generative AI models: Artificial intelligence models for generating feedback and advice, such as OpenAI GPT-3

[1013] Secure protocols: Protocols for securely sending and receiving user data, such as HTTPS

[1014] Data processing and calculation

[1015] 1. User Input

[1016] Users open a dedicated application and enter the events and feelings of the day in text format. For example, they might enter, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[1017] 2. Data Transmission

[1018] The device sends the entered text data to the server using the HTTPS protocol, encrypting the data and protecting the user's privacy.

[1019] 3. Receiving and analyzing text data

[1020] The server receives the text data from the device and passes it to a natural language analysis engine for analysis. The analysis involves sentiment analysis, keyword extraction, and theme extraction. For example, sentiment analysis may determine a negative sentiment, while keyword extraction may identify "meeting," "boss," and "lack of preparation."

[1021] 4. Generating feedback and advice

[1022] Based on the analysis results, the server uses a generative AI model to generate appropriate feedback and advice. It is important that the generated feedback is specific and suggests actions that can be used next time. For example, feedback such as "Let's think about what preparations are needed to make the next meeting a success" could be generated.

[1023] 5. Sending feedback and advice

[1024] The generated feedback and advice is then sent to the user terminal, again using a secure protocol.

[1025] 6. Viewing feedback and advice

[1026] The user device displays the feedback and advice to the user, who then opens the application and checks the provided feedback.

[1027] 7. User Reflection

[1028] The user reflects on the feedback provided and creates a plan of action for the next time, for example, "I will be more prepared for the next meeting and think of concrete steps to improve communication with my boss."

[1029] Specific examples

[1030] Example 1: Work reflection

[1031] The user types, "My presentation didn't go well today. Maybe I didn't practice enough."

[1032] The terminal transmits this text data to the server.

[1033] The server receives the text data and passes it to a natural language analysis engine.

[1034] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "presentation" and "lack of practice."

[1035] The server generates feedback such as, "Let's think about what practice you need to do to make your next presentation a success."

[1036] The server sends the feedback to the terminal, which displays it to the user.

[1037] Users read the feedback and create their own practice plan.

[1038] Example 2: Private introspection

[1039] A user types, "I had lunch with a friend and something I said or did made things awkward. How can I improve in the future?"

[1040] The terminal transmits this text data to the server.

[1041] The server receives the text data and passes it to a natural language analysis engine.

[1042] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "friends," "lunch," and "awkward."

[1043] The server generates feedback such as, "Let's think specifically about what kind of behavior we should be careful about at our next lunch."

[1044] The server sends the feedback to the terminal, which displays it to the user.

[1045] Users read the feedback, reflect on their own actions, and think about specific improvements.

[1046] This concludes the description of the preferred embodiment of the present invention. The system provides an effective tool for users to engage in self-reflection, learn from everyday occurrences, and promote self-improvement.

[1047] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1048] Step 1:

[1049] The user provides input.

[1050] Input: The user opens a dedicated application and enters the events and feelings of the day in text format.

[1051] Specific behavior: The user launches the application, enters daily events and emotions into the text input field, and presses the "Send" button.

[1052] Output: The input text data.

[1053] Step 2:

[1054] The terminal transmits the input text data to the server.

[1055] Input: Text data entered by the user.

[1056] Specific operation: The device sends the user's input to the server using the HTTPS protocol.

[1057] Output: The encrypted text data sent to the server.

[1058] Step 3:

[1059] The server passes the received text data to a natural language analysis engine.

[1060] Input: Encrypted text data.

[1061] What happens: The server receives an HTTPS request and passes the text data to a natural language analysis engine.

[1062] Output: Text data passed to the natural language analysis engine.

[1063] Step 4:

[1064] A natural language analysis engine analyzes the text data.

[1065] Input: Text data passed to the natural language analysis engine.

[1066] Specific operation: The natural language analysis engine performs sentiment analysis, keyword extraction, and theme extraction. For example, sentiment analysis may determine a message as "negative," and keywords such as "meeting," "boss," and "lack of preparation" may be extracted.

[1067] Output: Analysis results (sentiment, keywords, themes).

[1068] Step 5:

[1069] The server generates feedback and advice using a generative AI model based on the analysis results.

[1070] Input: Analysis results (sentiments, keywords, themes).

[1071] Specific operation: The server inputs prompts into the generative AI model and generates appropriate feedback and advice. For example, feedback such as "Let's think about what preparations you need to make to ensure the success of your next meeting" is generated.

[1072] Output: Generated feedback and advice.

[1073] Step 6:

[1074] The server sends the generated feedback and advice to the device.

[1075] Input: Generated feedback and advice.

[1076] Specific operation: The server sends the feedback to the device using the HTTPS protocol.

[1077] Output: Feedback and advice sent to the device.

[1078] Step 7:

[1079] The device will provide feedback and advice.

[1080] Input: Feedback and advice sent to your device.

[1081] Specific behavior: The device decodes the received feedback and displays it in the application's user interface.

[1082] Output: Feedback and advice displayed to the user.

[1083] Step 8:

[1084] The user performs a self-reflection activity.

[1085] Input: Any feedback or advice you see.

[1086] Specific Action: The user reads the feedback and creates a plan of action for the next time. For example, the user may perform a self-reflection activity such as, "I will think of specific steps to be more prepared for the next meeting and improve communication with my boss."

[1087] Output: A concrete action plan.

[1088] (Application example 1)

[1089] 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."

[1090] Conventional AI support systems require users to input events and emotions in text format, and do not provide voice-based diary entries or visual feedback using wearable devices. This makes it difficult for users to easily engage in self-reflection, and there is a risk that small everyday events or emotions may be overlooked. Furthermore, conventional systems are limited to text input, which makes them lack immediacy and convenience.

[1091] 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.

[1092] In this invention, the server includes a means for a user to input a diary or memo in text format, a means for transmitting the input text data to the server, a means for performing natural language analysis, a means for recording a diary or memo by voice input, a means for converting the voice input into text format, and a means for visually displaying feedback via a wearable device such as smart glasses. This allows the user to easily record a diary or memo through voice input and immediately receive appropriate feedback from the analysis results. Furthermore, visual feedback via the wearable device promotes introspection.

[1093] "User" refers to a user who uses this system to enter diary entries and memos and receive feedback and advice.

[1094] "Text format" is a data format based on character information entered by the user.

[1095] A "server" is a computer system that receives data sent by a user, analyzes it, generates feedback, and sends it back to the user.

[1096] "Natural language analysis" is the act of analyzing text data entered by a user using natural language processing technology to perform sentiment analysis, keyword extraction, etc.

[1097] "Feedback" refers to advice and improvement suggestions provided to users based on the analysis results.

[1098] "Advice" refers to specific improvement measures or suggested actions provided to users based on the analysis results.

[1099] "User terminal" means a device used by a user to operate the system and receive feedback and advice.

[1100] "Voice input" is a method in which a user inputs information by speaking.

[1101] "Convert to text" is the process of converting speech input into text data.

[1102] "Smart glasses" are wearable devices worn by users to present visual information.

[1103] A "wearable device" is a computing device that can be worn by a user.

[1104] "Visually displaying" means showing information to the user through the device's display.

[1105] This invention is an advanced AI support system that records the user's daily events and emotions and encourages introspection based on that data. Specific embodiments for realizing this system are described below.

[1106] The system components are:

[1107] 1. User terminals: including wearable devices such as smart glasses and smartphones.

[1108] 2. Voice input system: This system allows users to input daily events and emotions by voice and converts them into text. Specifically, it uses voice recognition APIs such as Google Cloud Speech-to-Text and IBM Watson.

[1109] 3. Natural Language Analysis System: A system that analyzes text data. It performs processes such as sentiment analysis, keyword extraction, and theme extraction. Specifically, it uses a sentiment analysis model that utilizes the Transformers pipeline.

[1110] 4. Feedback generation system: A system that generates appropriate feedback and advice for users based on the analysis results.

[1111] 5. Server: This is the central processing unit that performs the analysis and generation mentioned above, and receives data from users and processes and analyzes it.

[1112] Program processing overview

[1113] 1. User Input:

[1114] Using a wearable device such as smart glasses, users can input their day's events and emotions by voice, for example, "Today was a bad day at work."

[1115] 2. Data transmission:

[1116] The user terminal converts the voice data into text and sends it to the server via a secure protocol (e.g., HTTPS).

[1117] 3. Receiving and analyzing text data:

[1118] The server receives the text data and passes it to a natural language analysis system, which performs sentiment analysis, determining, for example, that the sentiment is "negative," and extracts keywords such as "work" and "didn't go well."

[1119] 4. Generate feedback and advice:

[1120] The server generates feedback based on the analysis results, creating advice such as, "Think about what preparations you need to make the next time you work."

[1121] 5. Sending and Viewing Feedback:

[1122] The server transmits the generated feedback to the user's device, which visually displays the feedback through the smart glasses.

[1123] Specific examples

[1124] Work reflection

[1125] A user speaks, "Today's meeting went wrong. Maybe I wasn't prepared enough."

[1126] The user terminal converts the voice data into text and sends it to the server.

[1127] The server receives the text data and performs sentiment analysis and keyword extraction.

[1128] The server generates feedback saying, "Let's think about how to prepare for the meeting."

[1129] Feedback is displayed to the user through the smart glasses.

[1130] Private introspection

[1131] The user speaks, "I said too much at dinner with a friend. How can I improve?"

[1132] The user terminal converts the voice data into text and sends it to the server.

[1133] The server receives the text data and performs sentiment analysis and keyword extraction.

[1134] The server generates feedback such as, "Let's think about what we should pay attention to the next time we dine here."

[1135] Feedback is displayed to the user through the smart glasses.

[1136] Prompt Sentence Examples

[1137] Here are some example prompts to encourage users to speak:

[1138] "How was your day today?"

[1139] "Tell me what happened today and how you felt."

[1140] "Is there anything you'd like to improve?"

[1141] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1142] Step 1:

[1143] Using a wearable device such as smart glasses, users input their day's events and emotions through voice, for example, "Today was a bad day at work."

[1144] Step 2:

[1145] The device receives voice input and converts it to text using a speech recognition system (for example, Google Cloud Speech-to-Text or IBM Watson). The input is voice data, and the output is text data. This conversion processes the user's voice into text information.

[1146] Step 3:

[1147] The terminal sends the converted text data to the server via a secure protocol (e.g., HTTPS). The input is the text data, and the output is the transmission completion status. Encryption technology is used for data transmission for security.

[1148] Step 4:

[1149] The server receives the text data and passes it to the natural language analysis system. The input is text data, and the received data is subjected to analysis. Here, the received data is added to a queue to be analyzed.

[1150] Step 5:

[1151] The server's natural language analysis system performs sentiment analysis, keyword extraction, and theme extraction on the text data. This uses a sentiment analysis model that utilizes a Transformers pipeline. The input is text data, and the output is a sentiment determination result and a keyword list. At this stage, the data is sentiment-tagged and key keywords are extracted.

[1152] Step 6:

[1153] Based on the analysis results, the server generates appropriate feedback and advice. For example, it creates advice such as, "Think about what preparations you need to make the next time you work." The input is the emotion determination result and a list of keywords, and the output is a feedback message. In this process, a generative AI model is used to generate appropriate advice for the user.

[1154] Step 7:

[1155] The server securely transmits the generated feedback message to the user terminal. The input is the feedback message, and the output is a transmission completion status. The transmission means uses a secure protocol.

[1156] Step 8:

[1157] The terminal visually displays the received feedback message to the user via a wearable device such as smart glasses. The input is the feedback message, and the output is the feedback content displayed on the glasses' display. Here, the user can confirm which specific areas need improvement.

[1158] The above is the flow of program processing and specific operations of the system that realizes the application example.

[1159] 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.

[1160] This invention is an AI support system that allows users to record their daily events and emotions and use that information to engage in effective self-reflection. The system analyzes the contents of diary entries and notes, and provides appropriate feedback and advice to promote self-improvement. Furthermore, by incorporating an emotion engine, the system can recognize the user's emotions and provide more appropriate feedback.

[1161] Explaining program processing in natural language

[1162] The system mainly consists of the following elements:

[1163] 1. User Input

[1164] Using a dedicated application, users can input the events, feelings, and thoughts of the day in text format. For example, they might input, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[1165] 2. Data Transmission

[1166] The device sends the entered text data to the server. Data transmission is performed using a secure protocol (e.g., HTTPS) to protect the user's privacy.

[1167] 3. Receiving and analyzing text data

[1168] The server receives the text data and passes it to a natural language analysis engine, which analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction.

[1169] The emotion engine also analyzes text data and recognizes the user's emotions.

[1170] 4. Generating feedback and advice

[1171] The server generates appropriate feedback and advice for the user based on the analysis results. For example, if the result of the sentiment analysis is determined to be "negative" and the keywords "meeting," "boss," and "lack of preparation" are extracted, the server generates the following feedback: "Let's think about what preparations you need to make to ensure the success of your next meeting."

[1172] The emotion engine may also tailor the feedback depending on the user's emotional state, for example, generating feedback such as "Think of concrete steps you can take to feel more positive about your upcoming meeting."

[1173] 5. Sending feedback and advice

[1174] The server generates feedback and advice and sends it to the device using a secure protocol.

[1175] 6. Viewing feedback and advice

[1176] The device displays feedback and advice to the user. When the user opens the application, the provided feedback and advice is displayed on the screen.

[1177] 7. User Reflection

[1178] Users can review the displayed feedback and advice and reflect on it in their own self-reflection activities, such as "I will prepare thoroughly for the next meeting and think of concrete steps to improve communication with my boss."

[1179] Specific examples

[1180] Example 1: Work reflection

[1181] The user types, "My presentation today didn't go well. Maybe I didn't practice enough."

[1182] The terminal transmits this text data to the server.

[1183] The server receives the text data and passes it to a natural language analysis engine.

[1184] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "presentation" and "lack of practice."

[1185] The emotion engine recognizes the user's emotional state and generates feedback accordingly.

[1186] The server generates feedback such as, "Let's think about what practice you need to do to make your next presentation a success."

[1187] The server sends the feedback to the terminal, which displays it to the user.

[1188] Users read the feedback and create their own practice plan.

[1189] Example 2: Private introspection

[1190] The user types, "I had lunch with a friend and something I said or did made things awkward. How can I improve in the future?"

[1191] The terminal transmits this text data to the server.

[1192] The server receives the text data and passes it to a natural language analysis engine.

[1193] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "friends," "lunch," and "awkward."

[1194] The emotion engine recognizes the user's emotional state and generates feedback accordingly.

[1195] The server generates feedback such as, "Let's think specifically about what kind of behavior you should be careful about at your next lunch."

[1196] The server sends the feedback to the terminal, which displays it to the user.

[1197] Users read the feedback, reflect on their own actions, and think about specific improvements.

[1198] The above is an embodiment of the present invention. This system is an efficient tool for users to engage in self-reflection, learn from everyday events, and promote self-improvement. In addition, by combining it with an emotion engine, it can provide appropriate support according to the user's emotions.

[1199] The processing flow will be explained below.

[1200] Step 1:

[1201] The user opens a dedicated application and enters the events, feelings, and thoughts of the day in text format. For example, they might enter, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[1202] Step 2:

[1203] The device sends the entered text data to the server. Data transmission is performed using a secure protocol (e.g., HTTPS) to protect the user's privacy.

[1204] Step 3:

[1205] The server receives the text data and passes it to a natural language analysis engine, which analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction.

[1206] Step 4:

[1207] A natural language analysis engine analyzes text data. First, it performs sentiment analysis to determine whether the input text is classified as "positive," "negative," or "neutral." Next, it performs keyword extraction to identify important words and phrases. Finally, it performs theme extraction to extract key topics and arguments.

[1208] Step 5:

[1209] The emotion engine analyzes the text data to recognize the user's emotional state. This analysis involves detailed classification of emotions sensed from the user's input text and identifies the current emotional state.

[1210] Step 6:

[1211] The server generates appropriate feedback and advice for the user based on the analysis results. For example, if the result of the sentiment analysis is determined to be "negative" and the keywords "meeting," "boss," and "lack of preparation" are extracted, the server generates feedback such as "Let's think about what kind of preparations you need to make to ensure the success of your next meeting." It also generates feedback such as "Let's think about specific steps to have a positive attitude toward future meetings," reflecting the results of the emotion engine.

[1212] Step 7:

[1213] The server generates feedback and advice and sends it to the device using a secure protocol.

[1214] Step 8:

[1215] The device displays the received feedback and advice to the user. When the user opens the application, the provided feedback and advice is displayed on the screen.

[1216] Step 9:

[1217] Users can review the displayed feedback and advice and reflect on it in their own self-reflection activities, such as "I will prepare thoroughly for the next meeting and think of concrete steps to improve communication with my boss."

[1218] Specific examples

[1219] Example 1: Work reflection

[1220] The user types, "My presentation today didn't go well. Maybe I didn't practice enough."

[1221] The terminal transmits this text data to the server.

[1222] The server receives the text data and passes it to a natural language analysis engine.

[1223] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "presentation" and "lack of practice."

[1224] The emotion engine recognizes the user's emotional state and generates feedback accordingly.

[1225] The server generates feedback such as, "Let's think about what practice you need to do to make your next presentation a success."

[1226] The server sends the feedback to the terminal, which displays it to the user.

[1227] Users read the feedback and create their own practice plan.

[1228] Example 2: Private introspection

[1229] The user types, "I had lunch with a friend and something I said or did made things awkward. How can I improve in the future?"

[1230] The terminal transmits this text data to the server.

[1231] The server receives the text data and passes it to a natural language analysis engine.

[1232] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "friends," "lunch," and "awkward."

[1233] The emotion engine recognizes the user's emotional state and generates feedback accordingly.

[1234] The server generates feedback such as, "Let's think specifically about what kind of behavior you should be careful about at your next lunch."

[1235] The server sends the feedback to the terminal, which displays it to the user.

[1236] Users read the feedback, reflect on their own actions, and think about specific improvements.

[1237] This allows users to self-reflect, learn from everyday events, and promote self-improvement. In addition, by combining it with an emotion engine, appropriate support is provided according to the user's emotions.

[1238] Example 2

[1239] 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."

[1240] While systems aimed at self-reflection and self-improvement exist, providing appropriate feedback based on the user's emotional state has been difficult. From a security perspective, ensuring the safety of data transmission has also been an issue. Furthermore, rather than simple feedback, users need messages that encourage specific actions and self-reflection steps, but there has been a lack of systems that address this need.

[1241] 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.

[1242] In this invention, the server includes means for a user to input a diary or memo in text format, means for transmitting the input text data to the server, means for the server to receive the text data and perform natural language analysis, means for generating feedback and advice based on the analysis results, means for transmitting the generated feedback and advice to the user terminal, means for the user terminal to display the feedback and advice, means for transmitting and receiving data using a secure protocol, means for analyzing the user's emotions from the input text using an emotion recognition engine, means for generating feedback and advice based on the analysis results using a generative AI model, and means for adjusting the generated feedback and advice according to the user's emotional state. This makes it possible to provide appropriate feedback according to the user's emotions, ensure secure transmission and reception of data, and further encourage the user to take specific introspection and action steps.

[1243] "User input" is a means by which system users use a dedicated application to input the events, feelings, and thoughts of the day in text format.

[1244] "Data transmission" refers to the act and means by which a terminal transmits input text data to a server, and data security is ensured by using a secure protocol such as HTTPS.

[1245] "Receiving text data" refers to the act and means by which the server receives text data sent from the terminal.

[1246] "Natural language analysis" refers to the techniques and means of analyzing input text data using techniques such as sentiment analysis, keyword extraction, and theme extraction.

[1247] An "emotion recognition engine" is a means of recognizing a user's emotional state from text data and analyzing emotions such as positive or negative.

[1248] "Generation of feedback and advice" is a means of generating specific feedback and advice for the user based on the results of natural language analysis and emotion recognition.

[1249] A "generative AI model" is an artificial intelligence model used to generate feedback and advice based on analytical results.

[1250] A "secure data transmission protocol" is a communication method used to encrypt and safely transmit and receive data, such as HTTPS.

[1251] "Feedback and advice display" refers to a means for visually displaying the feedback and advice received by the user terminal to the user.

[1252] "Reflection activities" are activities in which users review feedback and advice, review their own actions and thoughts, and consider ways to improve.

[1253] This invention is a system that allows users to record daily events and emotions and to use that information to effectively reflect on themselves. Specific steps for implementing this system are described below.

[1254] The system mainly consists of the following elements:

[1255] 1. User Input

[1256] Using a dedicated application, users can input the events, feelings, and thoughts of the day in text format. For example, they might input, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[1257] 2. Data Transmission

[1258] The device sends the entered text data to the server using a secure protocol such as HTTPS, which encrypts the data during the process and protects the user's privacy.

[1259] 3. Receiving and analyzing text data

[1260] Once the server receives the text data, it passes it to a natural language analysis engine (e.g., Google Cloud Natural Language API or IBM Watson NLU). The analysis engine analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction. Additionally, it uses an emotion recognition engine (e.g., the undisclosed TeNTEN emotion recognition API) to recognize the user's emotional state.

[1261] 4. Generating feedback and advice

[1262] The server generates appropriate feedback and advice for the user based on the analysis results. A trained generative AI model (e.g., GPT-3) is used here. For example, if the result of the sentiment analysis is determined to be "negative," the server generates feedback such as, "Let's think about what preparations you need to make to ensure the success of your next meeting." The emotion recognition engine further refines the feedback according to the user's emotional state, providing the user with the most appropriate advice.

[1263] 5. Sending feedback and advice

[1264] Server-generated feedback and advice is sent to the device using HTTPS.

[1265] 6. Viewing feedback and advice

[1266] The device displays feedback and advice to the user. When the user opens the application, the provided feedback and advice is displayed on the screen.

[1267] 7. User Reflection

[1268] Users can review the displayed feedback and advice and reflect on it in their own self-reflection activities, such as "I will prepare thoroughly for the next meeting and think of concrete steps to improve communication with my boss."

[1269] Specific examples

[1270] Example 1: Work reflection

[1271] The user types, "My presentation today didn't go well. Maybe I didn't practice enough."

[1272] The terminal transmits this text data to the server.

[1273] The server receives the text data and passes it to a natural language analysis engine.

[1274] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "presentation" and "lack of practice."

[1275] An emotion recognition engine recognizes the user's emotional state and generates feedback accordingly.

[1276] The server generates feedback such as, "Let's think about what practice you need to do to make your next presentation a success."

[1277] The server sends the feedback to the terminal, which displays it to the user.

[1278] Users read the feedback and create their own practice plan.

[1279] Example 2: Private introspection

[1280] The user types, "I had lunch with a friend and something I said or did made things awkward. How can I improve in the future?"

[1281] The terminal transmits this text data to the server.

[1282] The server receives the text data and passes it to a natural language analysis engine.

[1283] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "friends," "lunch," and "awkward."

[1284] An emotion recognition engine recognizes the user's emotional state and generates feedback accordingly.

[1285] The server generates feedback such as, "Let's think specifically about what kind of behavior you should be careful about at your next lunch."

[1286] The server sends the feedback to the terminal, which displays it to the user.

[1287] Users read the feedback, reflect on their own actions, and think about specific improvements.

[1288] This system is an efficient tool for users to engage in self-reflection, learn from everyday events, and promote self-improvement. It also combines an emotion engine to provide appropriate support based on the user's emotions. By using secure protocols, user privacy is also protected.

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

[1290] Step 1: User Input

[1291] Using a dedicated application, users input the events, feelings, and thoughts of the day in text format. This application runs on smartphones and PCs and provides an easily accessible user interface. For example, a user can input, "Today's meeting with my boss didn't go well. It may be because I wasn't prepared enough." This input data is saved in text format.

[1292] Step 2: Send data

[1293] The terminal sends the text data entered by the user to the server. A secure protocol such as HTTPS is used for transmission, and the data is encrypted before being sent. This protects the user's privacy. The input is the user's text data, and the output is a notification to the server that transmission has been completed.

[1294] Step 3: Receiving and parsing text data

[1295] The server decrypts the received text data and sends it to a natural language analysis engine. The natural language analysis engine analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction. For example, it uses Google Cloud Natural Language API or IBM Watson NLU. The input to the analysis engine is the text data sent by the user, and the output is the emotional state, extracted keywords, and themes. It also analyzes the user's emotions using an emotion recognition engine. Specifically, the emotion recognition API assigns emotional labels such as "positive" or "negative."

[1296] Step 4: Generate feedback and advice

[1297] The server generates appropriate feedback and advice based on the analysis results obtained from the natural language analysis engine and emotion recognition engine. A generative AI model (e.g., GPT-3) is used for this generation. The input is the analysis result, and the output is specific feedback or advice text. For example, if the result of the sentiment analysis is "negative," feedback such as "Let's think about what preparations are needed to make the next meeting a success" is generated. The generative AI model generates the feedback based on a prompt sentence. An example of a prompt sentence is, "What concerns does the user have about the meeting?"

[1298] Step 5: Send feedback and advice

[1299] The server sends the generated feedback and advice to the device. The data is encrypted and sent using a secure protocol (e.g., HTTPS). The input is the feedback or advice text, and the output is a notification to the device that transmission has been completed.

[1300] Step 6: View feedback and advice

[1301] The terminal decrypts the received feedback and advice and displays it to the user on the application. When the user opens the application, the provided feedback and advice is displayed on the screen. The input is the text of the feedback and advice received from the server, and the output is what is displayed to the user.

[1302] Step 7: User reflection

[1303] The user can review the displayed feedback and advice and reflect it in their own self-reflection activities. For example, they can take action such as writing down specific preparation items for the next meeting or taking notes on key points to communicate with their boss. The input is the text of the feedback and advice, and the output is the user's specific action plan. By inputting the results of this self-reflection activity back into the application at a later date, continuous self-improvement is possible.

[1304] (Application example 2)

[1305] 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."

[1306] Existing virtual stores do not adequately collect or analyze customer feedback, making it difficult to provide specific product suggestions or improvement proposals based on customer sentiment. They also lacked a function to recommend appropriate products that took past feedback history into account. For these reasons, a system was needed to improve customer satisfaction and achieve more effective marketing.

[1307] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1308] In this invention, the server includes: a means for a user to input diary entries and feedback in text format; a means for transmitting the input text data to the server; a means for the server to receive the text data and perform natural language analysis; a means for generating feedback and advice based on the analysis results; a means for transmitting the generated feedback and advice to the user terminal; and a means for the user terminal to display the feedback and advice and a means for recommending products appropriate to the emotional state of the user, taking into account past feedback history. This makes it possible to provide specific feedback based on the customer's emotions and improve customer satisfaction. Furthermore, the appropriate product recommendation function taking into account past feedback history enables more effective marketing.

[1309] A "user" is a person who uses the system and is the entity that inputs text data such as feedback and diaries.

[1310] The "text format" is a data format consisting of characters, and is used to represent input content such as diary entries and feedback.

[1311] "Feedback" refers to the textual input of a user's thoughts and opinions about their shopping experience in the virtual store and the products they purchased.

[1312] A "server" is a computer system that receives text data sent by a user, analyzes it, and generates feedback.

[1313] "Natural language analysis" is a technology that uses computers to process human language and understand its content, and includes sentiment analysis, keyword extraction, and theme extraction.

[1314] "Sentiment analysis" is a technique for determining the emotional state of text data, evaluating it as positive, negative, neutral, etc.

[1315] "Keyword extraction" is a technique for extracting important words and phrases from text data, which is useful for analyzing feedback.

[1316] "Theme extraction" is a technique for identifying major topics from text data and is used to understand the content of feedback.

[1317] "Advice" refers to specific suggestions or advice that the server provides to the user based on the analysis results.

[1318] "Past feedback history" is a record of feedback provided by the user to date, which is used for future analysis and product recommendations.

[1319] The "product recommendation function" is a function that suggests suitable products to the user, taking into account their past feedback history and current emotional state.

[1320] A "user terminal" is a device used by a user to input and display feedback and advice, and includes a smartphone, a personal computer, etc.

[1321] This invention is an AI support system for virtual stores that analyzes user input of diary entries and feedback, and provides appropriate feedback and product recommendations. This system mainly consists of the following elements:

[1322] 1. User Input

[1323] First, users access the virtual store using their smartphone, PC, or other device. They then enter text feedback about their shopping experience and the products they purchased. For example, they might enter feedback like, "I felt the battery life of my latest smartwatch was shorter than expected."

[1324] 2. Data Transmission

[1325] The device sends the entered text data to the server using a secure protocol such as HTTPS to protect the user's privacy.

[1326] 3. Receiving and analyzing text data

[1327] The server receives the text data and passes it to a natural language analysis engine, which analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction. Sentiment analysis is performed using libraries such as TextBlob.

[1328] 4. Generating feedback and advice

[1329] The server generates appropriate feedback and advice for the user based on the analysis results. For example, if the result of the sentiment analysis is determined to be "negative" and the keyword "battery life" is extracted, the server will generate the following feedback: "We apologize for the inconvenience. We will suggest a different model with a longer battery life."

[1330] 5. Sending feedback and advice

[1331] The server generates feedback and advice and sends it to the device using a secure protocol. The user device receives the feedback and advice and displays it to the user.

[1332] 6. Use of past feedback history

[1333] The server also takes into account past feedback history and provides a recommendation function to recommend products that suit the user's emotions and history, enabling more personalized advice and product suggestions.

[1334] By combining these elements, the system can provide specific feedback and appropriate product recommendations based on customer sentiment. In particular, natural language processing and sentiment analysis can be used to understand the content of user feedback and generate optimal advice.

[1335] Specific examples

[1336] Customer-provided feedback: "The battery life of my latest smartwatch is shorter than expected."

[1337] System feedback: Negative and says "We apologize for the inconvenience. We suggest you try another model with a longer battery life. Please check the link below."

[1338] Example prompt sentence:

[1339] "Customer provided feedback: The battery life of their latest smartwatch was shorter than expected. Generate a sentiment analysis and appropriate advice based on this feedback."

[1340] Thus, the present invention is directed to a system that efficiently analyzes user-entered feedback and provides specific suggestions for improving user satisfaction.

[1341] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1342] Step 1:

[1343] Users enter diary entries and feedback in text format.

[1344] Specifically, users access the virtual store's application using a smartphone or PC and enter their thoughts and opinions about their shopping experience and the products they purchased in text form, such as "I felt that the battery life of my latest smartwatch was shorter than expected."

[1345] Step 2:

[1346] The terminal transmits the input text data to the server.

[1347] Specifically, the terminal sends the user's feedback to the server using a secure protocol such as HTTPS, where the input is the text data entered by the user and the output is the data sent to the server.

[1348] Step 3:

[1349] The server receives the text data and passes it to a natural language analysis engine.

[1350] Specifically, the server receives data sent by the user and passes it to a natural language analysis engine (e.g., TextBlob library) for processing. The input is the text data sent by the user, and the output is the data passed to the analysis engine.

[1351] Step 4:

[1352] The natural language analysis engine analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction.

[1353] Specifically, the engine analyzes text data, determines whether the sentiment is positive or negative, and extracts key keywords and themes. The input is the text data passed from the server, and the output is the analysis results (e.g., sentiment analysis results, keywords, themes).

[1354] Step 5:

[1355] The server generates feedback and advice based on the analysis results.

[1356] Specifically, the server generates appropriate feedback and advice for the user based on the results of the sentiment analysis and the extracted keywords. For example, if the result is "negative" and the keyword "battery life" is extracted, the server generates the following feedback: "We apologize for the inconvenience. We will suggest a different model with a longer battery life." The input is the analysis result, and the output is the generated feedback and advice.

[1357] Step 6:

[1358] The server generates feedback and advice and sends it to the user's terminal.

[1359] Specifically, the server transmits the generated feedback and advice to the user terminal using a secure protocol. The input is the generated feedback and advice, and the output is transmission to the user terminal.

[1360] Step 7:

[1361] The user's device displays feedback and advice.

[1362] Specifically, the terminal displays the feedback and advice provided to the user so that the user can confirm the content. The input is the feedback and advice sent from the server, and the output is the display to the user.

[1363] Step 8:

[1364] The server takes into account past feedback history and recommends products that suit your emotional state.

[1365] Specifically, the server recommends products suitable for the user based on the user's past feedback history and current emotional state. For example, it proposes a "model with long battery life." The input is the past feedback history and the current analysis results, and the output is the recommended product information.

[1366] 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.

[1367] 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.

[1368] 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.

[1369] [Fourth embodiment]

[1370] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1371] 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.

[1372] 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).

[1373] 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.

[1374] 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.

[1375] 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).

[1376] 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.

[1377] 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.

[1378] 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.

[1379] 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.

[1380] 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.

[1381] 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.

[1382] 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."

[1383] This invention is an AI support system that allows users to record their daily events and emotions and use that information to engage in effective self-reflection. Users enter their diary entries and notes, and the system analyzes their content and provides appropriate feedback and advice to promote self-improvement.

[1384] Explaining program processing in natural language

[1385] The system mainly consists of the following elements:

[1386] 1. User Input

[1387] Using a dedicated application, users can input the events, feelings, and thoughts of the day in text format. For example, they might input, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[1388] 2. Data Transmission

[1389] The device sends the entered text data to the server, using a secure protocol (e.g., HTTPS) to protect the user's privacy.

[1390] 3. Receiving and analyzing text data

[1391] The server receives the text data and passes it to a natural language analysis engine, which analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction.

[1392] For example, sentiment analysis classifies the message as "negative," and keyword extraction extracts "meeting," "boss," and "lack of preparation."

[1393] 4. Generating feedback and advice

[1394] Based on the analysis results, the server generates appropriate feedback and advice for the user, particularly suggesting areas for improvement and specific actions to take next time.

[1395] For example, the generated feedback is, "Let's think about what preparations you need to make to make the next meeting a success."

[1396] 5. Sending feedback and advice

[1397] The server generates feedback and advice and sends it to the device, using a secure protocol for data transmission.

[1398] 6. Viewing feedback and advice

[1399] The device displays feedback and advice to the user, who can then open the application and review the feedback provided.

[1400] 7. User Reflection

[1401] The user reflects on the feedback and creates a plan of action for the next time. For example, they might take specific steps to better prepare for the next meeting and improve communication with their boss.

[1402] Specific examples

[1403] Example 1: Work reflection

[1404] The user types, "My presentation today didn't go well. Maybe I didn't practice enough."

[1405] The terminal transmits this text data to the server.

[1406] The server receives the text data and passes it to a natural language analysis engine.

[1407] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "presentation" and "lack of practice."

[1408] The server generates feedback such as, "Let's think about what practice you need to do to make your next presentation a success."

[1409] The server sends the feedback to the terminal, which displays it to the user.

[1410] Users read the feedback and create their own practice plan.

[1411] Example 2: Private introspection

[1412] The user types, "I had lunch with a friend and something I said or did made things awkward. How can I improve in the future?"

[1413] The terminal transmits this text data to the server.

[1414] The server receives the text data and passes it to a natural language analysis engine.

[1415] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "friends," "lunch," and "awkward."

[1416] The server generates feedback such as, "Let's think specifically about what kind of behavior you should be careful about at your next lunch."

[1417] The server sends the feedback to the terminal, which displays it to the user.

[1418] Users read the feedback, reflect on their own actions, and think about specific improvements.

[1419] This concludes the description of the preferred embodiment of the present invention, which provides users with an effective tool for self-reflection, learning from everyday occurrences, and promoting self-improvement.

[1420] The processing flow will be explained below.

[1421] Step 1:

[1422] The user opens a dedicated application and enters the events, feelings, and thoughts of the day in text format. For example, they might enter, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[1423] Step 2:

[1424] The device sends the entered text data to the server. Data transmission is performed using a secure protocol (e.g., HTTPS) to protect the user's privacy.

[1425] Step 3:

[1426] The server receives the text data and passes it to a natural language analysis engine, which analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction.

[1427] Step 4:

[1428] A natural language analysis engine analyzes text data. First, it performs sentiment analysis to determine whether the input text is classified as "positive," "negative," or "neutral." Next, it performs keyword extraction to identify important words and phrases. Finally, it performs theme extraction to extract key topics and arguments.

[1429] Step 5:

[1430] The server generates appropriate feedback and advice for the user based on the analysis results. For example, if the result of the sentiment analysis is determined to be "negative" and the keywords "meeting," "boss," and "lack of preparation" are extracted, the server generates the following feedback: "Let's think about what preparations you need to make to ensure the success of your next meeting."

[1431] Step 6:

[1432] The server generates feedback and advice and sends it to the device using a secure protocol.

[1433] Step 7:

[1434] The device displays the received feedback and advice to the user. When the user opens the application, the provided feedback and advice is displayed on the screen.

[1435] Step 8:

[1436] Users can review the displayed feedback and advice and reflect on it in their own self-reflection activities, such as "I will prepare thoroughly for the next meeting and think of concrete steps to improve communication with my boss."

[1437] Through these steps, the system efficiently and effectively supports the user's self-reflection activities and helps the user to continue self-improvement.

[1438] Example 1

[1439] 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."

[1440] Previously, systems that allowed users to record their daily events and emotions and use them for self-reflection had problems such as insufficient analysis of the input information and vague feedback provided. Furthermore, there were also cases where user privacy was not adequately protected. This resulted in issues such as ineffective self-reflection and difficulty in promoting self-improvement.

[1441] 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.

[1442] In this invention, the server includes means for performing sentiment analysis, keyword extraction, and theme extraction of input text using a natural language analysis engine, means for generating feedback and advice using a generative AI model, and means for transmitting and receiving the input text data and the generated feedback and advice using a secure protocol. This allows users to receive specific feedback and advice based on detailed analysis results, enabling them to engage in self-reflection activities with peace of mind.

[1443] A "user" is a person who uses this system to enter diary entries and memos.

[1444] "Text data" is data in which diary entries and memos entered by the user are expressed in text format.

[1445] The "server" is a device that receives text data sent by users, analyzes it, and generates and sends feedback and advice.

[1446] A "natural language analysis engine" is a program or device that analyzes text data and extracts the emotions, keywords, and themes contained therein.

[1447] A "generative AI model" is an artificial intelligence model that generates feedback and advice based on analysis results.

[1448] "Feedback and advice" refers to specific instructions and advice provided to the user based on the analysis results.

[1449] A "secure protocol" is a set of rules for encrypting communication content and sending and receiving data safely.

[1450] "Sentiment analysis" is a technology that identifies a user's emotions from the content of text data and determines whether those emotions are positive or negative.

[1451] "Keyword extraction" is a technique for identifying important words and phrases from text data.

[1452] "Theme extraction" is a technique for identifying major topics or themes within text data.

[1453] A "user terminal" is a device on which a user enters a diary or memo and checks feedback and advice. Examples of such devices include smartphones and tablets.

[1454] "Reflection" is an activity in which the user reflects on themselves based on the feedback and advice provided and makes a plan of action for the next time.

[1455] This invention is an AI support system that allows users to record their daily events and emotions and use that information to engage in effective self-reflection. Users use a dedicated application to enter their diary and notes, and the server analyzes the content and provides appropriate feedback and advice to promote self-improvement.

[1456] Hardware and Software Configuration

[1457] The system consists of the following elements:

[1458] User device: A device such as a smartphone or tablet where users input text data and receive feedback.

[1459] Server: A device that analyzes text data and generates and sends feedback and advice.

[1460] Natural language analysis engines: such as Google Natural Language API and Amazon Comprehend, which perform sentiment analysis, keyword extraction, and theme extraction on text data.

[1461] Generative AI models: Artificial intelligence models for generating feedback and advice, such as OpenAI GPT-3

[1462] Secure protocols: Protocols for securely sending and receiving user data, such as HTTPS

[1463] Data processing and calculation

[1464] 1. User Input

[1465] Users open a dedicated application and enter the events and feelings of the day in text format. For example, they might enter, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[1466] 2. Data Transmission

[1467] The device sends the entered text data to the server using the HTTPS protocol, encrypting the data and protecting the user's privacy.

[1468] 3. Receiving and analyzing text data

[1469] The server receives the text data from the device and passes it to a natural language analysis engine for analysis. The analysis involves sentiment analysis, keyword extraction, and theme extraction. For example, sentiment analysis may determine a negative sentiment, while keyword extraction may identify "meeting," "boss," and "lack of preparation."

[1470] 4. Generating feedback and advice

[1471] Based on the analysis results, the server uses a generative AI model to generate appropriate feedback and advice. It is important that the generated feedback is specific and suggests actions that can be used next time. For example, feedback such as "Let's think about what preparations are needed to make the next meeting a success" could be generated.

[1472] 5. Sending feedback and advice

[1473] The generated feedback and advice is then sent to the user terminal, again using a secure protocol.

[1474] 6. Viewing feedback and advice

[1475] The user device displays the feedback and advice to the user, who then opens the application and checks the provided feedback.

[1476] 7. User Reflection

[1477] The user reflects on the feedback provided and creates a plan of action for the next time, for example, "I will be more prepared for the next meeting and think of concrete steps to improve communication with my boss."

[1478] Specific examples

[1479] Example 1: Work reflection

[1480] The user types, "My presentation didn't go well today. Maybe I didn't practice enough."

[1481] The terminal transmits this text data to the server.

[1482] The server receives the text data and passes it to a natural language analysis engine.

[1483] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "presentation" and "lack of practice."

[1484] The server generates feedback such as, "Let's think about what practice you need to do to make your next presentation a success."

[1485] The server sends the feedback to the terminal, which displays it to the user.

[1486] Users read the feedback and create their own practice plan.

[1487] Example 2: Private introspection

[1488] A user types, "I had lunch with a friend and something I said or did made things awkward. How can I improve in the future?"

[1489] The terminal transmits this text data to the server.

[1490] The server receives the text data and passes it to a natural language analysis engine.

[1491] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "friends," "lunch," and "awkward."

[1492] The server generates feedback such as, "Let's think specifically about what kind of behavior we should be careful about at our next lunch."

[1493] The server sends the feedback to the terminal, which displays it to the user.

[1494] Users read the feedback, reflect on their own actions, and think about specific improvements.

[1495] This concludes the description of the preferred embodiment of the present invention. The system provides an effective tool for users to engage in self-reflection, learn from everyday occurrences, and promote self-improvement.

[1496] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1497] Step 1:

[1498] The user provides input.

[1499] Input: The user opens a dedicated application and enters the events and feelings of the day in text format.

[1500] Specific behavior: The user launches the application, enters daily events and emotions into the text input field, and presses the "Send" button.

[1501] Output: The input text data.

[1502] Step 2:

[1503] The terminal transmits the input text data to the server.

[1504] Input: Text data entered by the user.

[1505] Specific operation: The device sends the user's input to the server using the HTTPS protocol.

[1506] Output: The encrypted text data sent to the server.

[1507] Step 3:

[1508] The server passes the received text data to a natural language analysis engine.

[1509] Input: Encrypted text data.

[1510] What happens: The server receives an HTTPS request and passes the text data to a natural language analysis engine.

[1511] Output: Text data passed to the natural language analysis engine.

[1512] Step 4:

[1513] A natural language analysis engine analyzes the text data.

[1514] Input: Text data passed to the natural language analysis engine.

[1515] Specific operation: The natural language analysis engine performs sentiment analysis, keyword extraction, and theme extraction. For example, sentiment analysis may determine a message as "negative," and keywords such as "meeting," "boss," and "lack of preparation" may be extracted.

[1516] Output: Analysis results (sentiment, keywords, themes).

[1517] Step 5:

[1518] The server generates feedback and advice using a generative AI model based on the analysis results.

[1519] Input: Analysis results (sentiments, keywords, themes).

[1520] Specific operation: The server inputs prompts into the generative AI model and generates appropriate feedback and advice. For example, feedback such as "Let's think about what preparations you need to make to ensure the success of your next meeting" is generated.

[1521] Output: Generated feedback and advice.

[1522] Step 6:

[1523] The server sends the generated feedback and advice to the device.

[1524] Input: Generated feedback and advice.

[1525] Specific operation: The server sends the feedback to the device using the HTTPS protocol.

[1526] Output: Feedback and advice sent to the device.

[1527] Step 7:

[1528] The device will provide feedback and advice.

[1529] Input: Feedback and advice sent to your device.

[1530] Specific behavior: The device decodes the received feedback and displays it in the application's user interface.

[1531] Output: Feedback and advice displayed to the user.

[1532] Step 8:

[1533] The user performs a self-reflection activity.

[1534] Input: Any feedback or advice you see.

[1535] Specific Action: The user reads the feedback and creates a plan of action for the next time. For example, the user may perform a self-reflection activity such as, "I will think of specific steps to be more prepared for the next meeting and improve communication with my boss."

[1536] Output: A concrete action plan.

[1537] (Application example 1)

[1538] 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."

[1539] Conventional AI support systems require users to input events and emotions in text format, and do not provide voice-based diary entries or visual feedback using wearable devices. This makes it difficult for users to easily engage in self-reflection, and there is a risk that small everyday events or emotions may be overlooked. Furthermore, conventional systems are limited to text input, which makes them lack immediacy and convenience.

[1540] 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.

[1541] In this invention, the server includes a means for a user to input a diary or memo in text format, a means for transmitting the input text data to the server, a means for performing natural language analysis, a means for recording a diary or memo by voice input, a means for converting the voice input into text format, and a means for visually displaying feedback via a wearable device such as smart glasses. This allows the user to easily record a diary or memo through voice input and immediately receive appropriate feedback from the analysis results. Furthermore, visual feedback via the wearable device promotes introspection.

[1542] "User" refers to a user who uses this system to enter diary entries and memos and receive feedback and advice.

[1543] "Text format" is a data format based on character information entered by the user.

[1544] A "server" is a computer system that receives data sent by a user, analyzes it, generates feedback, and sends it back to the user.

[1545] "Natural language analysis" is the act of analyzing text data entered by a user using natural language processing technology to perform sentiment analysis, keyword extraction, etc.

[1546] "Feedback" refers to advice and improvement suggestions provided to users based on the analysis results.

[1547] "Advice" refers to specific improvement measures or suggested actions provided to users based on the analysis results.

[1548] "User terminal" means a device used by a user to operate the system and receive feedback and advice.

[1549] "Voice input" is a method in which a user inputs information by speaking.

[1550] "Convert to text" is the process of converting speech input into text data.

[1551] "Smart glasses" are wearable devices worn by users to present visual information.

[1552] A "wearable device" is a computing device that can be worn by a user.

[1553] "Visually displaying" means showing information to the user through the device's display.

[1554] This invention is an advanced AI support system that records the user's daily events and emotions and encourages introspection based on that data. Specific embodiments for realizing this system are described below.

[1555] The system components are:

[1556] 1. User terminals: including wearable devices such as smart glasses and smartphones.

[1557] 2. Voice input system: This system allows users to input daily events and emotions by voice and converts them into text. Specifically, it uses voice recognition APIs such as Google Cloud Speech-to-Text and IBM Watson.

[1558] 3. Natural Language Analysis System: A system that analyzes text data. It performs processes such as sentiment analysis, keyword extraction, and theme extraction. Specifically, it uses a sentiment analysis model that utilizes the Transformers pipeline.

[1559] 4. Feedback generation system: A system that generates appropriate feedback and advice for users based on the analysis results.

[1560] 5. Server: This is the central processing unit that performs the analysis and generation mentioned above, and receives data from users and processes and analyzes it.

[1561] Program processing overview

[1562] 1. User Input:

[1563] Using a wearable device such as smart glasses, users can input their day's events and emotions by voice, for example, "Today was a bad day at work."

[1564] 2. Data transmission:

[1565] The user terminal converts the voice data into text and sends it to the server via a secure protocol (e.g., HTTPS).

[1566] 3. Receiving and analyzing text data:

[1567] The server receives the text data and passes it to a natural language analysis system, which performs sentiment analysis, determining, for example, that the sentiment is "negative," and extracts keywords such as "work" and "didn't go well."

[1568] 4. Generate feedback and advice:

[1569] The server generates feedback based on the analysis results, creating advice such as, "Think about what preparations you need to make the next time you work."

[1570] 5. Sending and Viewing Feedback:

[1571] The server transmits the generated feedback to the user's device, which visually displays the feedback through the smart glasses.

[1572] Specific examples

[1573] Work reflection

[1574] A user speaks, "Today's meeting went wrong. Maybe I wasn't prepared enough."

[1575] The user terminal converts the voice data into text and sends it to the server.

[1576] The server receives the text data and performs sentiment analysis and keyword extraction.

[1577] The server generates feedback saying, "Let's think about how to prepare for the meeting."

[1578] Feedback is displayed to the user through the smart glasses.

[1579] Private introspection

[1580] The user speaks, "I said too much at dinner with a friend. How can I improve?"

[1581] The user terminal converts the voice data into text and sends it to the server.

[1582] The server receives the text data and performs sentiment analysis and keyword extraction.

[1583] The server generates feedback such as, "Let's think about what we should pay attention to the next time we dine here."

[1584] Feedback is displayed to the user through the smart glasses.

[1585] Prompt Sentence Examples

[1586] Here are some example prompts to encourage users to speak:

[1587] "How was your day today?"

[1588] "Tell me what happened today and how you felt."

[1589] "Is there anything you'd like to improve?"

[1590] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1591] Step 1:

[1592] Using a wearable device such as smart glasses, users input their day's events and emotions through voice, for example, "Today was a bad day at work."

[1593] Step 2:

[1594] The device receives voice input and converts it to text using a speech recognition system (for example, Google Cloud Speech-to-Text or IBM Watson). The input is voice data, and the output is text data. This conversion processes the user's voice into text information.

[1595] Step 3:

[1596] The terminal sends the converted text data to the server via a secure protocol (e.g., HTTPS). The input is the text data, and the output is the transmission completion status. Encryption technology is used for data transmission for security.

[1597] Step 4:

[1598] The server receives the text data and passes it to the natural language analysis system. The input is text data, and the received data is subjected to analysis. Here, the received data is added to a queue to be analyzed.

[1599] Step 5:

[1600] The server's natural language analysis system performs sentiment analysis, keyword extraction, and theme extraction on the text data. This uses a sentiment analysis model that utilizes a Transformers pipeline. The input is text data, and the output is a sentiment determination result and a keyword list. At this stage, the data is sentiment-tagged and key keywords are extracted.

[1601] Step 6:

[1602] Based on the analysis results, the server generates appropriate feedback and advice. For example, it creates advice such as, "Think about what preparations you need to make the next time you work." The input is the emotion determination result and a list of keywords, and the output is a feedback message. In this process, a generative AI model is used to generate appropriate advice for the user.

[1603] Step 7:

[1604] The server securely transmits the generated feedback message to the user terminal. The input is the feedback message, and the output is a transmission completion status. The transmission means uses a secure protocol.

[1605] Step 8:

[1606] The terminal visually displays the received feedback message to the user via a wearable device such as smart glasses. The input is the feedback message, and the output is the feedback content displayed on the glasses' display. Here, the user can confirm which specific areas need improvement.

[1607] The above is the flow of program processing and specific operations of the system that realizes the application example.

[1608] 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.

[1609] This invention is an AI support system that allows users to record their daily events and emotions and use that information to engage in effective self-reflection. The system analyzes the contents of diary entries and notes, and provides appropriate feedback and advice to promote self-improvement. Furthermore, by incorporating an emotion engine, the system can recognize the user's emotions and provide more appropriate feedback.

[1610] Explaining program processing in natural language

[1611] The system mainly consists of the following elements:

[1612] 1. User Input

[1613] Using a dedicated application, users can input the events, feelings, and thoughts of the day in text format. For example, they might input, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[1614] 2. Data Transmission

[1615] The device sends the entered text data to the server. Data transmission is performed using a secure protocol (e.g., HTTPS) to protect the user's privacy.

[1616] 3. Receiving and analyzing text data

[1617] The server receives the text data and passes it to a natural language analysis engine, which analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction.

[1618] The emotion engine also analyzes text data and recognizes the user's emotions.

[1619] 4. Generating feedback and advice

[1620] The server generates appropriate feedback and advice for the user based on the analysis results. For example, if the result of the sentiment analysis is determined to be "negative" and the keywords "meeting," "boss," and "lack of preparation" are extracted, the server generates the following feedback: "Let's think about what preparations you need to make to ensure the success of your next meeting."

[1621] The emotion engine may also tailor the feedback depending on the user's emotional state, for example, generating feedback such as "Think of concrete steps you can take to feel more positive about your upcoming meeting."

[1622] 5. Sending feedback and advice

[1623] The server generates feedback and advice and sends it to the device using a secure protocol.

[1624] 6. Viewing feedback and advice

[1625] The device displays feedback and advice to the user. When the user opens the application, the provided feedback and advice is displayed on the screen.

[1626] 7. User Reflection

[1627] Users can review the displayed feedback and advice and reflect on it in their own self-reflection activities, such as "I will prepare thoroughly for the next meeting and think of concrete steps to improve communication with my boss."

[1628] Specific examples

[1629] Example 1: Work reflection

[1630] The user types, "My presentation today didn't go well. Maybe I didn't practice enough."

[1631] The terminal transmits this text data to the server.

[1632] The server receives the text data and passes it to a natural language analysis engine.

[1633] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "presentation" and "lack of practice."

[1634] The emotion engine recognizes the user's emotional state and generates feedback accordingly.

[1635] The server generates feedback such as, "Let's think about what practice you need to do to make your next presentation a success."

[1636] The server sends the feedback to the terminal, which displays it to the user.

[1637] Users read the feedback and create their own practice plan.

[1638] Example 2: Private introspection

[1639] The user types, "I had lunch with a friend and something I said or did made things awkward. How can I improve in the future?"

[1640] The terminal transmits this text data to the server.

[1641] The server receives the text data and passes it to a natural language analysis engine.

[1642] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "friends," "lunch," and "awkward."

[1643] The emotion engine recognizes the user's emotional state and generates feedback accordingly.

[1644] The server generates feedback such as, "Let's think specifically about what kind of behavior you should be careful about at your next lunch."

[1645] The server sends the feedback to the terminal, which displays it to the user.

[1646] Users read the feedback, reflect on their own actions, and think about specific improvements.

[1647] The above is an embodiment of the present invention. This system is an efficient tool for users to engage in self-reflection, learn from everyday events, and promote self-improvement. In addition, by combining it with an emotion engine, it can provide appropriate support according to the user's emotions.

[1648] The processing flow will be explained below.

[1649] Step 1:

[1650] The user opens a dedicated application and enters the events, feelings, and thoughts of the day in text format. For example, they might enter, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[1651] Step 2:

[1652] The device sends the entered text data to the server. Data transmission is performed using a secure protocol (e.g., HTTPS) to protect the user's privacy.

[1653] Step 3:

[1654] The server receives the text data and passes it to a natural language analysis engine, which analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction.

[1655] Step 4:

[1656] A natural language analysis engine analyzes text data. First, it performs sentiment analysis to determine whether the input text is classified as "positive," "negative," or "neutral." Next, it performs keyword extraction to identify important words and phrases. Finally, it performs theme extraction to extract key topics and arguments.

[1657] Step 5:

[1658] The emotion engine analyzes the text data to recognize the user's emotional state. This analysis involves detailed classification of emotions sensed from the user's input text and identifies the current emotional state.

[1659] Step 6:

[1660] The server generates appropriate feedback and advice for the user based on the analysis results. For example, if the result of the sentiment analysis is determined to be "negative" and the keywords "meeting," "boss," and "lack of preparation" are extracted, the server generates feedback such as "Let's think about what kind of preparations you need to make to ensure the success of your next meeting." It also generates feedback such as "Let's think about specific steps to have a positive attitude toward future meetings," reflecting the results of the emotion engine.

[1661] Step 7:

[1662] The server generates feedback and advice and sends it to the device using a secure protocol.

[1663] Step 8:

[1664] The device displays the received feedback and advice to the user. When the user opens the application, the provided feedback and advice is displayed on the screen.

[1665] Step 9:

[1666] Users can review the displayed feedback and advice and reflect on it in their own self-reflection activities, such as "I will prepare thoroughly for the next meeting and think of concrete steps to improve communication with my boss."

[1667] Specific examples

[1668] Example 1: Work reflection

[1669] The user types, "My presentation today didn't go well. Maybe I didn't practice enough."

[1670] The terminal transmits this text data to the server.

[1671] The server receives the text data and passes it to a natural language analysis engine.

[1672] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "presentation" and "lack of practice."

[1673] The emotion engine recognizes the user's emotional state and generates feedback accordingly.

[1674] The server generates feedback such as, "Let's think about what practice you need to do to make your next presentation a success."

[1675] The server sends the feedback to the terminal, which displays it to the user.

[1676] Users read the feedback and create their own practice plan.

[1677] Example 2: Private introspection

[1678] The user types, "I had lunch with a friend and something I said or did made things awkward. How can I improve in the future?"

[1679] The terminal transmits this text data to the server.

[1680] The server receives the text data and passes it to a natural language analysis engine.

[1681] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "friends," "lunch," and "awkward."

[1682] The emotion engine recognizes the user's emotional state and generates feedback accordingly.

[1683] The server generates feedback such as, "Let's think specifically about what kind of behavior you should be careful about at your next lunch."

[1684] The server sends the feedback to the terminal, which displays it to the user.

[1685] Users read the feedback, reflect on their own actions, and think about specific improvements.

[1686] This allows users to self-reflect, learn from everyday events, and promote self-improvement. In addition, by combining it with an emotion engine, appropriate support is provided according to the user's emotions.

[1687] Example 2

[1688] 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."

[1689] While systems aimed at self-reflection and self-improvement exist, providing appropriate feedback based on the user's emotional state has been difficult. From a security perspective, ensuring the safety of data transmission has also been an issue. Furthermore, rather than simple feedback, users need messages that encourage specific actions and self-reflection steps, but there has been a lack of systems that address this need.

[1690] 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.

[1691] In this invention, the server includes means for a user to input a diary or memo in text format, means for transmitting the input text data to the server, means for the server to receive the text data and perform natural language analysis, means for generating feedback and advice based on the analysis results, means for transmitting the generated feedback and advice to the user terminal, means for the user terminal to display the feedback and advice, means for transmitting and receiving data using a secure protocol, means for analyzing the user's emotions from the input text using an emotion recognition engine, means for generating feedback and advice based on the analysis results using a generative AI model, and means for adjusting the generated feedback and advice according to the user's emotional state. This makes it possible to provide appropriate feedback according to the user's emotions, ensure secure transmission and reception of data, and further encourage the user to take specific introspection and action steps.

[1692] "User input" is a means by which system users use a dedicated application to input the events, feelings, and thoughts of the day in text format.

[1693] "Data transmission" refers to the act and means by which a terminal transmits input text data to a server, and data security is ensured by using a secure protocol such as HTTPS.

[1694] "Receiving text data" refers to the act and means by which the server receives text data sent from the terminal.

[1695] "Natural language analysis" refers to the techniques and means of analyzing input text data using techniques such as sentiment analysis, keyword extraction, and theme extraction.

[1696] An "emotion recognition engine" is a means of recognizing a user's emotional state from text data and analyzing emotions such as positive or negative.

[1697] "Generation of feedback and advice" is a means of generating specific feedback and advice for the user based on the results of natural language analysis and emotion recognition.

[1698] A "generative AI model" is an artificial intelligence model used to generate feedback and advice based on analytical results.

[1699] A "secure data transmission protocol" is a communication method used to encrypt and safely transmit and receive data, such as HTTPS.

[1700] "Feedback and advice display" refers to a means for visually displaying the feedback and advice received by the user terminal to the user.

[1701] "Reflection activities" are activities in which users review feedback and advice, review their own actions and thoughts, and consider ways to improve.

[1702] This invention is a system that allows users to record daily events and emotions and to use that information to effectively reflect on themselves. Specific steps for implementing this system are described below.

[1703] The system mainly consists of the following elements:

[1704] 1. User Input

[1705] Using a dedicated application, users can input the events, feelings, and thoughts of the day in text format. For example, they might input, "Today's meeting with my boss didn't go well. Maybe it's because I wasn't prepared enough."

[1706] 2. Data Transmission

[1707] The device sends the entered text data to the server using a secure protocol such as HTTPS, which encrypts the data during the process and protects the user's privacy.

[1708] 3. Receiving and analyzing text data

[1709] Once the server receives the text data, it passes it to a natural language analysis engine (e.g., Google Cloud Natural Language API or IBM Watson NLU). The analysis engine analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction. Additionally, it uses an emotion recognition engine (e.g., the undisclosed TeNTEN emotion recognition API) to recognize the user's emotional state.

[1710] 4. Generating feedback and advice

[1711] The server generates appropriate feedback and advice for the user based on the analysis results. A trained generative AI model (e.g., GPT-3) is used here. For example, if the result of the sentiment analysis is determined to be "negative," the server generates feedback such as, "Let's think about what preparations you need to make to ensure the success of your next meeting." The emotion recognition engine further refines the feedback according to the user's emotional state, providing the user with the most appropriate advice.

[1712] 5. Sending feedback and advice

[1713] Server-generated feedback and advice is sent to the device using HTTPS.

[1714] 6. Viewing feedback and advice

[1715] The device displays feedback and advice to the user. When the user opens the application, the provided feedback and advice is displayed on the screen.

[1716] 7. User Reflection

[1717] Users can review the displayed feedback and advice and reflect on it in their own self-reflection activities, such as "I will prepare thoroughly for the next meeting and think of concrete steps to improve communication with my boss."

[1718] Specific examples

[1719] Example 1: Work reflection

[1720] The user types, "My presentation today didn't go well. Maybe I didn't practice enough."

[1721] The terminal transmits this text data to the server.

[1722] The server receives the text data and passes it to a natural language analysis engine.

[1723] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "presentation" and "lack of practice."

[1724] An emotion recognition engine recognizes the user's emotional state and generates feedback accordingly.

[1725] The server generates feedback such as, "Let's think about what practice you need to do to make your next presentation a success."

[1726] The server sends the feedback to the terminal, which displays it to the user.

[1727] Users read the feedback and create their own practice plan.

[1728] Example 2: Private introspection

[1729] The user types, "I had lunch with a friend and something I said or did made things awkward. How can I improve in the future?"

[1730] The terminal transmits this text data to the server.

[1731] The server receives the text data and passes it to a natural language analysis engine.

[1732] The natural language analysis engine performs sentiment analysis, determines the sentiment to be "negative," and extracts the keywords "friends," "lunch," and "awkward."

[1733] An emotion recognition engine recognizes the user's emotional state and generates feedback accordingly.

[1734] The server generates feedback such as, "Let's think specifically about what kind of behavior you should be careful about at your next lunch."

[1735] The server sends the feedback to the terminal, which displays it to the user.

[1736] Users read the feedback, reflect on their own actions, and think about specific improvements.

[1737] This system is an efficient tool for users to engage in self-reflection, learn from everyday events, and promote self-improvement. It also combines an emotion engine to provide appropriate support based on the user's emotions. By using secure protocols, user privacy is also protected.

[1738] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1739] Step 1: User Input

[1740] Using a dedicated application, users input the events, feelings, and thoughts of the day in text format. This application runs on smartphones and PCs and provides an easily accessible user interface. For example, a user can input, "Today's meeting with my boss didn't go well. It may be because I wasn't prepared enough." This input data is saved in text format.

[1741] Step 2: Send data

[1742] The terminal sends the text data entered by the user to the server. A secure protocol such as HTTPS is used for transmission, and the data is encrypted before being sent. This protects the user's privacy. The input is the user's text data, and the output is a notification to the server that transmission has been completed.

[1743] Step 3: Receiving and parsing text data

[1744] The server decrypts the received text data and sends it to a natural language analysis engine. The natural language analysis engine analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction. For example, it uses Google Cloud Natural Language API or IBM Watson NLU. The input to the analysis engine is the text data sent by the user, and the output is the emotional state, extracted keywords, and themes. It also analyzes the user's emotions using an emotion recognition engine. Specifically, the emotion recognition API assigns emotional labels such as "positive" or "negative."

[1745] Step 4: Generate feedback and advice

[1746] The server generates appropriate feedback and advice based on the analysis results obtained from the natural language analysis engine and emotion recognition engine. A generative AI model (e.g., GPT-3) is used for this generation. The input is the analysis result, and the output is specific feedback or advice text. For example, if the result of the sentiment analysis is "negative," feedback such as "Let's think about what preparations are needed to make the next meeting a success" is generated. The generative AI model generates the feedback based on a prompt sentence. An example of a prompt sentence is, "What concerns does the user have about the meeting?"

[1747] Step 5: Send feedback and advice

[1748] The server sends the generated feedback and advice to the device. The data is encrypted and sent using a secure protocol (e.g., HTTPS). The input is the feedback or advice text, and the output is a notification to the device that transmission has been completed.

[1749] Step 6: View feedback and advice

[1750] The terminal decrypts the received feedback and advice and displays it to the user on the application. When the user opens the application, the provided feedback and advice is displayed on the screen. The input is the text of the feedback and advice received from the server, and the output is what is displayed to the user.

[1751] Step 7: User reflection

[1752] The user can review the displayed feedback and advice and reflect it in their own self-reflection activities. For example, they can take action such as writing down specific preparation items for the next meeting or taking notes on key points to communicate with their boss. The input is the text of the feedback and advice, and the output is the user's specific action plan. By inputting the results of this self-reflection activity back into the application at a later date, continuous self-improvement is possible.

[1753] (Application example 2)

[1754] 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."

[1755] Existing virtual stores do not adequately collect or analyze customer feedback, making it difficult to provide specific product suggestions or improvement proposals based on customer sentiment. They also lacked a function to recommend appropriate products that took past feedback history into account. For these reasons, a system was needed to improve customer satisfaction and achieve more effective marketing.

[1756] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1757] In this invention, the server includes: a means for a user to input diary entries and feedback in text format; a means for transmitting the input text data to the server; a means for the server to receive the text data and perform natural language analysis; a means for generating feedback and advice based on the analysis results; a means for transmitting the generated feedback and advice to the user terminal; and a means for the user terminal to display the feedback and advice and a means for recommending products appropriate to the emotional state of the user, taking into account past feedback history. This makes it possible to provide specific feedback based on the customer's emotions and improve customer satisfaction. Furthermore, the appropriate product recommendation function taking into account past feedback history enables more effective marketing.

[1758] A "user" is a person who uses the system and is the entity that inputs text data such as feedback and diaries.

[1759] The "text format" is a data format consisting of characters, and is used to represent input content such as diary entries and feedback.

[1760] "Feedback" refers to the textual input of a user's thoughts and opinions about their shopping experience in the virtual store and the products they purchased.

[1761] A "server" is a computer system that receives text data sent by a user, analyzes it, and generates feedback.

[1762] "Natural language analysis" is a technology that uses computers to process human language and understand its content, and includes sentiment analysis, keyword extraction, and theme extraction.

[1763] "Sentiment analysis" is a technique for determining the emotional state of text data, evaluating it as positive, negative, neutral, etc.

[1764] "Keyword extraction" is a technique for extracting important words and phrases from text data, which is useful for analyzing feedback.

[1765] "Theme extraction" is a technique for identifying major topics from text data and is used to understand the content of feedback.

[1766] "Advice" refers to specific suggestions or advice that the server provides to the user based on the analysis results.

[1767] "Past feedback history" is a record of feedback provided by the user to date, which is used for future analysis and product recommendations.

[1768] The "product recommendation function" is a function that suggests suitable products to the user, taking into account their past feedback history and current emotional state.

[1769] A "user terminal" is a device used by a user to input and display feedback and advice, and includes a smartphone, a personal computer, etc.

[1770] This invention is an AI support system for virtual stores that analyzes user input of diary entries and feedback, and provides appropriate feedback and product recommendations. This system mainly consists of the following elements:

[1771] 1. User Input

[1772] First, users access the virtual store using their smartphone, PC, or other device. They then enter text feedback about their shopping experience and the products they purchased. For example, they might enter feedback like, "I felt the battery life of my latest smartwatch was shorter than expected."

[1773] 2. Data Transmission

[1774] The device sends the entered text data to the server using a secure protocol such as HTTPS to protect the user's privacy.

[1775] 3. Receiving and analyzing text data

[1776] The server receives the text data and passes it to a natural language analysis engine, which analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction. Sentiment analysis is performed using libraries such as TextBlob.

[1777] 4. Generating feedback and advice

[1778] The server generates appropriate feedback and advice for the user based on the analysis results. For example, if the result of the sentiment analysis is determined to be "negative" and the keyword "battery life" is extracted, the server will generate the following feedback: "We apologize for the inconvenience. We will suggest a different model with a longer battery life."

[1779] 5. Sending feedback and advice

[1780] The server generates feedback and advice and sends it to the device using a secure protocol. The user device receives the feedback and advice and displays it to the user.

[1781] 6. Use of past feedback history

[1782] The server also takes into account past feedback history and provides a recommendation function to recommend products that suit the user's emotions and history, enabling more personalized advice and product suggestions.

[1783] By combining these elements, the system can provide specific feedback and appropriate product recommendations based on customer sentiment. In particular, natural language processing and sentiment analysis can be used to understand the content of user feedback and generate optimal advice.

[1784] Specific examples

[1785] Customer-provided feedback: "The battery life of my latest smartwatch is shorter than expected."

[1786] System feedback: Negative and says "We apologize for the inconvenience. We suggest you try another model with a longer battery life. Please check the link below."

[1787] Example prompt sentence:

[1788] "Customer provided feedback: The battery life of their latest smartwatch was shorter than expected. Generate a sentiment analysis and appropriate advice based on this feedback."

[1789] Thus, the present invention is directed to a system that efficiently analyzes user-entered feedback and provides specific suggestions for improving user satisfaction.

[1790] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1791] Step 1:

[1792] Users enter diary entries and feedback in text format.

[1793] Specifically, users access the virtual store's application using a smartphone or PC and enter their thoughts and opinions about their shopping experience and the products they purchased in text form, such as "I felt that the battery life of my latest smartwatch was shorter than expected."

[1794] Step 2:

[1795] The terminal transmits the input text data to the server.

[1796] Specifically, the terminal sends the user's feedback to the server using a secure protocol such as HTTPS, where the input is the text data entered by the user and the output is the data sent to the server.

[1797] Step 3:

[1798] The server receives the text data and passes it to a natural language analysis engine.

[1799] Specifically, the server receives data sent by the user and passes it to a natural language analysis engine (e.g., TextBlob library) for processing. The input is the text data sent by the user, and the output is the data passed to the analysis engine.

[1800] Step 4:

[1801] The natural language analysis engine analyzes the input text and performs sentiment analysis, keyword extraction, and theme extraction.

[1802] Specifically, the engine analyzes text data, determines whether the sentiment is positive or negative, and extracts key keywords and themes. The input is the text data passed from the server, and the output is the analysis results (e.g., sentiment analysis results, keywords, themes).

[1803] Step 5:

[1804] The server generates feedback and advice based on the analysis results.

[1805] Specifically, the server generates appropriate feedback and advice for the user based on the results of the sentiment analysis and the extracted keywords. For example, if the result is "negative" and the keyword "battery life" is extracted, the server generates the following feedback: "We apologize for the inconvenience. We will suggest a different model with a longer battery life." The input is the analysis result, and the output is the generated feedback and advice.

[1806] Step 6:

[1807] The server generates feedback and advice and sends it to the user's terminal.

[1808] Specifically, the server transmits the generated feedback and advice to the user terminal using a secure protocol. The input is the generated feedback and advice, and the output is transmission to the user terminal.

[1809] Step 7:

[1810] The user's device displays feedback and advice.

[1811] Specifically, the terminal displays the feedback and advice provided to the user so that the user can confirm the content. The input is the feedback and advice sent from the server, and the output is the display to the user.

[1812] Step 8:

[1813] The server takes into account past feedback history and recommends products that suit your emotional state.

[1814] Specifically, the server recommends products suitable for the user based on the user's past feedback history and current emotional state. For example, it proposes a "model with long battery life." The input is the past feedback history and the current analysis results, and the output is the recommended product information.

[1815] 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.

[1816] 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.

[1817] 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.

[1818] 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.

[1819] 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.

[1820] 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.

[1821] 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).

[1822] 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.

[1823] 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."

[1824] 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.

[1825] 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).

[1826] 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.

[1827] 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.

[1828] 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.

[1829] 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.

[1830] 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.

[1831] 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.

[1832] 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.

[1833] 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.

[1834] 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.

[1835] 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.

[1836] The following is further disclosed regarding the above embodiment.

[1837] (Claim 1)

[1838] A means for users to input diary entries and memos in text format;

[1839] means for transmitting input text data to a server;

[1840] A server receives the text data and performs natural language analysis;

[1841] A means of generating feedback and advice based on the analysis results;

[1842] means for transmitting the generated feedback and advice to a user terminal;

[1843] a means for displaying feedback and advice on the user terminal;

[1844] A system including:

[1845] (Claim 2)

[1846] 2. The system according to claim 1, further comprising means for generating a message prompting a user for self-reflection in the form of a question based on the analysis result.

[1847] (Claim 3)

[1848] 10. The system of claim 1, further comprising means for performing sentiment analysis, keyword extraction, and theme extraction of the input text using natural language processing techniques.

[1849] "Example 1"

[1850] (Claim 1)

[1851] A means for users to input diary entries and memos in text format;

[1852] means for transmitting input text data to a server;

[1853] A server receives the text data and performs natural language analysis;

[1854] means for performing sentiment analysis, keyword extraction, and theme extraction on input text using a natural language analysis engine;

[1855] A means of generating feedback and advice using a generative AI model based on the analysis results;

[1856] means for transmitting the generated feedback and advice to a user terminal;

[1857] a means for displaying feedback and advice on the user terminal;

[1858] A system including:

[1859] (Claim 2)

[1860] 2. The system according to claim 1, further comprising means for generating a message prompting a user for self-reflection in the form of a question based on the analysis result.

[1861] (Claim 3)

[1862] 10. The system of claim 1, further comprising means for transmitting and receiving the input text data and the generated feedback and advice using a secure protocol.

[1863] "Application Example 1"

[1864] (Claim 1)

[1865] A means for users to input diary entries and memos in text format;

[1866] means for transmitting input text data to a server;

[1867] A server receives the text data and performs natural language analysis;

[1868] A means of generating feedback and advice based on the analysis results;

[1869] means for transmitting the generated feedback and advice to a user terminal;

[1870] a means for displaying feedback and advice on the user terminal;

[1871] A means for recording diary entries and memos by voice input;

[1872] a means for converting voice input into text form;

[1873] a means for visually displaying the feedback via a wearable device such as smart glasses;

[1874] A system including:

[1875] (Claim 2)

[1876] 2. The system according to claim 1, further comprising means for generating a message prompting a user for self-reflection in the form of a question based on the analysis result.

[1877] (Claim 3)

[1878] 10. The system of claim 1, further comprising means for performing sentiment analysis, keyword extraction, and theme extraction of the input text using natural language processing techniques.

[1879] "Example 2: Combining Emotion Engines"

[1880] (Claim 1)

[1881] A means for users to input diary entries and memos in text format;

[1882] means for transmitting input text data to a server;

[1883] A server receives the text data and performs natural language analysis;

[1884] A means of generating feedback and advice based on the analysis results;

[1885] means for transmitting the generated feedback and advice to a user terminal;

[1886] a means for displaying feedback and advice on the user terminal;

[1887] a means for transmitting and receiving data using a secure protocol;

[1888] A system including:

[1889] (Claim 2)

[1890] 2. The system according to claim 1, further comprising means for generating a message prompting a user for self-reflection in the form of a question based on the analysis result.

[1891] (Claim 3)

[1892] 10. The system of claim 1, further comprising means for performing sentiment analysis, keyword extraction, and theme extraction of the input text using natural language processing techniques.

[1893] (Claim 4)

[1894] 10. The system of claim 1, further comprising means for parsing user emotions from input text using an emotion recognition engine.

[1895] (Claim 5)

[1896] 10. The system of claim 1, further comprising means for using the generative AI model to generate feedback or advice based on the analysis results.

[1897] (Claim 6)

[1898] 10. The system of claim 1, further comprising means for adjusting the generated feedback or advice depending on the user's emotional state.

[1899] "Application example 2 when combining emotion engines"

[1900] (Claim 1)

[1901] A means for users to enter diary entries and feedback in text format;

[1902] means for transmitting input text data to a server;

[1903] A server receives the text data and performs natural language analysis;

[1904] A means of generating feedback and advice based on the analysis results;

[1905] means for transmitting the generated feedback and advice to a user terminal;

[1906] a means for displaying feedback and advice on the user terminal;

[1907] A means of recommending products appropriate to an emotional state, taking into account past feedback history;

[1908] A system including:

[1909] (Claim 2)

[1910] 2. The system according to claim 1, further comprising means for generating a message prompting a user for self-reflection in the form of a question based on the analysis result.

[1911] (Claim 3)

[1912] 10. The system of claim 1, further comprising means for performing sentiment analysis, keyword extraction, and theme extraction of the input text using natural language processing techniques. [Explanation of symbols]

[1913] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to input diary entries and memos in text format; means for transmitting input text data to a server; A server receives the text data and performs natural language analysis; A means of generating feedback and advice based on the analysis results; means for transmitting the generated feedback and advice to a user terminal; a means for displaying feedback and advice on the user terminal; A system including:

2. The system according to claim 1 , further comprising means for generating a message prompting a user for self-reflection in the form of a question based on the analysis result.

3. The system of claim 1 , further comprising means for performing sentiment analysis, keyword extraction, and theme extraction of the input text using natural language processing techniques.

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