System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to input a diary; means for transmitting the input diary data to a server; means for the server to receive the diary data and analyze emotions and life patterns using a natural language processing engine; means for generating advice for the user based on the analysis result; means for transmitting the generated advice to a user terminal; and means for the user terminal to display the advice to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, busy lives and stressful environments often make it difficult to maintain mental health. In particular, lack of understanding of one's own emotions and lifestyle patterns and lack of appropriate advice makes self-improvement and improving one's quality of life difficult. To address these issues, there is a need for a system that allows users to analyze their own emotions and lifestyle patterns through a diary and receive specific advice based on that analysis. [Means for solving the problem]

[0005] The present invention solves the above problems by providing a system that includes a means for allowing a user to input a diary entry, a means for transmitting the input diary entry data to a server, and a means for the server to analyze the received diary entry data using a natural language processing engine to extract emotions and lifestyle patterns. It also provides a system that includes a means for generating specific advice for the user based on the analysis results, a means for transmitting the generated advice to a user terminal, and a means for the user terminal to display the advice to the user. This system allows users to visualize their own emotions and lifestyle patterns and receive useful advice to enrich their daily lives.

[0006] "User" refers to an individual who uses the system to enter a diary entry and receive analysis results and advice.

[0007] "Terminal" refers to the device used by the user to enter diary entries and communicate with the server to receive analysis results and advice.

[0008] A "diary" refers to a personal record in text format that a user enters via a device.

[0009] "Server" refers to a computer system that receives diary data sent by users, analyzes it, and generates analysis results and advice.

[0010] A "natural language processing engine" refers to algorithms and software that analyze text data and extract emotions and lifestyle patterns.

[0011] "Sentiment analysis" refers to the process of identifying emotions contained in a diary and analyzing the trends of those emotions.

[0012] "Lifestyle patterns" refer to the regularities in a user's lifestyle and behavior extracted from the user's diary data.

[0013] "Analysis results" refers to information about emotions and lifestyle patterns generated by a natural language processing engine based on the user's diary data.

[0014] "Advice" refers to specific suggestions or instructions generated based on the analysis results and provided to the user.

[0015] "Sending" refers to the act of sending diary data from a user device to a server, or sending analysis results or advice from the server to a user device. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention relates to a system that uses natural language processing technology to analyze diary data entered by a user, clarifying emotional tendencies and lifestyle patterns, and providing specific advice based on the results. This system operates in the following manner: the user enters the diary, the server analyzes the data, and the analysis results and advice are provided to the user.

[0038] User Actions

[0039] 1. Diary entry:

[0040] The user launches the application on their device and a text box appears where they can enter their diary entry.

[0041] Users enter information about their daily events and feelings into a text box.

[0042] Once the input is complete, the user presses the "Send" button to send the diary data to the server.

[0043] 2. Example:

[0044] For example, a user may enter "I had a lot of work today and I'm tired, but I met up with a friend in the evening and felt refreshed," and then press the send button. This sends the diary data to the server.

[0045] Device behavior

[0046] 1. Data transmission:

[0047] The device formats the diary data entered by the user into JSON format and sends it to the server's API endpoint.

[0048] 2. Receiving the analysis results:

[0049] The terminal waits for the analysis results and advice from the server.

[0050] Receives analysis results from the server and prepares them for display to the user.

[0051] 3. Results display:

[0052] The analysis results and advice received by the device are displayed on the UI, providing a visual notification to the user.

[0053] Server Operation

[0054] 1. Data reception:

[0055] The server receives the diary data sent from the device and converts the received data into an appropriate format.

[0056] 2. Analysis by natural language processing engine:

[0057] The server sends the diary data to a natural language processing engine, which analyzes the text data and extracts emotional trends (e.g., positive or negative emotions) and specific lifestyle patterns (e.g., causes of stress and ways to refresh).

[0058] The extracted data is then passed through a more detailed analysis algorithm to assess the user's happiness and stress levels.

[0059] 3. Generating Advice:

[0060] Based on the analysis results, the server generates specific advice for the user, such as "Spending more time with friends will help reduce stress."

[0061] The generated advice and analysis results are compiled into a single response data.

[0062] 4. Send results:

[0063] The server sends the compiled response data to the device and provides advice to the user.

[0064] Specific examples

[0065] The server receives diary data in which the user has entered "I had a lot of work today and I was tired, but I met up with a friend in the evening and felt refreshed."

[0066] The server sends this text to a natural language processing engine for analysis.

[0067] The engine extracts "tired" (negative emotion) and "energetic" (positive emotion).

[0068] Based on this, the server generates advice such as "Spending more time with friends is an effective way to relieve stress."

[0069] The server sends the analysis results and advice to the device, which then notifies the user.

[0070] This system is an effective tool for users to understand their own emotions and lifestyle patterns and receive specific advice to improve their daily lives.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] The user starts the application on the terminal to input the diary.

[0074] Step 2:

[0075] The user inputs a diary entry into a text box in the application and presses the "Send" button to instruct the application to send the diary entry.

[0076] Step 3:

[0077] The device formats the diary data entered by the user into JSON format.

[0078] Step 4:

[0079] The device sends the formatted diary data as a POST request to the server's API endpoint.

[0080] Step 5:

[0081] The server receives the diary data transmitted from the terminal.

[0082] Step 6:

[0083] The server sends the received diary data to a natural language processing engine and begins analysis.

[0084] Step 7:

[0085] A natural language processing engine analyzes the diary data and extracts emotional trends (positive, negative, etc.) and lifestyle patterns from the text.

[0086] Step 8:

[0087] The server passes the analysis results obtained from the natural language processing engine to an evaluation algorithm, which evaluates the user's happiness and stress levels.

[0088] Step 9:

[0089] The server generates specific advice for the user based on the analysis results and evaluation.

[0090] Step 10:

[0091] The advice and analysis results generated by the server are compiled and constructed as response data.

[0092] Step 11:

[0093] The server sends the response data to the terminal.

[0094] Step 12:

[0095] The terminal receives the response data from the server.

[0096] Step 13:

[0097] The analysis results and advice received by the terminal are visually displayed to the user.

[0098] Step 14:

[0099] The user checks the display on the device and uses the diary analysis results and advice as a reference.

[0100] For example, a user writes in their diary, "I had a lot of work today and I'm tired, but I felt better after meeting up with friends this evening," and presses the send button. This diary data is analyzed through the steps described above, and the server displays advice on the device, such as, "If you value the time you spend with friends, you will reduce stress." Through this process, the user can understand their own emotional tendencies and effective ways to deal with them.

[0101] Example 1

[0102] 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."

[0103] In recent years, there has been an increasing demand for tools to improve people's happiness in a stressful society. Conventional methods lacked a mechanism for analyzing users' emotions and lifestyle patterns in detail and providing specific advice based on that analysis. This made it difficult for users to understand their own emotions and lifestyle patterns and take appropriate action. The purpose of this invention is to provide a system that improves users' happiness by allowing users to easily input daily diary data, analyzing that data, and providing specific advice.

[0104] 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.

[0105] In this invention, the server includes means for a user to input a diary entry, means for transmitting the input diary data to the server, means for the server to receive the diary data and analyze emotions and lifestyle patterns using a natural language processing engine, means for generating advice for the user based on the analysis results, means for transmitting the generated advice to the user terminal, means for the user terminal to display the advice to the user, means for the user terminal to convert the diary data into JSON format and transmit it to the server, means for the server to convert the diary data into an appropriate format and pass it to the natural language processing engine, and means for using a text analysis engine as the natural language processing engine. This enables users to easily understand their own emotions and lifestyle patterns and receive specific advice based on that understanding.

[0106] "User" refers to a person who uses the system to input diary data and receive the analysis results.

[0107] "Diary data" refers to text data entered by users to describe their daily events and feelings.

[0108] "Server" refers to a computer system that receives diary data sent by users, analyzes it, and generates advice.

[0109] A "natural language processing engine" refers to a technical means of analyzing diary data and extracting emotions and lifestyle patterns from the text.

[0110] "Sentiment analysis" refers to the process of extracting and analyzing positive and negative emotions from diary data.

[0111] "Lifestyle patterns" refer to trends in a user's behavior and habits extracted from the user's diary data.

[0112] "Advice" refers to specific guidelines for action provided to users based on the analysis results.

[0113] "User device" refers to the device (e.g., smartphone, PC) used by the user to enter diary entries and receive analysis results and advice.

[0114] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a data format for structuring diary data and sending it to a server.

[0115] "Analysis results" refers to the output of data analyzed by a natural language processing engine, including information on emotions and lifestyle patterns.

[0116] "Generative AI model" refers to an artificial intelligence model used to generate specific advice based on analytical results.

[0117] This invention is a system that allows users to input diary data, analyze it using natural language processing technology to clarify emotional tendencies and lifestyle patterns, and provides specific advice based on the results. This system is implemented as follows: the user inputs the diary, the server analyzes the data, and the analysis results and advice are provided to the user.

[0118] Users start the application on their smartphone, PC, or other device and enter their diary entries. A text box for entering diary entries is displayed, and the user enters details about daily events and emotions into the text box. Once the entry is complete, the user presses the "Send" button to send the diary data to the server.

[0119] The device converts the diary data entered by the user into JSON format and sends it to the server's API endpoint. To do this, the device uses, for example, an HTTP POST request. The server receives the diary data sent from the device and converts the received data into an appropriate format. This process can be performed using, for example, a script written in Python or a web framework (e.g., Flask or Django).

[0120] The server sends the received diary data to a natural language processing engine. Libraries such as "spaCy" and "BERT" are used as natural language processing engines. "spaCy" is a fast and powerful natural language processing library, and "BERT" is a Transformer model based on deep learning. The engine analyzes the diary text and extracts emotional trends (positive, negative, etc.) and specific lifestyle patterns. For example, when analyzing the text "I had a lot of work today and I was tired, but I felt better after meeting up with friends in the evening," it extracts "tired" (negative emotion) and "energetic" (positive emotion).

[0121] The extracted data is passed to a more detailed analysis algorithm to evaluate the user's happiness and stress levels. The server generates specific advice based on the analysis results. A generative AI model can be used to generate advice. For example, it could provide a specific guideline for action, such as "Spending more time with friends will help reduce stress." The generated advice and analysis results are then combined into a single response data set.

[0122] The server sends the collected response data to the device and provides advice to the user. The device then displays the received analysis results and advice on the UI, visually notifying the user.

[0123] Examples of specific prompts include, "Analyze the user's diary data and extract phrases of positive and negative emotions," and "Evaluate lifestyle patterns and happiness levels based on emotional trends from the diary data and generate specific advice."

[0124] This system is an effective tool for users to understand their own emotions and lifestyle patterns and receive specific advice to improve their daily lives.

[0125] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0126] Step 1: User diary entry

[0127] A user starts the application on a device such as a smartphone or PC and enters their diary. They enter daily events and emotions in text form in the input field, and when they are finished, they press the "Send" button. The input here is the user's diary, and the output is the text data after pressing the send button.

[0128] Step 2: Send data by device

[0129] The device converts the diary text entered by the user into JSON format. After conversion, the device sends this JSON data to the server's API endpoint using an HTTP POST request. The input here is the diary text entered by the user, and the output is the diary data converted into JSON format.

[0130] Step 3: Server receives data

[0131] The server receives the JSON-formatted diary data sent from the device and converts it into an appropriate format. For example, it uses a Python script to convert the JSON data into text data. The input here is the JSON data sent from the device, and the output is text data to be passed to a natural language processing engine.

[0132] Step 4: Server sends to natural language processing engine

[0133] The server sends the converted text data to a natural language processing engine, such as "spaCy" or "BERT." The engine analyzes the text and extracts emotional trends and lifestyle patterns. The input here is the text data, and the output is the emotions and lifestyle patterns extracted based on the analysis results.

[0134] Step 5: Processing the analysis results by the server

[0135] The server receives the analysis results from the natural language processing engine and evaluates the user's happiness and stress levels using a detailed analysis algorithm. The analysis results are then processed using a generative AI model. The input here is the analysis results from the engine, and the output is evaluation data on the user's happiness and stress levels.

[0136] Step 6: Server Generates Advice

[0137] The server generates specific advice based on the evaluation data. For example, it generates a specific course of action such as "Spending more time with friends will reduce stress." The input here is the evaluation data on happiness and stress levels, and the output is the generated specific advice.

[0138] Step 7: Server sends results

[0139] The server compiles the response data, including the generated advice and analysis results, into JSON format and sends it to the terminal. The input here is the generated advice and analysis results, and the output is the response JSON data to be sent to the terminal.

[0140] Step 8: Displaying the results on your device

[0141] The terminal receives the response JSON data sent from the server and displays the analysis results and advice to the user. The input here is the response JSON data from the server, and the output is the analysis results and advice displayed on the user interface.

[0142] In this way, users can analyze their emotions and lifestyle patterns through their own diary and receive specific advice.

[0143] (Application example 1)

[0144] 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."

[0145] While existing systems that analyze users' diary data focus on analyzing emotions and lifestyle patterns, they are limited in providing specific advice based on those data. Furthermore, they lack a way to personalize the shopping experience based on users' emotions and lifestyle patterns. Therefore, further improvements are needed to improve user satisfaction.

[0146] 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.

[0147] In this invention, the server includes a means for providing personalized shopping advice based on the user's diary data, a means for using a generative AI model to generate personalized shopping advice, and a means for using a natural language processing engine to perform sentiment analysis and lifestyle pattern extraction, thereby enabling the provision of specific, personalized shopping advice based on the user's emotions and lifestyle patterns.

[0148] "User" refers to an individual who uses the system to enter a diary entry and receive analyzed advice.

[0149] "Diary data" refers to text data entered by users about daily events and emotions.

[0150] The term "server" refers to a computer system that receives and analyzes diary data, generates advice based on the results, and transmits it to the user terminal.

[0151] A "natural language processing engine" refers to a program or algorithm that analyzes text data to extract emotions and lifestyle patterns.

[0152] "Emotion" refers to the user's emotional state (e.g., positive, negative) analyzed from diary data.

[0153] "Lifestyle patterns" refers to the user's daily activities and various habits analyzed based on diary data.

[0154] "Advice" refers to specific guidelines and recommendations provided to users based on analyzed emotions and lifestyle patterns.

[0155] "User device" refers to the electronic device (e.g., smartphone, computer) used by the user to enter diary entries and receive analysis results and advice.

[0156] "Personalized shopping advice" refers to individualized purchasing suggestions and advice provided based on emotions and lifestyle patterns analyzed from a user's diary data.

[0157] "Generative AI Model" refers to the artificial intelligence model used to generate personalized shopping advice based on a user's diary data.

[0158] The present invention is a system that analyzes diary data entered by a user and provides the user with personalized shopping advice based on the analysis results. This system is composed of a user terminal, a server, and a network for communication between them.

[0159] User Actions

[0160] 1. Diary entry:

[0161] Users launch a dedicated application on their "user device" such as a smartphone or computer, and enter information about their daily events and emotions into the application's text box.

[0162] For example, a user might enter, "I've been busy at work lately and I'm feeling stressed every day. I'm looking for products that will help me relax." This input is treated as "diary data."

[0163] When the user presses the "Send" button, the diary data is sent to the server.

[0164] Device behavior

[0165] 1. Data transmission:

[0166] The user's device formats the entered diary data into JSON format and sends it to the server's API endpoint.

[0167] 2. Receiving the analysis results:

[0168] The user terminal waits for the analysis results and advice from the server.

[0169] Receives response data from the server and prepares it for display to the user.

[0170] 3. Results display:

[0171] The user device displays the received analysis results and advice on the UI, visually notifying the user.

[0172] Server Operation

[0173] 1. Data reception:

[0174] The server receives the diary data sent from the user's device and converts it into an appropriate format.

[0175] 2. Analysis by natural language processing engine:

[0176] The server sends the diary data to a "natural language processing engine" (e.g., BERT), which analyzes the text data and extracts emotions (e.g., positive, negative) and lifestyle patterns (e.g., stressful, relaxed).

[0177] 3. Generating Advice:

[0178] The server generates personalized "shopping advice" based on the analysis results, using a "generative AI model" in the process.

[0179] The generated advice and analysis results are compiled into a single response data.

[0180] 4. Send results:

[0181] The server sends the compiled response data to the user's device and delivers advice to the user.

[0182] Specific examples

[0183] Analysis results and advice examples

[0184] Input example: A user writes in their diary, "I've been busy at work lately and I'm feeling stressed every day. I'm looking for a product that will help me relax."

[0185] Analysis results: The server sends this text to a natural language processing engine (e.g., BERT), which extracts "negative emotions" and "stressful lifestyle patterns."

[0186] Generated advice: The server generates the advice "If you want to relax, try aroma oils and massage equipment."

[0187] Displaying results: The user device displays the received analysis results and advice to the user.

[0188] Prompt Sentence Examples

[0189] By inputting prompt sentences like the following into the generative AI model, appropriate shopping advice can be generated.

[0190] "A user wrote in their diary, 'I've been busy at work lately and I'm feeling stressed every day. I'm looking for products that will help me relax.' Analyze this user's emotions and lifestyle patterns to generate specific shopping advice."

[0191] The present invention allows users to receive personalized shopping advice based on their diary data, resulting in a more satisfying shopping experience. It also helps improve the quality of users' lives by providing specific guidelines for action along with the analysis results.

[0192] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0193] Step 1:

[0194] The user enters the diary entry.

[0195] Input: Users use smartphone or computer applications to input text about everyday events and feelings.

[0196] Specific behavior: The user launches the application and writes a diary entry in the text box that appears. For example, the user might type, "I've been busy at work lately and I'm feeling stressed every day. I'm looking for products that will help me relax."

[0197] Step 2:

[0198] The device transmits the diary data to the server.

[0199] Input: Diary data entered by the user (e.g., "Work has been busy lately, and I'm feeling stressed every day. I'm looking for products that will help me relax.").

[0200] Specific operation: The device formats the entered diary data into JSON format and sends it to the server's API endpoint.

[0201] Step 3:

[0202] The server receives the diary data and converts it into an appropriate format.

[0203] Input: Diary data sent from the device (JSON format).

[0204] Output: Diary data in a parseable text format.

[0205] Specific operation: The server converts the received diary data into an appropriate text format (e.g., checking and converting character encoding).

[0206] Step 4:

[0207] The server analyzes the diary data using a natural language processing engine.

[0208] Input: Text diary data.

[0209] Output: Sentiment analysis and lifestyle pattern extraction results (e.g., "negative emotions" or "stressful lifestyle patterns").

[0210] Specific operation: The server passes the diary data to a natural language processing engine (e.g., BERT) to analyze emotions and lifestyle patterns. The natural language processing engine extracts positive / negative emotions from the text and identifies lifestyle patterns.

[0211] Step 5:

[0212] The server uses the generative AI model to generate personalized shopping advice.

[0213] Input: Sentiment analysis and lifestyle pattern extraction results.

[0214] Output: Personalized shopping advice (e.g., "If you want to relax, try our aromatic oils and massage equipment.").

[0215] Specific operation: The server generates prompt sentences based on the analysis results and inputs them into the generative AI model, which then creates shopping advice based on the generated prompt sentences.

[0216] Step 6:

[0217] The server sends the analysis results and generation advice to the user's terminal.

[0218] Input: Personalized shopping advice and analytics.

[0219] Output: The response data sent to the user device.

[0220] Specific operation: The server combines the analysis results and generation advice into a single response data and sends it to the user terminal.

[0221] Step 7:

[0222] The user's device displays the analysis results and advice to the user.

[0223] Input: The response data received from the server.

[0224] Output: Advice and analysis results displayed to the user.

[0225] Specific operation: The user device displays the received analysis results and advice on the application UI and notifies the user, allowing the user to visually check and receive specific shopping advice.

[0226] 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.

[0227] This invention relates to a system that uses an emotion engine and a natural language processing engine to analyze diary entries entered by users and provides specific advice based on the results. This system allows users to visualize their emotions and lifestyle patterns through their diary entries and obtain useful information to improve their daily lives.

[0228] User Actions

[0229] 1. Diary entry:

[0230] The user launches the application on their device and a text box appears where they can enter their diary entry.

[0231] The user enters information about daily events and emotions into the text box and presses the "Send" button to send the diary data to the server.

[0232] 2. Example:

[0233] For example, a user enters "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do," and presses the send button. This sends the diary data to the server.

[0234] Device behavior

[0235] 1. Data transmission:

[0236] The device formats the diary data entered by the user into JSON format and sends it to the server's API endpoint.

[0237] 2. Receiving the analysis results:

[0238] The terminal waits for analysis results and advice from the server and receives data from the server.

[0239] 3. Results display:

[0240] The analysis results and advice received by the device are displayed on the UI, providing a visual notification to the user.

[0241] Server Operation

[0242] 1. Data reception:

[0243] The server receives the diary data sent from the device and converts the received data into an appropriate format.

[0244] 2. Analysis by emotion engine:

[0245] The server sends the received diary data to the emotion engine, which recognizes the user's emotions from the text.

[0246] The emotion engine extracts "happy" (positive emotion) and "tired" (negative emotion) and analyzes the tendencies of each emotion.

[0247] 3. Analysis by natural language processing engine:

[0248] Based on the results of the emotion engine, the server sends the diary data to a natural language processing engine to extract detailed lifestyle patterns and additional emotional factors.

[0249] 4. Evaluation of analysis results:

[0250] The server passes the analysis results obtained from the emotion engine and natural language processing engine to an evaluation algorithm, which then comprehensively evaluates the user's happiness and stress levels.

[0251] 5. Generating Advice:

[0252] Based on the analysis results, the server generates specific advice for the user, such as "If you feel stressed at work, it's a good idea to take a short break."

[0253] The generated advice and analysis results are compiled as response data.

[0254] 6. Sending the results:

[0255] The server sends the compiled response data to the terminal and delivers it to the user.

[0256] Specific examples

[0257] The server receives diary data in which the user has entered "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do."

[0258] The server sends this text to an emotion engine, which extracts positive emotions (happy) and negative emotions (tired).

[0259] The data is then sent to a natural language processing engine to analyze more detailed lifestyle patterns.

[0260] The server evaluates the user's happiness and stress levels based on their emotional tendencies and lifestyle patterns, and generates specific advice such as "If you're tired from work, it's a good idea to take a short break."

[0261] The generated advice and analysis results are sent to the device, which then notifies the user.

[0262] The system allows users to gain a deeper understanding of their emotions and lifestyle patterns and provides useful advice that can be put into practice in daily life.

[0263] The processing flow will be explained below.

[0264] Step 1:

[0265] The user starts the application on the terminal to input the diary.

[0266] Step 2:

[0267] The user inputs a diary entry into a text box in the application and presses the "Send" button to instruct the application to send the diary entry.

[0268] Step 3:

[0269] The device formats the diary data entered by the user into JSON format.

[0270] Step 4:

[0271] The device sends the formatted diary data as a POST request to the server's API endpoint.

[0272] Step 5:

[0273] The server receives the diary data sent from the device. The received data is saved in the internal storage and then the next process is performed.

[0274] Step 6:

[0275] The server sends the received diary data to an emotion engine, which analyzes the emotional trends.

[0276] Step 7:

[0277] The emotion engine analyzes the diary data and classifies emotions in the text into categories such as positive and negative.

[0278] Step 8:

[0279] The server receives the emotion analysis results from the emotion engine and sends them to the natural language processing engine.

[0280] Step 9:

[0281] A natural language processing engine uses the diary data and emotion analysis results to perform a more detailed analysis of lifestyle patterns and behavioral characteristics, for example, extracting regular sources of stress and ways to refresh oneself.

[0282] Step 10:

[0283] The server receives the analysis results from the natural language processing engine and passes the data to the evaluation algorithm.

[0284] Step 11:

[0285] The evaluation algorithm comprehensively evaluates the user's happiness and stress levels and generates specific advice based on that.

[0286] Step 12:

[0287] The server compiles the generated advice and the overall analysis results into response data.

[0288] Step 13:

[0289] The server sends the response data to the terminal.

[0290] Step 14:

[0291] The terminal receives the response data from the server.

[0292] Step 15:

[0293] The analysis results and advice received by the terminal are visually displayed to the user.

[0294] Step 16:

[0295] Users can check the analysis results and advice displayed on their device and use them in their daily lives.

[0296] As a concrete example, a user may enter "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do," and then press the send button. This diary data is analyzed through the steps described above, and the server displays advice on the device, such as "If you feel stressed at work, it's a good idea to take a short break." Through this process, the user can understand their own emotions and lifestyle patterns and receive help in achieving a better life.

[0297] Example 2

[0298] 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."

[0299] Conventional systems have been insufficient in analyzing a user's diary content and providing useful advice, due to insufficient emotional analysis and detailed analysis of lifestyle patterns. Furthermore, it has been difficult to generate specific advice based on the analysis results, making it difficult to provide effective advice to users. The present invention aims to solve these problems by providing a system that can analyze a user's diary content in detail and provide specific and effective advice.

[0300] 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.

[0301] In this invention, the server includes means for analyzing emotions using an emotion analysis engine, means for analyzing detailed lifestyle patterns using a natural language processing engine, means for comprehensively evaluating the analysis results using an evaluation algorithm, and means for generating specific advice using a generative AI model. This makes it possible to analyze the contents of a user's diary in detail and provide specific and effective advice based on the analysis results.

[0302] A "user" is someone who uses this system to enter a diary entry and receive advice.

[0303] "Diary data" is text information entered by the user about daily events and feelings.

[0304] A "terminal" is a device used to enter diary entries and receive analysis results and advice, and includes smartphones, tablets, PCs, etc.

[0305] A "server" is a computer system that receives, analyzes, evaluates, and generates advice on diary data.

[0306] An "emotion analysis engine" is a program that recognizes a user's emotions from text and generates an emotion score.

[0307] A "natural language processing engine" is a program that analyzes text data and extracts detailed lifestyle patterns and additional emotional factors.

[0308] The "evaluation algorithm" is a method for comprehensively evaluating a user's happiness and stress levels based on the results obtained from the sentiment analysis engine and natural language processing engine.

[0309] A "generative AI model" is an artificial intelligence model that generates specific advice for users based on the analysis results.

[0310] "JSON format" is the data format used when sending diary data to a server, and is a lightweight, highly readable text-based data exchange format.

[0311] An "API endpoint" is an interface for sending and receiving data from a terminal to a server.

[0312] "Response data" is data that includes analysis results and advice and is sent from the server to the terminal.

[0313] This invention is a system that analyzes diary entries entered by users using an emotion analysis engine and a natural language processing engine, and provides specific advice based on the analysis results. This system allows users to visualize their emotions and lifestyle patterns through their diary and obtain useful information to improve their daily lives.

[0314] The system uses the following hardware and software:

[0315] Hardware:

[0316] Devices: Smartphones, tablets, PCs, etc.

[0317] Server: Cloud server (e.g. AWS, Google Cloud, Azure, etc.)

[0318] software:

[0319] Applications: iOS app, Android app, Web app

[0320] Backend: Node.js, Django, Flask, etc.

[0321] Database: MySQL, PostgreSQL, MongoDB, etc.

[0322] Sentiment Analysis Engine: A sentiment analysis library built using machine learning frameworks such as TensorFlow and PyTorch

[0323] Natural Language Processing Engines: NLP libraries such as SpaCy, NLTK, BERT, etc.

[0324] User Action:

[0325] First, the user launches the application on their device. Next, a text box for entering diary entries is displayed. The user enters information about daily events and emotions into the text box and presses the "Send" button to send the diary data to the server.

[0326] For example, a user enters "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do," and presses the send button. This operation sends the diary data to the server.

[0327] Terminal behavior:

[0328] The diary data entered by the user is converted to JSON format on the device and sent to the server's API endpoint. The device then waits for and receives analysis results and advice from the server. The received results and advice are displayed on the device's UI and notified to the user.

[0329] Server behavior:

[0330] The server receives the diary data sent from the device and converts it into an appropriate format. It then uses an emotion analysis engine to analyze the diary data and recognize the user's emotions. For example, it extracts emotions such as "happy" (positive emotion) or "tired" (negative emotion).

[0331] The server then uses the results of the emotion analysis engine to further analyze the diary data using a natural language processing engine to extract lifestyle patterns and additional emotional factors, and then passes the analysis results to an evaluation algorithm to comprehensively evaluate the user's happiness and stress levels.

[0332] The server then uses the generative AI model to generate specific advice based on the analysis results. For example, it might generate advice such as, "If you feel stressed at work, it's a good idea to take a short break." The generated advice and analysis results are compiled as response data and sent to the device.

[0333] Examples:

[0334] The server receives diary data such as, "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do." The server sends this text to an emotion analysis engine, which extracts positive emotions (happy) and negative emotions (tired). Next, it uses a natural language processing engine to analyze lifestyle patterns in more detail. The server comprehensively evaluates the emotional trends and lifestyle patterns and generates specific advice such as, "If you're tired from work, it's a good idea to take a short break." The generated advice and analysis results are sent to the device, which notifies the user.

[0335] Example prompt sentence:

[0336] "Analyze the following sentence using an emotion engine and a natural language processing engine, and generate a specific piece of advice to provide to the user: 'I was happy that my boss praised me today, but I'm tired because I have a lot of work to do.'"

[0337] The system allows users to gain a deeper understanding of their emotions and lifestyle patterns and provides useful advice that can be put into practice in daily life.

[0338] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0339] Step 1:

[0340] User diary entry:

[0341] The user launches the application on their device, a text box appears for entering diary entries, and the user enters details about daily events and emotions and presses the "Send" button.

[0342] Input: Text data entered by the user.

[0343] Output: Diary data sent to the server.

[0344] Step 2:

[0345] Sending diary data:

[0346] The device formats the diary data entered by the user into JSON format, and then sends the formatted data to the server's API endpoint.

[0347] Input: Text data.

[0348] Output: Diary data in JSON format.

[0349] Specific operation: The device sends diary data in JSON format to the server using an HTTP POST request.

[0350] Step 3:

[0351] Data reception and preprocessing by the server:

[0352] The server receives the diary data in JSON format sent from the device and converts the received data into an internal format suitable for analysis.

[0353] Input: Diary data in JSON format.

[0354] Output: Diary data in internal format.

[0355] What happens: The server parses the JSON data and extracts the text fields.

[0356] Step 4:

[0357] Sentiment analysis using sentiment analysis engine:

[0358] The server passes the received diary data to an emotion analysis engine, which recognizes the user's emotions from the text. Specifically, it extracts emotions such as "happy" (positive emotion) or "tired" (negative emotion).

[0359] Input: Diary data in internal format.

[0360] Output: Sentiment analysis result (e.g., "Happy: 0.8, Tired: 0.6").

[0361] What it does: The sentiment analysis engine analyzes the text and generates a sentiment score.

[0362] Step 5:

[0363] Detailed analysis using natural language processing engine:

[0364] The server passes the results of the emotion analysis to a natural language processing engine, which then performs a detailed analysis of the diary data, extracting lifestyle patterns and additional emotional factors.

[0365] Input: Sentiment analysis results and diary data in internal format.

[0366] Output: Life pattern analysis results and additional emotional factors.

[0367] What it does: A natural language processing engine tokenizes and tags text for parts of speech, extracting contextual information.

[0368] Step 6:

[0369] Evaluation of analysis results:

[0370] The server passes the results obtained from the sentiment analysis engine and natural language processing engine to an evaluation algorithm, which then comprehensively evaluates the user's happiness and stress levels.

[0371] Input: Sentiment analysis results and lifestyle pattern analysis results.

[0372] Output: Overall rating (e.g., "Happiness score: 7, Stress score: 5").

[0373] Specific operation: The evaluation algorithm calculates an overall score based on the emotion score and lifestyle patterns.

[0374] Step 7:

[0375] Generative AI models generate advice:

[0376] The server uses a generative AI model based on the analysis results to generate specific advice for the user, such as "If you feel stressed at work, it's a good idea to take a short break."

[0377] Input: Overall evaluation results.

[0378] Output: Specific advice.

[0379] Specific operation: The generative AI model selects the most appropriate advice based on the evaluation results and outputs it as text.

[0380] Step 8:

[0381] Sending analysis results and advice:

[0382] The server compiles the generated advice and analysis results as response data and sends it to the terminal.

[0383] Input: Specific advice and analysis results.

[0384] Output: The response data.

[0385] Specific operation: The server uses an HTTP response to send response data to the terminal.

[0386] Step 9:

[0387] Receive and display results on your device:

[0388] The device receives the response data from the server, and then displays the results and advice on the UI to notify the user.

[0389] Input: Response data.

[0390] Output: Analysis results and specific advice displayed to the user.

[0391] What it does: The application parses the JSON data and displays the results in a user-friendly format.

[0392] (Application example 2)

[0393] 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."

[0394] There is a lack of specific, individualized support that employees need to manage the emotions and stress they experience daily at work and improve their productivity and performance. There is also a lack of ways to understand employees' happiness and stress levels in real time and quickly improve the work environment. As a result, there is a growing risk of employee motivation decreasing and turnover increasing.

[0395] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for using a natural language processing engine to perform emotion analysis and lifestyle pattern extraction, means for evaluating a user's happiness and stress level and generating work environment improvement proposals based on the analysis results, and means for employees to input daily events, analyze their emotions and lifestyle patterns at work, and provide specific advice for work improvement. This makes it possible to appropriately manage employee emotions and stress and quickly improve the work environment.

[0396] A "diary" is text data that allows users to record daily events and feelings.

[0397] "Means" is a general term for methods, operations, devices, etc. used to achieve a specific purpose.

[0398] A "user device" is a communication device used by a user, such as a smartphone or tablet.

[0399] A "server" is a computer system that analyzes data received from a user terminal and returns the results.

[0400] "Sentiment analysis" is the process of extracting the type and intensity of a user's emotions from input text data.

[0401] "Lifestyle patterns" is a concept that refers to the tendencies of a user's daily activities and behavior.

[0402] A "natural language processing engine" is an algorithm or software that analyzes text data and understands its meaning.

[0403] "Advice" refers to specific advice or suggestions for improving user behavior based on the analysis results.

[0404] "Employee" refers to the staff or officers working for a company or organization.

[0405] To implement this invention, a system is used in which users input their diary entries and the system analyzes the entries. The system is implemented as a smartphone application and provides an interface where employees can input their daily events and emotions.

[0406] System Configuration

[0407] 1. User Action:

[0408] The user launches the smartphone application and enters their diary entry. The entered text data is properly formatted within the application.

[0409] For example, an employee might type, "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do."

[0410] 2. Data transmission:

[0411] The smartphone application formats the entered diary data into JSON format and sends it to the server's API endpoint.

[0412] 3. Server Operation:

[0413] Data reception:

[0414] The server receives the diary data sent from the device and converts it into an appropriate format (e.g., JSON format).

[0415] Emotion analysis:

[0416] The "emotion analysis engine" running on the server analyzes the received diary data and recognizes the user's emotions. In this process, positive and negative emotions are extracted from the text data.

[0417] Lifestyle pattern analysis:

[0418] A server-based "natural language processing engine" is also used to extract detailed lifestyle patterns and additional emotional factors from the diary data, identifying patterns such as "busy" or "high stress."

[0419] Advice Generation:

[0420] Based on the analysis results, the server uses an "advice generation engine" to generate specific advice, such as "take short breaks between work sessions."

[0421] 4. Send and view results:

[0422] The server sends the generated advice and analysis results to the user's device, and the smartphone application displays the received data on its UI to visually notify the user.

[0423] Specific examples

[0424] For example, if an employee types, "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do," the following will happen:

[0425] 1. The user enters their diary into the app and submits it.

[0426] 2. The server receives the data, and the emotion analysis engine extracts the positive emotion of "happy" and the negative emotion of "tired."

[0427] 3. The natural language processing engine extracts "busy" as a lifestyle pattern.

[0428] 4. The advice generation engine generates the advice "Take short breaks between work."

[0429] 5. The app displays the advice to the user.

[0430] Prompt Sentence Examples

[0431] "How did you feel today? Write in your journal."

[0432] As described above, this invention is a system that can analyze employees' daily emotions and lifestyle patterns and provide specific advice to help improve the work environment. This system is expected to improve work efficiency by managing employees' happiness and stress levels.

[0433] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0434] Step 1:

[0435] The user launches the smartphone app and enters their diary entry.

[0436] Specifically, the user inputs text data such as "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do." By pressing the "Send" button, the input data is formatted into JSON format and sent to the server.

[0437] Step 2:

[0438] The user's device formats the diary data into JSON format and sends it to the server.

[0439] Specifically, the input text data is converted to JSON format and an HTTP POST request is sent to the server's API endpoint. The input is the diary text data entered by the user, and the output is JSON format data.

[0440] Step 3:

[0441] The server receives the diary data sent from the user terminal.

[0442] Specifically, the server receives an HTTP request via an API endpoint and receives JSON-formatted diary data for analysis. The input is the JSON-formatted diary data, and the output is the received data.

[0443] Step 4:

[0444] The server uses an emotion analysis engine to analyze the diary data and recognize the user's emotions.

[0445] Specifically, the server inputs the received diary data into an emotion analysis engine, which extracts positive emotions ("happy") and negative emotions ("tired") from the text. The input is the received diary data, and the output is the emotion analysis results.

[0446] Step 5:

[0447] The server analyzes lifestyle patterns using a natural language processing engine.

[0448] Specifically, based on the emotion analysis results, the server inputs the diary data into a natural language processing engine to extract detailed lifestyle patterns ("busy") and additional emotional factors ("high stress"). The inputs are the emotion analysis results and diary data, and the output is the lifestyle pattern analysis results.

[0449] Step 6:

[0450] The server generates specific advice based on the analysis results.

[0451] Specifically, the server uses an advice generation engine to generate specific advice such as "Take short breaks between work" based on the results of emotion analysis and lifestyle pattern analysis. The input is the results of emotion analysis and lifestyle pattern analysis, and the output is the generated advice.

[0452] Step 7:

[0453] The server sends the generated advice and analysis results to the user's device.

[0454] Specifically, the server formats the generated advice and analysis results in JSON format and sends them to the user's device as an HTTP response. The input is the generated advice and analysis results, and the output is JSON format data.

[0455] Step 8:

[0456] The user's device displays the advice and analysis results received from the server.

[0457] Specifically, the user device analyzes the JSON format data received as an HTTP response and displays advice and analysis results on the UI. The input is the JSON data received from the server, and the output is the display result on the UI.

[0458] 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.

[0459] 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.

[0460] 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.

[0461] [Second embodiment]

[0462] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0463] 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.

[0464] 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).

[0465] 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.

[0466] 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.

[0467] 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).

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] 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."

[0474] This invention relates to a system that uses natural language processing technology to analyze diary data entered by a user, clarifying emotional tendencies and lifestyle patterns, and providing specific advice based on the results. This system operates in the following manner: the user enters the diary, the server analyzes the data, and the analysis results and advice are provided to the user.

[0475] User Actions

[0476] 1. Diary entry:

[0477] The user launches the application on their device and a text box appears where they can enter their diary entry.

[0478] Users enter information about their daily events and feelings into a text box.

[0479] Once the input is complete, the user presses the "Send" button to send the diary data to the server.

[0480] 2. Example:

[0481] For example, a user may enter "I had a lot of work today and I'm tired, but I met up with a friend in the evening and felt refreshed," and then press the send button. This sends the diary data to the server.

[0482] Device behavior

[0483] 1. Data transmission:

[0484] The device formats the diary data entered by the user into JSON format and sends it to the server's API endpoint.

[0485] 2. Receiving the analysis results:

[0486] The terminal waits for the analysis results and advice from the server.

[0487] Receives analysis results from the server and prepares them for display to the user.

[0488] 3. Results display:

[0489] The analysis results and advice received by the device are displayed on the UI, providing a visual notification to the user.

[0490] Server Operation

[0491] 1. Data reception:

[0492] The server receives the diary data sent from the device and converts the received data into an appropriate format.

[0493] 2. Analysis by natural language processing engine:

[0494] The server sends the diary data to a natural language processing engine, which analyzes the text data and extracts emotional trends (e.g., positive or negative emotions) and specific lifestyle patterns (e.g., causes of stress and ways to refresh).

[0495] The extracted data is then passed through a more detailed analysis algorithm to assess the user's happiness and stress levels.

[0496] 3. Generating Advice:

[0497] Based on the analysis results, the server generates specific advice for the user, such as "Spending more time with friends will help reduce stress."

[0498] The generated advice and analysis results are compiled into a single response data.

[0499] 4. Send results:

[0500] The server sends the compiled response data to the device and provides advice to the user.

[0501] Specific examples

[0502] The server receives diary data in which the user has entered "I had a lot of work today and I was tired, but I met up with a friend in the evening and felt refreshed."

[0503] The server sends this text to a natural language processing engine for analysis.

[0504] The engine extracts "tired" (negative emotion) and "energetic" (positive emotion).

[0505] Based on this, the server generates advice such as "Spending more time with friends is an effective way to relieve stress."

[0506] The server sends the analysis results and advice to the device, which then notifies the user.

[0507] This system is an effective tool for users to understand their own emotions and lifestyle patterns and receive specific advice to improve their daily lives.

[0508] The processing flow will be explained below.

[0509] Step 1:

[0510] The user starts the application on the terminal to input the diary.

[0511] Step 2:

[0512] The user inputs a diary entry into a text box in the application and presses the "Send" button to instruct the application to send the diary entry.

[0513] Step 3:

[0514] The device formats the diary data entered by the user into JSON format.

[0515] Step 4:

[0516] The device sends the formatted diary data as a POST request to the server's API endpoint.

[0517] Step 5:

[0518] The server receives the diary data transmitted from the terminal.

[0519] Step 6:

[0520] The server sends the received diary data to a natural language processing engine and begins analysis.

[0521] Step 7:

[0522] A natural language processing engine analyzes the diary data and extracts emotional trends (positive, negative, etc.) and lifestyle patterns from the text.

[0523] Step 8:

[0524] The server passes the analysis results obtained from the natural language processing engine to an evaluation algorithm, which evaluates the user's happiness and stress levels.

[0525] Step 9:

[0526] The server generates specific advice for the user based on the analysis results and evaluation.

[0527] Step 10:

[0528] The advice and analysis results generated by the server are compiled and constructed as response data.

[0529] Step 11:

[0530] The server sends the response data to the terminal.

[0531] Step 12:

[0532] The terminal receives the response data from the server.

[0533] Step 13:

[0534] The analysis results and advice received by the terminal are visually displayed to the user.

[0535] Step 14:

[0536] The user checks the display on the device and uses the diary analysis results and advice as a reference.

[0537] For example, a user writes in their diary, "I had a lot of work today and I'm tired, but I felt better after meeting up with friends this evening," and presses the send button. This diary data is analyzed through the steps described above, and the server displays advice on the device, such as, "If you value the time you spend with friends, you will reduce stress." Through this process, the user can understand their own emotional tendencies and effective ways to deal with them.

[0538] Example 1

[0539] 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."

[0540] In recent years, there has been an increasing demand for tools to improve people's happiness in a stressful society. Conventional methods lacked a mechanism for analyzing users' emotions and lifestyle patterns in detail and providing specific advice based on that analysis. This made it difficult for users to understand their own emotions and lifestyle patterns and take appropriate action. The purpose of this invention is to provide a system that improves users' happiness by allowing users to easily input daily diary data, analyzing that data, and providing specific advice.

[0541] 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.

[0542] In this invention, the server includes means for a user to input a diary entry, means for transmitting the input diary data to the server, means for the server to receive the diary data and analyze emotions and lifestyle patterns using a natural language processing engine, means for generating advice for the user based on the analysis results, means for transmitting the generated advice to the user terminal, means for the user terminal to display the advice to the user, means for the user terminal to convert the diary data into JSON format and transmit it to the server, means for the server to convert the diary data into an appropriate format and pass it to the natural language processing engine, and means for using a text analysis engine as the natural language processing engine. This enables users to easily understand their own emotions and lifestyle patterns and receive specific advice based on that understanding.

[0543] "User" refers to a person who uses the system to input diary data and receive the analysis results.

[0544] "Diary data" refers to text data entered by users to describe their daily events and feelings.

[0545] "Server" refers to a computer system that receives diary data sent by users, analyzes it, and generates advice.

[0546] A "natural language processing engine" refers to a technical means of analyzing diary data and extracting emotions and lifestyle patterns from the text.

[0547] "Sentiment analysis" refers to the process of extracting and analyzing positive and negative emotions from diary data.

[0548] "Lifestyle patterns" refer to trends in a user's behavior and habits extracted from the user's diary data.

[0549] "Advice" refers to specific guidelines for action provided to users based on the analysis results.

[0550] "User device" refers to the device (e.g., smartphone, PC) used by the user to enter diary entries and receive analysis results and advice.

[0551] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a data format for structuring diary data and sending it to a server.

[0552] "Analysis results" refers to the output of data analyzed by a natural language processing engine, including information on emotions and lifestyle patterns.

[0553] "Generative AI model" refers to an artificial intelligence model used to generate specific advice based on analytical results.

[0554] This invention is a system that allows users to input diary data, analyze it using natural language processing technology to clarify emotional tendencies and lifestyle patterns, and provides specific advice based on the results. This system is implemented as follows: the user inputs the diary, the server analyzes the data, and the analysis results and advice are provided to the user.

[0555] Users start the application on their smartphone, PC, or other device and enter their diary entries. A text box for entering diary entries is displayed, and the user enters details about daily events and emotions into the text box. Once the entry is complete, the user presses the "Send" button to send the diary data to the server.

[0556] The device converts the diary data entered by the user into JSON format and sends it to the server's API endpoint. To do this, the device uses, for example, an HTTP POST request. The server receives the diary data sent from the device and converts the received data into an appropriate format. This process can be performed using, for example, a script written in Python or a web framework (e.g., Flask or Django).

[0557] The server sends the received diary data to a natural language processing engine. Libraries such as "spaCy" and "BERT" are used as natural language processing engines. "spaCy" is a fast and powerful natural language processing library, and "BERT" is a Transformer model based on deep learning. The engine analyzes the diary text and extracts emotional trends (positive, negative, etc.) and specific lifestyle patterns. For example, when analyzing the text "I had a lot of work today and I was tired, but I felt better after meeting up with friends in the evening," it extracts "tired" (negative emotion) and "energetic" (positive emotion).

[0558] The extracted data is passed to a more detailed analysis algorithm to evaluate the user's happiness and stress levels. The server generates specific advice based on the analysis results. A generative AI model can be used to generate advice. For example, it could provide a specific guideline for action, such as "Spending more time with friends will help reduce stress." The generated advice and analysis results are then combined into a single response data set.

[0559] The server sends the collected response data to the device and provides advice to the user. The device then displays the received analysis results and advice on the UI, visually notifying the user.

[0560] Examples of specific prompts include, "Analyze the user's diary data and extract phrases of positive and negative emotions," and "Evaluate lifestyle patterns and happiness levels based on emotional trends from the diary data and generate specific advice."

[0561] This system is an effective tool for users to understand their own emotions and lifestyle patterns and receive specific advice to improve their daily lives.

[0562] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0563] Step 1: User diary entry

[0564] A user starts the application on a device such as a smartphone or PC and enters their diary. They enter daily events and emotions in text form in the input field, and when they are finished, they press the "Send" button. The input here is the user's diary, and the output is the text data after pressing the send button.

[0565] Step 2: Send data by device

[0566] The device converts the diary text entered by the user into JSON format. After conversion, the device sends this JSON data to the server's API endpoint using an HTTP POST request. The input here is the diary text entered by the user, and the output is the diary data converted into JSON format.

[0567] Step 3: Server receives data

[0568] The server receives the JSON-formatted diary data sent from the device and converts it into an appropriate format. For example, it uses a Python script to convert the JSON data into text data. The input here is the JSON data sent from the device, and the output is text data to be passed to a natural language processing engine.

[0569] Step 4: Server sends to natural language processing engine

[0570] The server sends the converted text data to a natural language processing engine, such as "spaCy" or "BERT." The engine analyzes the text and extracts emotional trends and lifestyle patterns. The input here is the text data, and the output is the emotions and lifestyle patterns extracted based on the analysis results.

[0571] Step 5: Processing the analysis results by the server

[0572] The server receives the analysis results from the natural language processing engine and evaluates the user's happiness and stress levels using a detailed analysis algorithm. The analysis results are then processed using a generative AI model. The input here is the analysis results from the engine, and the output is evaluation data on the user's happiness and stress levels.

[0573] Step 6: Server Generates Advice

[0574] The server generates specific advice based on the evaluation data. For example, it generates a specific course of action such as "Spending more time with friends will reduce stress." The input here is the evaluation data on happiness and stress levels, and the output is the generated specific advice.

[0575] Step 7: Server sends results

[0576] The server compiles the response data, including the generated advice and analysis results, into JSON format and sends it to the terminal. The input here is the generated advice and analysis results, and the output is the response JSON data to be sent to the terminal.

[0577] Step 8: Displaying the results on your device

[0578] The terminal receives the response JSON data sent from the server and displays the analysis results and advice to the user. The input here is the response JSON data from the server, and the output is the analysis results and advice displayed on the user interface.

[0579] In this way, users can analyze their emotions and lifestyle patterns through their own diary and receive specific advice.

[0580] (Application example 1)

[0581] 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."

[0582] While existing systems that analyze users' diary data focus on analyzing emotions and lifestyle patterns, they are limited in providing specific advice based on those data. Furthermore, they lack a way to personalize the shopping experience based on users' emotions and lifestyle patterns. Therefore, further improvements are needed to improve user satisfaction.

[0583] 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.

[0584] In this invention, the server includes a means for providing personalized shopping advice based on the user's diary data, a means for using a generative AI model to generate personalized shopping advice, and a means for using a natural language processing engine to perform sentiment analysis and lifestyle pattern extraction, thereby enabling the provision of specific, personalized shopping advice based on the user's emotions and lifestyle patterns.

[0585] "User" refers to an individual who uses the system to enter a diary entry and receive analyzed advice.

[0586] "Diary data" refers to text data entered by users about daily events and emotions.

[0587] The term "server" refers to a computer system that receives and analyzes diary data, generates advice based on the results, and transmits it to the user terminal.

[0588] A "natural language processing engine" refers to a program or algorithm that analyzes text data to extract emotions and lifestyle patterns.

[0589] "Emotion" refers to the user's emotional state (e.g., positive, negative) analyzed from diary data.

[0590] "Lifestyle patterns" refers to the user's daily activities and various habits analyzed based on diary data.

[0591] "Advice" refers to specific guidelines and recommendations provided to users based on analyzed emotions and lifestyle patterns.

[0592] "User device" refers to the electronic device (e.g., smartphone, computer) used by the user to enter diary entries and receive analysis results and advice.

[0593] "Personalized shopping advice" refers to individualized purchasing suggestions and advice provided based on emotions and lifestyle patterns analyzed from a user's diary data.

[0594] "Generative AI Model" refers to the artificial intelligence model used to generate personalized shopping advice based on a user's diary data.

[0595] The present invention is a system that analyzes diary data entered by a user and provides the user with personalized shopping advice based on the analysis results. This system is composed of a user terminal, a server, and a network for communication between them.

[0596] User Actions

[0597] 1. Diary entry:

[0598] Users launch a dedicated application on their "user device" such as a smartphone or computer, and enter information about their daily events and emotions into the application's text box.

[0599] For example, a user might enter, "I've been busy at work lately and I'm feeling stressed every day. I'm looking for products that will help me relax." This input is treated as "diary data."

[0600] When the user presses the "Send" button, the diary data is sent to the server.

[0601] Device behavior

[0602] 1. Data transmission:

[0603] The user's device formats the entered diary data into JSON format and sends it to the server's API endpoint.

[0604] 2. Receiving the analysis results:

[0605] The user terminal waits for the analysis results and advice from the server.

[0606] Receives response data from the server and prepares it for display to the user.

[0607] 3. Results display:

[0608] The user device displays the received analysis results and advice on the UI, visually notifying the user.

[0609] Server Operation

[0610] 1. Data reception:

[0611] The server receives the diary data sent from the user's device and converts it into an appropriate format.

[0612] 2. Analysis by natural language processing engine:

[0613] The server sends the diary data to a "natural language processing engine" (e.g., BERT), which analyzes the text data and extracts emotions (e.g., positive, negative) and lifestyle patterns (e.g., stressful, relaxed).

[0614] 3. Generating Advice:

[0615] The server generates personalized "shopping advice" based on the analysis results, using a "generative AI model" in the process.

[0616] The generated advice and analysis results are compiled into a single response data.

[0617] 4. Send results:

[0618] The server sends the compiled response data to the user's device and delivers advice to the user.

[0619] Specific examples

[0620] Analysis results and advice examples

[0621] Input example: A user writes in their diary, "I've been busy at work lately and I'm feeling stressed every day. I'm looking for a product that will help me relax."

[0622] Analysis results: The server sends this text to a natural language processing engine (e.g., BERT), which extracts "negative emotions" and "stressful lifestyle patterns."

[0623] Generated advice: The server generates the advice "If you want to relax, try aroma oils and massage equipment."

[0624] Displaying results: The user device displays the received analysis results and advice to the user.

[0625] Prompt Sentence Examples

[0626] By inputting prompt sentences like the following into the generative AI model, appropriate shopping advice can be generated.

[0627] "A user wrote in their diary, 'I've been busy at work lately and I'm feeling stressed every day. I'm looking for products that will help me relax.' Analyze this user's emotions and lifestyle patterns to generate specific shopping advice."

[0628] The present invention allows users to receive personalized shopping advice based on their diary data, resulting in a more satisfying shopping experience. It also helps improve the quality of users' lives by providing specific guidelines for action along with the analysis results.

[0629] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0630] Step 1:

[0631] The user enters the diary entry.

[0632] Input: Users use smartphone or computer applications to input text about everyday events and feelings.

[0633] Specific behavior: The user launches the application and writes a diary entry in the text box that appears. For example, the user might type, "I've been busy at work lately and I'm feeling stressed every day. I'm looking for products that will help me relax."

[0634] Step 2:

[0635] The device transmits the diary data to the server.

[0636] Input: Diary data entered by the user (e.g., "Work has been busy lately, and I'm feeling stressed every day. I'm looking for products that will help me relax.").

[0637] Specific operation: The device formats the entered diary data into JSON format and sends it to the server's API endpoint.

[0638] Step 3:

[0639] The server receives the diary data and converts it into an appropriate format.

[0640] Input: Diary data sent from the device (JSON format).

[0641] Output: Diary data in a parseable text format.

[0642] Specific operation: The server converts the received diary data into an appropriate text format (e.g., checking and converting character encoding).

[0643] Step 4:

[0644] The server analyzes the diary data using a natural language processing engine.

[0645] Input: Text diary data.

[0646] Output: Sentiment analysis and lifestyle pattern extraction results (e.g., "negative emotions" or "stressful lifestyle patterns").

[0647] Specific operation: The server passes the diary data to a natural language processing engine (e.g., BERT) to analyze emotions and lifestyle patterns. The natural language processing engine extracts positive / negative emotions from the text and identifies lifestyle patterns.

[0648] Step 5:

[0649] The server uses the generative AI model to generate personalized shopping advice.

[0650] Input: Sentiment analysis and lifestyle pattern extraction results.

[0651] Output: Personalized shopping advice (e.g., "If you want to relax, try our aromatic oils and massage equipment.").

[0652] Specific operation: The server generates prompt sentences based on the analysis results and inputs them into the generative AI model, which then creates shopping advice based on the generated prompt sentences.

[0653] Step 6:

[0654] The server sends the analysis results and generation advice to the user's terminal.

[0655] Input: Personalized shopping advice and analytics.

[0656] Output: The response data sent to the user device.

[0657] Specific operation: The server combines the analysis results and generation advice into a single response data and sends it to the user terminal.

[0658] Step 7:

[0659] The user's device displays the analysis results and advice to the user.

[0660] Input: The response data received from the server.

[0661] Output: Advice and analysis results displayed to the user.

[0662] Specific operation: The user device displays the received analysis results and advice on the application UI and notifies the user, allowing the user to visually check and receive specific shopping advice.

[0663] 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.

[0664] This invention relates to a system that uses an emotion engine and a natural language processing engine to analyze diary entries entered by users and provides specific advice based on the results. This system allows users to visualize their emotions and lifestyle patterns through their diary entries and obtain useful information to improve their daily lives.

[0665] User Actions

[0666] 1. Diary entry:

[0667] The user launches the application on their device and a text box appears where they can enter their diary entry.

[0668] The user enters information about daily events and emotions into the text box and presses the "Send" button to send the diary data to the server.

[0669] 2. Example:

[0670] For example, a user enters "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do," and presses the send button. This sends the diary data to the server.

[0671] Device behavior

[0672] 1. Data transmission:

[0673] The device formats the diary data entered by the user into JSON format and sends it to the server's API endpoint.

[0674] 2. Receiving the analysis results:

[0675] The terminal waits for analysis results and advice from the server and receives data from the server.

[0676] 3. Results display:

[0677] The analysis results and advice received by the device are displayed on the UI, providing a visual notification to the user.

[0678] Server Operation

[0679] 1. Data reception:

[0680] The server receives the diary data sent from the device and converts the received data into an appropriate format.

[0681] 2. Analysis by emotion engine:

[0682] The server sends the received diary data to the emotion engine, which recognizes the user's emotions from the text.

[0683] The emotion engine extracts "happy" (positive emotion) and "tired" (negative emotion) and analyzes the tendencies of each emotion.

[0684] 3. Analysis by natural language processing engine:

[0685] Based on the results of the emotion engine, the server sends the diary data to a natural language processing engine to extract detailed lifestyle patterns and additional emotional factors.

[0686] 4. Evaluation of analysis results:

[0687] The server passes the analysis results obtained from the emotion engine and natural language processing engine to an evaluation algorithm, which then comprehensively evaluates the user's happiness and stress levels.

[0688] 5. Generating Advice:

[0689] Based on the analysis results, the server generates specific advice for the user, such as "If you feel stressed at work, it's a good idea to take a short break."

[0690] The generated advice and analysis results are compiled as response data.

[0691] 6. Sending the results:

[0692] The server sends the compiled response data to the terminal and delivers it to the user.

[0693] Specific examples

[0694] The server receives diary data in which the user has entered "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do."

[0695] The server sends this text to an emotion engine, which extracts positive emotions (happy) and negative emotions (tired).

[0696] The data is then sent to a natural language processing engine to analyze more detailed lifestyle patterns.

[0697] The server evaluates the user's happiness and stress levels based on their emotional tendencies and lifestyle patterns, and generates specific advice such as "If you're tired from work, it's a good idea to take a short break."

[0698] The generated advice and analysis results are sent to the device, which then notifies the user.

[0699] The system allows users to gain a deeper understanding of their emotions and lifestyle patterns and provides useful advice that can be put into practice in daily life.

[0700] The processing flow will be explained below.

[0701] Step 1:

[0702] The user starts the application on the terminal to input the diary.

[0703] Step 2:

[0704] The user inputs a diary entry into a text box in the application and presses the "Send" button to instruct the application to send the diary entry.

[0705] Step 3:

[0706] The device formats the diary data entered by the user into JSON format.

[0707] Step 4:

[0708] The device sends the formatted diary data as a POST request to the server's API endpoint.

[0709] Step 5:

[0710] The server receives the diary data sent from the device. The received data is saved in the internal storage and then the next process is performed.

[0711] Step 6:

[0712] The server sends the received diary data to an emotion engine, which analyzes the emotional trends.

[0713] Step 7:

[0714] The emotion engine analyzes the diary data and classifies emotions in the text into categories such as positive and negative.

[0715] Step 8:

[0716] The server receives the emotion analysis results from the emotion engine and sends them to the natural language processing engine.

[0717] Step 9:

[0718] A natural language processing engine uses the diary data and emotion analysis results to perform a more detailed analysis of lifestyle patterns and behavioral characteristics, for example, extracting regular sources of stress and ways to refresh oneself.

[0719] Step 10:

[0720] The server receives the analysis results from the natural language processing engine and passes the data to the evaluation algorithm.

[0721] Step 11:

[0722] The evaluation algorithm comprehensively evaluates the user's happiness and stress levels and generates specific advice based on that.

[0723] Step 12:

[0724] The server compiles the generated advice and the overall analysis results into response data.

[0725] Step 13:

[0726] The server sends the response data to the terminal.

[0727] Step 14:

[0728] The terminal receives the response data from the server.

[0729] Step 15:

[0730] The analysis results and advice received by the terminal are visually displayed to the user.

[0731] Step 16:

[0732] Users can check the analysis results and advice displayed on their device and use them in their daily lives.

[0733] As a concrete example, a user may enter "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do," and then press the send button. This diary data is analyzed through the steps described above, and the server displays advice on the device, such as "If you feel stressed at work, it's a good idea to take a short break." Through this process, the user can understand their own emotions and lifestyle patterns and receive help in achieving a better life.

[0734] Example 2

[0735] 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."

[0736] Conventional systems have been insufficient in analyzing a user's diary content and providing useful advice, due to insufficient emotional analysis and detailed analysis of lifestyle patterns. Furthermore, it has been difficult to generate specific advice based on the analysis results, making it difficult to provide effective advice to users. The present invention aims to solve these problems by providing a system that can analyze a user's diary content in detail and provide specific and effective advice.

[0737] 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.

[0738] In this invention, the server includes means for analyzing emotions using an emotion analysis engine, means for analyzing detailed lifestyle patterns using a natural language processing engine, means for comprehensively evaluating the analysis results using an evaluation algorithm, and means for generating specific advice using a generative AI model. This makes it possible to analyze the contents of a user's diary in detail and provide specific and effective advice based on the analysis results.

[0739] A "user" is someone who uses this system to enter a diary entry and receive advice.

[0740] "Diary data" is text information entered by the user about daily events and feelings.

[0741] A "terminal" is a device used to enter diary entries and receive analysis results and advice, and includes smartphones, tablets, PCs, etc.

[0742] A "server" is a computer system that receives, analyzes, evaluates, and generates advice on diary data.

[0743] An "emotion analysis engine" is a program that recognizes a user's emotions from text and generates an emotion score.

[0744] A "natural language processing engine" is a program that analyzes text data and extracts detailed lifestyle patterns and additional emotional factors.

[0745] The "evaluation algorithm" is a method for comprehensively evaluating a user's happiness and stress levels based on the results obtained from the sentiment analysis engine and natural language processing engine.

[0746] A "generative AI model" is an artificial intelligence model that generates specific advice for users based on the analysis results.

[0747] "JSON format" is the data format used when sending diary data to a server, and is a lightweight, highly readable text-based data exchange format.

[0748] An "API endpoint" is an interface for sending and receiving data from a terminal to a server.

[0749] "Response data" is data that includes analysis results and advice and is sent from the server to the terminal.

[0750] This invention is a system that analyzes diary entries entered by users using an emotion analysis engine and a natural language processing engine, and provides specific advice based on the analysis results. This system allows users to visualize their emotions and lifestyle patterns through their diary and obtain useful information to improve their daily lives.

[0751] The system uses the following hardware and software:

[0752] Hardware:

[0753] Devices: Smartphones, tablets, PCs, etc.

[0754] Server: Cloud server (e.g. AWS, Google Cloud, Azure, etc.)

[0755] software:

[0756] Applications: iOS app, Android app, Web app

[0757] Backend: Node.js, Django, Flask, etc.

[0758] Database: MySQL, PostgreSQL, MongoDB, etc.

[0759] Sentiment Analysis Engine: A sentiment analysis library built using machine learning frameworks such as TensorFlow and PyTorch

[0760] Natural Language Processing Engines: NLP libraries such as SpaCy, NLTK, BERT, etc.

[0761] User Action:

[0762] First, the user launches the application on their device. Next, a text box for entering diary entries is displayed. The user enters information about daily events and emotions into the text box and presses the "Send" button to send the diary data to the server.

[0763] For example, a user enters "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do," and presses the send button. This operation sends the diary data to the server.

[0764] Terminal behavior:

[0765] The diary data entered by the user is converted to JSON format on the device and sent to the server's API endpoint. The device then waits for and receives analysis results and advice from the server. The received results and advice are displayed on the device's UI and notified to the user.

[0766] Server behavior:

[0767] The server receives the diary data sent from the device and converts it into an appropriate format. It then uses an emotion analysis engine to analyze the diary data and recognize the user's emotions. For example, it extracts emotions such as "happy" (positive emotion) or "tired" (negative emotion).

[0768] The server then uses the results of the emotion analysis engine to further analyze the diary data using a natural language processing engine to extract lifestyle patterns and additional emotional factors, and then passes the analysis results to an evaluation algorithm to comprehensively evaluate the user's happiness and stress levels.

[0769] The server then uses the generative AI model to generate specific advice based on the analysis results. For example, it might generate advice such as, "If you feel stressed at work, it's a good idea to take a short break." The generated advice and analysis results are compiled as response data and sent to the device.

[0770] Examples:

[0771] The server receives diary data such as, "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do." The server sends this text to an emotion analysis engine, which extracts positive emotions (happy) and negative emotions (tired). Next, it uses a natural language processing engine to analyze lifestyle patterns in more detail. The server comprehensively evaluates the emotional trends and lifestyle patterns and generates specific advice such as, "If you're tired from work, it's a good idea to take a short break." The generated advice and analysis results are sent to the device, which notifies the user.

[0772] Example prompt sentence:

[0773] "Analyze the following sentence using an emotion engine and a natural language processing engine, and generate a specific piece of advice to provide to the user: 'I was happy that my boss praised me today, but I'm tired because I have a lot of work to do.'"

[0774] The system allows users to gain a deeper understanding of their emotions and lifestyle patterns and provides useful advice that can be put into practice in daily life.

[0775] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0776] Step 1:

[0777] User diary entry:

[0778] The user launches the application on their device, a text box appears for entering diary entries, and the user enters details about daily events and emotions and presses the "Send" button.

[0779] Input: Text data entered by the user.

[0780] Output: Diary data sent to the server.

[0781] Step 2:

[0782] Sending diary data:

[0783] The device formats the diary data entered by the user into JSON format, and then sends the formatted data to the server's API endpoint.

[0784] Input: Text data.

[0785] Output: Diary data in JSON format.

[0786] Specific operation: The device sends diary data in JSON format to the server using an HTTP POST request.

[0787] Step 3:

[0788] Data reception and preprocessing by the server:

[0789] The server receives the diary data in JSON format sent from the device and converts the received data into an internal format suitable for analysis.

[0790] Input: Diary data in JSON format.

[0791] Output: Diary data in internal format.

[0792] What happens: The server parses the JSON data and extracts the text fields.

[0793] Step 4:

[0794] Sentiment analysis using sentiment analysis engine:

[0795] The server passes the received diary data to an emotion analysis engine, which recognizes the user's emotions from the text. Specifically, it extracts emotions such as "happy" (positive emotion) or "tired" (negative emotion).

[0796] Input: Diary data in internal format.

[0797] Output: Sentiment analysis result (e.g., "Happy: 0.8, Tired: 0.6").

[0798] What it does: The sentiment analysis engine analyzes the text and generates a sentiment score.

[0799] Step 5:

[0800] Detailed analysis using natural language processing engine:

[0801] The server passes the results of the emotion analysis to a natural language processing engine, which then performs a detailed analysis of the diary data, extracting lifestyle patterns and additional emotional factors.

[0802] Input: Sentiment analysis results and diary data in internal format.

[0803] Output: Life pattern analysis results and additional emotional factors.

[0804] What it does: A natural language processing engine tokenizes and tags text for parts of speech, extracting contextual information.

[0805] Step 6:

[0806] Evaluation of analysis results:

[0807] The server passes the results obtained from the sentiment analysis engine and natural language processing engine to an evaluation algorithm, which then comprehensively evaluates the user's happiness and stress levels.

[0808] Input: Sentiment analysis results and lifestyle pattern analysis results.

[0809] Output: Overall rating (e.g., "Happiness score: 7, Stress score: 5").

[0810] Specific operation: The evaluation algorithm calculates an overall score based on the emotion score and lifestyle patterns.

[0811] Step 7:

[0812] Generative AI models generate advice:

[0813] The server uses a generative AI model based on the analysis results to generate specific advice for the user, such as "If you feel stressed at work, it's a good idea to take a short break."

[0814] Input: Overall evaluation results.

[0815] Output: Specific advice.

[0816] Specific operation: The generative AI model selects the most appropriate advice based on the evaluation results and outputs it as text.

[0817] Step 8:

[0818] Sending analysis results and advice:

[0819] The server compiles the generated advice and analysis results as response data and sends it to the terminal.

[0820] Input: Specific advice and analysis results.

[0821] Output: The response data.

[0822] Specific operation: The server uses an HTTP response to send response data to the terminal.

[0823] Step 9:

[0824] Receive and display results on your device:

[0825] The device receives the response data from the server, and then displays the results and advice on the UI to notify the user.

[0826] Input: Response data.

[0827] Output: Analysis results and specific advice displayed to the user.

[0828] What it does: The application parses the JSON data and displays the results in a user-friendly format.

[0829] (Application example 2)

[0830] 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."

[0831] There is a lack of specific, individualized support that employees need to manage the emotions and stress they experience daily at work and improve their productivity and performance. There is also a lack of ways to understand employees' happiness and stress levels in real time and quickly improve the work environment. As a result, there is a growing risk of employee motivation decreasing and turnover increasing.

[0832] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for using a natural language processing engine to perform emotion analysis and lifestyle pattern extraction, means for evaluating a user's happiness and stress level and generating work environment improvement proposals based on the analysis results, and means for employees to input daily events, analyze their emotions and lifestyle patterns at work, and provide specific advice for work improvement. This makes it possible to appropriately manage employee emotions and stress and quickly improve the work environment.

[0833] A "diary" is text data that allows users to record daily events and feelings.

[0834] "Means" is a general term for methods, operations, devices, etc. used to achieve a specific purpose.

[0835] A "user device" is a communication device used by a user, such as a smartphone or tablet.

[0836] A "server" is a computer system that analyzes data received from a user terminal and returns the results.

[0837] "Sentiment analysis" is the process of extracting the type and intensity of a user's emotions from input text data.

[0838] "Lifestyle patterns" is a concept that refers to the tendencies of a user's daily activities and behavior.

[0839] A "natural language processing engine" is an algorithm or software that analyzes text data and understands its meaning.

[0840] "Advice" refers to specific advice or suggestions for improving user behavior based on the analysis results.

[0841] "Employee" refers to the staff or officers working for a company or organization.

[0842] To implement this invention, a system is used in which users input their diary entries and the system analyzes the entries. The system is implemented as a smartphone application and provides an interface where employees can input their daily events and emotions.

[0843] System Configuration

[0844] 1. User Action:

[0845] The user launches the smartphone application and enters their diary entry. The entered text data is properly formatted within the application.

[0846] For example, an employee might type, "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do."

[0847] 2. Data transmission:

[0848] The smartphone application formats the entered diary data into JSON format and sends it to the server's API endpoint.

[0849] 3. Server Operation:

[0850] Data reception:

[0851] The server receives the diary data sent from the device and converts it into an appropriate format (e.g., JSON format).

[0852] Emotion analysis:

[0853] The "emotion analysis engine" running on the server analyzes the received diary data and recognizes the user's emotions. In this process, positive and negative emotions are extracted from the text data.

[0854] Lifestyle pattern analysis:

[0855] A server-based "natural language processing engine" is also used to extract detailed lifestyle patterns and additional emotional factors from the diary data, identifying patterns such as "busy" or "high stress."

[0856] Advice Generation:

[0857] Based on the analysis results, the server uses an "advice generation engine" to generate specific advice, such as "take short breaks between work sessions."

[0858] 4. Send and view results:

[0859] The server sends the generated advice and analysis results to the user's device, and the smartphone application displays the received data on its UI to visually notify the user.

[0860] Specific examples

[0861] For example, if an employee types, "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do," the following will happen:

[0862] 1. The user enters their diary into the app and submits it.

[0863] 2. The server receives the data, and the emotion analysis engine extracts the positive emotion of "happy" and the negative emotion of "tired."

[0864] 3. The natural language processing engine extracts "busy" as a lifestyle pattern.

[0865] 4. The advice generation engine generates the advice "Take short breaks between work."

[0866] 5. The app displays the advice to the user.

[0867] Prompt Sentence Examples

[0868] "How did you feel today? Write in your journal."

[0869] As described above, this invention is a system that can analyze employees' daily emotions and lifestyle patterns and provide specific advice to help improve the work environment. This system is expected to improve work efficiency by managing employees' happiness and stress levels.

[0870] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0871] Step 1:

[0872] The user launches the smartphone app and enters their diary entry.

[0873] Specifically, the user inputs text data such as "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do." By pressing the "Send" button, the input data is formatted into JSON format and sent to the server.

[0874] Step 2:

[0875] The user's device formats the diary data into JSON format and sends it to the server.

[0876] Specifically, the input text data is converted to JSON format and an HTTP POST request is sent to the server's API endpoint. The input is the diary text data entered by the user, and the output is JSON format data.

[0877] Step 3:

[0878] The server receives the diary data sent from the user terminal.

[0879] Specifically, the server receives an HTTP request via an API endpoint and receives JSON-formatted diary data for analysis. The input is the JSON-formatted diary data, and the output is the received data.

[0880] Step 4:

[0881] The server uses an emotion analysis engine to analyze the diary data and recognize the user's emotions.

[0882] Specifically, the server inputs the received diary data into an emotion analysis engine, which extracts positive emotions ("happy") and negative emotions ("tired") from the text. The input is the received diary data, and the output is the emotion analysis results.

[0883] Step 5:

[0884] The server analyzes lifestyle patterns using a natural language processing engine.

[0885] Specifically, based on the emotion analysis results, the server inputs the diary data into a natural language processing engine to extract detailed lifestyle patterns ("busy") and additional emotional factors ("high stress"). The inputs are the emotion analysis results and diary data, and the output is the lifestyle pattern analysis results.

[0886] Step 6:

[0887] The server generates specific advice based on the analysis results.

[0888] Specifically, the server uses an advice generation engine to generate specific advice such as "Take short breaks between work" based on the results of emotion analysis and lifestyle pattern analysis. The input is the results of emotion analysis and lifestyle pattern analysis, and the output is the generated advice.

[0889] Step 7:

[0890] The server sends the generated advice and analysis results to the user's device.

[0891] Specifically, the server formats the generated advice and analysis results in JSON format and sends them to the user's device as an HTTP response. The input is the generated advice and analysis results, and the output is JSON format data.

[0892] Step 8:

[0893] The user's device displays the advice and analysis results received from the server.

[0894] Specifically, the user device analyzes the JSON format data received as an HTTP response and displays advice and analysis results on the UI. The input is the JSON data received from the server, and the output is the display result on the UI.

[0895] 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.

[0896] 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.

[0897] 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.

[0898] [Third embodiment]

[0899] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0900] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0901] 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).

[0902] 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.

[0903] 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.

[0904] 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).

[0905] 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.

[0906] 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.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] 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."

[0911] This invention relates to a system that uses natural language processing technology to analyze diary data entered by a user, clarifying emotional tendencies and lifestyle patterns, and providing specific advice based on the results. This system operates in the following manner: the user enters the diary, the server analyzes the data, and the analysis results and advice are provided to the user.

[0912] User Actions

[0913] 1. Diary entry:

[0914] The user launches the application on their device and a text box appears where they can enter their diary entry.

[0915] Users enter information about their daily events and feelings into a text box.

[0916] Once the input is complete, the user presses the "Send" button to send the diary data to the server.

[0917] 2. Example:

[0918] For example, a user may enter "I had a lot of work today and I'm tired, but I met up with a friend in the evening and felt refreshed," and then press the send button. This sends the diary data to the server.

[0919] Device behavior

[0920] 1. Data transmission:

[0921] The device formats the diary data entered by the user into JSON format and sends it to the server's API endpoint.

[0922] 2. Receiving the analysis results:

[0923] The terminal waits for the analysis results and advice from the server.

[0924] Receives analysis results from the server and prepares them for display to the user.

[0925] 3. Results display:

[0926] The analysis results and advice received by the device are displayed on the UI, providing a visual notification to the user.

[0927] Server Operation

[0928] 1. Data reception:

[0929] The server receives the diary data sent from the device and converts the received data into an appropriate format.

[0930] 2. Analysis by natural language processing engine:

[0931] The server sends the diary data to a natural language processing engine, which analyzes the text data and extracts emotional trends (e.g., positive or negative emotions) and specific lifestyle patterns (e.g., causes of stress and ways to refresh).

[0932] The extracted data is then passed through a more detailed analysis algorithm to assess the user's happiness and stress levels.

[0933] 3. Generating Advice:

[0934] Based on the analysis results, the server generates specific advice for the user, such as "Spending more time with friends will help reduce stress."

[0935] The generated advice and analysis results are compiled into a single response data.

[0936] 4. Send results:

[0937] The server sends the compiled response data to the device and provides advice to the user.

[0938] Specific examples

[0939] The server receives diary data in which the user has entered "I had a lot of work today and I was tired, but I met up with a friend in the evening and felt refreshed."

[0940] The server sends this text to a natural language processing engine for analysis.

[0941] The engine extracts "tired" (negative emotion) and "energetic" (positive emotion).

[0942] Based on this, the server generates advice such as "Spending more time with friends is an effective way to relieve stress."

[0943] The server sends the analysis results and advice to the device, which then notifies the user.

[0944] This system is an effective tool for users to understand their own emotions and lifestyle patterns and receive specific advice to improve their daily lives.

[0945] The processing flow will be explained below.

[0946] Step 1:

[0947] The user starts the application on the terminal to input the diary.

[0948] Step 2:

[0949] The user inputs a diary entry into a text box in the application and presses the "Send" button to instruct the application to send the diary entry.

[0950] Step 3:

[0951] The device formats the diary data entered by the user into JSON format.

[0952] Step 4:

[0953] The device sends the formatted diary data as a POST request to the server's API endpoint.

[0954] Step 5:

[0955] The server receives the diary data transmitted from the terminal.

[0956] Step 6:

[0957] The server sends the received diary data to a natural language processing engine and begins analysis.

[0958] Step 7:

[0959] A natural language processing engine analyzes the diary data and extracts emotional trends (positive, negative, etc.) and lifestyle patterns from the text.

[0960] Step 8:

[0961] The server passes the analysis results obtained from the natural language processing engine to an evaluation algorithm, which evaluates the user's happiness and stress levels.

[0962] Step 9:

[0963] The server generates specific advice for the user based on the analysis results and evaluation.

[0964] Step 10:

[0965] The advice and analysis results generated by the server are compiled and constructed as response data.

[0966] Step 11:

[0967] The server sends the response data to the terminal.

[0968] Step 12:

[0969] The terminal receives the response data from the server.

[0970] Step 13:

[0971] The analysis results and advice received by the terminal are visually displayed to the user.

[0972] Step 14:

[0973] The user checks the display on the device and uses the diary analysis results and advice as a reference.

[0974] For example, a user writes in their diary, "I had a lot of work today and I'm tired, but I felt better after meeting up with friends this evening," and presses the send button. This diary data is analyzed through the steps described above, and the server displays advice on the device, such as, "If you value the time you spend with friends, you will reduce stress." Through this process, the user can understand their own emotional tendencies and effective ways to deal with them.

[0975] Example 1

[0976] 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."

[0977] In recent years, there has been an increasing demand for tools to improve people's happiness in a stressful society. Conventional methods lacked a mechanism for analyzing users' emotions and lifestyle patterns in detail and providing specific advice based on that analysis. This made it difficult for users to understand their own emotions and lifestyle patterns and take appropriate action. The purpose of this invention is to provide a system that improves users' happiness by allowing users to easily input daily diary data, analyzing that data, and providing specific advice.

[0978] 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.

[0979] In this invention, the server includes means for a user to input a diary entry, means for transmitting the input diary data to the server, means for the server to receive the diary data and analyze emotions and lifestyle patterns using a natural language processing engine, means for generating advice for the user based on the analysis results, means for transmitting the generated advice to the user terminal, means for the user terminal to display the advice to the user, means for the user terminal to convert the diary data into JSON format and transmit it to the server, means for the server to convert the diary data into an appropriate format and pass it to the natural language processing engine, and means for using a text analysis engine as the natural language processing engine. This enables users to easily understand their own emotions and lifestyle patterns and receive specific advice based on that understanding.

[0980] "User" refers to a person who uses the system to input diary data and receive the analysis results.

[0981] "Diary data" refers to text data entered by users to describe their daily events and feelings.

[0982] "Server" refers to a computer system that receives diary data sent by users, analyzes it, and generates advice.

[0983] A "natural language processing engine" refers to a technical means of analyzing diary data and extracting emotions and lifestyle patterns from the text.

[0984] "Sentiment analysis" refers to the process of extracting and analyzing positive and negative emotions from diary data.

[0985] "Lifestyle patterns" refer to trends in a user's behavior and habits extracted from the user's diary data.

[0986] "Advice" refers to specific guidelines for action provided to users based on the analysis results.

[0987] "User device" refers to the device (e.g., smartphone, PC) used by the user to enter diary entries and receive analysis results and advice.

[0988] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a data format for structuring diary data and sending it to a server.

[0989] "Analysis results" refers to the output of data analyzed by a natural language processing engine, including information on emotions and lifestyle patterns.

[0990] "Generative AI model" refers to an artificial intelligence model used to generate specific advice based on analytical results.

[0991] This invention is a system that allows users to input diary data, analyze it using natural language processing technology to clarify emotional tendencies and lifestyle patterns, and provides specific advice based on the results. This system is implemented as follows: the user inputs the diary, the server analyzes the data, and the analysis results and advice are provided to the user.

[0992] Users start the application on their smartphone, PC, or other device and enter their diary entries. A text box for entering diary entries is displayed, and the user enters details about daily events and emotions into the text box. Once the entry is complete, the user presses the "Send" button to send the diary data to the server.

[0993] The device converts the diary data entered by the user into JSON format and sends it to the server's API endpoint. To do this, the device uses, for example, an HTTP POST request. The server receives the diary data sent from the device and converts the received data into an appropriate format. This process can be performed using, for example, a script written in Python or a web framework (e.g., Flask or Django).

[0994] The server sends the received diary data to a natural language processing engine. Libraries such as "spaCy" and "BERT" are used as natural language processing engines. "spaCy" is a fast and powerful natural language processing library, and "BERT" is a Transformer model based on deep learning. The engine analyzes the diary text and extracts emotional trends (positive, negative, etc.) and specific lifestyle patterns. For example, when analyzing the text "I had a lot of work today and I was tired, but I felt better after meeting up with friends in the evening," it extracts "tired" (negative emotion) and "energetic" (positive emotion).

[0995] The extracted data is passed to a more detailed analysis algorithm to evaluate the user's happiness and stress levels. The server generates specific advice based on the analysis results. A generative AI model can be used to generate advice. For example, it could provide a specific guideline for action, such as "Spending more time with friends will help reduce stress." The generated advice and analysis results are then combined into a single response data set.

[0996] The server sends the collected response data to the device and provides advice to the user. The device then displays the received analysis results and advice on the UI, visually notifying the user.

[0997] Examples of specific prompts include, "Analyze the user's diary data and extract phrases of positive and negative emotions," and "Evaluate lifestyle patterns and happiness levels based on emotional trends from the diary data and generate specific advice."

[0998] This system is an effective tool for users to understand their own emotions and lifestyle patterns and receive specific advice to improve their daily lives.

[0999] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1000] Step 1: User diary entry

[1001] A user starts the application on a device such as a smartphone or PC and enters their diary. They enter daily events and emotions in text form in the input field, and when they are finished, they press the "Send" button. The input here is the user's diary, and the output is the text data after pressing the send button.

[1002] Step 2: Send data by device

[1003] The device converts the diary text entered by the user into JSON format. After conversion, the device sends this JSON data to the server's API endpoint using an HTTP POST request. The input here is the diary text entered by the user, and the output is the diary data converted into JSON format.

[1004] Step 3: Server receives data

[1005] The server receives the JSON-formatted diary data sent from the device and converts it into an appropriate format. For example, it uses a Python script to convert the JSON data into text data. The input here is the JSON data sent from the device, and the output is text data to be passed to a natural language processing engine.

[1006] Step 4: Server sends to natural language processing engine

[1007] The server sends the converted text data to a natural language processing engine, such as "spaCy" or "BERT." The engine analyzes the text and extracts emotional trends and lifestyle patterns. The input here is the text data, and the output is the emotions and lifestyle patterns extracted based on the analysis results.

[1008] Step 5: Processing the analysis results by the server

[1009] The server receives the analysis results from the natural language processing engine and evaluates the user's happiness and stress levels using a detailed analysis algorithm. The analysis results are then processed using a generative AI model. The input here is the analysis results from the engine, and the output is evaluation data on the user's happiness and stress levels.

[1010] Step 6: Server Generates Advice

[1011] The server generates specific advice based on the evaluation data. For example, it generates a specific course of action such as "Spending more time with friends will reduce stress." The input here is the evaluation data on happiness and stress levels, and the output is the generated specific advice.

[1012] Step 7: Server sends results

[1013] The server compiles the response data, including the generated advice and analysis results, into JSON format and sends it to the terminal. The input here is the generated advice and analysis results, and the output is the response JSON data to be sent to the terminal.

[1014] Step 8: Displaying the results on your device

[1015] The terminal receives the response JSON data sent from the server and displays the analysis results and advice to the user. The input here is the response JSON data from the server, and the output is the analysis results and advice displayed on the user interface.

[1016] In this way, users can analyze their emotions and lifestyle patterns through their own diary and receive specific advice.

[1017] (Application example 1)

[1018] 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."

[1019] While existing systems that analyze users' diary data focus on analyzing emotions and lifestyle patterns, they are limited in providing specific advice based on those data. Furthermore, they lack a way to personalize the shopping experience based on users' emotions and lifestyle patterns. Therefore, further improvements are needed to improve user satisfaction.

[1020] 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.

[1021] In this invention, the server includes a means for providing personalized shopping advice based on the user's diary data, a means for using a generative AI model to generate personalized shopping advice, and a means for using a natural language processing engine to perform sentiment analysis and lifestyle pattern extraction, thereby enabling the provision of specific, personalized shopping advice based on the user's emotions and lifestyle patterns.

[1022] "User" refers to an individual who uses the system to enter a diary entry and receive analyzed advice.

[1023] "Diary data" refers to text data entered by users about daily events and emotions.

[1024] The term "server" refers to a computer system that receives and analyzes diary data, generates advice based on the results, and transmits it to the user terminal.

[1025] A "natural language processing engine" refers to a program or algorithm that analyzes text data to extract emotions and lifestyle patterns.

[1026] "Emotion" refers to the user's emotional state (e.g., positive, negative) analyzed from diary data.

[1027] "Lifestyle patterns" refers to the user's daily activities and various habits analyzed based on diary data.

[1028] "Advice" refers to specific guidelines and recommendations provided to users based on analyzed emotions and lifestyle patterns.

[1029] "User device" refers to the electronic device (e.g., smartphone, computer) used by the user to enter diary entries and receive analysis results and advice.

[1030] "Personalized shopping advice" refers to individualized purchasing suggestions and advice provided based on emotions and lifestyle patterns analyzed from a user's diary data.

[1031] "Generative AI Model" refers to the artificial intelligence model used to generate personalized shopping advice based on a user's diary data.

[1032] The present invention is a system that analyzes diary data entered by a user and provides the user with personalized shopping advice based on the analysis results. This system is composed of a user terminal, a server, and a network for communication between them.

[1033] User Actions

[1034] 1. Diary entry:

[1035] Users launch a dedicated application on their "user device" such as a smartphone or computer, and enter information about their daily events and emotions into the application's text box.

[1036] For example, a user might enter, "I've been busy at work lately and I'm feeling stressed every day. I'm looking for products that will help me relax." This input is treated as "diary data."

[1037] When the user presses the "Send" button, the diary data is sent to the server.

[1038] Device behavior

[1039] 1. Data transmission:

[1040] The user's device formats the entered diary data into JSON format and sends it to the server's API endpoint.

[1041] 2. Receiving the analysis results:

[1042] The user terminal waits for the analysis results and advice from the server.

[1043] Receives response data from the server and prepares it for display to the user.

[1044] 3. Results display:

[1045] The user device displays the received analysis results and advice on the UI, visually notifying the user.

[1046] Server Operation

[1047] 1. Data reception:

[1048] The server receives the diary data sent from the user's device and converts it into an appropriate format.

[1049] 2. Analysis by natural language processing engine:

[1050] The server sends the diary data to a "natural language processing engine" (e.g., BERT), which analyzes the text data and extracts emotions (e.g., positive, negative) and lifestyle patterns (e.g., stressful, relaxed).

[1051] 3. Generating Advice:

[1052] The server generates personalized "shopping advice" based on the analysis results, using a "generative AI model" in the process.

[1053] The generated advice and analysis results are compiled into a single response data.

[1054] 4. Send results:

[1055] The server sends the compiled response data to the user's device and delivers advice to the user.

[1056] Specific examples

[1057] Analysis results and advice examples

[1058] Input example: A user writes in their diary, "I've been busy at work lately and I'm feeling stressed every day. I'm looking for a product that will help me relax."

[1059] Analysis results: The server sends this text to a natural language processing engine (e.g., BERT), which extracts "negative emotions" and "stressful lifestyle patterns."

[1060] Generated advice: The server generates the advice "If you want to relax, try aroma oils and massage equipment."

[1061] Displaying results: The user device displays the received analysis results and advice to the user.

[1062] Prompt Sentence Examples

[1063] By inputting prompt sentences like the following into the generative AI model, appropriate shopping advice can be generated.

[1064] "A user wrote in their diary, 'I've been busy at work lately and I'm feeling stressed every day. I'm looking for products that will help me relax.' Analyze this user's emotions and lifestyle patterns to generate specific shopping advice."

[1065] The present invention allows users to receive personalized shopping advice based on their diary data, resulting in a more satisfying shopping experience. It also helps improve the quality of users' lives by providing specific guidelines for action along with the analysis results.

[1066] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1067] Step 1:

[1068] The user enters the diary entry.

[1069] Input: Users use smartphone or computer applications to input text about everyday events and feelings.

[1070] Specific behavior: The user launches the application and writes a diary entry in the text box that appears. For example, the user might type, "I've been busy at work lately and I'm feeling stressed every day. I'm looking for products that will help me relax."

[1071] Step 2:

[1072] The device transmits the diary data to the server.

[1073] Input: Diary data entered by the user (e.g., "Work has been busy lately, and I'm feeling stressed every day. I'm looking for products that will help me relax.").

[1074] Specific operation: The device formats the entered diary data into JSON format and sends it to the server's API endpoint.

[1075] Step 3:

[1076] The server receives the diary data and converts it into an appropriate format.

[1077] Input: Diary data sent from the device (JSON format).

[1078] Output: Diary data in a parseable text format.

[1079] Specific operation: The server converts the received diary data into an appropriate text format (e.g., checking and converting character encoding).

[1080] Step 4:

[1081] The server analyzes the diary data using a natural language processing engine.

[1082] Input: Text diary data.

[1083] Output: Sentiment analysis and lifestyle pattern extraction results (e.g., "negative emotions" or "stressful lifestyle patterns").

[1084] Specific operation: The server passes the diary data to a natural language processing engine (e.g., BERT) to analyze emotions and lifestyle patterns. The natural language processing engine extracts positive / negative emotions from the text and identifies lifestyle patterns.

[1085] Step 5:

[1086] The server uses the generative AI model to generate personalized shopping advice.

[1087] Input: Sentiment analysis and lifestyle pattern extraction results.

[1088] Output: Personalized shopping advice (e.g., "If you want to relax, try our aromatic oils and massage equipment.").

[1089] Specific operation: The server generates prompt sentences based on the analysis results and inputs them into the generative AI model, which then creates shopping advice based on the generated prompt sentences.

[1090] Step 6:

[1091] The server sends the analysis results and generation advice to the user's terminal.

[1092] Input: Personalized shopping advice and analytics.

[1093] Output: The response data sent to the user device.

[1094] Specific operation: The server combines the analysis results and generation advice into a single response data and sends it to the user terminal.

[1095] Step 7:

[1096] The user's device displays the analysis results and advice to the user.

[1097] Input: The response data received from the server.

[1098] Output: Advice and analysis results displayed to the user.

[1099] Specific operation: The user device displays the received analysis results and advice on the application UI and notifies the user, allowing the user to visually check and receive specific shopping advice.

[1100] 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.

[1101] This invention relates to a system that uses an emotion engine and a natural language processing engine to analyze diary entries entered by users and provides specific advice based on the results. This system allows users to visualize their emotions and lifestyle patterns through their diary entries and obtain useful information to improve their daily lives.

[1102] User Actions

[1103] 1. Diary entry:

[1104] The user launches the application on their device and a text box appears where they can enter their diary entry.

[1105] The user enters information about daily events and emotions into the text box and presses the "Send" button to send the diary data to the server.

[1106] 2. Example:

[1107] For example, a user enters "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do," and presses the send button. This sends the diary data to the server.

[1108] Device behavior

[1109] 1. Data transmission:

[1110] The device formats the diary data entered by the user into JSON format and sends it to the server's API endpoint.

[1111] 2. Receiving the analysis results:

[1112] The terminal waits for analysis results and advice from the server and receives data from the server.

[1113] 3. Results display:

[1114] The analysis results and advice received by the device are displayed on the UI, providing a visual notification to the user.

[1115] Server Operation

[1116] 1. Data reception:

[1117] The server receives the diary data sent from the device and converts the received data into an appropriate format.

[1118] 2. Analysis by emotion engine:

[1119] The server sends the received diary data to the emotion engine, which recognizes the user's emotions from the text.

[1120] The emotion engine extracts "happy" (positive emotion) and "tired" (negative emotion) and analyzes the tendencies of each emotion.

[1121] 3. Analysis by natural language processing engine:

[1122] Based on the results of the emotion engine, the server sends the diary data to a natural language processing engine to extract detailed lifestyle patterns and additional emotional factors.

[1123] 4. Evaluation of analysis results:

[1124] The server passes the analysis results obtained from the emotion engine and natural language processing engine to an evaluation algorithm, which then comprehensively evaluates the user's happiness and stress levels.

[1125] 5. Generating Advice:

[1126] Based on the analysis results, the server generates specific advice for the user, such as "If you feel stressed at work, it's a good idea to take a short break."

[1127] The generated advice and analysis results are compiled as response data.

[1128] 6. Sending the results:

[1129] The server sends the compiled response data to the terminal and delivers it to the user.

[1130] Specific examples

[1131] The server receives diary data in which the user has entered "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do."

[1132] The server sends this text to an emotion engine, which extracts positive emotions (happy) and negative emotions (tired).

[1133] The data is then sent to a natural language processing engine to analyze more detailed lifestyle patterns.

[1134] The server evaluates the user's happiness and stress levels based on their emotional tendencies and lifestyle patterns, and generates specific advice such as "If you're tired from work, it's a good idea to take a short break."

[1135] The generated advice and analysis results are sent to the device, which then notifies the user.

[1136] The system allows users to gain a deeper understanding of their emotions and lifestyle patterns and provides useful advice that can be put into practice in daily life.

[1137] The processing flow will be explained below.

[1138] Step 1:

[1139] The user starts the application on the terminal to input the diary.

[1140] Step 2:

[1141] The user inputs a diary entry into a text box in the application and presses the "Send" button to instruct the application to send the diary entry.

[1142] Step 3:

[1143] The device formats the diary data entered by the user into JSON format.

[1144] Step 4:

[1145] The device sends the formatted diary data as a POST request to the server's API endpoint.

[1146] Step 5:

[1147] The server receives the diary data sent from the device. The received data is saved in the internal storage and then the next process is performed.

[1148] Step 6:

[1149] The server sends the received diary data to an emotion engine, which analyzes the emotional trends.

[1150] Step 7:

[1151] The emotion engine analyzes the diary data and classifies emotions in the text into categories such as positive and negative.

[1152] Step 8:

[1153] The server receives the emotion analysis results from the emotion engine and sends them to the natural language processing engine.

[1154] Step 9:

[1155] A natural language processing engine uses the diary data and emotion analysis results to perform a more detailed analysis of lifestyle patterns and behavioral characteristics, for example, extracting regular sources of stress and ways to refresh oneself.

[1156] Step 10:

[1157] The server receives the analysis results from the natural language processing engine and passes the data to the evaluation algorithm.

[1158] Step 11:

[1159] The evaluation algorithm comprehensively evaluates the user's happiness and stress levels and generates specific advice based on that.

[1160] Step 12:

[1161] The server compiles the generated advice and the overall analysis results into response data.

[1162] Step 13:

[1163] The server sends the response data to the terminal.

[1164] Step 14:

[1165] The terminal receives the response data from the server.

[1166] Step 15:

[1167] The analysis results and advice received by the terminal are visually displayed to the user.

[1168] Step 16:

[1169] Users can check the analysis results and advice displayed on their device and use them in their daily lives.

[1170] As a concrete example, a user may enter "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do," and then press the send button. This diary data is analyzed through the steps described above, and the server displays advice on the device, such as "If you feel stressed at work, it's a good idea to take a short break." Through this process, the user can understand their own emotions and lifestyle patterns and receive help in achieving a better life.

[1171] Example 2

[1172] 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."

[1173] Conventional systems have been insufficient in analyzing a user's diary content and providing useful advice, due to insufficient emotional analysis and detailed analysis of lifestyle patterns. Furthermore, it has been difficult to generate specific advice based on the analysis results, making it difficult to provide effective advice to users. The present invention aims to solve these problems by providing a system that can analyze a user's diary content in detail and provide specific and effective advice.

[1174] 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.

[1175] In this invention, the server includes means for analyzing emotions using an emotion analysis engine, means for analyzing detailed lifestyle patterns using a natural language processing engine, means for comprehensively evaluating the analysis results using an evaluation algorithm, and means for generating specific advice using a generative AI model. This makes it possible to analyze the contents of a user's diary in detail and provide specific and effective advice based on the analysis results.

[1176] A "user" is someone who uses this system to enter a diary entry and receive advice.

[1177] "Diary data" is text information entered by the user about daily events and feelings.

[1178] A "terminal" is a device used to enter diary entries and receive analysis results and advice, and includes smartphones, tablets, PCs, etc.

[1179] A "server" is a computer system that receives, analyzes, evaluates, and generates advice on diary data.

[1180] An "emotion analysis engine" is a program that recognizes a user's emotions from text and generates an emotion score.

[1181] A "natural language processing engine" is a program that analyzes text data and extracts detailed lifestyle patterns and additional emotional factors.

[1182] The "evaluation algorithm" is a method for comprehensively evaluating a user's happiness and stress levels based on the results obtained from the sentiment analysis engine and natural language processing engine.

[1183] A "generative AI model" is an artificial intelligence model that generates specific advice for users based on the analysis results.

[1184] "JSON format" is the data format used when sending diary data to a server, and is a lightweight, highly readable text-based data exchange format.

[1185] An "API endpoint" is an interface for sending and receiving data from a terminal to a server.

[1186] "Response data" is data that includes analysis results and advice and is sent from the server to the terminal.

[1187] This invention is a system that analyzes diary entries entered by users using an emotion analysis engine and a natural language processing engine, and provides specific advice based on the analysis results. This system allows users to visualize their emotions and lifestyle patterns through their diary and obtain useful information to improve their daily lives.

[1188] The system uses the following hardware and software:

[1189] Hardware:

[1190] Devices: Smartphones, tablets, PCs, etc.

[1191] Server: Cloud server (e.g. AWS, Google Cloud, Azure, etc.)

[1192] software:

[1193] Applications: iOS app, Android app, Web app

[1194] Backend: Node.js, Django, Flask, etc.

[1195] Database: MySQL, PostgreSQL, MongoDB, etc.

[1196] Sentiment Analysis Engine: A sentiment analysis library built using machine learning frameworks such as TensorFlow and PyTorch

[1197] Natural Language Processing Engines: NLP libraries such as SpaCy, NLTK, BERT, etc.

[1198] User Action:

[1199] First, the user launches the application on their device. Next, a text box for entering diary entries is displayed. The user enters information about daily events and emotions into the text box and presses the "Send" button to send the diary data to the server.

[1200] For example, a user enters "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do," and presses the send button. This operation sends the diary data to the server.

[1201] Terminal behavior:

[1202] The diary data entered by the user is converted to JSON format on the device and sent to the server's API endpoint. The device then waits for and receives analysis results and advice from the server. The received results and advice are displayed on the device's UI and notified to the user.

[1203] Server behavior:

[1204] The server receives the diary data sent from the device and converts it into an appropriate format. It then uses an emotion analysis engine to analyze the diary data and recognize the user's emotions. For example, it extracts emotions such as "happy" (positive emotion) or "tired" (negative emotion).

[1205] The server then uses the results of the emotion analysis engine to further analyze the diary data using a natural language processing engine to extract lifestyle patterns and additional emotional factors, and then passes the analysis results to an evaluation algorithm to comprehensively evaluate the user's happiness and stress levels.

[1206] The server then uses the generative AI model to generate specific advice based on the analysis results. For example, it might generate advice such as, "If you feel stressed at work, it's a good idea to take a short break." The generated advice and analysis results are compiled as response data and sent to the device.

[1207] Examples:

[1208] The server receives diary data such as, "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do." The server sends this text to an emotion analysis engine, which extracts positive emotions (happy) and negative emotions (tired). Next, it uses a natural language processing engine to analyze lifestyle patterns in more detail. The server comprehensively evaluates the emotional trends and lifestyle patterns and generates specific advice such as, "If you're tired from work, it's a good idea to take a short break." The generated advice and analysis results are sent to the device, which notifies the user.

[1209] Example prompt sentence:

[1210] "Analyze the following sentence using an emotion engine and a natural language processing engine, and generate a specific piece of advice to provide to the user: 'I was happy that my boss praised me today, but I'm tired because I have a lot of work to do.'"

[1211] The system allows users to gain a deeper understanding of their emotions and lifestyle patterns and provides useful advice that can be put into practice in daily life.

[1212] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1213] Step 1:

[1214] User diary entry:

[1215] The user launches the application on their device, a text box appears for entering diary entries, and the user enters details about daily events and emotions and presses the "Send" button.

[1216] Input: Text data entered by the user.

[1217] Output: Diary data sent to the server.

[1218] Step 2:

[1219] Sending diary data:

[1220] The device formats the diary data entered by the user into JSON format, and then sends the formatted data to the server's API endpoint.

[1221] Input: Text data.

[1222] Output: Diary data in JSON format.

[1223] Specific operation: The device sends diary data in JSON format to the server using an HTTP POST request.

[1224] Step 3:

[1225] Data reception and preprocessing by the server:

[1226] The server receives the diary data in JSON format sent from the device and converts the received data into an internal format suitable for analysis.

[1227] Input: Diary data in JSON format.

[1228] Output: Diary data in internal format.

[1229] What happens: The server parses the JSON data and extracts the text fields.

[1230] Step 4:

[1231] Sentiment analysis using sentiment analysis engine:

[1232] The server passes the received diary data to an emotion analysis engine, which recognizes the user's emotions from the text. Specifically, it extracts emotions such as "happy" (positive emotion) or "tired" (negative emotion).

[1233] Input: Diary data in internal format.

[1234] Output: Sentiment analysis result (e.g., "Happy: 0.8, Tired: 0.6").

[1235] What it does: The sentiment analysis engine analyzes the text and generates a sentiment score.

[1236] Step 5:

[1237] Detailed analysis using natural language processing engine:

[1238] The server passes the results of the emotion analysis to a natural language processing engine, which then performs a detailed analysis of the diary data, extracting lifestyle patterns and additional emotional factors.

[1239] Input: Sentiment analysis results and diary data in internal format.

[1240] Output: Life pattern analysis results and additional emotional factors.

[1241] What it does: A natural language processing engine tokenizes and tags text for parts of speech, extracting contextual information.

[1242] Step 6:

[1243] Evaluation of analysis results:

[1244] The server passes the results obtained from the sentiment analysis engine and natural language processing engine to an evaluation algorithm, which then comprehensively evaluates the user's happiness and stress levels.

[1245] Input: Sentiment analysis results and lifestyle pattern analysis results.

[1246] Output: Overall rating (e.g., "Happiness score: 7, Stress score: 5").

[1247] Specific operation: The evaluation algorithm calculates an overall score based on the emotion score and lifestyle patterns.

[1248] Step 7:

[1249] Generative AI models generate advice:

[1250] The server uses a generative AI model based on the analysis results to generate specific advice for the user, such as "If you feel stressed at work, it's a good idea to take a short break."

[1251] Input: Overall evaluation results.

[1252] Output: Specific advice.

[1253] Specific operation: The generative AI model selects the most appropriate advice based on the evaluation results and outputs it as text.

[1254] Step 8:

[1255] Sending analysis results and advice:

[1256] The server compiles the generated advice and analysis results as response data and sends it to the terminal.

[1257] Input: Specific advice and analysis results.

[1258] Output: The response data.

[1259] Specific operation: The server uses an HTTP response to send response data to the terminal.

[1260] Step 9:

[1261] Receive and display results on your device:

[1262] The device receives the response data from the server, and then displays the results and advice on the UI to notify the user.

[1263] Input: Response data.

[1264] Output: Analysis results and specific advice displayed to the user.

[1265] What it does: The application parses the JSON data and displays the results in a user-friendly format.

[1266] (Application example 2)

[1267] 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."

[1268] There is a lack of specific, individualized support that employees need to manage the emotions and stress they experience daily at work and improve their productivity and performance. There is also a lack of ways to understand employees' happiness and stress levels in real time and quickly improve the work environment. As a result, there is a growing risk of employee motivation decreasing and turnover increasing.

[1269] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for using a natural language processing engine to perform emotion analysis and lifestyle pattern extraction, means for evaluating a user's happiness and stress level and generating work environment improvement proposals based on the analysis results, and means for employees to input daily events, analyze their emotions and lifestyle patterns at work, and provide specific advice for work improvement. This makes it possible to appropriately manage employee emotions and stress and quickly improve the work environment.

[1270] A "diary" is text data that allows users to record daily events and feelings.

[1271] "Means" is a general term for methods, operations, devices, etc. used to achieve a specific purpose.

[1272] A "user device" is a communication device used by a user, such as a smartphone or tablet.

[1273] A "server" is a computer system that analyzes data received from a user terminal and returns the results.

[1274] "Sentiment analysis" is the process of extracting the type and intensity of a user's emotions from input text data.

[1275] "Lifestyle patterns" is a concept that refers to the tendencies of a user's daily activities and behavior.

[1276] A "natural language processing engine" is an algorithm or software that analyzes text data and understands its meaning.

[1277] "Advice" refers to specific advice or suggestions for improving user behavior based on the analysis results.

[1278] "Employee" refers to the staff or officers working for a company or organization.

[1279] To implement this invention, a system is used in which users input their diary entries and the system analyzes the entries. The system is implemented as a smartphone application and provides an interface where employees can input their daily events and emotions.

[1280] System Configuration

[1281] 1. User Action:

[1282] The user launches the smartphone application and enters their diary entry. The entered text data is properly formatted within the application.

[1283] For example, an employee might type, "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do."

[1284] 2. Data transmission:

[1285] The smartphone application formats the entered diary data into JSON format and sends it to the server's API endpoint.

[1286] 3. Server Operation:

[1287] Data reception:

[1288] The server receives the diary data sent from the device and converts it into an appropriate format (e.g., JSON format).

[1289] Emotion analysis:

[1290] The "emotion analysis engine" running on the server analyzes the received diary data and recognizes the user's emotions. In this process, positive and negative emotions are extracted from the text data.

[1291] Lifestyle pattern analysis:

[1292] A server-based "natural language processing engine" is also used to extract detailed lifestyle patterns and additional emotional factors from the diary data, identifying patterns such as "busy" or "high stress."

[1293] Advice Generation:

[1294] Based on the analysis results, the server uses an "advice generation engine" to generate specific advice, such as "take short breaks between work sessions."

[1295] 4. Send and view results:

[1296] The server sends the generated advice and analysis results to the user's device, and the smartphone application displays the received data on its UI to visually notify the user.

[1297] Specific examples

[1298] For example, if an employee types, "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do," the following will happen:

[1299] 1. The user enters their diary into the app and submits it.

[1300] 2. The server receives the data, and the emotion analysis engine extracts the positive emotion of "happy" and the negative emotion of "tired."

[1301] 3. The natural language processing engine extracts "busy" as a lifestyle pattern.

[1302] 4. The advice generation engine generates the advice "Take short breaks between work."

[1303] 5. The app displays the advice to the user.

[1304] Prompt Sentence Examples

[1305] "How did you feel today? Write in your journal."

[1306] As described above, this invention is a system that can analyze employees' daily emotions and lifestyle patterns and provide specific advice to help improve the work environment. This system is expected to improve work efficiency by managing employees' happiness and stress levels.

[1307] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1308] Step 1:

[1309] The user launches the smartphone app and enters their diary entry.

[1310] Specifically, the user inputs text data such as "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do." By pressing the "Send" button, the input data is formatted into JSON format and sent to the server.

[1311] Step 2:

[1312] The user's device formats the diary data into JSON format and sends it to the server.

[1313] Specifically, the input text data is converted to JSON format and an HTTP POST request is sent to the server's API endpoint. The input is the diary text data entered by the user, and the output is JSON format data.

[1314] Step 3:

[1315] The server receives the diary data sent from the user terminal.

[1316] Specifically, the server receives an HTTP request via an API endpoint and receives JSON-formatted diary data for analysis. The input is the JSON-formatted diary data, and the output is the received data.

[1317] Step 4:

[1318] The server uses an emotion analysis engine to analyze the diary data and recognize the user's emotions.

[1319] Specifically, the server inputs the received diary data into an emotion analysis engine, which extracts positive emotions ("happy") and negative emotions ("tired") from the text. The input is the received diary data, and the output is the emotion analysis results.

[1320] Step 5:

[1321] The server analyzes lifestyle patterns using a natural language processing engine.

[1322] Specifically, based on the emotion analysis results, the server inputs the diary data into a natural language processing engine to extract detailed lifestyle patterns ("busy") and additional emotional factors ("high stress"). The inputs are the emotion analysis results and diary data, and the output is the lifestyle pattern analysis results.

[1323] Step 6:

[1324] The server generates specific advice based on the analysis results.

[1325] Specifically, the server uses an advice generation engine to generate specific advice such as "Take short breaks between work" based on the results of emotion analysis and lifestyle pattern analysis. The input is the results of emotion analysis and lifestyle pattern analysis, and the output is the generated advice.

[1326] Step 7:

[1327] The server sends the generated advice and analysis results to the user's device.

[1328] Specifically, the server formats the generated advice and analysis results in JSON format and sends them to the user's device as an HTTP response. The input is the generated advice and analysis results, and the output is JSON format data.

[1329] Step 8:

[1330] The user's device displays the advice and analysis results received from the server.

[1331] Specifically, the user device analyzes the JSON format data received as an HTTP response and displays advice and analysis results on the UI. The input is the JSON data received from the server, and the output is the display result on the UI.

[1332] 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.

[1333] 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.

[1334] 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.

[1335] [Fourth embodiment]

[1336] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1337] 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.

[1338] 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).

[1339] 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.

[1340] 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.

[1341] 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).

[1342] 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.

[1343] 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.

[1344] 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.

[1345] 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.

[1346] 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.

[1347] 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.

[1348] 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."

[1349] This invention relates to a system that uses natural language processing technology to analyze diary data entered by a user, clarifying emotional tendencies and lifestyle patterns, and providing specific advice based on the results. This system operates in the following manner: the user enters the diary, the server analyzes the data, and the analysis results and advice are provided to the user.

[1350] User Actions

[1351] 1. Diary entry:

[1352] The user launches the application on their device and a text box appears where they can enter their diary entry.

[1353] Users enter information about their daily events and feelings into a text box.

[1354] Once the input is complete, the user presses the "Send" button to send the diary data to the server.

[1355] 2. Example:

[1356] For example, a user may enter "I had a lot of work today and I'm tired, but I met up with a friend in the evening and felt refreshed," and then press the send button. This sends the diary data to the server.

[1357] Device behavior

[1358] 1. Data transmission:

[1359] The device formats the diary data entered by the user into JSON format and sends it to the server's API endpoint.

[1360] 2. Receiving the analysis results:

[1361] The terminal waits for the analysis results and advice from the server.

[1362] Receives analysis results from the server and prepares them for display to the user.

[1363] 3. Results display:

[1364] The analysis results and advice received by the device are displayed on the UI, providing a visual notification to the user.

[1365] Server Operation

[1366] 1. Data reception:

[1367] The server receives the diary data sent from the device and converts the received data into an appropriate format.

[1368] 2. Analysis by natural language processing engine:

[1369] The server sends the diary data to a natural language processing engine, which analyzes the text data and extracts emotional trends (e.g., positive or negative emotions) and specific lifestyle patterns (e.g., causes of stress and ways to refresh).

[1370] The extracted data is then passed through a more detailed analysis algorithm to assess the user's happiness and stress levels.

[1371] 3. Generating Advice:

[1372] Based on the analysis results, the server generates specific advice for the user, such as "Spending more time with friends will help reduce stress."

[1373] The generated advice and analysis results are compiled into a single response data.

[1374] 4. Send results:

[1375] The server sends the compiled response data to the device and provides advice to the user.

[1376] Specific examples

[1377] The server receives diary data in which the user has entered "I had a lot of work today and I was tired, but I met up with a friend in the evening and felt refreshed."

[1378] The server sends this text to a natural language processing engine for analysis.

[1379] The engine extracts "tired" (negative emotion) and "energetic" (positive emotion).

[1380] Based on this, the server generates advice such as "Spending more time with friends is an effective way to relieve stress."

[1381] The server sends the analysis results and advice to the device, which then notifies the user.

[1382] This system is an effective tool for users to understand their own emotions and lifestyle patterns and receive specific advice to improve their daily lives.

[1383] The processing flow will be explained below.

[1384] Step 1:

[1385] The user starts the application on the terminal to input the diary.

[1386] Step 2:

[1387] The user inputs a diary entry into a text box in the application and presses the "Send" button to instruct the application to send the diary entry.

[1388] Step 3:

[1389] The device formats the diary data entered by the user into JSON format.

[1390] Step 4:

[1391] The device sends the formatted diary data as a POST request to the server's API endpoint.

[1392] Step 5:

[1393] The server receives the diary data transmitted from the terminal.

[1394] Step 6:

[1395] The server sends the received diary data to a natural language processing engine and begins analysis.

[1396] Step 7:

[1397] A natural language processing engine analyzes the diary data and extracts emotional trends (positive, negative, etc.) and lifestyle patterns from the text.

[1398] Step 8:

[1399] The server passes the analysis results obtained from the natural language processing engine to an evaluation algorithm, which evaluates the user's happiness and stress levels.

[1400] Step 9:

[1401] The server generates specific advice for the user based on the analysis results and evaluation.

[1402] Step 10:

[1403] The advice and analysis results generated by the server are compiled and constructed as response data.

[1404] Step 11:

[1405] The server sends the response data to the terminal.

[1406] Step 12:

[1407] The terminal receives the response data from the server.

[1408] Step 13:

[1409] The analysis results and advice received by the terminal are visually displayed to the user.

[1410] Step 14:

[1411] The user checks the display on the device and uses the diary analysis results and advice as a reference.

[1412] For example, a user writes in their diary, "I had a lot of work today and I'm tired, but I felt better after meeting up with friends this evening," and presses the send button. This diary data is analyzed through the steps described above, and the server displays advice on the device, such as, "If you value the time you spend with friends, you will reduce stress." Through this process, the user can understand their own emotional tendencies and effective ways to deal with them.

[1413] Example 1

[1414] 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."

[1415] In recent years, there has been an increasing demand for tools to improve people's happiness in a stressful society. Conventional methods lacked a mechanism for analyzing users' emotions and lifestyle patterns in detail and providing specific advice based on that analysis. This made it difficult for users to understand their own emotions and lifestyle patterns and take appropriate action. The purpose of this invention is to provide a system that improves users' happiness by allowing users to easily input daily diary data, analyzing that data, and providing specific advice.

[1416] 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.

[1417] In this invention, the server includes means for a user to input a diary entry, means for transmitting the input diary data to the server, means for the server to receive the diary data and analyze emotions and lifestyle patterns using a natural language processing engine, means for generating advice for the user based on the analysis results, means for transmitting the generated advice to the user terminal, means for the user terminal to display the advice to the user, means for the user terminal to convert the diary data into JSON format and transmit it to the server, means for the server to convert the diary data into an appropriate format and pass it to the natural language processing engine, and means for using a text analysis engine as the natural language processing engine. This enables users to easily understand their own emotions and lifestyle patterns and receive specific advice based on that understanding.

[1418] "User" refers to a person who uses the system to input diary data and receive the analysis results.

[1419] "Diary data" refers to text data entered by users to describe their daily events and feelings.

[1420] "Server" refers to a computer system that receives diary data sent by users, analyzes it, and generates advice.

[1421] A "natural language processing engine" refers to a technical means of analyzing diary data and extracting emotions and lifestyle patterns from the text.

[1422] "Sentiment analysis" refers to the process of extracting and analyzing positive and negative emotions from diary data.

[1423] "Lifestyle patterns" refer to trends in a user's behavior and habits extracted from the user's diary data.

[1424] "Advice" refers to specific guidelines for action provided to users based on the analysis results.

[1425] "User device" refers to the device (e.g., smartphone, PC) used by the user to enter diary entries and receive analysis results and advice.

[1426] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a data format for structuring diary data and sending it to a server.

[1427] "Analysis results" refers to the output of data analyzed by a natural language processing engine, including information on emotions and lifestyle patterns.

[1428] "Generative AI model" refers to an artificial intelligence model used to generate specific advice based on analytical results.

[1429] This invention is a system that allows users to input diary data, analyze it using natural language processing technology to clarify emotional tendencies and lifestyle patterns, and provides specific advice based on the results. This system is implemented as follows: the user inputs the diary, the server analyzes the data, and the analysis results and advice are provided to the user.

[1430] Users start the application on their smartphone, PC, or other device and enter their diary entries. A text box for entering diary entries is displayed, and the user enters details about daily events and emotions into the text box. Once the entry is complete, the user presses the "Send" button to send the diary data to the server.

[1431] The device converts the diary data entered by the user into JSON format and sends it to the server's API endpoint. To do this, the device uses, for example, an HTTP POST request. The server receives the diary data sent from the device and converts the received data into an appropriate format. This process can be performed using, for example, a script written in Python or a web framework (e.g., Flask or Django).

[1432] The server sends the received diary data to a natural language processing engine. Libraries such as "spaCy" and "BERT" are used as natural language processing engines. "spaCy" is a fast and powerful natural language processing library, and "BERT" is a Transformer model based on deep learning. The engine analyzes the diary text and extracts emotional trends (positive, negative, etc.) and specific lifestyle patterns. For example, when analyzing the text "I had a lot of work today and I was tired, but I felt better after meeting up with friends in the evening," it extracts "tired" (negative emotion) and "energetic" (positive emotion).

[1433] The extracted data is passed to a more detailed analysis algorithm to evaluate the user's happiness and stress levels. The server generates specific advice based on the analysis results. A generative AI model can be used to generate advice. For example, it could provide a specific guideline for action, such as "Spending more time with friends will help reduce stress." The generated advice and analysis results are then combined into a single response data set.

[1434] The server sends the collected response data to the device and provides advice to the user. The device then displays the received analysis results and advice on the UI, visually notifying the user.

[1435] Examples of specific prompts include, "Analyze the user's diary data and extract phrases of positive and negative emotions," and "Evaluate lifestyle patterns and happiness levels based on emotional trends from the diary data and generate specific advice."

[1436] This system is an effective tool for users to understand their own emotions and lifestyle patterns and receive specific advice to improve their daily lives.

[1437] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1438] Step 1: User diary entry

[1439] A user starts the application on a device such as a smartphone or PC and enters their diary. They enter daily events and emotions in text form in the input field, and when they are finished, they press the "Send" button. The input here is the user's diary, and the output is the text data after pressing the send button.

[1440] Step 2: Send data by device

[1441] The device converts the diary text entered by the user into JSON format. After conversion, the device sends this JSON data to the server's API endpoint using an HTTP POST request. The input here is the diary text entered by the user, and the output is the diary data converted into JSON format.

[1442] Step 3: Server receives data

[1443] The server receives the JSON-formatted diary data sent from the device and converts it into an appropriate format. For example, it uses a Python script to convert the JSON data into text data. The input here is the JSON data sent from the device, and the output is text data to be passed to a natural language processing engine.

[1444] Step 4: Server sends to natural language processing engine

[1445] The server sends the converted text data to a natural language processing engine, such as "spaCy" or "BERT." The engine analyzes the text and extracts emotional trends and lifestyle patterns. The input here is the text data, and the output is the emotions and lifestyle patterns extracted based on the analysis results.

[1446] Step 5: Processing the analysis results by the server

[1447] The server receives the analysis results from the natural language processing engine and evaluates the user's happiness and stress levels using a detailed analysis algorithm. The analysis results are then processed using a generative AI model. The input here is the analysis results from the engine, and the output is evaluation data on the user's happiness and stress levels.

[1448] Step 6: Server Generates Advice

[1449] The server generates specific advice based on the evaluation data. For example, it generates a specific course of action such as "Spending more time with friends will reduce stress." The input here is the evaluation data on happiness and stress levels, and the output is the generated specific advice.

[1450] Step 7: Server sends results

[1451] The server compiles the response data, including the generated advice and analysis results, into JSON format and sends it to the terminal. The input here is the generated advice and analysis results, and the output is the response JSON data to be sent to the terminal.

[1452] Step 8: Displaying the results on your device

[1453] The terminal receives the response JSON data sent from the server and displays the analysis results and advice to the user. The input here is the response JSON data from the server, and the output is the analysis results and advice displayed on the user interface.

[1454] In this way, users can analyze their emotions and lifestyle patterns through their own diary and receive specific advice.

[1455] (Application example 1)

[1456] 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."

[1457] While existing systems that analyze users' diary data focus on analyzing emotions and lifestyle patterns, they are limited in providing specific advice based on those data. Furthermore, they lack a way to personalize the shopping experience based on users' emotions and lifestyle patterns. Therefore, further improvements are needed to improve user satisfaction.

[1458] 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.

[1459] In this invention, the server includes a means for providing personalized shopping advice based on the user's diary data, a means for using a generative AI model to generate personalized shopping advice, and a means for using a natural language processing engine to perform sentiment analysis and lifestyle pattern extraction, thereby enabling the provision of specific, personalized shopping advice based on the user's emotions and lifestyle patterns.

[1460] "User" refers to an individual who uses the system to enter a diary entry and receive analyzed advice.

[1461] "Diary data" refers to text data entered by users about daily events and emotions.

[1462] The term "server" refers to a computer system that receives and analyzes diary data, generates advice based on the results, and transmits it to the user terminal.

[1463] A "natural language processing engine" refers to a program or algorithm that analyzes text data to extract emotions and lifestyle patterns.

[1464] "Emotion" refers to the user's emotional state (e.g., positive, negative) analyzed from diary data.

[1465] "Lifestyle patterns" refers to the user's daily activities and various habits analyzed based on diary data.

[1466] "Advice" refers to specific guidelines and recommendations provided to users based on analyzed emotions and lifestyle patterns.

[1467] "User device" refers to the electronic device (e.g., smartphone, computer) used by the user to enter diary entries and receive analysis results and advice.

[1468] "Personalized shopping advice" refers to individualized purchasing suggestions and advice provided based on emotions and lifestyle patterns analyzed from a user's diary data.

[1469] "Generative AI Model" refers to the artificial intelligence model used to generate personalized shopping advice based on a user's diary data.

[1470] The present invention is a system that analyzes diary data entered by a user and provides the user with personalized shopping advice based on the analysis results. This system is composed of a user terminal, a server, and a network for communication between them.

[1471] User Actions

[1472] 1. Diary entry:

[1473] Users launch a dedicated application on their "user device" such as a smartphone or computer, and enter information about their daily events and emotions into the application's text box.

[1474] For example, a user might enter, "I've been busy at work lately and I'm feeling stressed every day. I'm looking for products that will help me relax." This input is treated as "diary data."

[1475] When the user presses the "Send" button, the diary data is sent to the server.

[1476] Device behavior

[1477] 1. Data transmission:

[1478] The user's device formats the entered diary data into JSON format and sends it to the server's API endpoint.

[1479] 2. Receiving the analysis results:

[1480] The user terminal waits for the analysis results and advice from the server.

[1481] Receives response data from the server and prepares it for display to the user.

[1482] 3. Results display:

[1483] The user device displays the received analysis results and advice on the UI, visually notifying the user.

[1484] Server Operation

[1485] 1. Data reception:

[1486] The server receives the diary data sent from the user's device and converts it into an appropriate format.

[1487] 2. Analysis by natural language processing engine:

[1488] The server sends the diary data to a "natural language processing engine" (e.g., BERT), which analyzes the text data and extracts emotions (e.g., positive, negative) and lifestyle patterns (e.g., stressful, relaxed).

[1489] 3. Generating Advice:

[1490] The server generates personalized "shopping advice" based on the analysis results, using a "generative AI model" in the process.

[1491] The generated advice and analysis results are compiled into a single response data.

[1492] 4. Send results:

[1493] The server sends the compiled response data to the user's device and delivers advice to the user.

[1494] Specific examples

[1495] Analysis results and advice examples

[1496] Input example: A user writes in their diary, "I've been busy at work lately and I'm feeling stressed every day. I'm looking for a product that will help me relax."

[1497] Analysis results: The server sends this text to a natural language processing engine (e.g., BERT), which extracts "negative emotions" and "stressful lifestyle patterns."

[1498] Generated advice: The server generates the advice "If you want to relax, try aroma oils and massage equipment."

[1499] Displaying results: The user device displays the received analysis results and advice to the user.

[1500] Prompt Sentence Examples

[1501] By inputting prompt sentences like the following into the generative AI model, appropriate shopping advice can be generated.

[1502] "A user wrote in their diary, 'I've been busy at work lately and I'm feeling stressed every day. I'm looking for products that will help me relax.' Analyze this user's emotions and lifestyle patterns to generate specific shopping advice."

[1503] The present invention allows users to receive personalized shopping advice based on their diary data, resulting in a more satisfying shopping experience. It also helps improve the quality of users' lives by providing specific guidelines for action along with the analysis results.

[1504] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1505] Step 1:

[1506] The user enters the diary entry.

[1507] Input: Users use smartphone or computer applications to input text about everyday events and feelings.

[1508] Specific behavior: The user launches the application and writes a diary entry in the text box that appears. For example, the user might type, "I've been busy at work lately and I'm feeling stressed every day. I'm looking for products that will help me relax."

[1509] Step 2:

[1510] The device transmits the diary data to the server.

[1511] Input: Diary data entered by the user (e.g., "Work has been busy lately, and I'm feeling stressed every day. I'm looking for products that will help me relax.").

[1512] Specific operation: The device formats the entered diary data into JSON format and sends it to the server's API endpoint.

[1513] Step 3:

[1514] The server receives the diary data and converts it into an appropriate format.

[1515] Input: Diary data sent from the device (JSON format).

[1516] Output: Diary data in a parseable text format.

[1517] Specific operation: The server converts the received diary data into an appropriate text format (e.g., checking and converting character encoding).

[1518] Step 4:

[1519] The server analyzes the diary data using a natural language processing engine.

[1520] Input: Text diary data.

[1521] Output: Sentiment analysis and lifestyle pattern extraction results (e.g., "negative emotions" or "stressful lifestyle patterns").

[1522] Specific operation: The server passes the diary data to a natural language processing engine (e.g., BERT) to analyze emotions and lifestyle patterns. The natural language processing engine extracts positive / negative emotions from the text and identifies lifestyle patterns.

[1523] Step 5:

[1524] The server uses the generative AI model to generate personalized shopping advice.

[1525] Input: Sentiment analysis and lifestyle pattern extraction results.

[1526] Output: Personalized shopping advice (e.g., "If you want to relax, try our aromatic oils and massage equipment.").

[1527] Specific operation: The server generates prompt sentences based on the analysis results and inputs them into the generative AI model, which then creates shopping advice based on the generated prompt sentences.

[1528] Step 6:

[1529] The server sends the analysis results and generation advice to the user's terminal.

[1530] Input: Personalized shopping advice and analytics.

[1531] Output: The response data sent to the user device.

[1532] Specific operation: The server combines the analysis results and generation advice into a single response data and sends it to the user terminal.

[1533] Step 7:

[1534] The user's device displays the analysis results and advice to the user.

[1535] Input: The response data received from the server.

[1536] Output: Advice and analysis results displayed to the user.

[1537] Specific operation: The user device displays the received analysis results and advice on the application UI and notifies the user, allowing the user to visually check and receive specific shopping advice.

[1538] 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.

[1539] This invention relates to a system that uses an emotion engine and a natural language processing engine to analyze diary entries entered by users and provides specific advice based on the results. This system allows users to visualize their emotions and lifestyle patterns through their diary entries and obtain useful information to improve their daily lives.

[1540] User Actions

[1541] 1. Diary entry:

[1542] The user launches the application on their device and a text box appears where they can enter their diary entry.

[1543] The user enters information about daily events and emotions into the text box and presses the "Send" button to send the diary data to the server.

[1544] 2. Example:

[1545] For example, a user enters "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do," and presses the send button. This sends the diary data to the server.

[1546] Device behavior

[1547] 1. Data transmission:

[1548] The device formats the diary data entered by the user into JSON format and sends it to the server's API endpoint.

[1549] 2. Receiving the analysis results:

[1550] The terminal waits for analysis results and advice from the server and receives data from the server.

[1551] 3. Results display:

[1552] The analysis results and advice received by the device are displayed on the UI, providing a visual notification to the user.

[1553] Server Operation

[1554] 1. Data reception:

[1555] The server receives the diary data sent from the device and converts the received data into an appropriate format.

[1556] 2. Analysis by emotion engine:

[1557] The server sends the received diary data to the emotion engine, which recognizes the user's emotions from the text.

[1558] The emotion engine extracts "happy" (positive emotion) and "tired" (negative emotion) and analyzes the tendencies of each emotion.

[1559] 3. Analysis by natural language processing engine:

[1560] Based on the results of the emotion engine, the server sends the diary data to a natural language processing engine to extract detailed lifestyle patterns and additional emotional factors.

[1561] 4. Evaluation of analysis results:

[1562] The server passes the analysis results obtained from the emotion engine and natural language processing engine to an evaluation algorithm, which then comprehensively evaluates the user's happiness and stress levels.

[1563] 5. Generating Advice:

[1564] Based on the analysis results, the server generates specific advice for the user, such as "If you feel stressed at work, it's a good idea to take a short break."

[1565] The generated advice and analysis results are compiled as response data.

[1566] 6. Sending the results:

[1567] The server sends the compiled response data to the terminal and delivers it to the user.

[1568] Specific examples

[1569] The server receives diary data in which the user has entered "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do."

[1570] The server sends this text to an emotion engine, which extracts positive emotions (happy) and negative emotions (tired).

[1571] The data is then sent to a natural language processing engine to analyze more detailed lifestyle patterns.

[1572] The server evaluates the user's happiness and stress levels based on their emotional tendencies and lifestyle patterns, and generates specific advice such as "If you're tired from work, it's a good idea to take a short break."

[1573] The generated advice and analysis results are sent to the device, which then notifies the user.

[1574] The system allows users to gain a deeper understanding of their emotions and lifestyle patterns and provides useful advice that can be put into practice in daily life.

[1575] The processing flow will be explained below.

[1576] Step 1:

[1577] The user starts the application on the terminal to input the diary.

[1578] Step 2:

[1579] The user inputs a diary entry into a text box in the application and presses the "Send" button to instruct the application to send the diary entry.

[1580] Step 3:

[1581] The device formats the diary data entered by the user into JSON format.

[1582] Step 4:

[1583] The device sends the formatted diary data as a POST request to the server's API endpoint.

[1584] Step 5:

[1585] The server receives the diary data sent from the device. The received data is saved in the internal storage and then the next process is performed.

[1586] Step 6:

[1587] The server sends the received diary data to an emotion engine, which analyzes the emotional trends.

[1588] Step 7:

[1589] The emotion engine analyzes the diary data and classifies emotions in the text into categories such as positive and negative.

[1590] Step 8:

[1591] The server receives the emotion analysis results from the emotion engine and sends them to the natural language processing engine.

[1592] Step 9:

[1593] A natural language processing engine uses the diary data and emotion analysis results to perform a more detailed analysis of lifestyle patterns and behavioral characteristics, for example, extracting regular sources of stress and ways to refresh oneself.

[1594] Step 10:

[1595] The server receives the analysis results from the natural language processing engine and passes the data to the evaluation algorithm.

[1596] Step 11:

[1597] The evaluation algorithm comprehensively evaluates the user's happiness and stress levels and generates specific advice based on that.

[1598] Step 12:

[1599] The server compiles the generated advice and the overall analysis results into response data.

[1600] Step 13:

[1601] The server sends the response data to the terminal.

[1602] Step 14:

[1603] The terminal receives the response data from the server.

[1604] Step 15:

[1605] The analysis results and advice received by the terminal are visually displayed to the user.

[1606] Step 16:

[1607] Users can check the analysis results and advice displayed on their device and use them in their daily lives.

[1608] As a concrete example, a user may enter "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do," and then press the send button. This diary data is analyzed through the steps described above, and the server displays advice on the device, such as "If you feel stressed at work, it's a good idea to take a short break." Through this process, the user can understand their own emotions and lifestyle patterns and receive help in achieving a better life.

[1609] Example 2

[1610] 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."

[1611] Conventional systems have been insufficient in analyzing a user's diary content and providing useful advice, due to insufficient emotional analysis and detailed analysis of lifestyle patterns. Furthermore, it has been difficult to generate specific advice based on the analysis results, making it difficult to provide effective advice to users. The present invention aims to solve these problems by providing a system that can analyze a user's diary content in detail and provide specific and effective advice.

[1612] 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.

[1613] In this invention, the server includes means for analyzing emotions using an emotion analysis engine, means for analyzing detailed lifestyle patterns using a natural language processing engine, means for comprehensively evaluating the analysis results using an evaluation algorithm, and means for generating specific advice using a generative AI model. This makes it possible to analyze the contents of a user's diary in detail and provide specific and effective advice based on the analysis results.

[1614] A "user" is someone who uses this system to enter a diary entry and receive advice.

[1615] "Diary data" is text information entered by the user about daily events and feelings.

[1616] A "terminal" is a device used to enter diary entries and receive analysis results and advice, and includes smartphones, tablets, PCs, etc.

[1617] A "server" is a computer system that receives, analyzes, evaluates, and generates advice on diary data.

[1618] An "emotion analysis engine" is a program that recognizes a user's emotions from text and generates an emotion score.

[1619] A "natural language processing engine" is a program that analyzes text data and extracts detailed lifestyle patterns and additional emotional factors.

[1620] The "evaluation algorithm" is a method for comprehensively evaluating a user's happiness and stress levels based on the results obtained from the sentiment analysis engine and natural language processing engine.

[1621] A "generative AI model" is an artificial intelligence model that generates specific advice for users based on the analysis results.

[1622] "JSON format" is the data format used when sending diary data to a server, and is a lightweight, highly readable text-based data exchange format.

[1623] An "API endpoint" is an interface for sending and receiving data from a terminal to a server.

[1624] "Response data" is data that includes analysis results and advice and is sent from the server to the terminal.

[1625] This invention is a system that analyzes diary entries entered by users using an emotion analysis engine and a natural language processing engine, and provides specific advice based on the analysis results. This system allows users to visualize their emotions and lifestyle patterns through their diary and obtain useful information to improve their daily lives.

[1626] The system uses the following hardware and software:

[1627] Hardware:

[1628] Devices: Smartphones, tablets, PCs, etc.

[1629] Server: Cloud server (e.g. AWS, Google Cloud, Azure, etc.)

[1630] software:

[1631] Applications: iOS app, Android app, Web app

[1632] Backend: Node.js, Django, Flask, etc.

[1633] Database: MySQL, PostgreSQL, MongoDB, etc.

[1634] Sentiment Analysis Engine: A sentiment analysis library built using machine learning frameworks such as TensorFlow and PyTorch

[1635] Natural Language Processing Engines: NLP libraries such as SpaCy, NLTK, BERT, etc.

[1636] User Action:

[1637] First, the user launches the application on their device. Next, a text box for entering diary entries is displayed. The user enters information about daily events and emotions into the text box and presses the "Send" button to send the diary data to the server.

[1638] For example, a user enters "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do," and presses the send button. This operation sends the diary data to the server.

[1639] Terminal behavior:

[1640] The diary data entered by the user is converted to JSON format on the device and sent to the server's API endpoint. The device then waits for and receives analysis results and advice from the server. The received results and advice are displayed on the device's UI and notified to the user.

[1641] Server behavior:

[1642] The server receives the diary data sent from the device and converts it into an appropriate format. It then uses an emotion analysis engine to analyze the diary data and recognize the user's emotions. For example, it extracts emotions such as "happy" (positive emotion) or "tired" (negative emotion).

[1643] The server then uses the results of the emotion analysis engine to further analyze the diary data using a natural language processing engine to extract lifestyle patterns and additional emotional factors, and then passes the analysis results to an evaluation algorithm to comprehensively evaluate the user's happiness and stress levels.

[1644] The server then uses the generative AI model to generate specific advice based on the analysis results. For example, it might generate advice such as, "If you feel stressed at work, it's a good idea to take a short break." The generated advice and analysis results are compiled as response data and sent to the device.

[1645] Examples:

[1646] The server receives diary data such as, "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do." The server sends this text to an emotion analysis engine, which extracts positive emotions (happy) and negative emotions (tired). Next, it uses a natural language processing engine to analyze lifestyle patterns in more detail. The server comprehensively evaluates the emotional trends and lifestyle patterns and generates specific advice such as, "If you're tired from work, it's a good idea to take a short break." The generated advice and analysis results are sent to the device, which notifies the user.

[1647] Example prompt sentence:

[1648] "Analyze the following sentence using an emotion engine and a natural language processing engine, and generate a specific piece of advice to provide to the user: 'I was happy that my boss praised me today, but I'm tired because I have a lot of work to do.'"

[1649] The system allows users to gain a deeper understanding of their emotions and lifestyle patterns and provides useful advice that can be put into practice in daily life.

[1650] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1651] Step 1:

[1652] User diary entry:

[1653] The user launches the application on their device, a text box appears for entering diary entries, and the user enters details about daily events and emotions and presses the "Send" button.

[1654] Input: Text data entered by the user.

[1655] Output: Diary data sent to the server.

[1656] Step 2:

[1657] Sending diary data:

[1658] The device formats the diary data entered by the user into JSON format, and then sends the formatted data to the server's API endpoint.

[1659] Input: Text data.

[1660] Output: Diary data in JSON format.

[1661] Specific operation: The device sends diary data in JSON format to the server using an HTTP POST request.

[1662] Step 3:

[1663] Data reception and preprocessing by the server:

[1664] The server receives the diary data in JSON format sent from the device and converts the received data into an internal format suitable for analysis.

[1665] Input: Diary data in JSON format.

[1666] Output: Diary data in internal format.

[1667] What happens: The server parses the JSON data and extracts the text fields.

[1668] Step 4:

[1669] Sentiment analysis using sentiment analysis engine:

[1670] The server passes the received diary data to an emotion analysis engine, which recognizes the user's emotions from the text. Specifically, it extracts emotions such as "happy" (positive emotion) or "tired" (negative emotion).

[1671] Input: Diary data in internal format.

[1672] Output: Sentiment analysis result (e.g., "Happy: 0.8, Tired: 0.6").

[1673] What it does: The sentiment analysis engine analyzes the text and generates a sentiment score.

[1674] Step 5:

[1675] Detailed analysis using natural language processing engine:

[1676] The server passes the results of the emotion analysis to a natural language processing engine, which then performs a detailed analysis of the diary data, extracting lifestyle patterns and additional emotional factors.

[1677] Input: Sentiment analysis results and diary data in internal format.

[1678] Output: Life pattern analysis results and additional emotional factors.

[1679] What it does: A natural language processing engine tokenizes and tags text for parts of speech, extracting contextual information.

[1680] Step 6:

[1681] Evaluation of analysis results:

[1682] The server passes the results obtained from the sentiment analysis engine and natural language processing engine to an evaluation algorithm, which then comprehensively evaluates the user's happiness and stress levels.

[1683] Input: Sentiment analysis results and lifestyle pattern analysis results.

[1684] Output: Overall rating (e.g., "Happiness score: 7, Stress score: 5").

[1685] Specific operation: The evaluation algorithm calculates an overall score based on the emotion score and lifestyle patterns.

[1686] Step 7:

[1687] Generative AI models generate advice:

[1688] The server uses a generative AI model based on the analysis results to generate specific advice for the user, such as "If you feel stressed at work, it's a good idea to take a short break."

[1689] Input: Overall evaluation results.

[1690] Output: Specific advice.

[1691] Specific operation: The generative AI model selects the most appropriate advice based on the evaluation results and outputs it as text.

[1692] Step 8:

[1693] Sending analysis results and advice:

[1694] The server compiles the generated advice and analysis results as response data and sends it to the terminal.

[1695] Input: Specific advice and analysis results.

[1696] Output: The response data.

[1697] Specific operation: The server uses an HTTP response to send response data to the terminal.

[1698] Step 9:

[1699] Receive and display results on your device:

[1700] The device receives the response data from the server, and then displays the results and advice on the UI to notify the user.

[1701] Input: Response data.

[1702] Output: Analysis results and specific advice displayed to the user.

[1703] What it does: The application parses the JSON data and displays the results in a user-friendly format.

[1704] (Application example 2)

[1705] 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."

[1706] There is a lack of specific, individualized support that employees need to manage the emotions and stress they experience daily at work and improve their productivity and performance. There is also a lack of ways to understand employees' happiness and stress levels in real time and quickly improve the work environment. As a result, there is a growing risk of employee motivation decreasing and turnover increasing.

[1707] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for using a natural language processing engine to perform emotion analysis and lifestyle pattern extraction, means for evaluating a user's happiness and stress level and generating work environment improvement proposals based on the analysis results, and means for employees to input daily events, analyze their emotions and lifestyle patterns at work, and provide specific advice for work improvement. This makes it possible to appropriately manage employee emotions and stress and quickly improve the work environment.

[1708] A "diary" is text data that allows users to record daily events and feelings.

[1709] "Means" is a general term for methods, operations, devices, etc. used to achieve a specific purpose.

[1710] A "user device" is a communication device used by a user, such as a smartphone or tablet.

[1711] A "server" is a computer system that analyzes data received from a user terminal and returns the results.

[1712] "Sentiment analysis" is the process of extracting the type and intensity of a user's emotions from input text data.

[1713] "Lifestyle patterns" is a concept that refers to the tendencies of a user's daily activities and behavior.

[1714] A "natural language processing engine" is an algorithm or software that analyzes text data and understands its meaning.

[1715] "Advice" refers to specific advice or suggestions for improving user behavior based on the analysis results.

[1716] "Employee" refers to the staff or officers working for a company or organization.

[1717] To implement this invention, a system is used in which users input their diary entries and the system analyzes the entries. The system is implemented as a smartphone application and provides an interface where employees can input their daily events and emotions.

[1718] System Configuration

[1719] 1. User Action:

[1720] The user launches the smartphone application and enters their diary entry. The entered text data is properly formatted within the application.

[1721] For example, an employee might type, "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do."

[1722] 2. Data transmission:

[1723] The smartphone application formats the entered diary data into JSON format and sends it to the server's API endpoint.

[1724] 3. Server Operation:

[1725] Data reception:

[1726] The server receives the diary data sent from the device and converts it into an appropriate format (e.g., JSON format).

[1727] Emotion analysis:

[1728] The "emotion analysis engine" running on the server analyzes the received diary data and recognizes the user's emotions. In this process, positive and negative emotions are extracted from the text data.

[1729] Lifestyle pattern analysis:

[1730] A server-based "natural language processing engine" is also used to extract detailed lifestyle patterns and additional emotional factors from the diary data, identifying patterns such as "busy" or "high stress."

[1731] Advice Generation:

[1732] Based on the analysis results, the server uses an "advice generation engine" to generate specific advice, such as "take short breaks between work sessions."

[1733] 4. Send and view results:

[1734] The server sends the generated advice and analysis results to the user's device, and the smartphone application displays the received data on its UI to visually notify the user.

[1735] Specific examples

[1736] For example, if an employee types, "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do," the following will happen:

[1737] 1. The user enters their diary into the app and submits it.

[1738] 2. The server receives the data, and the emotion analysis engine extracts the positive emotion of "happy" and the negative emotion of "tired."

[1739] 3. The natural language processing engine extracts "busy" as a lifestyle pattern.

[1740] 4. The advice generation engine generates the advice "Take short breaks between work."

[1741] 5. The app displays the advice to the user.

[1742] Prompt Sentence Examples

[1743] "How did you feel today? Write in your journal."

[1744] As described above, this invention is a system that can analyze employees' daily emotions and lifestyle patterns and provide specific advice to help improve the work environment. This system is expected to improve work efficiency by managing employees' happiness and stress levels.

[1745] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1746] Step 1:

[1747] The user launches the smartphone app and enters their diary entry.

[1748] Specifically, the user inputs text data such as "I was happy that my boss praised me today, but I'm tired because I have a lot of work to do." By pressing the "Send" button, the input data is formatted into JSON format and sent to the server.

[1749] Step 2:

[1750] The user's device formats the diary data into JSON format and sends it to the server.

[1751] Specifically, the input text data is converted to JSON format and an HTTP POST request is sent to the server's API endpoint. The input is the diary text data entered by the user, and the output is JSON format data.

[1752] Step 3:

[1753] The server receives the diary data sent from the user terminal.

[1754] Specifically, the server receives an HTTP request via an API endpoint and receives JSON-formatted diary data for analysis. The input is the JSON-formatted diary data, and the output is the received data.

[1755] Step 4:

[1756] The server uses an emotion analysis engine to analyze the diary data and recognize the user's emotions.

[1757] Specifically, the server inputs the received diary data into an emotion analysis engine, which extracts positive emotions ("happy") and negative emotions ("tired") from the text. The input is the received diary data, and the output is the emotion analysis results.

[1758] Step 5:

[1759] The server analyzes lifestyle patterns using a natural language processing engine.

[1760] Specifically, based on the emotion analysis results, the server inputs the diary data into a natural language processing engine to extract detailed lifestyle patterns ("busy") and additional emotional factors ("high stress"). The inputs are the emotion analysis results and diary data, and the output is the lifestyle pattern analysis results.

[1761] Step 6:

[1762] The server generates specific advice based on the analysis results.

[1763] Specifically, the server uses an advice generation engine to generate specific advice such as "Take short breaks between work" based on the results of emotion analysis and lifestyle pattern analysis. The input is the results of emotion analysis and lifestyle pattern analysis, and the output is the generated advice.

[1764] Step 7:

[1765] The server sends the generated advice and analysis results to the user's device.

[1766] Specifically, the server formats the generated advice and analysis results in JSON format and sends them to the user's device as an HTTP response. The input is the generated advice and analysis results, and the output is JSON format data.

[1767] Step 8:

[1768] The user's device displays the advice and analysis results received from the server.

[1769] Specifically, the user device analyzes the JSON format data received as an HTTP response and displays advice and analysis results on the UI. The input is the JSON data received from the server, and the output is the display result on the UI.

[1770] 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.

[1771] 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.

[1772] 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.

[1773] 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.

[1774] 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.

[1775] 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.

[1776] 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).

[1777] 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.

[1778] 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."

[1779] 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.

[1780] 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).

[1781] 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.

[1782] 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.

[1783] 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.

[1784] 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.

[1785] 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.

[1786] 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.

[1787] 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.

[1788] 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.

[1789] 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.

[1790] 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.

[1791] The following is further disclosed regarding the above embodiment.

[1792] (Claim 1)

[1793] A means for a user to input a diary;

[1794] means for transmitting the input diary data to a server;

[1795] A server receives the diary data and analyzes emotions and lifestyle patterns using a natural language processing engine;

[1796] A means for generating advice for a user based on the analysis results;

[1797] means for transmitting the generated advice to a user terminal;

[1798] a means for the user device to display the advice to the user;

[1799] A system including:

[1800] (Claim 2)

[1801] 2. The system of claim 1, wherein the server uses a natural language processing engine to perform sentiment analysis and life pattern extraction.

[1802] (Claim 3)

[1803] The system of claim 1, wherein the server has means for assessing the happiness and stress levels of the user.

[1804] "Example 1"

[1805] (Claim 1)

[1806] A means for a user to input a diary;

[1807] means for transmitting the input diary data to a server;

[1808] A server receives the diary data and analyzes emotions and lifestyle patterns using a natural language processing engine;

[1809] A means for generating advice for a user based on the analysis results;

[1810] means for transmitting the generated advice to a user terminal;

[1811] a means for the user device to display the advice to the user;

[1812] A means for the user device to convert diary data into JSON format and send it to the server,

[1813] A means for the server to convert the diary data into an appropriate format and pass it to a natural language processing engine;

[1814] a means for using a text analysis engine as a natural language processing engine;

[1815] A system including:

[1816] (Claim 2)

[1817] 2. The system of claim 1, wherein the server uses a natural language processing engine to perform sentiment analysis and life pattern extraction.

[1818] (Claim 3)

[1819] The system of claim 1, wherein the server has a means for assessing the user's happiness and stress level, and generates specific advice using a generative AI model.

[1820] "Application Example 1"

[1821] (Claim 1)

[1822] A means for a user to input a diary;

[1823] means for transmitting the input diary data to a server;

[1824] A server receives the diary data and analyzes emotions and lifestyle patterns using a natural language processing engine;

[1825] A means for generating advice for a user based on the analysis results;

[1826] means for transmitting the generated advice to a user terminal;

[1827] a means for the user device to display the advice to the user;

[1828] a means for providing personalized shopping advice based on the user's diary data;

[1829] a means for using the generative AI model to generate personalized shopping advice;

[1830] A system including:

[1831] (Claim 2)

[1832] 2. The system of claim 1, wherein the server uses a natural language processing engine to perform sentiment analysis and life pattern extraction.

[1833] (Claim 3)

[1834] The system of claim 1, wherein the server has means for assessing the happiness and stress levels of the user.

[1835] "Example 2: Combining Emotion Engines"

[1836] (Claim 1)

[1837] A means for a user to input a diary;

[1838] means for transmitting the input diary data to a server;

[1839] A server receives the diary data and analyzes emotions using an emotion analysis engine;

[1840] A means of analyzing detailed lifestyle patterns based on the results of emotion analysis using a natural language processing engine;

[1841] A means for comprehensively evaluating the analysis results using an evaluation algorithm;

[1842] A means for generating specific advice using a generative AI model based on the analysis results; and

[1843] means for transmitting the generated advice to a user terminal;

[1844] a means for the user device to display the advice to the user;

[1845] A system including:

[1846] (Claim 2)

[1847] 2. The system of claim 1, wherein the server uses a natural language processing engine to perform sentiment analysis and life pattern extraction.

[1848] (Claim 3)

[1849] The system of claim 1, wherein the server has means for assessing the happiness and stress levels of the user.

[1850] "Application example 2 when combining emotion engines"

[1851] (Claim 1)

[1852] A means for a user to input a diary;

[1853] means for transmitting the input diary data to a server;

[1854] A server receives the diary data and analyzes emotions and lifestyle patterns using a natural language processing engine;

[1855] A means for generating advice for a user based on the analysis results;

[1856] means for transmitting the generated advice to a user terminal;

[1857] a means for the user device to display the advice to the user;

[1858] A means for employees to input their daily events, analyze their emotions and lifestyle patterns at work, and provide specific advice on how to improve their work.

[1859] A system including:

[1860] (Claim 2)

[1861] 2. The system of claim 1, wherein the server uses a natural language processing engine to perform sentiment analysis and life pattern extraction.

[1862] (Claim 3)

[1863] The system according to claim 1, wherein the server has a means for evaluating the user's happiness and stress level and generating suggestions for improving the working environment based on the analysis results. [Explanation of symbols]

[1864] 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 a user to input a diary; means for transmitting the input diary data to a server; A server receives the diary data and analyzes emotions and lifestyle patterns using a natural language processing engine; A means for generating advice for a user based on the analysis results; means for transmitting the generated advice to a user terminal; a means for the user device to display the advice to the user; A system including:

2. The system of claim 1 , wherein the server uses a natural language processing engine to perform sentiment analysis and life pattern extraction.

3. The system of claim 1, wherein the server comprises means for assessing the happiness and stress levels of the user.

Images