system

Abstract

Provide a system. A method for collecting professional coaching data; A means to analyze the collected coaching data and identify important topics and effective coaching methods, A means for collecting user attribute information; means for generating a customized coaching plan based on the user's concerns and needs; means for conducting a coaching session for the user based on the generated coaching plan; a means for collecting and analyzing user feedback after a coaching session; The analysis results will be stored in a database and reflected in the next coaching plan. A system including:

Description

[Technical Field]

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

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

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

[0004] High costs and time constraints are challenges for business people seeking professional business coaching. It is also often difficult to provide customized coaching tailored to the concerns and needs of each individual business person. Therefore, there is a need for a method that allows many business people to receive high-quality coaching easily and efficiently. [Means for solving the problem]

[0005] This invention provides a system that collects professional coaching data and stores it in a database, and analyzes the collected data to identify important topics and effective coaching methods. It also provides a system that includes a means for collecting user attribute information, a means for generating a customized coaching plan based on the user's concerns and needs, a means for conducting coaching sessions with the user based on the generated coaching plan, a means for collecting and analyzing user feedback after the coaching session, and a means for storing the analysis results in a database and reflecting them in the next coaching plan. This system enables many business people to receive effective business coaching at low cost.

[0006] "Professional coaching data" refers to information including audio data, text data, and image data generated during a coaching session conducted by a professional business coach.

[0007] A "database" is a digital system in which collected information is efficiently stored, managed, and retrieved.

[0008] "Analysis" refers to the process of analyzing collected data to identify important topics and effective methods.

[0009] "User attribute information" includes basic and professional information about the user, such as name, age, job title, and industry affiliation.

[0010] A "coaching plan" is a plan that includes specific coaching methods and action plans generated based on the user's needs and concerns.

[0011] A "coaching session" is a series of activities in which a user and an AI interact based on a generated coaching plan.

[0012] "Feedback" refers to information such as satisfaction, evaluation, and opinions provided by users after a coaching session.

[0013] "Analysis results" refers to the results of analyzing collected feedback using natural language processing or other analytical methods. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The present invention is a system for enabling businessmen to receive professional coaching efficiently and effectively, and is implemented through the following specific processes.

[0036] 1. Data accumulation

[0037] Subject: Server

[0038] The server collects audio, text, and image data from coaching sessions conducted by professional business coaches and stores the data in a database. The collected data is analyzed using natural language processing and machine learning algorithms to identify important topics and effective coaching methods. This identified information is used to provide effective coaching to other users.

[0039] Examples:

[0040] The server stores recordings of the professional coach's sessions in cloud storage and analyzes them as text data using voice recognition technology.

[0041] The server converts the professional coach's feedback notes into a database using OCR technology and stores them.

[0042] 2. Collection of User Information

[0043] Subject: Device

[0044] The device provides an interface for collecting basic information and professional information (such as name, age, job title, and industry) from the user. Users can input this information through the device and communicate their concerns and coaching needs in detail.

[0045] Examples:

[0046] The terminal displays the question "What is your current job title?" to the user and transmits the answer to the server.

[0047] The device provides a form in which users can enter their concerns by category, and the contents are stored in a database.

[0048] 3. Create a coaching plan

[0049] Subject: Server

[0050] The server generates a customized coaching plan based on the information entered by the user and past coaching data. An AI algorithm selects the coaching method that best suits the user's attributes and concerns, and creates a specific action plan.

[0051] Examples:

[0052] Based on information such as "a user who is a sales manager is seeking advice on stress management," the server extracts effective stress management techniques from past data and creates a customized plan.

[0053] Based on the plan, the server generates specific actions, such as "perform a short meditation before starting work each day," and presents them to the user.

[0054] 4. Conducting a coaching session

[0055] Subject: Device

[0056] The terminal conducts a coaching session in an interactive manner with the user based on the generated coaching plan, and provides advice and support to the user in voice and text format.

[0057] Examples:

[0058] The device asks the user, "Please tell us about specific situations that caused you stress at work today," and provides advice based on the user's response.

[0059] The device instructs the user to "set a goal for this week and review next week to see if you achieved it."

[0060] 5. Gather and incorporate feedback

[0061] Subject: User

[0062] After a coaching session, users fill out a feedback form to evaluate the session content and their satisfaction. This feedback is sent to the server and used to improve the quality of the next session.

[0063] Examples:

[0064] The user inputs feedback such as "This stress management advice was very helpful" and sends it to the server.

[0065] The server analyzes the feedback and stores it in a database to be reflected in future coaching plans.

[0066] In this way, the system of the present invention allows business people to easily receive high-quality coaching, and by automating the process, it increases efficiency and effectiveness.

[0067] The processing flow will be explained below.

[0068] Program processing flow

[0069] 1. Data accumulation

[0070] Subject: Server

[0071] Step 1:

[0072] Data collection for professional coaching sessions

[0073] The server collects audio data, text data, and image data of coaching sessions by professional coaches.

[0074] Examples:

[0075] The server uploads the audio data from the recording device to cloud storage.

[0076] The server stores the feedback notes and log data received from emails and digital notes in a database.

[0077] Step 2:

[0078] Data analysis

[0079] The server analyzes the collected coaching data and identifies important topics and effective coaching methods.

[0080] Examples:

[0081] The server uses speech recognition algorithms to convert the audio data into text and then uses natural language processing (NLP) to classify it into categories.

[0082] The server generates metadata to identify existing coaching methods and evaluate their effectiveness.

[0083] Step 3:

[0084] Stored in a database

[0085] The server stores the analyzed results in a database for easy access later.

[0086] Examples:

[0087] The server enters the analysis results into specific fields in a database, making them searchable.

[0088] 2. Collection of User Information

[0089] Subject: Device

[0090] Step 4:

[0091] User Preferences

[0092] The terminal provides an interface for inputting basic information about the user (such as name, age, job title, industry, etc.).

[0093] Examples:

[0094] The terminal displays a form with fields such as "Please enter your name."

[0095] The information entered by the user is sent to the server.

[0096] Step 5:

[0097] Identifying the problem

[0098] The terminal displays a detailed questionnaire form to gather the user's specific concerns and coaching needs.

[0099] Examples:

[0100] The device asks the user, "What is the biggest business challenge you are currently facing?"

[0101] The terminal collects the user's answers and sends them to the server.

[0102] 3. Create a coaching plan

[0103] Subject: Server

[0104] Step 6:

[0105] Information Integration

[0106] The server integrates the collected user information with the accumulated coaching data to create a coaching plan that best suits the user's needs.

[0107] Examples:

[0108] The server links the attribute "sales manager" with the concern "stress management" and extracts the optimal method from past data.

[0109] Step 7:

[0110] Generate a coaching plan

[0111] The server generates a specific coaching plan suited to the user based on the integrated information.

[0112] Examples:

[0113] The server suggests specific actions that meet the user's needs, such as "daily reflection time."

[0114] 4. Conducting a coaching session

[0115] Subject: Device

[0116] Step 8:

[0117] Starting a Session

[0118] The terminal starts a coaching session at a time set by the user, and the session proceeds interactively.

[0119] Examples:

[0120] The device sends a message to the user saying, "Good morning. Let's talk about your goals for today."

[0121] Step 9:

[0122] Session Progression

[0123] The device provides specific advice and support to the user based on a coaching plan generated by AI.

[0124] Examples:

[0125] The device asks, "Please tell us how stressed you felt at work yesterday," and suggests ways to deal with the situation based on the user's answer.

[0126] 5. Gather and incorporate feedback

[0127] Subject: User

[0128] Step 10:

[0129] Providing feedback

[0130] After the coaching session is over, the user fills out a feedback form to rate the session.

[0131] Examples:

[0132] The user enters feedback such as "I was satisfied with today's session."

[0133] Subject: Server

[0134] Step 11:

[0135] Analyzing and incorporating feedback

[0136] The server analyzes the user's feedback and stores it as data to be used in future sessions.

[0137] Examples:

[0138] The server analyzes the collected feedback and identifies patterns, such as "stress management advice was helpful," and incorporates them into the next coaching plan.

[0139] This series of steps allows users to receive consistent, customized business coaching in an efficient manner.

[0140] Example 1

[0141] 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."

[0142] Traditional business coaching is primarily conducted through face-to-face sessions, which, while effective, require a great deal of time and effort. Furthermore, while individually customized coaching plans are required, generating them is time-consuming and often insufficient. Furthermore, the process of incorporating user feedback into the next coaching session is manual and therefore difficult to do efficiently. Therefore, an efficient and effective system that can solve these issues is needed.

[0143] 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.

[0144] In this invention, the server includes means for collecting professional coaching data, means for analyzing the collected coaching data to identify important topics and effective coaching methods, and means for collecting user attribute information. This enables the server to automatically collect and analyze professional coaching data and generate and provide a customized coaching plan that is optimal for the user. The server also includes means for converting the coaching data into text data using voice recognition technology and optical character recognition technology, means for collecting user information via a user interface and storing it in a database, and means for analyzing user information and past coaching data using an AI algorithm to generate an optimal coaching plan. This enables the server to automatically generate a coaching plan based on the user's concerns and needs and incorporate feedback into the next session.

[0145] "Professional Coaching Data" refers to the audio, text, and image data provided by a business coach during a coaching session.

[0146] A "coaching session" refers to a series of interactions or activities conducted by a coach to provide advice and support to a user.

[0147] "Speech recognition technology" refers to technology for converting voice data into text data.

[0148] "Optical character recognition technology" refers to technology for converting character information contained in image data into text data.

[0149] "User interface" refers to the interface through which a user interacts with a system.

[0150] "AI algorithm" refers to an algorithm that uses artificial intelligence technology to perform data analysis and predictions.

[0151] The present invention is a system for enabling businessmen to receive professional coaching efficiently and effectively, and is implemented through the following specific processes.

[0152] Data accumulation

[0153] Subject: Server

[0154] The server collects audio data, text data, and image data from coaching sessions conducted by professional business coaches and stores this data in a database. To achieve this, the server uses cloud storage (e.g., Amazon S3). The server also converts the audio data into text data using voice recognition technology (e.g., Google® Speech-to-Text) and the image data into text data using optical character recognition technology (e.g., Tesseract OCR). This data is analyzed using machine learning algorithms to identify important topics and effective coaching methods. This identified information is used to provide effective coaching to other users.

[0155] Examples:

[0156] The server stores the recordings of the professional coaches' sessions in cloud storage and analyzes them as audio data using Google Speech-to-Text.

[0157] The server converts the professional coach's feedback notes into a database using Tesseract OCR and stores them.

[0158] Collection of User Information

[0159] Subject: Terminal

[0160] The device provides an interface for collecting basic information and professional information (such as name, age, job title, and industry). Users can enter this information through the device and provide detailed information about their concerns and coaching needs. The collected information is sent to a server in real time and stored in a database.

[0161] Examples:

[0162] The terminal displays the question "What is your current job title?" to the user and transmits the answer to the server.

[0163] The device provides a form in which users can enter their concerns by category, and the contents are stored in a database.

[0164] Generate a coaching plan

[0165] Subject: Server

[0166] The server generates a customized coaching plan based on the information entered by the user and past coaching data. An AI algorithm (e.g., TENSORFLOW (registered trademark), PyTorch) selects the coaching method that best suits the user's attributes and concerns, and creates a specific action plan. The generated coaching plan is provided to the user for implementation.

[0167] Examples:

[0168] Based on information such as "a user who is a sales manager is seeking advice on stress management," the server extracts effective stress management techniques from past data and creates a customized plan.

[0169] Based on the plan, the server generates specific actions, such as "perform a short meditation before starting work each day," and presents them to the user.

[0170] Conducting a coaching session

[0171] Subject: Terminal

[0172] The device conducts a coaching session in an interactive format with the user based on the generated coaching plan, providing advice and support to the user in voice and text format using chatbot frameworks (e.g., Dialogflow, Microsoft® Bot Framework) and speech synthesis technologies (e.g., Google Text-to-Speech, Amazon Polly).

[0173] Examples:

[0174] The device asks the user, "Please tell us about specific situations that caused you stress at work today," and provides advice based on the user's response.

[0175] The device instructs the user to "set a goal for this week and review next week to see if you achieved it."

[0176] Gathering and implementing feedback

[0177] Subject: User

[0178] After a coaching session, users fill out a feedback form to evaluate the content of the session and their satisfaction. This feedback is sent to the server and used to improve the quality of the next session. The server analyzes the feedback and reflects it in the next coaching plan.

[0179] Examples:

[0180] The user inputs feedback such as "This stress management advice was very helpful" and sends it to the server.

[0181] The server analyzes the feedback and stores it in a database to be reflected in future coaching plans.

[0182] Prompt Sentence Examples

[0183] Below are examples of prompt sentences to input to the generative AI model.

[0184] 1. Data Collection Prompt:

[0185] What are the specific steps to converting a business coach's audio sessions into text and identifying effective coaching methods?

[0186] 2. User Information Collection Prompt:

[0187] Please propose the best UI design to collect the user's basic information and occupational information.

[0188] 3. Coaching Plan Generation Prompts:

[0189] Describe the detailed design of the algorithm to create the stress management coaching plan required by the sales manager.

[0190] 4. Coaching Session Prompts:

[0191] How can I design a chatbot that provides advice to users in a conversational way?

[0192] 5. Feedback collection prompts:

[0193] What is the best way to efficiently collect and analyze feedback after a coaching session?

[0194] The above is a description of the mode for carrying out the present invention. This system enables business people to receive professional coaching efficiently and effectively, and increases efficiency and effectiveness by automating the process.

[0195] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0196] Processing Steps

[0197] Step 1: Data accumulation

[0198] Subject: Server

[0199] input:

[0200] Audio, text, and image data from professional coaching sessions

[0201] Specific behavior:

[0202] The server records audio data of sessions conducted by professional business coaches and stores it in cloud storage (e.g., Amazon S3).

[0203] The server converts the stored voice data into text data using voice recognition technology (e.g., Google Speech-to-Text).

[0204] The server scans the professional coach's handwritten feedback notes and converts them into text data using optical character recognition technology (e.g., Tesseract OCR).

[0205] The server stores the converted text data in a database.

[0206] output:

[0207] Results of converting audio and image data into text data

[0208] Text data stored in a database

[0209] Step 2: Collect user information

[0210] Subject: Terminal

[0211] input:

[0212] User basic information (name, age, job title, industry, etc.)

[0213] User concerns and coaching needs

[0214] Specific behavior:

[0215] The terminal displays a form to collect basic information and occupational information from the user.

[0216] The terminal provides an interface for inputting the user's concerns and coaching needs.

[0217] The device transmits the collected information to the server in real time.

[0218] output:

[0219] User information and coaching needs

[0220] Information sent to the server for storage in the database

[0221] Step 3: Create a coaching plan

[0222] Subject: Server

[0223] input:

[0224] Information entered by the user

[0225] Past coaching data

[0226] Specific behavior:

[0227] The server retrieves the information received from the user from a database and uses AI algorithms (e.g., TensorFlow, PyTorch) to analyze the optimal coaching method.

[0228] The server generates a customized coaching plan based on the analysis results.

[0229] The server prepares the generated plan for serving to the user.

[0230] output:

[0231] Customized Coaching Plans

[0232] A concrete action plan provided to the user

[0233] Step 4: Conduct a coaching session

[0234] Subject: Terminal

[0235] input:

[0236] Coaching plan sent from the server

[0237] Specific behavior:

[0238] The terminal conducts a session in an interactive format with the user based on the generated coaching plan.

[0239] The device uses speech synthesis technology (e.g., Google Text-to-Speech, Amazon Polly) to provide advice and support via voice.

[0240] The device receives the user's questions and answers in real time and provides appropriate feedback.

[0241] output:

[0242] Coaching session content provided to users

[0243] Real-time feedback from users

[0244] Step 5: Gather and incorporate feedback

[0245] Subject: User, Server

[0246] input:

[0247] User Feedback

[0248] Collected session results

[0249] Specific behavior:

[0250] After a coaching session, the user fills in a feedback form and submits it to the server.

[0251] The server analyzes the submitted feedback and stores it in a database to improve the quality of the next session.

[0252] output:

[0253] User feedback data

[0254] Database updates to be reflected in the next coaching plan

[0255] (Application example 1)

[0256] 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."

[0257] Conventional coaching systems have not adequately addressed the efficiency of business coaching or the identification of effective coaching methods, making it difficult to quickly provide customized coaching plans for specific industries. There is a particular need for coaching systems that can appropriately address the challenges faced by online shopping site operators and staff. Conventional systems are unable to provide efficient coaching because they do not integrate processes such as collecting and analyzing coaching data, collecting user attribute information, generating customized coaching plans, conducting interactive coaching sessions with users, and collecting and incorporating feedback.

[0258] 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.

[0259] In this invention, the server includes means for collecting professional coaching data, means for analyzing the collected coaching data and identifying important topics and effective coaching methods, means for collecting user attribute information, means for generating a customized coaching plan based on the user's concerns and needs, means for conducting coaching sessions with the user based on the generated coaching plan, means for collecting and analyzing user feedback after the coaching session, means for storing the analysis results in a database and reflecting them in the next coaching plan, means for providing customized coaching to operators and staff of an online shopping site that runs on a smartphone, and means for generating coaching plans from prompt sentences based on the user's attributes and concerns using a generative AI model. This makes it possible to generate and implement customized coaching plans that can quickly and effectively address the unique challenges faced by operators and staff of an online shopping site.

[0260] "Professional Coaching Data" is data including recordings of coaching sessions and feedback provided by a business coach.

[0261] "Collecting coaching data" is the process of acquiring audio, text, and image data from professional coaching sessions.

[0262] "Analysis" refers to the process of examining collected data using machine learning algorithms and natural language processing techniques to identify important topics and effective coaching techniques.

[0263] "User attribute information" is basic information about the coachee, such as name, age, job title, and information about related industries.

[0264] A "customized coaching plan" is an individualized coaching strategy or action plan that is identified based on a user's concerns and needs.

[0265] A "coaching session" is an interactive activity in which a user receives advice and support based on a generated coaching plan.

[0266] "Gathering feedback" is the process of collecting opinions from users after a coaching session about the content of the session and their satisfaction with the session.

[0267] A "smartphone" is a mobile device that can connect to the Internet and install applications.

[0268] An "online shopping site" is a website that sells products and services over the Internet.

[0269] A "generative AI model" is an artificial intelligence algorithm that generates an appropriate coaching plan based on the user's attributes and concerns.

[0270] A "prompt sentence" is an input sentence provided to a generative AI model to generate a coaching plan.

[0271] This invention is a system that enables operators and staff of online shopping sites to receive professional coaching efficiently and effectively, and is specifically implemented as follows.

[0272] 1. Data accumulation

[0273] server

[0274] The server collects audio, text, and image data from coaching sessions conducted by professional business coaches and stores the data in a database. The collected data is analyzed using natural language processing and machine learning algorithms to identify important topics and effective coaching methods. This identified information is used to provide effective coaching to other users.

[0275] Examples:

[0276] The server stores recordings of the professional coach's sessions in cloud storage and analyzes them as text data using voice recognition technology.

[0277] The server converts the professional coach's feedback notes into a database using OCR technology and stores them.

[0278] 2. Collection of User Information

[0279] Terminal

[0280] The device provides an interface for collecting basic information and professional information (such as name, age, job title, and industry) from the user. Users can input this information through the device and communicate their concerns and coaching needs in detail.

[0281] Examples:

[0282] The terminal displays the question "What is your current job title?" to the user and transmits the answer to the server.

[0283] The device provides a form in which users can enter their concerns by category, and the contents are stored in a database.

[0284] 3. Create a coaching plan

[0285] server

[0286] The server generates a customized coaching plan based on the information entered by the user and past coaching data. Using a generative AI model, it selects the coaching method that best suits the user's attributes and concerns, and creates a specific action plan.

[0287] Examples:

[0288] Based on information such as "a customer support representative at an online shopping site is seeking advice on how to improve customer service," the server extracts effective methods of customer service from past data and creates a customized plan.

[0289] An example of a prompt sentence would be "User name: Taro Tanaka, Job title: Marketing manager, Concern: How to improve customer service," and based on that, the AI ​​model would generate an appropriate coaching plan.

[0290] 4. Conducting a coaching session

[0291] Terminal

[0292] The terminal conducts a coaching session in an interactive manner with the user based on the generated coaching plan, and provides advice and support to the user in voice and text format.

[0293] Examples:

[0294] The device asks the user, "Please tell us about specific situations that caused you stress at work today," and provides advice based on the user's response.

[0295] The device instructs the user to "set a goal for this week and review next week to see if you achieved it."

[0296] 5. Gather and incorporate feedback

[0297] User

[0298] After a coaching session, users fill out a feedback form to evaluate the session content and their satisfaction. This feedback is sent to the server and used to improve the quality of the next session.

[0299] Examples:

[0300] The user inputs feedback such as "This stress management advice was very helpful" and sends it to the server.

[0301] The server analyzes the feedback and stores it in a database to be reflected in future coaching plans.

[0302] Through the system of the present invention, operators and staff of online shopping sites can easily receive high-quality coaching, and by automating the process, efficiency and effectiveness can be increased.

[0303] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0304] Step 1:

[0305] Data accumulation

[0306] The server collects audio data, text data, and image data provided by professional business coaches. The collected data is stored in cloud storage. The audio data is converted into text data using speech recognition technology (e.g., Google Speech Recognition), and the text data is analyzed using natural language processing technology. The image data is converted into text data using OCR technology.

[0307] input:

[0308] Audio, text, and image data from professional business coaches

[0309] output:

[0310] Analyzed text and image data

[0311] Step 2:

[0312] Collection of User Information

[0313] The terminal provides an interface for collecting basic information and professional information (such as name, age, job title, and industry) entered by the user, which is then sent from the terminal to the server.

[0314] input:

[0315] User-supplied information such as name, age, job title, and industry affiliation

[0316] output:

[0317] User information sent to the server

[0318] Step 3:

[0319] Generate a coaching plan

[0320] The server uses a generative AI model based on the information entered by the user and past coaching data to generate a customized coaching plan. Prompt statements are input into the generative AI model to generate a coaching plan that is optimal for the user's attributes and concerns.

[0321] input:

[0322] User information, past coaching data, generative AI model

[0323] output:

[0324] Customized Coaching Plans

[0325] Step 4:

[0326] Conducting a coaching session

[0327] The device then conducts a coaching session interactively with the user based on the generated coaching plan, providing advice and support to the user in voice and text format and suggesting next actions based on the user's responses.

[0328] input:

[0329] Customized Coaching Plans

[0330] output:

[0331] User advice, support, and next steps

[0332] Step 5:

[0333] Gathering and implementing feedback

[0334] After a coaching session, users fill out a feedback form to evaluate the content of the session and their satisfaction. The feedback is sent to the server and analyzed. The analysis results are stored in a database and reflected in the next coaching plan.

[0335] input:

[0336] User Feedback

[0337] output:

[0338] Feedback data analyzed and stored in a database

[0339] Through these steps, online shopping site operators and staff can receive efficient and effective coaching.

[0340] 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.

[0341] The present invention is a system that enables businessmen to receive professional coaching efficiently and effectively, and is implemented through the following specific processes. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide the user with more personalized coaching.

[0342] 1. Data accumulation

[0343] Subject: Server

[0344] The server collects audio, text, and image data from coaching sessions conducted by professional coaches and stores them in a database. The collected data is analyzed using natural language processing and machine learning algorithms to identify important topics and effective coaching methods. This identified information is used to provide effective coaching to other users.

[0345] Examples:

[0346] The server stores recordings of the professional coach's sessions in cloud storage and analyzes them as text data using voice recognition technology.

[0347] The server converts the professional coach's feedback notes into a database using OCR technology and stores them.

[0348] 2. Collection of User Information

[0349] Subject: Device

[0350] The device provides an interface for collecting basic information and professional information (such as name, age, job title, and industry) from the user. Users can input this information through the device and communicate their concerns and coaching needs in detail.

[0351] Examples:

[0352] The terminal displays the question "What is your current job title?" to the user and transmits the answer to the server.

[0353] The device provides a form in which users can enter their concerns by category, and the contents are stored in a database.

[0354] 3. Create a coaching plan

[0355] Subject: Server

[0356] The server generates a customized coaching plan based on the information entered by the user and past coaching data. An AI algorithm selects the coaching method that best suits the user's attributes and concerns, and creates a specific action plan. In addition, an emotion engine collects the user's emotional data and adjusts the plan accordingly.

[0357] Examples:

[0358] Based on information such as "a user who is a sales manager is seeking advice on stress management," the server extracts effective stress management techniques from past data and creates a customized plan.

[0359] If the emotion engine identifies the user's emotional state as "fatigue," it will suggest additional relaxation techniques to the stress management plan.

[0360] 4. Conducting a coaching session

[0361] Subject: Device

[0362] The device conducts a coaching session interactively with the user based on the generated coaching plan. It provides advice and support to the user in voice and text format. The emotion engine monitors and responds to the user's emotional state in real time.

[0363] Examples:

[0364] The device asks the user, "Please tell us about specific situations that caused you stress at work today," and provides advice based on the user's response.

[0365] If the emotion engine recognizes that the user is feeling "anxious," it takes an approach that emphasizes encouragement and support within the session.

[0366] 5. Gather and incorporate feedback

[0367] Subject: User

[0368] After a coaching session, users fill out a feedback form to evaluate the session content and their satisfaction. This feedback is sent to the server and used to improve the quality of the next session.

[0369] Examples:

[0370] The user inputs feedback such as "This stress management advice was very helpful" and sends it to the server.

[0371] Subject: Server

[0372] The server analyzes the collected feedback and stores it as data to be used in future sessions, taking into account the analysis results of the emotion engine.

[0373] Examples:

[0374] The server analyzes the collected feedback and identifies patterns, such as "stress management advice was helpful," and incorporates them into the next coaching plan.

[0375] Data from the sentiment engine can tell you when a user has positive feelings about a particular action, and then reinforce that action and include it in your next plan.

[0376] In this way, the system of the present invention allows businessmen to easily receive high-quality coaching, and the addition of an emotion engine makes it possible to provide even more personalized coaching.

[0377] The processing flow will be explained below.

[0378] Program processing flow

[0379] 1. Data accumulation

[0380] Subject: Server

[0381] Step 1:

[0382] Data collection for professional coaching sessions

[0383] The server collects audio data, text data, and image data of coaching sessions by professional coaches.

[0384] Examples:

[0385] The server uploads the audio data from the recording device to cloud storage.

[0386] The server stores the feedback notes and log data received from emails and digital notes in a database.

[0387] Step 2:

[0388] Data analysis

[0389] The server analyzes the collected coaching data and identifies important topics and effective coaching methods.

[0390] Examples:

[0391] The server uses speech recognition algorithms to convert the audio data into text and then uses natural language processing (NLP) to classify it into categories.

[0392] The server generates metadata to identify existing coaching methods and evaluate their effectiveness.

[0393] Step 3:

[0394] Stored in a database

[0395] The server stores the analyzed results in a database for easy access later.

[0396] Examples:

[0397] The server enters the analysis results into specific fields in a database, making them searchable.

[0398] 2. Collection of User Information

[0399] Subject: Device

[0400] Step 4:

[0401] User Preferences

[0402] The terminal provides an interface for inputting basic information about the user (such as name, age, job title, industry, etc.).

[0403] Examples:

[0404] The terminal displays a form with fields such as "Please enter your name."

[0405] The information entered by the user is sent to the server.

[0406] Step 5:

[0407] Identifying the problem

[0408] The terminal displays a detailed questionnaire form to gather the user's specific concerns and coaching needs.

[0409] Examples:

[0410] The device asks the user, "What is the biggest business challenge you are currently facing?"

[0411] The terminal collects the user's answers and sends them to the server.

[0412] 3. Create a coaching plan

[0413] Subject: Server

[0414] Step 6:

[0415] Information Integration

[0416] The server integrates the collected user information with the accumulated coaching data to create a coaching plan that best suits the user's needs.

[0417] Examples:

[0418] The server links the attribute "sales manager" with the concern "stress management" and extracts the optimal method from past data.

[0419] Step 7:

[0420] Sentiment analysis with emotion engine

[0421] The server collects the user's emotion data using an emotion engine, which recognizes the user's emotion using voice analysis, facial expression recognition, and text analysis, and reflects the collected data in the coaching plan.

[0422] Examples:

[0423] The server analyzes the user's voice data, and the emotion engine detects "anxiety" and "stress."

[0424] The server incorporates the detected emotional information into a coaching plan, emphasizing relaxation techniques and positive feedback.

[0425] Step 8:

[0426] Final generation of coaching plan

[0427] The server then uses the integrated information to ultimately generate a specific coaching plan suited to the user.

[0428] Examples:

[0429] The server suggests specific actions such as "daily reflection time."

[0430] Generate additional actions based on your emotional state, such as "Try this relaxation technique today."

[0431] 4. Conducting a coaching session

[0432] Subject: Device

[0433] Step 9:

[0434] Starting a Session

[0435] The terminal starts a coaching session at a time set by the user, and the session proceeds interactively.

[0436] Examples:

[0437] The device sends a message to the user saying, "Good morning. Let's talk about your goals for today."

[0438] Step 10:

[0439] Session Progression

[0440] The device provides specific advice and support to the user based on the generated coaching plan, and the emotion engine monitors the user's emotions in real time and adapts the session content accordingly.

[0441] Examples:

[0442] The device asks, "Please tell us how stressed you felt at work yesterday," and suggests ways to deal with the situation based on the user's answer.

[0443] If the emotion engine detects the user's "anxiety," it takes an approach that emphasizes encouragement and support.

[0444] 5. Gather and incorporate feedback

[0445] Subject: User

[0446] Step 11:

[0447] Providing feedback

[0448] After the coaching session is over, the user fills out a feedback form to rate the session.

[0449] Examples:

[0450] The user enters feedback such as "I was satisfied with today's session."

[0451] Subject: Server

[0452] Step 12:

[0453] Analyzing and incorporating feedback

[0454] The server analyzes the user's feedback and stores it as data to be used in future sessions, taking into account the analysis results of the emotion engine.

[0455] Examples:

[0456] The server analyzes the collected feedback and identifies patterns such as "stress management advice was helpful."

[0457] Data from the sentiment engine can tell you when a user has positive feelings about a particular action, and then reinforce that action and include it in your next plan.

[0458] This series of steps allows users to receive consistent, personalized business coaching efficiently.

[0459] Example 2

[0460] 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."

[0461] Conventional coaching systems have not been able to fully realize the creation of professional coaching content or customized plans based on the user's individual needs, and have only been able to provide general coaching methods. Furthermore, they are unable to respond in real time taking into account the user's emotional state, making it difficult to provide optimal coaching to the user. As a result, user satisfaction and effectiveness have been limited.

[0462] 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.

[0463] In this invention, the server includes means for collecting professional coaching data, means for analyzing the collected coaching data and identifying important topics and effective coaching methods, means for collecting user attribute information, means for generating a coaching plan customized based on the user's concerns and needs, means for conducting a coaching session for the user based on the generated coaching plan, means for collecting and analyzing user feedback after the coaching session, means for collecting user emotional data in real time and adjusting the coaching plan, and means for storing the analysis results in a database and reflecting them in the next coaching plan. This makes it possible to provide an optimized coaching plan for the user and respond to the user's emotional state in real time.

[0464] "Professional coaching data" is a general term for data including audio data, text data, image data, etc. of coaching sessions provided by professional coaches.

[0465] "Analysis" is the process of processing collected data using statistical and machine learning techniques to identify important topics and effective coaching methods.

[0466] "User attribute information" is information that indicates the characteristics of a user, including information about the user's name, age, job title, and related industry.

[0467] A "customized coaching plan" is a coaching action plan that is optimized for a specific user based on the user's concerns and needs.

[0468] A "coaching session" is a process of providing guidance and advice to a user in an interactive format based on the generated coaching plan.

[0469] "Feedback" refers to the evaluation and impressions provided by the user after a coaching session, and is used to improve the quality of the next coaching plan.

[0470] "Collecting user emotional data in real time" refers to the process of using technologies such as voice recognition and emotion analysis to monitor the user's emotional state in real time during a coaching session.

[0471] A "database" is an information system for storing collected and analyzed data and feedback.

[0472] The present invention provides a system for receiving professional coaching that provides a customized coaching plan tailored to the individual needs of a user. This system is composed of elements including a server, a terminal, and a user.

[0473] Server Features

[0474] The server collects and analyzes coaching data provided by professional coaches and generates a customized coaching plan for each user. Specific hardware uses cloud storage (e.g., Amazon S3), and software uses voice recognition technology (e.g., Google Cloud Speech-to-Text), natural language processing AI algorithms (e.g., GPT-4®), and emotion engines (e.g., IBM Watson® Tone Analyzer).

[0475] The server uploads the sessions recorded by the professional coaches to cloud storage and collects the audio data from there. The collected audio data is converted into text data using voice recognition technology. Image data such as the professional coaches' feedback notes are also converted into text data using OCR technology and stored in a database.

[0476] Device Features

[0477] The device acts as an interface with the user, collecting information from the user and conducting coaching sessions. The user enters basic information, occupational information, and information about their worries and needs through the device, which is then sent to the server. The device then conducts the session in an interactive format with the user based on the generated coaching plan. The device also collects and analyzes the user's emotional data in real time and adjusts advice and guidance as needed.

[0478] User Roles

[0479] Users enter basic information and occupational information through their devices and provide details of their current concerns and needs. Based on the generated coaching plan, the user receives a coaching session. After the session, the user provides feedback, which is sent to the server and used to improve the quality of the next session.

[0480] Specific examples

[0481] The server inputs the prompt "The sales manager is seeking advice on stress management" into GPT-4, and has it generate a stress management coaching plan. If the emotion engine recognizes the user's emotional state as "fatigue," it will suggest adding relaxation techniques to the plan.

[0482] The device asks the user questions such as "What is your current job title?" and sends the answer to the server. If the user enters "Sales Manager," the server uses that information to generate a customized coaching plan.

[0483] During the session, the device asks, "Please tell us about specific situations that caused you stress at work today," and provides advice based on the user's input. If the emotion engine detects "anxiety" in real time, the device will display encouraging words such as, "It's important to take a break."

[0484] Example prompt sentence:

[0485] "Create a customized coaching plan for your sales manager who wants stress management advice. Also, include relaxation techniques if your users are feeling burned out by their work."

[0486] This system configuration makes it possible to provide coaching plans suited to the individual needs and emotional state of the user, thereby improving the quality of coaching.

[0487] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0488] Step 1:

[0489] Collecting professional coaching data

[0490] Subject: Server

[0491] The server collects audio data of coaching sessions recorded by professional coaches. The audio data is retrieved from cloud storage (e.g., Amazon S3). The retrieved audio data is converted into text data using speech recognition technology (e.g., Google Cloud Speech-to-Text). The converted text data is stored in a database. The input of this process is audio data, and the output is text data.

[0492] Specific behavior:

[0493] The server accesses Amazon S3 cloud storage and downloads the audio file.

[0494] Input the downloaded audio file into the Google Cloud Speech-to-Text API and convert it into text.

[0495] The converted text data is stored in a database.

[0496] Step 2:

[0497] Collection of User Information

[0498] Subject: Device

[0499] The terminal provides an interface for inputting basic information (such as name, age, and job title) and occupational information of the user. The user inputs this information and sends it from the terminal to the server. The input of this process is the basic information of the user, and the output is the user information sent to the server.

[0500] Specific behavior:

[0501] A form such as "Please enter your name" or "Please enter your age" will be displayed on the device screen.

[0502] The information entered by the user is sent to the server.

[0503] Step 3:

[0504] Collecting user concerns and needs

[0505] Subject: Device

[0506] The terminal provides a form for the user to input their concerns and coaching needs. The user inputs this information and sends it from the terminal to the server. The input of this process is the user's concerns and needs, and the output is the user information sent to the server.

[0507] Specific behavior:

[0508] The device screen displays the message "Please tell us what your current concerns are" and asks the user to select an item by category.

[0509] Information about the worries and needs selected by the user is sent to the server.

[0510] Step 4:

[0511] Generate a customized coaching plan

[0512] Subject: Server

[0513] The server generates a customized coaching plan using an AI algorithm (e.g., GPT-4) based on the user's input information and past coaching data. It then analyzes the user's emotional data using an emotion engine (e.g., IBM Watson Tone Analyzer) to adjust the coaching plan. The input of this process is the user's information and past coaching data, and the output is a customized coaching plan.

[0514] Specific behavior:

[0515] The server inputs the prompt "Sales manager wants advice on stress management" into GPT-4 and has it generate a customized plan.

[0516] The emotion engine analyzes the user's emotional state and adds suggestions such as relaxation techniques to the plan.

[0517] Step 5:

[0518] Conducting a coaching session

[0519] Subject: Device

[0520] The device interactively conducts a coaching session with the user based on the generated coaching plan. During the session, the device monitors the user's emotional state in real time and adjusts the plan as needed. The inputs to this process are the coaching plan and real-time emotional data, and the output is advice provided to the user.

[0521] Specific behavior:

[0522] The device asks the user, "Please tell us about a specific situation that caused you stress during work today."

[0523] Based on the user's answers, appropriate advice is provided.

[0524] If the emotion engine detects anxiety, it will display encouraging words such as "It's important to take breaks."

[0525] Step 6:

[0526] Gathering and implementing feedback

[0527] Subject: User and Server

[0528] The user provides feedback after a coaching session. The feedback is sent to the server and used to improve the quality of the next coaching plan. The server analyzes the collected feedback and incorporates it into the next coaching plan. The inputs to this process are the user's feedback and session data, and the output is an improved coaching plan.

[0529] Specific behavior:

[0530] The user inputs feedback into the terminal, such as "This stress management advice was very helpful."

[0531] The device sends the feedback to the server.

[0532] The server analyzes the feedback data and reflects patterns such as "the stress management advice was helpful" in the next plan.

[0533] (Application example 2)

[0534] 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."

[0535] Coaching for modern business people and store employees often involves a uniform approach, which means it is difficult to adequately address individual concerns and emotional states. Furthermore, it is difficult to provide coaching that responds to real-time emotional changes, which can result in ineffective guidance.

[0536] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0537] In this invention, the server includes means for collecting professional coaching data, means for analyzing the collected coaching data and identifying important topics and effective coaching methods, means for collecting user attribute information, means for generating a customized coaching plan based on the user's concerns and requests, means for conducting a coaching session with the user based on the generated coaching plan, means for collecting and analyzing user feedback after the coaching session, means for storing the analysis results in a database and reflecting them in the next coaching plan, and means for collecting user emotion data and adjusting the coaching plan in real time using an emotion engine. This enables effective and personalized coaching according to the user's individual concerns and emotional state.

[0538] "Professional instructional data" refers to educational or training information provided by a professional, and includes audio data, text data, and image data.

[0539] "Instructional Data" means educational or training information collected for a specific purpose, and includes audio data, text data, and image data.

[0540] "Key topics" refer to themes extracted from the analyzed data that have a significant impact on a particular situation or issue.

[0541] "Effective teaching methods" refer to educational or training techniques that have a proven track record of helping users achieve their goals.

[0542] "User demographic information" means basic data about an individual User, including name, age, job title, and industry-related information.

[0543] "User concerns and requests" refers to the problems the user is facing or the specific situations they wish to resolve.

[0544] "Customized instruction plan" refers to an educational or training program that is individually designed based on the user's demographic information and concerns or requests.

[0545] "Coaching Session" means an educational or training interactive program delivered to a User.

[0546] "Feedback" refers to the evaluation, opinions, and impressions provided by users after a coaching session.

[0547] "Emotional Data" refers to information used to measure and analyze a user's emotional state.

[0548] "Emotion engine" refers to the algorithms and systems that analyze users' emotional data in real time and adjust appropriate teaching plans.

[0549] The present invention is a system that enables users to receive professional guidance efficiently and effectively, and aims to provide personalized guidance that is tailored to the user's individual concerns and emotional state.

[0550] 1. Data accumulation

[0551] The server collects professional teaching data and stores it in a database. The teaching data includes audio data, text data, and image data. This data is analyzed using natural language processing and machine learning algorithms to identify important topics and effective teaching methods. This identified information is used to provide effective teaching to other users.

[0552] 2. Collection of User Information

[0553] The device provides an interface for collecting basic information and professional information (such as name, age, job title, and industry) from users. Users can input this information through the device and provide detailed information about their concerns and guidance needs.

[0554] 3. Generate lesson plans

[0555] The server generates a customized instruction plan based on the information entered by the user and past instruction data. An AI algorithm selects the instruction method that best suits the user's attributes and concerns, and creates a specific action plan. In addition, an emotion engine collects the user's emotional data and adjusts the plan accordingly.

[0556] 4. Conducting a mentoring session

[0557] The device conducts a teaching session with the user in an interactive format based on the generated teaching plan. It provides advice and support to the user in voice and text format. The emotion engine monitors and responds to the user's emotional state in real time.

[0558] 5. Gather and incorporate feedback

[0559] After a training session, users fill out a feedback form to evaluate the content of the session and their satisfaction. This feedback is sent to the server and used to improve the quality of the next session. The server analyzes the collected feedback and saves it as data to be reflected in future sessions. It also takes into account the analysis results of the emotion engine.

[0560] Hardware and software used

[0561] Server: Used to store and analyze data, generate teaching plans, and collect and incorporate feedback. Python's nltk and spaCy are used for natural language processing, and scikit-learn is used for machine learning algorithms.

[0562] Devices: Used to collect user information, conduct coaching sessions, and gather feedback. This includes smartphones, tablets, laptops, etc.

[0563] Emotion engine: Used to collect user emotion data and analyze it in real time. Emotion analysis uses emotion recognition API and TensorFlow.

[0564] Prompt Sentence Examples

[0565] "Please tell us about the most stressful experience you have had recently while dealing with a customer."

[0566] "Please tell me a bit more about your current position and your concerns."

[0567] "Was the content of the coaching session helpful? Please give us specific feedback."

[0568] In this way, the system of the present invention allows users to easily receive high-quality instruction, and by adding an emotion engine, it is possible to provide even more personalized instruction.

[0569] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0570] Step 1:

[0571] The server collects professional teaching data and stores it in a database. As input, it receives audio, text, and image data from teaching sessions. It analyzes this data using natural language processing techniques (e.g., Python's nltk or spaCy) to identify important topics and effective teaching methods. As output, the analysis results are stored in a database.

[0572] Step 2:

[0573] The terminal provides an interface to collect basic information and professional information about the user. Input includes name, age, job title, and industry affiliation, which are entered by the user on the terminal. This information is sent to the server and stored in a database. Specifically, the terminal displays an input form and allows the user to enter information.

[0574] Step 3:

[0575] The server collects users' concerns and guidance needs. The input includes specific concerns and requests that users enter on their devices. This information is also stored in the database. Specifically, the device provides a category-specific input form, allowing users to enter detailed concerns.

[0576] Step 4:

[0577] The server generates a customized teaching plan based on collected user information and past teaching data. The inputs include the user's attribute information, concerns and requests, and past teaching data. Using the generative AI model, it selects the optimal teaching method and creates a specific action plan. The output is the generated customized plan.

[0578] Step 5:

[0579] The device conducts a teaching session interactively with the user based on the generated teaching plan. The device takes the teaching plan as input. It provides advice and support to the user in voice or text format, and an emotion engine monitors the user's emotional state in real time. The device outputs instruction tailored to the user's emotional state.

[0580] Step 6:

[0581] Users provide feedback after a training session. Input includes ratings, opinions, and impressions that users enter on their devices. This feedback is sent to the server and stored in a database. Specifically, the device displays a feedback form for users to fill out.

[0582] Step 7:

[0583] The server analyzes the collected feedback and reflects it in the next teaching plan. The input is feedback data from users. This is analyzed and stored in a database. The analysis results are taken into consideration when generating the next teaching plan. Specifically, the server analyzes the feedback data using natural language processing technology and extracts important opinions and evaluations.

[0584] Step 8:

[0585] The server collects the user's emotional data and adjusts the teaching plan in real time using an emotion engine. The input is emotional information from the user's voice and text data. Based on this information, the server uses an emotion recognition API and TensorFlow to analyze the user's emotional state and adjust the teaching plan as needed. Specifically, the emotion engine analyzes the user's tone of voice and vocabulary to determine their emotional state.

[0586] 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.

[0587] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0588] 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.

[0589] [Second embodiment]

[0590] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0591] 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.

[0592] 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).

[0593] 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.

[0594] 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.

[0595] 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).

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0601] 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."

[0602] The present invention is a system for enabling businessmen to receive professional coaching efficiently and effectively, and is implemented through the following specific processes.

[0603] 1. Data accumulation

[0604] Subject: Server

[0605] The server collects audio, text, and image data from coaching sessions conducted by professional business coaches and stores the data in a database. The collected data is analyzed using natural language processing and machine learning algorithms to identify important topics and effective coaching methods. This identified information is used to provide effective coaching to other users.

[0606] Examples:

[0607] The server stores recordings of the professional coach's sessions in cloud storage and analyzes them as text data using voice recognition technology.

[0608] The server converts the professional coach's feedback notes into a database using OCR technology and stores them.

[0609] 2. Collection of User Information

[0610] Subject: Device

[0611] The device provides an interface for collecting basic information and professional information (such as name, age, job title, and industry) from the user. Users can input this information through the device and communicate their concerns and coaching needs in detail.

[0612] Examples:

[0613] The terminal displays the question "What is your current job title?" to the user and transmits the answer to the server.

[0614] The device provides a form in which users can enter their concerns by category, and the contents are stored in a database.

[0615] 3. Create a coaching plan

[0616] Subject: Server

[0617] The server generates a customized coaching plan based on the information entered by the user and past coaching data. An AI algorithm selects the coaching method that best suits the user's attributes and concerns, and creates a specific action plan.

[0618] Examples:

[0619] Based on information such as "a user who is a sales manager is seeking advice on stress management," the server extracts effective stress management techniques from past data and creates a customized plan.

[0620] Based on the plan, the server generates specific actions, such as "perform a short meditation before starting work each day," and presents them to the user.

[0621] 4. Conducting a coaching session

[0622] Subject: Device

[0623] The terminal conducts a coaching session in an interactive manner with the user based on the generated coaching plan, and provides advice and support to the user in voice and text format.

[0624] Examples:

[0625] The device asks the user, "Please tell us about specific situations that caused you stress at work today," and provides advice based on the user's response.

[0626] The device instructs the user to "set a goal for this week and review next week to see if you achieved it."

[0627] 5. Gather and incorporate feedback

[0628] Subject: User

[0629] After a coaching session, users fill out a feedback form to evaluate the session content and their satisfaction. This feedback is sent to the server and used to improve the quality of the next session.

[0630] Examples:

[0631] The user inputs feedback such as "This stress management advice was very helpful" and sends it to the server.

[0632] The server analyzes the feedback and stores it in a database to be reflected in future coaching plans.

[0633] In this way, the system of the present invention allows business people to easily receive high-quality coaching, and by automating the process, it increases efficiency and effectiveness.

[0634] The processing flow will be explained below.

[0635] Program processing flow

[0636] 1. Data accumulation

[0637] Subject: Server

[0638] Step 1:

[0639] Data collection for professional coaching sessions

[0640] The server collects audio data, text data, and image data of coaching sessions by professional coaches.

[0641] Examples:

[0642] The server uploads the audio data from the recording device to cloud storage.

[0643] The server stores the feedback notes and log data received from emails and digital notes in a database.

[0644] Step 2:

[0645] Data analysis

[0646] The server analyzes the collected coaching data and identifies important topics and effective coaching methods.

[0647] Examples:

[0648] The server uses speech recognition algorithms to convert the audio data into text and then uses natural language processing (NLP) to classify it into categories.

[0649] The server generates metadata to identify existing coaching methods and evaluate their effectiveness.

[0650] Step 3:

[0651] Stored in a database

[0652] The server stores the analyzed results in a database for easy access later.

[0653] Examples:

[0654] The server enters the analysis results into specific fields in a database, making them searchable.

[0655] 2. Collection of User Information

[0656] Subject: Device

[0657] Step 4:

[0658] User Preferences

[0659] The terminal provides an interface for inputting basic information about the user (such as name, age, job title, industry, etc.).

[0660] Examples:

[0661] The terminal displays a form with fields such as "Please enter your name."

[0662] The information entered by the user is sent to the server.

[0663] Step 5:

[0664] Identifying the problem

[0665] The terminal displays a detailed questionnaire form to gather the user's specific concerns and coaching needs.

[0666] Examples:

[0667] The device asks the user, "What is the biggest business challenge you are currently facing?"

[0668] The terminal collects the user's answers and sends them to the server.

[0669] 3. Create a coaching plan

[0670] Subject: Server

[0671] Step 6:

[0672] Information Integration

[0673] The server integrates the collected user information with the accumulated coaching data to create a coaching plan that best suits the user's needs.

[0674] Examples:

[0675] The server links the attribute "sales manager" with the concern "stress management" and extracts the optimal method from past data.

[0676] Step 7:

[0677] Generate a coaching plan

[0678] The server generates a specific coaching plan suited to the user based on the integrated information.

[0679] Examples:

[0680] The server suggests specific actions that meet the user's needs, such as "daily reflection time."

[0681] 4. Conducting a coaching session

[0682] Subject: Device

[0683] Step 8:

[0684] Starting a Session

[0685] The terminal starts a coaching session at a time set by the user, and the session proceeds interactively.

[0686] Examples:

[0687] The device sends a message to the user saying, "Good morning. Let's talk about your goals for today."

[0688] Step 9:

[0689] Session Progression

[0690] The device provides specific advice and support to the user based on a coaching plan generated by AI.

[0691] Examples:

[0692] The device asks, "Please tell us how stressed you felt at work yesterday," and suggests ways to deal with the situation based on the user's answer.

[0693] 5. Gather and incorporate feedback

[0694] Subject: User

[0695] Step 10:

[0696] Providing feedback

[0697] After the coaching session is over, the user fills out a feedback form to rate the session.

[0698] Examples:

[0699] The user enters feedback such as "I was satisfied with today's session."

[0700] Subject: Server

[0701] Step 11:

[0702] Analyzing and incorporating feedback

[0703] The server analyzes the user's feedback and stores it as data to be used in future sessions.

[0704] Examples:

[0705] The server analyzes the collected feedback and identifies patterns, such as "stress management advice was helpful," and incorporates them into the next coaching plan.

[0706] This series of steps allows users to receive consistent, customized business coaching in an efficient manner.

[0707] Example 1

[0708] 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."

[0709] Traditional business coaching is primarily conducted through face-to-face sessions, which, while effective, require a great deal of time and effort. Furthermore, while individually customized coaching plans are required, generating them is time-consuming and often insufficient. Furthermore, the process of incorporating user feedback into the next coaching session is manual and therefore difficult to do efficiently. Therefore, an efficient and effective system that can solve these issues is needed.

[0710] 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.

[0711] In this invention, the server includes means for collecting professional coaching data, means for analyzing the collected coaching data to identify important topics and effective coaching methods, and means for collecting user attribute information. This enables the server to automatically collect and analyze professional coaching data and generate and provide a customized coaching plan that is optimal for the user. The server also includes means for converting the coaching data into text data using voice recognition technology and optical character recognition technology, means for collecting user information via a user interface and storing it in a database, and means for analyzing user information and past coaching data using an AI algorithm to generate an optimal coaching plan. This enables the server to automatically generate a coaching plan based on the user's concerns and needs and incorporate feedback into the next session.

[0712] "Professional Coaching Data" refers to the audio, text, and image data provided by a business coach during a coaching session.

[0713] A "coaching session" refers to a series of interactions or activities conducted by a coach to provide advice and support to a user.

[0714] "Speech recognition technology" refers to technology for converting voice data into text data.

[0715] "Optical character recognition technology" refers to technology for converting character information contained in image data into text data.

[0716] "User interface" refers to the interface through which a user interacts with a system.

[0717] "AI algorithm" refers to an algorithm that uses artificial intelligence technology to perform data analysis and predictions.

[0718] The present invention is a system for enabling businessmen to receive professional coaching efficiently and effectively, and is implemented through the following specific processes.

[0719] Data accumulation

[0720] Subject: Server

[0721] The server collects audio data, text data, and image data from coaching sessions conducted by professional business coaches and stores this data in a database. To achieve this, the server uses cloud storage (e.g., Amazon S3). The server also converts audio data into text data using voice recognition technology (e.g., Google Speech-to-Text) and image data into text data using optical character recognition technology (e.g., Tesseract OCR). This data is analyzed using machine learning algorithms to identify important topics and effective coaching methods. This identified information is used to provide effective coaching to other users.

[0722] Examples:

[0723] The server stores the recordings of the professional coaches' sessions in cloud storage and analyzes them as audio data using Google Speech-to-Text.

[0724] The server converts the professional coach's feedback notes into a database using Tesseract OCR and stores them.

[0725] Collection of User Information

[0726] Subject: Terminal

[0727] The device provides an interface for collecting basic information and professional information (such as name, age, job title, and industry). Users can enter this information through the device and provide detailed information about their concerns and coaching needs. The collected information is sent to a server in real time and stored in a database.

[0728] Examples:

[0729] The terminal displays the question "What is your current job title?" to the user and transmits the answer to the server.

[0730] The device provides a form in which users can enter their concerns by category, and the contents are stored in a database.

[0731] Generate a coaching plan

[0732] Subject: Server

[0733] The server generates a customized coaching plan based on the information entered by the user and past coaching data. AI algorithms (e.g., TensorFlow, PyTorch) select the coaching method that best suits the user's attributes and concerns, and create a specific action plan. The generated coaching plan is provided to the user for implementation.

[0734] Examples:

[0735] Based on information such as "a user who is a sales manager is seeking advice on stress management," the server extracts effective stress management techniques from past data and creates a customized plan.

[0736] Based on the plan, the server generates specific actions, such as "perform a short meditation before starting work each day," and presents them to the user.

[0737] Conducting a coaching session

[0738] Subject: Terminal

[0739] The device conducts a coaching session interactively with the user based on the generated coaching plan, providing advice and support to the user in voice and text format using chatbot frameworks (e.g., Dialogflow, Microsoft Bot Framework) and speech synthesis technologies (e.g., Google Text-to-Speech, Amazon Polly).

[0740] Examples:

[0741] The device asks the user, "Please tell us about specific situations that caused you stress at work today," and provides advice based on the user's response.

[0742] The device instructs the user to "set a goal for this week and review next week to see if you achieved it."

[0743] Gathering and implementing feedback

[0744] Subject: User

[0745] After a coaching session, users fill out a feedback form to evaluate the content of the session and their satisfaction. This feedback is sent to the server and used to improve the quality of the next session. The server analyzes the feedback and reflects it in the next coaching plan.

[0746] Examples:

[0747] The user inputs feedback such as "This stress management advice was very helpful" and sends it to the server.

[0748] The server analyzes the feedback and stores it in a database to be reflected in future coaching plans.

[0749] Prompt Sentence Examples

[0750] Below are examples of prompt sentences to input to the generative AI model.

[0751] 1. Data Collection Prompt:

[0752] What are the specific steps to converting a business coach's audio sessions into text and identifying effective coaching methods?

[0753] 2. User Information Collection Prompt:

[0754] Please propose the best UI design to collect the user's basic information and occupational information.

[0755] 3. Coaching Plan Generation Prompts:

[0756] Describe the detailed design of the algorithm to create the stress management coaching plan required by the sales manager.

[0757] 4. Coaching Session Prompts:

[0758] How can I design a chatbot that provides advice to users in a conversational way?

[0759] 5. Feedback collection prompts:

[0760] What is the best way to efficiently collect and analyze feedback after a coaching session?

[0761] The above is a description of the mode for carrying out the present invention. This system enables business people to receive professional coaching efficiently and effectively, and increases efficiency and effectiveness by automating the process.

[0762] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0763] Processing Steps

[0764] Step 1: Data accumulation

[0765] Subject: Server

[0766] input:

[0767] Audio, text, and image data from professional coaching sessions

[0768] Specific behavior:

[0769] The server records audio data of sessions conducted by professional business coaches and stores it in cloud storage (e.g., Amazon S3).

[0770] The server converts the stored voice data into text data using voice recognition technology (e.g., Google Speech-to-Text).

[0771] The server scans the professional coach's handwritten feedback notes and converts them into text data using optical character recognition technology (e.g., Tesseract OCR).

[0772] The server stores the converted text data in a database.

[0773] output:

[0774] Results of converting audio and image data into text data

[0775] Text data stored in a database

[0776] Step 2: Collect user information

[0777] Subject: Terminal

[0778] input:

[0779] User basic information (name, age, job title, industry, etc.)

[0780] User concerns and coaching needs

[0781] Specific behavior:

[0782] The terminal displays a form to collect basic information and occupational information from the user.

[0783] The terminal provides an interface for inputting the user's concerns and coaching needs.

[0784] The device transmits the collected information to the server in real time.

[0785] output:

[0786] User information and coaching needs

[0787] Information sent to the server for storage in the database

[0788] Step 3: Create a coaching plan

[0789] Subject: Server

[0790] input:

[0791] Information entered by the user

[0792] Past coaching data

[0793] Specific behavior:

[0794] The server retrieves the information received from the user from a database and uses AI algorithms (e.g., TensorFlow, PyTorch) to analyze the optimal coaching method.

[0795] The server generates a customized coaching plan based on the analysis results.

[0796] The server prepares the generated plan for serving to the user.

[0797] output:

[0798] Customized Coaching Plans

[0799] A concrete action plan provided to the user

[0800] Step 4: Conduct a coaching session

[0801] Subject: Terminal

[0802] input:

[0803] Coaching plan sent from the server

[0804] Specific behavior:

[0805] The terminal conducts a session in an interactive format with the user based on the generated coaching plan.

[0806] The device uses speech synthesis technology (e.g., Google Text-to-Speech, Amazon Polly) to provide advice and support via voice.

[0807] The device receives the user's questions and answers in real time and provides appropriate feedback.

[0808] output:

[0809] Coaching session content provided to users

[0810] Real-time feedback from users

[0811] Step 5: Gather and incorporate feedback

[0812] Subject: User, Server

[0813] input:

[0814] User Feedback

[0815] Collected session results

[0816] Specific behavior:

[0817] After a coaching session, the user fills in a feedback form and submits it to the server.

[0818] The server analyzes the submitted feedback and stores it in a database to improve the quality of the next session.

[0819] output:

[0820] User feedback data

[0821] Database updates to be reflected in the next coaching plan

[0822] (Application example 1)

[0823] 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."

[0824] Conventional coaching systems have not adequately addressed the efficiency of business coaching or the identification of effective coaching methods, making it difficult to quickly provide customized coaching plans for specific industries. There is a particular need for coaching systems that can appropriately address the challenges faced by online shopping site operators and staff. Conventional systems are unable to provide efficient coaching because they do not integrate processes such as collecting and analyzing coaching data, collecting user attribute information, generating customized coaching plans, conducting interactive coaching sessions with users, and collecting and incorporating feedback.

[0825] 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.

[0826] In this invention, the server includes means for collecting professional coaching data, means for analyzing the collected coaching data and identifying important topics and effective coaching methods, means for collecting user attribute information, means for generating a customized coaching plan based on the user's concerns and needs, means for conducting coaching sessions with the user based on the generated coaching plan, means for collecting and analyzing user feedback after the coaching session, means for storing the analysis results in a database and reflecting them in the next coaching plan, means for providing customized coaching to operators and staff of an online shopping site that runs on a smartphone, and means for generating coaching plans from prompt sentences based on the user's attributes and concerns using a generative AI model. This makes it possible to generate and implement customized coaching plans that can quickly and effectively address the unique challenges faced by operators and staff of an online shopping site.

[0827] "Professional Coaching Data" is data including recordings of coaching sessions and feedback provided by a business coach.

[0828] "Collecting coaching data" is the process of acquiring audio, text, and image data from professional coaching sessions.

[0829] "Analysis" refers to the process of examining collected data using machine learning algorithms and natural language processing techniques to identify important topics and effective coaching techniques.

[0830] "User attribute information" is basic information about the coachee, such as name, age, job title, and information about related industries.

[0831] A "customized coaching plan" is an individualized coaching strategy or action plan that is identified based on a user's concerns and needs.

[0832] A "coaching session" is an interactive activity in which a user receives advice and support based on a generated coaching plan.

[0833] "Gathering feedback" is the process of collecting opinions from users after a coaching session about the content of the session and their satisfaction with the session.

[0834] A "smartphone" is a mobile device that can connect to the Internet and install applications.

[0835] An "online shopping site" is a website that sells products and services over the Internet.

[0836] A "generative AI model" is an artificial intelligence algorithm that generates an appropriate coaching plan based on the user's attributes and concerns.

[0837] A "prompt sentence" is an input sentence provided to a generative AI model to generate a coaching plan.

[0838] This invention is a system that enables operators and staff of online shopping sites to receive professional coaching efficiently and effectively, and is specifically implemented as follows.

[0839] 1. Data accumulation

[0840] server

[0841] The server collects audio, text, and image data from coaching sessions conducted by professional business coaches and stores the data in a database. The collected data is analyzed using natural language processing and machine learning algorithms to identify important topics and effective coaching methods. This identified information is used to provide effective coaching to other users.

[0842] Examples:

[0843] The server stores recordings of the professional coach's sessions in cloud storage and analyzes them as text data using voice recognition technology.

[0844] The server converts the professional coach's feedback notes into a database using OCR technology and stores them.

[0845] 2. Collection of User Information

[0846] Terminal

[0847] The device provides an interface for collecting basic information and professional information (such as name, age, job title, and industry) from the user. Users can input this information through the device and communicate their concerns and coaching needs in detail.

[0848] Examples:

[0849] The terminal displays the question "What is your current job title?" to the user and transmits the answer to the server.

[0850] The device provides a form in which users can enter their concerns by category, and the contents are stored in a database.

[0851] 3. Create a coaching plan

[0852] server

[0853] The server generates a customized coaching plan based on the information entered by the user and past coaching data. Using a generative AI model, it selects the coaching method that best suits the user's attributes and concerns, and creates a specific action plan.

[0854] Examples:

[0855] Based on information such as "a customer support representative at an online shopping site is seeking advice on how to improve customer service," the server extracts effective methods of customer service from past data and creates a customized plan.

[0856] An example of a prompt sentence would be "User name: Taro Tanaka, Job title: Marketing manager, Concern: How to improve customer service," and based on that, the AI ​​model would generate an appropriate coaching plan.

[0857] 4. Conducting a coaching session

[0858] Terminal

[0859] The terminal conducts a coaching session in an interactive manner with the user based on the generated coaching plan, and provides advice and support to the user in voice and text format.

[0860] Examples:

[0861] The device asks the user, "Please tell us about specific situations that caused you stress at work today," and provides advice based on the user's response.

[0862] The device instructs the user to "set a goal for this week and review next week to see if you achieved it."

[0863] 5. Gather and incorporate feedback

[0864] User

[0865] After a coaching session, users fill out a feedback form to evaluate the session content and their satisfaction. This feedback is sent to the server and used to improve the quality of the next session.

[0866] Examples:

[0867] The user inputs feedback such as "This stress management advice was very helpful" and sends it to the server.

[0868] The server analyzes the feedback and stores it in a database to be reflected in future coaching plans.

[0869] Through the system of the present invention, operators and staff of online shopping sites can easily receive high-quality coaching, and by automating the process, efficiency and effectiveness can be increased.

[0870] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0871] Step 1:

[0872] Data accumulation

[0873] The server collects audio data, text data, and image data provided by professional business coaches. The collected data is stored in cloud storage. The audio data is converted into text data using speech recognition technology (e.g., Google Speech Recognition), and the text data is analyzed using natural language processing technology. The image data is converted into text data using OCR technology.

[0874] input:

[0875] Audio, text, and image data from professional business coaches

[0876] output:

[0877] Analyzed text and image data

[0878] Step 2:

[0879] Collection of User Information

[0880] The terminal provides an interface for collecting basic information and professional information (such as name, age, job title, and industry) entered by the user, which is then sent from the terminal to the server.

[0881] input:

[0882] User-supplied information such as name, age, job title, and industry affiliation

[0883] output:

[0884] User information sent to the server

[0885] Step 3:

[0886] Generate a coaching plan

[0887] The server uses a generative AI model based on the information entered by the user and past coaching data to generate a customized coaching plan. Prompt statements are input into the generative AI model to generate a coaching plan that is optimal for the user's attributes and concerns.

[0888] input:

[0889] User information, past coaching data, generative AI model

[0890] output:

[0891] Customized Coaching Plans

[0892] Step 4:

[0893] Conducting a coaching session

[0894] The device then conducts a coaching session interactively with the user based on the generated coaching plan, providing advice and support to the user in voice and text format and suggesting next actions based on the user's responses.

[0895] input:

[0896] Customized Coaching Plans

[0897] output:

[0898] User advice, support, and next steps

[0899] Step 5:

[0900] Gathering and implementing feedback

[0901] After a coaching session, users fill out a feedback form to evaluate the content of the session and their satisfaction. The feedback is sent to the server and analyzed. The analysis results are stored in a database and reflected in the next coaching plan.

[0902] input:

[0903] User Feedback

[0904] output:

[0905] Feedback data analyzed and stored in a database

[0906] Through these steps, online shopping site operators and staff can receive efficient and effective coaching.

[0907] 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.

[0908] The present invention is a system that enables businessmen to receive professional coaching efficiently and effectively, and is implemented through the following specific processes. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide the user with more personalized coaching.

[0909] 1. Data accumulation

[0910] Subject: Server

[0911] The server collects audio, text, and image data from coaching sessions conducted by professional coaches and stores them in a database. The collected data is analyzed using natural language processing and machine learning algorithms to identify important topics and effective coaching methods. This identified information is used to provide effective coaching to other users.

[0912] Examples:

[0913] The server stores recordings of the professional coach's sessions in cloud storage and analyzes them as text data using voice recognition technology.

[0914] The server converts the professional coach's feedback notes into a database using OCR technology and stores them.

[0915] 2. Collection of User Information

[0916] Subject: Device

[0917] The device provides an interface for collecting basic information and professional information (such as name, age, job title, and industry) from the user. Users can input this information through the device and communicate their concerns and coaching needs in detail.

[0918] Examples:

[0919] The terminal displays the question "What is your current job title?" to the user and transmits the answer to the server.

[0920] The device provides a form in which users can enter their concerns by category, and the contents are stored in a database.

[0921] 3. Create a coaching plan

[0922] Subject: Server

[0923] The server generates a customized coaching plan based on the information entered by the user and past coaching data. An AI algorithm selects the coaching method that best suits the user's attributes and concerns, and creates a specific action plan. In addition, an emotion engine collects the user's emotional data and adjusts the plan accordingly.

[0924] Examples:

[0925] Based on information such as "a user who is a sales manager is seeking advice on stress management," the server extracts effective stress management techniques from past data and creates a customized plan.

[0926] If the emotion engine identifies the user's emotional state as "fatigue," it will suggest additional relaxation techniques to the stress management plan.

[0927] 4. Conducting a coaching session

[0928] Subject: Device

[0929] The device conducts a coaching session interactively with the user based on the generated coaching plan. It provides advice and support to the user in voice and text format. The emotion engine monitors and responds to the user's emotional state in real time.

[0930] Examples:

[0931] The device asks the user, "Please tell us about specific situations that caused you stress at work today," and provides advice based on the user's response.

[0932] If the emotion engine recognizes that the user is feeling "anxious," it takes an approach that emphasizes encouragement and support within the session.

[0933] 5. Gather and incorporate feedback

[0934] Subject: User

[0935] After a coaching session, users fill out a feedback form to evaluate the session content and their satisfaction. This feedback is sent to the server and used to improve the quality of the next session.

[0936] Examples:

[0937] The user inputs feedback such as "This stress management advice was very helpful" and sends it to the server.

[0938] Subject: Server

[0939] The server analyzes the collected feedback and stores it as data to be used in future sessions, taking into account the analysis results of the emotion engine.

[0940] Examples:

[0941] The server analyzes the collected feedback and identifies patterns, such as "stress management advice was helpful," and incorporates them into the next coaching plan.

[0942] Data from the sentiment engine can tell you when a user has positive feelings about a particular action, and then reinforce that action and include it in your next plan.

[0943] In this way, the system of the present invention allows businessmen to easily receive high-quality coaching, and the addition of an emotion engine makes it possible to provide even more personalized coaching.

[0944] The processing flow will be explained below.

[0945] Program processing flow

[0946] 1. Data accumulation

[0947] Subject: Server

[0948] Step 1:

[0949] Data collection for professional coaching sessions

[0950] The server collects audio data, text data, and image data of coaching sessions by professional coaches.

[0951] Examples:

[0952] The server uploads the audio data from the recording device to cloud storage.

[0953] The server stores the feedback notes and log data received from emails and digital notes in a database.

[0954] Step 2:

[0955] Data analysis

[0956] The server analyzes the collected coaching data and identifies important topics and effective coaching methods.

[0957] Examples:

[0958] The server uses speech recognition algorithms to convert the audio data into text and then uses natural language processing (NLP) to classify it into categories.

[0959] The server generates metadata to identify existing coaching methods and evaluate their effectiveness.

[0960] Step 3:

[0961] Stored in a database

[0962] The server stores the analyzed results in a database for easy access later.

[0963] Examples:

[0964] The server enters the analysis results into specific fields in a database, making them searchable.

[0965] 2. Collection of User Information

[0966] Subject: Device

[0967] Step 4:

[0968] User Preferences

[0969] The terminal provides an interface for inputting basic information about the user (such as name, age, job title, industry, etc.).

[0970] Examples:

[0971] The terminal displays a form with fields such as "Please enter your name."

[0972] The information entered by the user is sent to the server.

[0973] Step 5:

[0974] Identifying the problem

[0975] The terminal displays a detailed questionnaire form to gather the user's specific concerns and coaching needs.

[0976] Examples:

[0977] The device asks the user, "What is the biggest business challenge you are currently facing?"

[0978] The terminal collects the user's answers and sends them to the server.

[0979] 3. Create a coaching plan

[0980] Subject: Server

[0981] Step 6:

[0982] Information Integration

[0983] The server integrates the collected user information with the accumulated coaching data to create a coaching plan that best suits the user's needs.

[0984] Examples:

[0985] The server links the attribute "sales manager" with the concern "stress management" and extracts the optimal method from past data.

[0986] Step 7:

[0987] Sentiment analysis with emotion engine

[0988] The server collects the user's emotion data using an emotion engine, which recognizes the user's emotion using voice analysis, facial expression recognition, and text analysis, and reflects the collected data in the coaching plan.

[0989] Examples:

[0990] The server analyzes the user's voice data, and the emotion engine detects "anxiety" and "stress."

[0991] The server incorporates the detected emotional information into a coaching plan, emphasizing relaxation techniques and positive feedback.

[0992] Step 8:

[0993] Final generation of coaching plan

[0994] The server then uses the integrated information to ultimately generate a specific coaching plan suited to the user.

[0995] Examples:

[0996] The server suggests specific actions such as "daily reflection time."

[0997] Generate additional actions based on your emotional state, such as "Try this relaxation technique today."

[0998] 4. Conducting a coaching session

[0999] Subject: Device

[1000] Step 9:

[1001] Starting a Session

[1002] The terminal starts a coaching session at a time set by the user, and the session proceeds interactively.

[1003] Examples:

[1004] The device sends a message to the user saying, "Good morning. Let's talk about your goals for today."

[1005] Step 10:

[1006] Session Progression

[1007] The device provides specific advice and support to the user based on the generated coaching plan, and the emotion engine monitors the user's emotions in real time and adapts the session content accordingly.

[1008] Examples:

[1009] The device asks, "Please tell us how stressed you felt at work yesterday," and suggests ways to deal with the situation based on the user's answer.

[1010] If the emotion engine detects the user's "anxiety," it takes an approach that emphasizes encouragement and support.

[1011] 5. Gather and incorporate feedback

[1012] Subject: User

[1013] Step 11:

[1014] Providing feedback

[1015] After the coaching session is over, the user fills out a feedback form to rate the session.

[1016] Examples:

[1017] The user enters feedback such as "I was satisfied with today's session."

[1018] Subject: Server

[1019] Step 12:

[1020] Analyzing and incorporating feedback

[1021] The server analyzes the user's feedback and stores it as data to be used in future sessions, taking into account the analysis results of the emotion engine.

[1022] Examples:

[1023] The server analyzes the collected feedback and identifies patterns such as "stress management advice was helpful."

[1024] Data from the sentiment engine can tell you when a user has positive feelings about a particular action, and then reinforce that action and include it in your next plan.

[1025] This series of steps allows users to receive consistent, personalized business coaching efficiently.

[1026] Example 2

[1027] 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."

[1028] Conventional coaching systems have not been able to fully realize the creation of professional coaching content or customized plans based on the user's individual needs, and have only been able to provide general coaching methods. Furthermore, they are unable to respond in real time taking into account the user's emotional state, making it difficult to provide optimal coaching to the user. As a result, user satisfaction and effectiveness have been limited.

[1029] 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.

[1030] In this invention, the server includes means for collecting professional coaching data, means for analyzing the collected coaching data and identifying important topics and effective coaching methods, means for collecting user attribute information, means for generating a coaching plan customized based on the user's concerns and needs, means for conducting a coaching session for the user based on the generated coaching plan, means for collecting and analyzing user feedback after the coaching session, means for collecting user emotional data in real time and adjusting the coaching plan, and means for storing the analysis results in a database and reflecting them in the next coaching plan. This makes it possible to provide an optimized coaching plan for the user and respond to the user's emotional state in real time.

[1031] "Professional coaching data" is a general term for data including audio data, text data, image data, etc. of coaching sessions provided by professional coaches.

[1032] "Analysis" is the process of processing collected data using statistical and machine learning techniques to identify important topics and effective coaching methods.

[1033] "User attribute information" is information that indicates the characteristics of a user, including information about the user's name, age, job title, and related industry.

[1034] A "customized coaching plan" is a coaching action plan that is optimized for a specific user based on the user's concerns and needs.

[1035] A "coaching session" is a process of providing guidance and advice to a user in an interactive format based on the generated coaching plan.

[1036] "Feedback" refers to the evaluation and impressions provided by the user after a coaching session, and is used to improve the quality of the next coaching plan.

[1037] "Collecting user emotional data in real time" refers to the process of using technologies such as voice recognition and emotion analysis to monitor the user's emotional state in real time during a coaching session.

[1038] A "database" is an information system for storing collected and analyzed data and feedback.

[1039] The present invention provides a system for receiving professional coaching that provides a customized coaching plan tailored to the individual needs of a user. This system is composed of elements including a server, a terminal, and a user.

[1040] Server Features

[1041] The server collects and analyzes coaching data provided by professional coaches and generates a customized coaching plan for each user. Specific hardware includes cloud storage (e.g., Amazon S3), and software includes voice recognition technology (e.g., Google Cloud Speech-to-Text), natural language processing AI algorithms (e.g., GPT-4), and emotion engines (e.g., IBM Watson Tone Analyzer).

[1042] The server uploads the sessions recorded by the professional coaches to cloud storage and collects the audio data from there. The collected audio data is converted into text data using voice recognition technology. Image data such as the professional coaches' feedback notes are also converted into text data using OCR technology and stored in a database.

[1043] Device Features

[1044] The device acts as an interface with the user, collecting information from the user and conducting coaching sessions. The user enters basic information, occupational information, and information about their worries and needs through the device, which is then sent to the server. The device then conducts the session in an interactive format with the user based on the generated coaching plan. The device also collects and analyzes the user's emotional data in real time and adjusts advice and guidance as needed.

[1045] User Roles

[1046] Users enter basic information and occupational information through their devices and provide details of their current concerns and needs. Based on the generated coaching plan, the user receives a coaching session. After the session, the user provides feedback, which is sent to the server and used to improve the quality of the next session.

[1047] Specific examples

[1048] The server inputs the prompt "The sales manager is seeking advice on stress management" into GPT-4, and has it generate a stress management coaching plan. If the emotion engine recognizes the user's emotional state as "fatigue," it will suggest adding relaxation techniques to the plan.

[1049] The device asks the user questions such as "What is your current job title?" and sends the answer to the server. If the user enters "Sales Manager," the server uses that information to generate a customized coaching plan.

[1050] During the session, the device asks, "Please tell us about specific situations that caused you stress at work today," and provides advice based on the user's input. If the emotion engine detects "anxiety" in real time, the device will display encouraging words such as, "It's important to take a break."

[1051] Example prompt sentence:

[1052] "Create a customized coaching plan for your sales manager who wants stress management advice. Also, include relaxation techniques if your users are feeling burned out by their work."

[1053] This system configuration makes it possible to provide coaching plans suited to the individual needs and emotional state of the user, thereby improving the quality of coaching.

[1054] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1055] Step 1:

[1056] Collecting professional coaching data

[1057] Subject: Server

[1058] The server collects audio data of coaching sessions recorded by professional coaches. The audio data is retrieved from cloud storage (e.g., Amazon S3). The retrieved audio data is converted into text data using speech recognition technology (e.g., Google Cloud Speech-to-Text). The converted text data is stored in a database. The input of this process is audio data, and the output is text data.

[1059] Specific behavior:

[1060] The server accesses Amazon S3 cloud storage and downloads the audio file.

[1061] Input the downloaded audio file into the Google Cloud Speech-to-Text API and convert it into text.

[1062] The converted text data is stored in a database.

[1063] Step 2:

[1064] Collection of User Information

[1065] Subject: Device

[1066] The terminal provides an interface for inputting basic information (such as name, age, and job title) and occupational information of the user. The user inputs this information and sends it from the terminal to the server. The input of this process is the basic information of the user, and the output is the user information sent to the server.

[1067] Specific behavior:

[1068] A form such as "Please enter your name" or "Please enter your age" will be displayed on the device screen.

[1069] The information entered by the user is sent to the server.

[1070] Step 3:

[1071] Collecting user concerns and needs

[1072] Subject: Device

[1073] The terminal provides a form for the user to input their concerns and coaching needs. The user inputs this information and sends it from the terminal to the server. The input of this process is the user's concerns and needs, and the output is the user information sent to the server.

[1074] Specific behavior:

[1075] The device screen displays the message "Please tell us what your current concerns are" and asks the user to select an item by category.

[1076] Information about the worries and needs selected by the user is sent to the server.

[1077] Step 4:

[1078] Generate a customized coaching plan

[1079] Subject: Server

[1080] The server generates a customized coaching plan using an AI algorithm (e.g., GPT-4) based on the user's input information and past coaching data. It then analyzes the user's emotional data using an emotion engine (e.g., IBM Watson Tone Analyzer) to adjust the coaching plan. The input of this process is the user's information and past coaching data, and the output is a customized coaching plan.

[1081] Specific behavior:

[1082] The server inputs the prompt "Sales manager wants advice on stress management" into GPT-4 and has it generate a customized plan.

[1083] The emotion engine analyzes the user's emotional state and adds suggestions such as relaxation techniques to the plan.

[1084] Step 5:

[1085] Conducting a coaching session

[1086] Subject: Device

[1087] The device interactively conducts a coaching session with the user based on the generated coaching plan. During the session, the device monitors the user's emotional state in real time and adjusts the plan as needed. The inputs to this process are the coaching plan and real-time emotional data, and the output is advice provided to the user.

[1088] Specific behavior:

[1089] The device asks the user, "Please tell us about a specific situation that caused you stress during work today."

[1090] Based on the user's answers, appropriate advice is provided.

[1091] If the emotion engine detects anxiety, it will display encouraging words such as "It's important to take breaks."

[1092] Step 6:

[1093] Gathering and implementing feedback

[1094] Subject: User and Server

[1095] The user provides feedback after a coaching session. The feedback is sent to the server and used to improve the quality of the next coaching plan. The server analyzes the collected feedback and incorporates it into the next coaching plan. The inputs to this process are the user's feedback and session data, and the output is an improved coaching plan.

[1096] Specific behavior:

[1097] The user inputs feedback into the terminal, such as "This stress management advice was very helpful."

[1098] The device sends the feedback to the server.

[1099] The server analyzes the feedback data and reflects patterns such as "the stress management advice was helpful" in the next plan.

[1100] (Application example 2)

[1101] 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."

[1102] Coaching for modern business people and store employees often involves a uniform approach, which means it is difficult to adequately address individual concerns and emotional states. Furthermore, it is difficult to provide coaching that responds to real-time emotional changes, which can result in ineffective guidance.

[1103] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1104] In this invention, the server includes means for collecting professional coaching data, means for analyzing the collected coaching data and identifying important topics and effective coaching methods, means for collecting user attribute information, means for generating a customized coaching plan based on the user's concerns and requests, means for conducting a coaching session with the user based on the generated coaching plan, means for collecting and analyzing user feedback after the coaching session, means for storing the analysis results in a database and reflecting them in the next coaching plan, and means for collecting user emotion data and adjusting the coaching plan in real time using an emotion engine. This enables effective and personalized coaching according to the user's individual concerns and emotional state.

[1105] "Professional instructional data" refers to educational or training information provided by a professional, and includes audio data, text data, and image data.

[1106] "Instructional Data" means educational or training information collected for a specific purpose, and includes audio data, text data, and image data.

[1107] "Key topics" refer to themes extracted from the analyzed data that have a significant impact on a particular situation or issue.

[1108] "Effective teaching methods" refer to educational or training techniques that have a proven track record of helping users achieve their goals.

[1109] "User demographic information" means basic data about an individual User, including name, age, job title, and industry-related information.

[1110] "User concerns and requests" refers to the problems the user is facing or the specific situations they wish to resolve.

[1111] "Customized instruction plan" refers to an educational or training program that is individually designed based on the user's demographic information and concerns or requests.

[1112] "Coaching Session" means an educational or training interactive program delivered to a User.

[1113] "Feedback" refers to the evaluation, opinions, and impressions provided by users after a coaching session.

[1114] "Emotional Data" refers to information used to measure and analyze a user's emotional state.

[1115] "Emotion engine" refers to the algorithms and systems that analyze users' emotional data in real time and adjust appropriate teaching plans.

[1116] The present invention is a system that enables users to receive professional guidance efficiently and effectively, and aims to provide personalized guidance that is tailored to the user's individual concerns and emotional state.

[1117] 1. Data accumulation

[1118] The server collects professional teaching data and stores it in a database. The teaching data includes audio data, text data, and image data. This data is analyzed using natural language processing and machine learning algorithms to identify important topics and effective teaching methods. This identified information is used to provide effective teaching to other users.

[1119] 2. Collection of User Information

[1120] The device provides an interface for collecting basic information and professional information (such as name, age, job title, and industry) from users. Users can input this information through the device and provide detailed information about their concerns and guidance needs.

[1121] 3. Generate lesson plans

[1122] The server generates a customized instruction plan based on the information entered by the user and past instruction data. An AI algorithm selects the instruction method that best suits the user's attributes and concerns, and creates a specific action plan. In addition, an emotion engine collects the user's emotional data and adjusts the plan accordingly.

[1123] 4. Conducting a mentoring session

[1124] The device conducts a teaching session with the user in an interactive format based on the generated teaching plan. It provides advice and support to the user in voice and text format. The emotion engine monitors and responds to the user's emotional state in real time.

[1125] 5. Gather and incorporate feedback

[1126] After a training session, users fill out a feedback form to evaluate the content of the session and their satisfaction. This feedback is sent to the server and used to improve the quality of the next session. The server analyzes the collected feedback and saves it as data to be reflected in future sessions. It also takes into account the analysis results of the emotion engine.

[1127] Hardware and software used

[1128] Server: Used to store and analyze data, generate teaching plans, and collect and incorporate feedback. Python's nltk and spaCy are used for natural language processing, and scikit-learn is used for machine learning algorithms.

[1129] Devices: Used to collect user information, conduct coaching sessions, and gather feedback. This includes smartphones, tablets, laptops, etc.

[1130] Emotion engine: Used to collect user emotion data and analyze it in real time. Emotion analysis uses emotion recognition API and TensorFlow.

[1131] Prompt Sentence Examples

[1132] "Please tell us about the most stressful experience you have had recently while dealing with a customer."

[1133] "Please tell me a bit more about your current position and your concerns."

[1134] "Was the content of the coaching session helpful? Please give us specific feedback."

[1135] In this way, the system of the present invention allows users to easily receive high-quality instruction, and by adding an emotion engine, it is possible to provide even more personalized instruction.

[1136] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1137] Step 1:

[1138] The server collects professional teaching data and stores it in a database. As input, it receives audio, text, and image data from teaching sessions. It analyzes this data using natural language processing techniques (e.g., Python's nltk or spaCy) to identify important topics and effective teaching methods. As output, the analysis results are stored in a database.

[1139] Step 2:

[1140] The terminal provides an interface to collect basic information and professional information about the user. Input includes name, age, job title, and industry affiliation, which are entered by the user on the terminal. This information is sent to the server and stored in a database. Specifically, the terminal displays an input form and allows the user to enter information.

[1141] Step 3:

[1142] The server collects users' concerns and guidance needs. The input includes specific concerns and requests that users enter on their devices. This information is also stored in the database. Specifically, the device provides a category-specific input form, allowing users to enter detailed concerns.

[1143] Step 4:

[1144] The server generates a customized teaching plan based on collected user information and past teaching data. The inputs include the user's attribute information, concerns and requests, and past teaching data. Using the generative AI model, it selects the optimal teaching method and creates a specific action plan. The output is the generated customized plan.

[1145] Step 5:

[1146] The device conducts a teaching session interactively with the user based on the generated teaching plan. The device takes the teaching plan as input. It provides advice and support to the user in voice or text format, and an emotion engine monitors the user's emotional state in real time. The device outputs instruction tailored to the user's emotional state.

[1147] Step 6:

[1148] Users provide feedback after a training session. Input includes ratings, opinions, and impressions that users enter on their devices. This feedback is sent to the server and stored in a database. Specifically, the device displays a feedback form for users to fill out.

[1149] Step 7:

[1150] The server analyzes the collected feedback and reflects it in the next teaching plan. The input is feedback data from users. This is analyzed and stored in a database. The analysis results are taken into consideration when generating the next teaching plan. Specifically, the server analyzes the feedback data using natural language processing technology and extracts important opinions and evaluations.

[1151] Step 8:

[1152] The server collects the user's emotional data and adjusts the teaching plan in real time using an emotion engine. The input is emotional information from the user's voice and text data. Based on this information, the server uses an emotion recognition API and TensorFlow to analyze the user's emotional state and adjust the teaching plan as needed. Specifically, the emotion engine analyzes the user's tone of voice and vocabulary to determine their emotional state.

[1153] 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.

[1154] 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.

[1155] 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.

[1156] [Third embodiment]

[1157] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1158] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1159] 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).

[1160] 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.

[1161] 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.

[1162] 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).

[1163] 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.

[1164] 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.

[1165] 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.

[1166] 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.

[1167] 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.

[1168] 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."

[1169] The present invention is a system for enabling businessmen to receive professional coaching efficiently and effectively, and is implemented through the following specific processes.

[1170] 1. Data accumulation

[1171] Subject: Server

[1172] The server collects audio, text, and image data from coaching sessions conducted by professional business coaches and stores the data in a database. The collected data is analyzed using natural language processing and machine learning algorithms to identify important topics and effective coaching methods. This identified information is used to provide effective coaching to other users.

[1173] Examples:

[1174] The server stores recordings of the professional coach's sessions in cloud storage and analyzes them as text data using voice recognition technology.

[1175] The server converts the professional coach's feedback notes into a database using OCR technology and stores them.

[1176] 2. Collection of User Information

[1177] Subject: Device

[1178] The device provides an interface for collecting basic information and professional information (such as name, age, job title, and industry) from the user. Users can input this information through the device and communicate their concerns and coaching needs in detail.

[1179] Examples:

[1180] The terminal displays the question "What is your current job title?" to the user and transmits the answer to the server.

[1181] The device provides a form in which users can enter their concerns by category, and the contents are stored in a database.

[1182] 3. Create a coaching plan

[1183] Subject: Server

[1184] The server generates a customized coaching plan based on the information entered by the user and past coaching data. An AI algorithm selects the coaching method that best suits the user's attributes and concerns, and creates a specific action plan.

[1185] Examples:

[1186] Based on information such as "a user who is a sales manager is seeking advice on stress management," the server extracts effective stress management techniques from past data and creates a customized plan.

[1187] Based on the plan, the server generates specific actions, such as "perform a short meditation before starting work each day," and presents them to the user.

[1188] 4. Conducting a coaching session

[1189] Subject: Device

[1190] The terminal conducts a coaching session in an interactive manner with the user based on the generated coaching plan, and provides advice and support to the user in voice and text format.

[1191] Examples:

[1192] The device asks the user, "Please tell us about specific situations that caused you stress at work today," and provides advice based on the user's response.

[1193] The device instructs the user to "set a goal for this week and review next week to see if you achieved it."

[1194] 5. Gather and incorporate feedback

[1195] Subject: User

[1196] After a coaching session, users fill out a feedback form to evaluate the session content and their satisfaction. This feedback is sent to the server and used to improve the quality of the next session.

[1197] Examples:

[1198] The user inputs feedback such as "This stress management advice was very helpful" and sends it to the server.

[1199] The server analyzes the feedback and stores it in a database to be reflected in future coaching plans.

[1200] In this way, the system of the present invention allows business people to easily receive high-quality coaching, and by automating the process, it increases efficiency and effectiveness.

[1201] The processing flow will be explained below.

[1202] Program processing flow

[1203] 1. Data accumulation

[1204] Subject: Server

[1205] Step 1:

[1206] Data collection for professional coaching sessions

[1207] The server collects audio data, text data, and image data of coaching sessions by professional coaches.

[1208] Examples:

[1209] The server uploads the audio data from the recording device to cloud storage.

[1210] The server stores the feedback notes and log data received from emails and digital notes in a database.

[1211] Step 2:

[1212] Data analysis

[1213] The server analyzes the collected coaching data and identifies important topics and effective coaching methods.

[1214] Examples:

[1215] The server uses speech recognition algorithms to convert the audio data into text and then uses natural language processing (NLP) to classify it into categories.

[1216] The server generates metadata to identify existing coaching methods and evaluate their effectiveness.

[1217] Step 3:

[1218] Stored in a database

[1219] The server stores the analyzed results in a database for easy access later.

[1220] Examples:

[1221] The server enters the analysis results into specific fields in a database, making them searchable.

[1222] 2. Collection of User Information

[1223] Subject: Device

[1224] Step 4:

[1225] User Preferences

[1226] The terminal provides an interface for inputting basic information about the user (such as name, age, job title, industry, etc.).

[1227] Examples:

[1228] The terminal displays a form with fields such as "Please enter your name."

[1229] The information entered by the user is sent to the server.

[1230] Step 5:

[1231] Identifying the problem

[1232] The terminal displays a detailed questionnaire form to gather the user's specific concerns and coaching needs.

[1233] Examples:

[1234] The device asks the user, "What is the biggest business challenge you are currently facing?"

[1235] The terminal collects the user's answers and sends them to the server.

[1236] 3. Create a coaching plan

[1237] Subject: Server

[1238] Step 6:

[1239] Information Integration

[1240] The server integrates the collected user information with the accumulated coaching data to create a coaching plan that best suits the user's needs.

[1241] Examples:

[1242] The server links the attribute "sales manager" with the concern "stress management" and extracts the optimal method from past data.

[1243] Step 7:

[1244] Generate a coaching plan

[1245] The server generates a specific coaching plan suited to the user based on the integrated information.

[1246] Examples:

[1247] The server suggests specific actions that meet the user's needs, such as "daily reflection time."

[1248] 4. Conducting a coaching session

[1249] Subject: Device

[1250] Step 8:

[1251] Starting a Session

[1252] The terminal starts a coaching session at a time set by the user, and the session proceeds interactively.

[1253] Examples:

[1254] The device sends a message to the user saying, "Good morning. Let's talk about your goals for today."

[1255] Step 9:

[1256] Session Progression

[1257] The device provides specific advice and support to the user based on a coaching plan generated by AI.

[1258] Examples:

[1259] The device asks, "Please tell us how stressed you felt at work yesterday," and suggests ways to deal with the situation based on the user's answer.

[1260] 5. Gather and incorporate feedback

[1261] Subject: User

[1262] Step 10:

[1263] Providing feedback

[1264] After the coaching session is over, the user fills out a feedback form to rate the session.

[1265] Examples:

[1266] The user enters feedback such as "I was satisfied with today's session."

[1267] Subject: Server

[1268] Step 11:

[1269] Analyzing and incorporating feedback

[1270] The server analyzes the user's feedback and stores it as data to be used in future sessions.

[1271] Examples:

[1272] The server analyzes the collected feedback and identifies patterns, such as "stress management advice was helpful," and incorporates them into the next coaching plan.

[1273] This series of steps allows users to receive consistent, customized business coaching in an efficient manner.

[1274] Example 1

[1275] 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."

[1276] Traditional business coaching is primarily conducted through face-to-face sessions, which, while effective, require a great deal of time and effort. Furthermore, while individually customized coaching plans are required, generating them is time-consuming and often insufficient. Furthermore, the process of incorporating user feedback into the next coaching session is manual and therefore difficult to do efficiently. Therefore, an efficient and effective system that can solve these issues is needed.

[1277] 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.

[1278] In this invention, the server includes means for collecting professional coaching data, means for analyzing the collected coaching data to identify important topics and effective coaching methods, and means for collecting user attribute information. This enables the server to automatically collect and analyze professional coaching data and generate and provide a customized coaching plan that is optimal for the user. The server also includes means for converting the coaching data into text data using voice recognition technology and optical character recognition technology, means for collecting user information via a user interface and storing it in a database, and means for analyzing user information and past coaching data using an AI algorithm to generate an optimal coaching plan. This enables the server to automatically generate a coaching plan based on the user's concerns and needs and incorporate feedback into the next session.

[1279] "Professional Coaching Data" refers to the audio, text, and image data provided by a business coach during a coaching session.

[1280] A "coaching session" refers to a series of interactions or activities conducted by a coach to provide advice and support to a user.

[1281] "Speech recognition technology" refers to technology for converting voice data into text data.

[1282] "Optical character recognition technology" refers to technology for converting character information contained in image data into text data.

[1283] "User interface" refers to the interface through which a user interacts with a system.

[1284] "AI algorithm" refers to an algorithm that uses artificial intelligence technology to perform data analysis and predictions.

[1285] The present invention is a system for enabling businessmen to receive professional coaching efficiently and effectively, and is implemented through the following specific processes.

[1286] Data accumulation

[1287] Subject: Server

[1288] The server collects audio data, text data, and image data from coaching sessions conducted by professional business coaches and stores this data in a database. To achieve this, the server uses cloud storage (e.g., Amazon S3). The server also converts audio data into text data using voice recognition technology (e.g., Google Speech-to-Text) and image data into text data using optical character recognition technology (e.g., Tesseract OCR). This data is analyzed using machine learning algorithms to identify important topics and effective coaching methods. This identified information is used to provide effective coaching to other users.

[1289] Examples:

[1290] The server stores the recordings of the professional coaches' sessions in cloud storage and analyzes them as audio data using Google Speech-to-Text.

[1291] The server converts the professional coach's feedback notes into a database using Tesseract OCR and stores them.

[1292] Collection of User Information

[1293] Subject: Terminal

[1294] The device provides an interface for collecting basic information and professional information (such as name, age, job title, and industry). Users can enter this information through the device and provide detailed information about their concerns and coaching needs. The collected information is sent to a server in real time and stored in a database.

[1295] Examples:

[1296] The terminal displays the question "What is your current job title?" to the user and transmits the answer to the server.

[1297] The device provides a form in which users can enter their concerns by category, and the contents are stored in a database.

[1298] Generate a coaching plan

[1299] Subject: Server

[1300] The server generates a customized coaching plan based on the information entered by the user and past coaching data. AI algorithms (e.g., TensorFlow, PyTorch) select the coaching method that best suits the user's attributes and concerns, and create a specific action plan. The generated coaching plan is provided to the user for implementation.

[1301] Examples:

[1302] Based on information such as "a user who is a sales manager is seeking advice on stress management," the server extracts effective stress management techniques from past data and creates a customized plan.

[1303] Based on the plan, the server generates specific actions, such as "perform a short meditation before starting work each day," and presents them to the user.

[1304] Conducting a coaching session

[1305] Subject: Terminal

[1306] The device conducts a coaching session interactively with the user based on the generated coaching plan, providing advice and support to the user in voice and text format using chatbot frameworks (e.g., Dialogflow, Microsoft Bot Framework) and speech synthesis technologies (e.g., Google Text-to-Speech, Amazon Polly).

[1307] Examples:

[1308] The device asks the user, "Please tell us about specific situations that caused you stress at work today," and provides advice based on the user's response.

[1309] The device instructs the user to "set a goal for this week and review next week to see if you achieved it."

[1310] Gathering and implementing feedback

[1311] Subject: User

[1312] After a coaching session, users fill out a feedback form to evaluate the content of the session and their satisfaction. This feedback is sent to the server and used to improve the quality of the next session. The server analyzes the feedback and reflects it in the next coaching plan.

[1313] Examples:

[1314] The user inputs feedback such as "This stress management advice was very helpful" and sends it to the server.

[1315] The server analyzes the feedback and stores it in a database to be reflected in future coaching plans.

[1316] Prompt Sentence Examples

[1317] Below are examples of prompt sentences to input to the generative AI model.

[1318] 1. Data Collection Prompt:

[1319] What are the specific steps to converting a business coach's audio sessions into text and identifying effective coaching methods?

[1320] 2. User Information Collection Prompt:

[1321] Please propose the best UI design to collect the user's basic information and occupational information.

[1322] 3. Coaching Plan Generation Prompts:

[1323] Describe the detailed design of the algorithm to create the stress management coaching plan required by the sales manager.

[1324] 4. Coaching Session Prompts:

[1325] How can I design a chatbot that provides advice to users in a conversational way?

[1326] 5. Feedback collection prompts:

[1327] What is the best way to efficiently collect and analyze feedback after a coaching session?

[1328] The above is a description of the mode for carrying out the present invention. This system enables business people to receive professional coaching efficiently and effectively, and increases efficiency and effectiveness by automating the process.

[1329] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1330] Processing Steps

[1331] Step 1: Data accumulation

[1332] Subject: Server

[1333] input:

[1334] Audio, text, and image data from professional coaching sessions

[1335] Specific behavior:

[1336] The server records audio data of sessions conducted by professional business coaches and stores it in cloud storage (e.g., Amazon S3).

[1337] The server converts the stored voice data into text data using voice recognition technology (e.g., Google Speech-to-Text).

[1338] The server scans the professional coach's handwritten feedback notes and converts them into text data using optical character recognition technology (e.g., Tesseract OCR).

[1339] The server stores the converted text data in a database.

[1340] output:

[1341] Results of converting audio and image data into text data

[1342] Text data stored in a database

[1343] Step 2: Collect user information

[1344] Subject: Terminal

[1345] input:

[1346] User basic information (name, age, job title, industry, etc.)

[1347] User concerns and coaching needs

[1348] Specific behavior:

[1349] The terminal displays a form to collect basic information and occupational information from the user.

[1350] The terminal provides an interface for inputting the user's concerns and coaching needs.

[1351] The device transmits the collected information to the server in real time.

[1352] output:

[1353] User information and coaching needs

[1354] Information sent to the server for storage in the database

[1355] Step 3: Create a coaching plan

[1356] Subject: Server

[1357] input:

[1358] Information entered by the user

[1359] Past coaching data

[1360] Specific behavior:

[1361] The server retrieves the information received from the user from a database and uses AI algorithms (e.g., TensorFlow, PyTorch) to analyze the optimal coaching method.

[1362] The server generates a customized coaching plan based on the analysis results.

[1363] The server prepares the generated plan for serving to the user.

[1364] output:

[1365] Customized Coaching Plans

[1366] A concrete action plan provided to the user

[1367] Step 4: Conduct a coaching session

[1368] Subject: Terminal

[1369] input:

[1370] Coaching plan sent from the server

[1371] Specific behavior:

[1372] The terminal conducts a session in an interactive format with the user based on the generated coaching plan.

[1373] The device uses speech synthesis technology (e.g., Google Text-to-Speech, Amazon Polly) to provide advice and support via voice.

[1374] The device receives the user's questions and answers in real time and provides appropriate feedback.

[1375] output:

[1376] Coaching session content provided to users

[1377] Real-time feedback from users

[1378] Step 5: Gather and incorporate feedback

[1379] Subject: User, Server

[1380] input:

[1381] User Feedback

[1382] Collected session results

[1383] Specific behavior:

[1384] After a coaching session, the user fills in a feedback form and submits it to the server.

[1385] The server analyzes the submitted feedback and stores it in a database to improve the quality of the next session.

[1386] output:

[1387] User feedback data

[1388] Database updates to be reflected in the next coaching plan

[1389] (Application example 1)

[1390] 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."

[1391] Conventional coaching systems have not adequately addressed the efficiency of business coaching or the identification of effective coaching methods, making it difficult to quickly provide customized coaching plans for specific industries. There is a particular need for coaching systems that can appropriately address the challenges faced by online shopping site operators and staff. Conventional systems are unable to provide efficient coaching because they do not integrate processes such as collecting and analyzing coaching data, collecting user attribute information, generating customized coaching plans, conducting interactive coaching sessions with users, and collecting and incorporating feedback.

[1392] 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.

[1393] In this invention, the server includes means for collecting professional coaching data, means for analyzing the collected coaching data and identifying important topics and effective coaching methods, means for collecting user attribute information, means for generating a customized coaching plan based on the user's concerns and needs, means for conducting coaching sessions with the user based on the generated coaching plan, means for collecting and analyzing user feedback after the coaching session, means for storing the analysis results in a database and reflecting them in the next coaching plan, means for providing customized coaching to operators and staff of an online shopping site that runs on a smartphone, and means for generating coaching plans from prompt sentences based on the user's attributes and concerns using a generative AI model. This makes it possible to generate and implement customized coaching plans that can quickly and effectively address the unique challenges faced by operators and staff of an online shopping site.

[1394] "Professional Coaching Data" is data including recordings of coaching sessions and feedback provided by a business coach.

[1395] "Collecting coaching data" is the process of acquiring audio, text, and image data from professional coaching sessions.

[1396] "Analysis" refers to the process of examining collected data using machine learning algorithms and natural language processing techniques to identify important topics and effective coaching techniques.

[1397] "User attribute information" is basic information about the coachee, such as name, age, job title, and information about related industries.

[1398] A "customized coaching plan" is an individualized coaching strategy or action plan that is identified based on a user's concerns and needs.

[1399] A "coaching session" is an interactive activity in which a user receives advice and support based on a generated coaching plan.

[1400] "Gathering feedback" is the process of collecting opinions from users after a coaching session about the content of the session and their satisfaction with the session.

[1401] A "smartphone" is a mobile device that can connect to the Internet and install applications.

[1402] An "online shopping site" is a website that sells products and services over the Internet.

[1403] A "generative AI model" is an artificial intelligence algorithm that generates an appropriate coaching plan based on the user's attributes and concerns.

[1404] A "prompt sentence" is an input sentence provided to a generative AI model to generate a coaching plan.

[1405] This invention is a system that enables operators and staff of online shopping sites to receive professional coaching efficiently and effectively, and is specifically implemented as follows.

[1406] 1. Data accumulation

[1407] server

[1408] The server collects audio, text, and image data from coaching sessions conducted by professional business coaches and stores the data in a database. The collected data is analyzed using natural language processing and machine learning algorithms to identify important topics and effective coaching methods. This identified information is used to provide effective coaching to other users.

[1409] Examples:

[1410] The server stores recordings of the professional coach's sessions in cloud storage and analyzes them as text data using voice recognition technology.

[1411] The server converts the professional coach's feedback notes into a database using OCR technology and stores them.

[1412] 2. Collection of User Information

[1413] Terminal

[1414] The device provides an interface for collecting basic information and professional information (such as name, age, job title, and industry) from the user. Users can input this information through the device and communicate their concerns and coaching needs in detail.

[1415] Examples:

[1416] The terminal displays the question "What is your current job title?" to the user and transmits the answer to the server.

[1417] The device provides a form in which users can enter their concerns by category, and the contents are stored in a database.

[1418] 3. Create a coaching plan

[1419] server

[1420] The server generates a customized coaching plan based on the information entered by the user and past coaching data. Using a generative AI model, it selects the coaching method that best suits the user's attributes and concerns, and creates a specific action plan.

[1421] Examples:

[1422] Based on information such as "a customer support representative at an online shopping site is seeking advice on how to improve customer service," the server extracts effective methods of customer service from past data and creates a customized plan.

[1423] An example of a prompt sentence would be "User name: Taro Tanaka, Job title: Marketing manager, Concern: How to improve customer service," and based on that, the AI ​​model would generate an appropriate coaching plan.

[1424] 4. Conducting a coaching session

[1425] Terminal

[1426] The terminal conducts a coaching session in an interactive manner with the user based on the generated coaching plan, and provides advice and support to the user in voice and text format.

[1427] Examples:

[1428] The device asks the user, "Please tell us about specific situations that caused you stress at work today," and provides advice based on the user's response.

[1429] The device instructs the user to "set a goal for this week and review next week to see if you achieved it."

[1430] 5. Gather and incorporate feedback

[1431] User

[1432] After a coaching session, users fill out a feedback form to evaluate the session content and their satisfaction. This feedback is sent to the server and used to improve the quality of the next session.

[1433] Examples:

[1434] The user inputs feedback such as "This stress management advice was very helpful" and sends it to the server.

[1435] The server analyzes the feedback and stores it in a database to be reflected in future coaching plans.

[1436] Through the system of the present invention, operators and staff of online shopping sites can easily receive high-quality coaching, and by automating the process, efficiency and effectiveness can be increased.

[1437] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1438] Step 1:

[1439] Data accumulation

[1440] The server collects audio data, text data, and image data provided by professional business coaches. The collected data is stored in cloud storage. The audio data is converted into text data using speech recognition technology (e.g., Google Speech Recognition), and the text data is analyzed using natural language processing technology. The image data is converted into text data using OCR technology.

[1441] input:

[1442] Audio, text, and image data from professional business coaches

[1443] output:

[1444] Analyzed text and image data

[1445] Step 2:

[1446] Collection of User Information

[1447] The terminal provides an interface for collecting basic information and professional information (such as name, age, job title, and industry) entered by the user, which is then sent from the terminal to the server.

[1448] input:

[1449] User-supplied information such as name, age, job title, and industry affiliation

[1450] output:

[1451] User information sent to the server

[1452] Step 3:

[1453] Generate a coaching plan

[1454] The server uses a generative AI model based on the information entered by the user and past coaching data to generate a customized coaching plan. Prompt statements are input into the generative AI model to generate a coaching plan that is optimal for the user's attributes and concerns.

[1455] input:

[1456] User information, past coaching data, generative AI model

[1457] output:

[1458] Customized Coaching Plans

[1459] Step 4:

[1460] Conducting a coaching session

[1461] The device then conducts a coaching session interactively with the user based on the generated coaching plan, providing advice and support to the user in voice and text format and suggesting next actions based on the user's responses.

[1462] input:

[1463] Customized Coaching Plans

[1464] output:

[1465] User advice, support, and next steps

[1466] Step 5:

[1467] Gathering and implementing feedback

[1468] After a coaching session, users fill out a feedback form to evaluate the content of the session and their satisfaction. The feedback is sent to the server and analyzed. The analysis results are stored in a database and reflected in the next coaching plan.

[1469] input:

[1470] User Feedback

[1471] output:

[1472] Feedback data analyzed and stored in a database

[1473] Through these steps, online shopping site operators and staff can receive efficient and effective coaching.

[1474] 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.

[1475] The present invention is a system that enables businessmen to receive professional coaching efficiently and effectively, and is implemented through the following specific processes. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide the user with more personalized coaching.

[1476] 1. Data accumulation

[1477] Subject: Server

[1478] The server collects audio, text, and image data from coaching sessions conducted by professional coaches and stores them in a database. The collected data is analyzed using natural language processing and machine learning algorithms to identify important topics and effective coaching methods. This identified information is used to provide effective coaching to other users.

[1479] Examples:

[1480] The server stores recordings of the professional coach's sessions in cloud storage and analyzes them as text data using voice recognition technology.

[1481] The server converts the professional coach's feedback notes into a database using OCR technology and stores them.

[1482] 2. Collection of User Information

[1483] Subject: Device

[1484] The device provides an interface for collecting basic information and professional information (such as name, age, job title, and industry) from the user. Users can input this information through the device and communicate their concerns and coaching needs in detail.

[1485] Examples:

[1486] The terminal displays the question "What is your current job title?" to the user and transmits the answer to the server.

[1487] The device provides a form in which users can enter their concerns by category, and the contents are stored in a database.

[1488] 3. Create a coaching plan

[1489] Subject: Server

[1490] The server generates a customized coaching plan based on the information entered by the user and past coaching data. An AI algorithm selects the coaching method that best suits the user's attributes and concerns, and creates a specific action plan. In addition, an emotion engine collects the user's emotional data and adjusts the plan accordingly.

[1491] Examples:

[1492] Based on information such as "a user who is a sales manager is seeking advice on stress management," the server extracts effective stress management techniques from past data and creates a customized plan.

[1493] If the emotion engine identifies the user's emotional state as "fatigue," it will suggest additional relaxation techniques to the stress management plan.

[1494] 4. Conducting a coaching session

[1495] Subject: Device

[1496] The device conducts a coaching session interactively with the user based on the generated coaching plan. It provides advice and support to the user in voice and text format. The emotion engine monitors and responds to the user's emotional state in real time.

[1497] Examples:

[1498] The device asks the user, "Please tell us about specific situations that caused you stress at work today," and provides advice based on the user's response.

[1499] If the emotion engine recognizes that the user is feeling "anxious," it takes an approach that emphasizes encouragement and support within the session.

[1500] 5. Gather and incorporate feedback

[1501] Subject: User

[1502] After a coaching session, users fill out a feedback form to evaluate the session content and their satisfaction. This feedback is sent to the server and used to improve the quality of the next session.

[1503] Examples:

[1504] The user inputs feedback such as "This stress management advice was very helpful" and sends it to the server.

[1505] Subject: Server

[1506] The server analyzes the collected feedback and stores it as data to be used in future sessions, taking into account the analysis results of the emotion engine.

[1507] Examples:

[1508] The server analyzes the collected feedback and identifies patterns, such as "stress management advice was helpful," and incorporates them into the next coaching plan.

[1509] Data from the sentiment engine can tell you when a user has positive feelings about a particular action, and then reinforce that action and include it in your next plan.

[1510] In this way, the system of the present invention allows businessmen to easily receive high-quality coaching, and the addition of an emotion engine makes it possible to provide even more personalized coaching.

[1511] The processing flow will be explained below.

[1512] Program processing flow

[1513] 1. Data accumulation

[1514] Subject: Server

[1515] Step 1:

[1516] Data collection for professional coaching sessions

[1517] The server collects audio data, text data, and image data of coaching sessions by professional coaches.

[1518] Examples:

[1519] The server uploads the audio data from the recording device to cloud storage.

[1520] The server stores the feedback notes and log data received from emails and digital notes in a database.

[1521] Step 2:

[1522] Data analysis

[1523] The server analyzes the collected coaching data and identifies important topics and effective coaching methods.

[1524] Examples:

[1525] The server uses speech recognition algorithms to convert the audio data into text and then uses natural language processing (NLP) to classify it into categories.

[1526] The server generates metadata to identify existing coaching methods and evaluate their effectiveness.

[1527] Step 3:

[1528] Stored in a database

[1529] The server stores the analyzed results in a database for easy access later.

[1530] Examples:

[1531] The server enters the analysis results into specific fields in a database, making them searchable.

[1532] 2. Collection of User Information

[1533] Subject: Device

[1534] Step 4:

[1535] User Preferences

[1536] The terminal provides an interface for inputting basic information about the user (such as name, age, job title, industry, etc.).

[1537] Examples:

[1538] The terminal displays a form with fields such as "Please enter your name."

[1539] The information entered by the user is sent to the server.

[1540] Step 5:

[1541] Identifying the problem

[1542] The terminal displays a detailed questionnaire form to gather the user's specific concerns and coaching needs.

[1543] Examples:

[1544] The device asks the user, "What is the biggest business challenge you are currently facing?"

[1545] The terminal collects the user's answers and sends them to the server.

[1546] 3. Create a coaching plan

[1547] Subject: Server

[1548] Step 6:

[1549] Information Integration

[1550] The server integrates the collected user information with the accumulated coaching data to create a coaching plan that best suits the user's needs.

[1551] Examples:

[1552] The server links the attribute "sales manager" with the concern "stress management" and extracts the optimal method from past data.

[1553] Step 7:

[1554] Sentiment analysis with emotion engine

[1555] The server collects the user's emotion data using an emotion engine, which recognizes the user's emotion using voice analysis, facial expression recognition, and text analysis, and reflects the collected data in the coaching plan.

[1556] Examples:

[1557] The server analyzes the user's voice data, and the emotion engine detects "anxiety" and "stress."

[1558] The server incorporates the detected emotional information into a coaching plan, emphasizing relaxation techniques and positive feedback.

[1559] Step 8:

[1560] Final generation of coaching plan

[1561] The server then uses the integrated information to ultimately generate a specific coaching plan suited to the user.

[1562] Examples:

[1563] The server suggests specific actions such as "daily reflection time."

[1564] Generate additional actions based on your emotional state, such as "Try this relaxation technique today."

[1565] 4. Conducting a coaching session

[1566] Subject: Device

[1567] Step 9:

[1568] Starting a Session

[1569] The terminal starts a coaching session at a time set by the user, and the session proceeds interactively.

[1570] Examples:

[1571] The device sends a message to the user saying, "Good morning. Let's talk about your goals for today."

[1572] Step 10:

[1573] Session Progression

[1574] The device provides specific advice and support to the user based on the generated coaching plan, and the emotion engine monitors the user's emotions in real time and adapts the session content accordingly.

[1575] Examples:

[1576] The device asks, "Please tell us how stressed you felt at work yesterday," and suggests ways to deal with the situation based on the user's answer.

[1577] If the emotion engine detects the user's "anxiety," it takes an approach that emphasizes encouragement and support.

[1578] 5. Gather and incorporate feedback

[1579] Subject: User

[1580] Step 11:

[1581] Providing feedback

[1582] After the coaching session is over, the user fills out a feedback form to rate the session.

[1583] Examples:

[1584] The user enters feedback such as "I was satisfied with today's session."

[1585] Subject: Server

[1586] Step 12:

[1587] Analyzing and incorporating feedback

[1588] The server analyzes the user's feedback and stores it as data to be used in future sessions, taking into account the analysis results of the emotion engine.

[1589] Examples:

[1590] The server analyzes the collected feedback and identifies patterns such as "stress management advice was helpful."

[1591] Data from the sentiment engine can tell you when a user has positive feelings about a particular action, and then reinforce that action and include it in your next plan.

[1592] This series of steps allows users to receive consistent, personalized business coaching efficiently.

[1593] Example 2

[1594] 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."

[1595] Conventional coaching systems have not been able to fully realize the creation of professional coaching content or customized plans based on the user's individual needs, and have only been able to provide general coaching methods. Furthermore, they are unable to respond in real time taking into account the user's emotional state, making it difficult to provide optimal coaching to the user. As a result, user satisfaction and effectiveness have been limited.

[1596] 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.

[1597] In this invention, the server includes means for collecting professional coaching data, means for analyzing the collected coaching data and identifying important topics and effective coaching methods, means for collecting user attribute information, means for generating a coaching plan customized based on the user's concerns and needs, means for conducting a coaching session for the user based on the generated coaching plan, means for collecting and analyzing user feedback after the coaching session, means for collecting user emotional data in real time and adjusting the coaching plan, and means for storing the analysis results in a database and reflecting them in the next coaching plan. This makes it possible to provide an optimized coaching plan for the user and respond to the user's emotional state in real time.

[1598] "Professional coaching data" is a general term for data including audio data, text data, image data, etc. of coaching sessions provided by professional coaches.

[1599] "Analysis" is the process of processing collected data using statistical and machine learning techniques to identify important topics and effective coaching methods.

[1600] "User attribute information" is information that indicates the characteristics of a user, including information about the user's name, age, job title, and related industry.

[1601] A "customized coaching plan" is a coaching action plan that is optimized for a specific user based on the user's concerns and needs.

[1602] A "coaching session" is a process of providing guidance and advice to a user in an interactive format based on the generated coaching plan.

[1603] "Feedback" refers to the evaluation and impressions provided by the user after a coaching session, and is used to improve the quality of the next coaching plan.

[1604] "Collecting user emotional data in real time" refers to the process of using technologies such as voice recognition and emotion analysis to monitor the user's emotional state in real time during a coaching session.

[1605] A "database" is an information system for storing collected and analyzed data and feedback.

[1606] The present invention provides a system for receiving professional coaching that provides a customized coaching plan tailored to the individual needs of a user. This system is composed of elements including a server, a terminal, and a user.

[1607] Server Features

[1608] The server collects and analyzes coaching data provided by professional coaches and generates a customized coaching plan for each user. Specific hardware includes cloud storage (e.g., Amazon S3), and software includes voice recognition technology (e.g., Google Cloud Speech-to-Text), natural language processing AI algorithms (e.g., GPT-4), and emotion engines (e.g., IBM Watson Tone Analyzer).

[1609] The server uploads the sessions recorded by the professional coaches to cloud storage and collects the audio data from there. The collected audio data is converted into text data using voice recognition technology. Image data such as the professional coaches' feedback notes are also converted into text data using OCR technology and stored in a database.

[1610] Device Features

[1611] The device acts as an interface with the user, collecting information from the user and conducting coaching sessions. The user enters basic information, occupational information, and information about their worries and needs through the device, which is then sent to the server. The device then conducts the session in an interactive format with the user based on the generated coaching plan. The device also collects and analyzes the user's emotional data in real time and adjusts advice and guidance as needed.

[1612] User Roles

[1613] Users enter basic information and occupational information through their devices and provide details of their current concerns and needs. Based on the generated coaching plan, the user receives a coaching session. After the session, the user provides feedback, which is sent to the server and used to improve the quality of the next session.

[1614] Specific examples

[1615] The server inputs the prompt "The sales manager is seeking advice on stress management" into GPT-4, and has it generate a stress management coaching plan. If the emotion engine recognizes the user's emotional state as "fatigue," it will suggest adding relaxation techniques to the plan.

[1616] The device asks the user questions such as "What is your current job title?" and sends the answer to the server. If the user enters "Sales Manager," the server uses that information to generate a customized coaching plan.

[1617] During the session, the device asks, "Please tell us about specific situations that caused you stress at work today," and provides advice based on the user's input. If the emotion engine detects "anxiety" in real time, the device will display encouraging words such as, "It's important to take a break."

[1618] Example prompt sentence:

[1619] "Create a customized coaching plan for your sales manager who wants stress management advice. Also, include relaxation techniques if your users are feeling burned out by their work."

[1620] This system configuration makes it possible to provide coaching plans suited to the individual needs and emotional state of the user, thereby improving the quality of coaching.

[1621] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1622] Step 1:

[1623] Collecting professional coaching data

[1624] Subject: Server

[1625] The server collects audio data of coaching sessions recorded by professional coaches. The audio data is retrieved from cloud storage (e.g., Amazon S3). The retrieved audio data is converted into text data using speech recognition technology (e.g., Google Cloud Speech-to-Text). The converted text data is stored in a database. The input of this process is audio data, and the output is text data.

[1626] Specific behavior:

[1627] The server accesses Amazon S3 cloud storage and downloads the audio file.

[1628] Input the downloaded audio file into the Google Cloud Speech-to-Text API and convert it into text.

[1629] The converted text data is stored in a database.

[1630] Step 2:

[1631] Collection of User Information

[1632] Subject: Device

[1633] The terminal provides an interface for inputting basic information (such as name, age, and job title) and occupational information of the user. The user inputs this information and sends it from the terminal to the server. The input of this process is the basic information of the user, and the output is the user information sent to the server.

[1634] Specific behavior:

[1635] A form such as "Please enter your name" or "Please enter your age" will be displayed on the device screen.

[1636] The information entered by the user is sent to the server.

[1637] Step 3:

[1638] Collecting user concerns and needs

[1639] Subject: Device

[1640] The terminal provides a form for the user to input their concerns and coaching needs. The user inputs this information and sends it from the terminal to the server. The input of this process is the user's concerns and needs, and the output is the user information sent to the server.

[1641] Specific behavior:

[1642] The device screen displays the message "Please tell us what your current concerns are" and asks the user to select an item by category.

[1643] Information about the worries and needs selected by the user is sent to the server.

[1644] Step 4:

[1645] Generate a customized coaching plan

[1646] Subject: Server

[1647] The server generates a customized coaching plan using an AI algorithm (e.g., GPT-4) based on the user's input information and past coaching data. It then analyzes the user's emotional data using an emotion engine (e.g., IBM Watson Tone Analyzer) to adjust the coaching plan. The input of this process is the user's information and past coaching data, and the output is a customized coaching plan.

[1648] Specific behavior:

[1649] The server inputs the prompt "Sales manager wants advice on stress management" into GPT-4 and has it generate a customized plan.

[1650] The emotion engine analyzes the user's emotional state and adds suggestions such as relaxation techniques to the plan.

[1651] Step 5:

[1652] Conducting a coaching session

[1653] Subject: Device

[1654] The device interactively conducts a coaching session with the user based on the generated coaching plan. During the session, the device monitors the user's emotional state in real time and adjusts the plan as needed. The inputs to this process are the coaching plan and real-time emotional data, and the output is advice provided to the user.

[1655] Specific behavior:

[1656] The device asks the user, "Please tell us about a specific situation that caused you stress during work today."

[1657] Based on the user's answers, appropriate advice is provided.

[1658] If the emotion engine detects anxiety, it will display encouraging words such as "It's important to take breaks."

[1659] Step 6:

[1660] Gathering and implementing feedback

[1661] Subject: User and Server

[1662] The user provides feedback after a coaching session. The feedback is sent to the server and used to improve the quality of the next coaching plan. The server analyzes the collected feedback and incorporates it into the next coaching plan. The inputs to this process are the user's feedback and session data, and the output is an improved coaching plan.

[1663] Specific behavior:

[1664] The user inputs feedback into the terminal, such as "This stress management advice was very helpful."

[1665] The device sends the feedback to the server.

[1666] The server analyzes the feedback data and reflects patterns such as "the stress management advice was helpful" in the next plan.

[1667] (Application example 2)

[1668] 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."

[1669] Coaching for modern business people and store employees often involves a uniform approach, which means it is difficult to adequately address individual concerns and emotional states. Furthermore, it is difficult to provide coaching that responds to real-time emotional changes, which can result in ineffective guidance.

[1670] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1671] In this invention, the server includes means for collecting professional coaching data, means for analyzing the collected coaching data and identifying important topics and effective coaching methods, means for collecting user attribute information, means for generating a customized coaching plan based on the user's concerns and requests, means for conducting a coaching session with the user based on the generated coaching plan, means for collecting and analyzing user feedback after the coaching session, means for storing the analysis results in a database and reflecting them in the next coaching plan, and means for collecting user emotion data and adjusting the coaching plan in real time using an emotion engine. This enables effective and personalized coaching according to the user's individual concerns and emotional state.

[1672] "Professional instructional data" refers to educational or training information provided by a professional, and includes audio data, text data, and image data.

[1673] "Instructional Data" means educational or training information collected for a specific purpose, and includes audio data, text data, and image data.

[1674] "Key topics" refer to themes extracted from the analyzed data that have a significant impact on a particular situation or issue.

[1675] "Effective teaching methods" refer to educational or training techniques that have a proven track record of helping users achieve their goals.

[1676] "User demographic information" means basic data about an individual User, including name, age, job title, and industry-related information.

[1677] "User concerns and requests" refers to the problems the user is facing or the specific situations they wish to resolve.

[1678] "Customized instruction plan" refers to an educational or training program that is individually designed based on the user's demographic information and concerns or requests.

[1679] "Coaching Session" means an educational or training interactive program delivered to a User.

[1680] "Feedback" refers to the evaluation, opinions, and impressions provided by users after a coaching session.

[1681] "Emotional Data" refers to information used to measure and analyze a user's emotional state.

[1682] "Emotion engine" refers to the algorithms and systems that analyze users' emotional data in real time and adjust appropriate teaching plans.

[1683] The present invention is a system that enables users to receive professional guidance efficiently and effectively, and aims to provide personalized guidance that is tailored to the user's individual concerns and emotional state.

[1684] 1. Data accumulation

[1685] The server collects professional teaching data and stores it in a database. The teaching data includes audio data, text data, and image data. This data is analyzed using natural language processing and machine learning algorithms to identify important topics and effective teaching methods. This identified information is used to provide effective teaching to other users.

[1686] 2. Collection of User Information

[1687] The device provides an interface for collecting basic information and professional information (such as name, age, job title, and industry) from users. Users can input this information through the device and provide detailed information about their concerns and guidance needs.

[1688] 3. Generate lesson plans

[1689] The server generates a customized instruction plan based on the information entered by the user and past instruction data. An AI algorithm selects the instruction method that best suits the user's attributes and concerns, and creates a specific action plan. In addition, an emotion engine collects the user's emotional data and adjusts the plan accordingly.

[1690] 4. Conducting a mentoring session

[1691] The device conducts a teaching session with the user in an interactive format based on the generated teaching plan. It provides advice and support to the user in voice and text format. The emotion engine monitors and responds to the user's emotional state in real time.

[1692] 5. Gather and incorporate feedback

[1693] After a training session, users fill out a feedback form to evaluate the content of the session and their satisfaction. This feedback is sent to the server and used to improve the quality of the next session. The server analyzes the collected feedback and saves it as data to be reflected in future sessions. It also takes into account the analysis results of the emotion engine.

[1694] Hardware and software used

[1695] Server: Used to store and analyze data, generate teaching plans, and collect and incorporate feedback. Python's nltk and spaCy are used for natural language processing, and scikit-learn is used for machine learning algorithms.

[1696] Devices: Used to collect user information, conduct coaching sessions, and gather feedback. This includes smartphones, tablets, laptops, etc.

[1697] Emotion engine: Used to collect user emotion data and analyze it in real time. Emotion analysis uses emotion recognition API and TensorFlow.

[1698] Prompt Sentence Examples

[1699] "Please tell us about the most stressful experience you have had recently while dealing with a customer."

[1700] "Please tell me a bit more about your current position and your concerns."

[1701] "Was the content of the coaching session helpful? Please give us specific feedback."

[1702] In this way, the system of the present invention allows users to easily receive high-quality instruction, and by adding an emotion engine, it is possible to provide even more personalized instruction.

[1703] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1704] Step 1:

[1705] The server collects professional teaching data and stores it in a database. As input, it receives audio, text, and image data from teaching sessions. It analyzes this data using natural language processing techniques (e.g., Python's nltk or spaCy) to identify important topics and effective teaching methods. As output, the analysis results are stored in a database.

[1706] Step 2:

[1707] The terminal provides an interface to collect basic information and professional information about the user. Input includes name, age, job title, and industry affiliation, which are entered by the user on the terminal. This information is sent to the server and stored in a database. Specifically, the terminal displays an input form and allows the user to enter information.

[1708] Step 3:

[1709] The server collects users' concerns and guidance needs. The input includes specific concerns and requests that users enter on their devices. This information is also stored in the database. Specifically, the device provides a category-specific input form, allowing users to enter detailed concerns.

[1710] Step 4:

[1711] The server generates a customized teaching plan based on collected user information and past teaching data. The inputs include the user's attribute information, concerns and requests, and past teaching data. Using the generative AI model, it selects the optimal teaching method and creates a specific action plan. The output is the generated customized plan.

[1712] Step 5:

[1713] The device conducts a teaching session interactively with the user based on the generated teaching plan. The device takes the teaching plan as input. It provides advice and support to the user in voice or text format, and an emotion engine monitors the user's emotional state in real time. The device outputs instruction tailored to the user's emotional state.

[1714] Step 6:

[1715] Users provide feedback after a training session. Input includes ratings, opinions, and impressions that users enter on their devices. This feedback is sent to the server and stored in a database. Specifically, the device displays a feedback form for users to fill out.

[1716] Step 7:

[1717] The server analyzes the collected feedback and reflects it in the next teaching plan. The input is feedback data from users. This is analyzed and stored in a database. The analysis results are taken into consideration when generating the next teaching plan. Specifically, the server analyzes the feedback data using natural language processing technology and extracts important opinions and evaluations.

[1718] Step 8:

[1719] The server collects the user's emotional data and adjusts the teaching plan in real time using an emotion engine. The input is emotional information from the user's voice and text data. Based on this information, the server uses an emotion recognition API and TensorFlow to analyze the user's emotional state and adjust the teaching plan as needed. Specifically, the emotion engine analyzes the user's tone of voice and vocabulary to determine their emotional state.

[1720] 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.

[1721] 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.

[1722] 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.

[1723] [Fourth embodiment]

[1724] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1725] 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.

[1726] 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).

[1727] 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.

[1728] 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.

[1729] 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).

[1730] 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.

[1731] 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.

[1732] 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.

[1733] 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.

[1734] 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.

[1735] 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.

[1736] 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."

[1737] The present invention is a system for enabling businessmen to receive professional coaching efficiently and effectively, and is implemented through the following specific processes.

[1738] 1. Data accumulation

[1739] Subject: Server

[1740] The server collects audio, text, and image data from coaching sessions conducted by professional business coaches and stores the data in a database. The collected data is analyzed using natural language processing and machine learning algorithms to identify important topics and effective coaching methods. This identified information is used to provide effective coaching to other users.

[1741] Examples:

[1742] The server stores recordings of the professional coach's sessions in cloud storage and analyzes them as text data using voice recognition technology.

[1743] The server converts the professional coach's feedback notes into a database using OCR technology and stores them.

[1744] 2. Collection of User Information

[1745] Subject: Device

[1746] The device provides an interface for collecting basic information and professional information (such as name, age, job title, and industry) from the user. Users can input this information through the device and communicate their concerns and coaching needs in detail.

[1747] Examples:

[1748] The terminal displays the question "What is your current job title?" to the user and transmits the answer to the server.

[1749] The device provides a form in which users can enter their concerns by category, and the contents are stored in a database.

[1750] 3. Create a coaching plan

[1751] Subject: Server

[1752] The server generates a customized coaching plan based on the information entered by the user and past coaching data. An AI algorithm selects the coaching method that best suits the user's attributes and concerns, and creates a specific action plan.

[1753] Examples:

[1754] Based on information such as "a user who is a sales manager is seeking advice on stress management," the server extracts effective stress management techniques from past data and creates a customized plan.

[1755] Based on the plan, the server generates specific actions, such as "perform a short meditation before starting work each day," and presents them to the user.

[1756] 4. Conducting a coaching session

[1757] Subject: Device

[1758] The terminal conducts a coaching session in an interactive manner with the user based on the generated coaching plan, and provides advice and support to the user in voice and text format.

[1759] Examples:

[1760] The device asks the user, "Please tell us about specific situations that caused you stress at work today," and provides advice based on the user's response.

[1761] The device instructs the user to "set a goal for this week and review next week to see if you achieved it."

[1762] 5. Gather and incorporate feedback

[1763] Subject: User

[1764] After a coaching session, users fill out a feedback form to evaluate the session content and their satisfaction. This feedback is sent to the server and used to improve the quality of the next session.

[1765] Examples:

[1766] The user inputs feedback such as "This stress management advice was very helpful" and sends it to the server.

[1767] The server analyzes the feedback and stores it in a database to be reflected in future coaching plans.

[1768] In this way, the system of the present invention allows business people to easily receive high-quality coaching, and by automating the process, it increases efficiency and effectiveness.

[1769] The processing flow will be explained below.

[1770] Program processing flow

[1771] 1. Data accumulation

[1772] Subject: Server

[1773] Step 1:

[1774] Data collection for professional coaching sessions

[1775] The server collects audio data, text data, and image data of coaching sessions by professional coaches.

[1776] Examples:

[1777] The server uploads the audio data from the recording device to cloud storage.

[1778] The server stores the feedback notes and log data received from emails and digital notes in a database.

[1779] Step 2:

[1780] Data analysis

[1781] The server analyzes the collected coaching data and identifies important topics and effective coaching methods.

[1782] Examples:

[1783] The server uses speech recognition algorithms to convert the audio data into text and then uses natural language processing (NLP) to classify it into categories.

[1784] The server generates metadata to identify existing coaching methods and evaluate their effectiveness.

[1785] Step 3:

[1786] Stored in a database

[1787] The server stores the analyzed results in a database for easy access later.

[1788] Examples:

[1789] The server enters the analysis results into specific fields in a database, making them searchable.

[1790] 2. Collection of User Information

[1791] Subject: Device

[1792] Step 4:

[1793] User Preferences

[1794] The terminal provides an interface for inputting basic information about the user (such as name, age, job title, industry, etc.).

[1795] Examples:

[1796] The terminal displays a form with fields such as "Please enter your name."

[1797] The information entered by the user is sent to the server.

[1798] Step 5:

[1799] Identifying the problem

[1800] The terminal displays a detailed questionnaire form to gather the user's specific concerns and coaching needs.

[1801] Examples:

[1802] The device asks the user, "What is the biggest business challenge you are currently facing?"

[1803] The terminal collects the user's answers and sends them to the server.

[1804] 3. Create a coaching plan

[1805] Subject: Server

[1806] Step 6:

[1807] Information Integration

[1808] The server integrates the collected user information with the accumulated coaching data to create a coaching plan that best suits the user's needs.

[1809] Examples:

[1810] The server links the attribute "sales manager" with the concern "stress management" and extracts the optimal method from past data.

[1811] Step 7:

[1812] Generate a coaching plan

[1813] The server generates a specific coaching plan suited to the user based on the integrated information.

[1814] Examples:

[1815] The server suggests specific actions that meet the user's needs, such as "daily reflection time."

[1816] 4. Conducting a coaching session

[1817] Subject: Device

[1818] Step 8:

[1819] Starting a Session

[1820] The terminal starts a coaching session at a time set by the user, and the session proceeds interactively.

[1821] Examples:

[1822] The device sends a message to the user saying, "Good morning. Let's talk about your goals for today."

[1823] Step 9:

[1824] Session Progression

[1825] The device provides specific advice and support to the user based on a coaching plan generated by AI.

[1826] Examples:

[1827] The device asks, "Please tell us how stressed you felt at work yesterday," and suggests ways to deal with the situation based on the user's answer.

[1828] 5. Gather and incorporate feedback

[1829] Subject: User

[1830] Step 10:

[1831] Providing feedback

[1832] After the coaching session is over, the user fills out a feedback form to rate the session.

[1833] Examples:

[1834] The user enters feedback such as "I was satisfied with today's session."

[1835] Subject: Server

[1836] Step 11:

[1837] Analyzing and incorporating feedback

[1838] The server analyzes the user's feedback and stores it as data to be used in future sessions.

[1839] Examples:

[1840] The server analyzes the collected feedback and identifies patterns, such as "stress management advice was helpful," and incorporates them into the next coaching plan.

[1841] This series of steps allows users to receive consistent, customized business coaching in an efficient manner.

[1842] Example 1

[1843] 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."

[1844] Traditional business coaching is primarily conducted through face-to-face sessions, which, while effective, require a great deal of time and effort. Furthermore, while individually customized coaching plans are required, generating them is time-consuming and often insufficient. Furthermore, the process of incorporating user feedback into the next coaching session is manual and therefore difficult to do efficiently. Therefore, an efficient and effective system that can solve these issues is needed.

[1845] 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.

[1846] In this invention, the server includes means for collecting professional coaching data, means for analyzing the collected coaching data to identify important topics and effective coaching methods, and means for collecting user attribute information. This enables the server to automatically collect and analyze professional coaching data and generate and provide a customized coaching plan that is optimal for the user. The server also includes means for converting the coaching data into text data using voice recognition technology and optical character recognition technology, means for collecting user information via a user interface and storing it in a database, and means for analyzing user information and past coaching data using an AI algorithm to generate an optimal coaching plan. This enables the server to automatically generate a coaching plan based on the user's concerns and needs and incorporate feedback into the next session.

[1847] "Professional Coaching Data" refers to the audio, text, and image data provided by a business coach during a coaching session.

[1848] A "coaching session" refers to a series of interactions or activities conducted by a coach to provide advice and support to a user.

[1849] "Speech recognition technology" refers to technology for converting voice data into text data.

[1850] "Optical character recognition technology" refers to technology for converting character information contained in image data into text data.

[1851] "User interface" refers to the interface through which a user interacts with a system.

[1852] "AI algorithm" refers to an algorithm that uses artificial intelligence technology to perform data analysis and predictions.

[1853] The present invention is a system for enabling businessmen to receive professional coaching efficiently and effectively, and is implemented through the following specific processes.

[1854] Data accumulation

[1855] Subject: Server

[1856] The server collects audio data, text data, and image data from coaching sessions conducted by professional business coaches and stores this data in a database. To achieve this, the server uses cloud storage (e.g., Amazon S3). The server also converts audio data into text data using voice recognition technology (e.g., Google Speech-to-Text) and image data into text data using optical character recognition technology (e.g., Tesseract OCR). This data is analyzed using machine learning algorithms to identify important topics and effective coaching methods. This identified information is used to provide effective coaching to other users.

[1857] Examples:

[1858] The server stores the recordings of the professional coaches' sessions in cloud storage and analyzes them as audio data using Google Speech-to-Text.

[1859] The server converts the professional coach's feedback notes into a database using Tesseract OCR and stores them.

[1860] Collection of User Information

[1861] Subject: Terminal

[1862] The device provides an interface for collecting basic information and professional information (such as name, age, job title, and industry). Users can enter this information through the device and provide detailed information about their concerns and coaching needs. The collected information is sent to a server in real time and stored in a database.

[1863] Examples:

[1864] The terminal displays the question "What is your current job title?" to the user and transmits the answer to the server.

[1865] The device provides a form in which users can enter their concerns by category, and the contents are stored in a database.

[1866] Generate a coaching plan

[1867] Subject: Server

[1868] The server generates a customized coaching plan based on the information entered by the user and past coaching data. AI algorithms (e.g., TensorFlow, PyTorch) select the coaching method that best suits the user's attributes and concerns, and create a specific action plan. The generated coaching plan is provided to the user for implementation.

[1869] Examples:

[1870] Based on information such as "a user who is a sales manager is seeking advice on stress management," the server extracts effective stress management techniques from past data and creates a customized plan.

[1871] Based on the plan, the server generates specific actions, such as "perform a short meditation before starting work each day," and presents them to the user.

[1872] Conducting a coaching session

[1873] Subject: Terminal

[1874] The device conducts a coaching session interactively with the user based on the generated coaching plan, providing advice and support to the user in voice and text format using chatbot frameworks (e.g., Dialogflow, Microsoft Bot Framework) and speech synthesis technologies (e.g., Google Text-to-Speech, Amazon Polly).

[1875] Examples:

[1876] The device asks the user, "Please tell us about specific situations that caused you stress at work today," and provides advice based on the user's response.

[1877] The device instructs the user to "set a goal for this week and review next week to see if you achieved it."

[1878] Gathering and implementing feedback

[1879] Subject: User

[1880] After a coaching session, users fill out a feedback form to evaluate the content of the session and their satisfaction. This feedback is sent to the server and used to improve the quality of the next session. The server analyzes the feedback and reflects it in the next coaching plan.

[1881] Examples:

[1882] The user inputs feedback such as "This stress management advice was very helpful" and sends it to the server.

[1883] The server analyzes the feedback and stores it in a database to be reflected in future coaching plans.

[1884] Prompt Sentence Examples

[1885] Below are examples of prompt sentences to input to the generative AI model.

[1886] 1. Data Collection Prompt:

[1887] What are the specific steps to converting a business coach's audio sessions into text and identifying effective coaching methods?

[1888] 2. User Information Collection Prompt:

[1889] Please propose the best UI design to collect the user's basic information and occupational information.

[1890] 3. Coaching Plan Generation Prompts:

[1891] Describe the detailed design of the algorithm to create the stress management coaching plan required by the sales manager.

[1892] 4. Coaching Session Prompts:

[1893] How can I design a chatbot that provides advice to users in a conversational way?

[1894] 5. Feedback collection prompts:

[1895] What is the best way to efficiently collect and analyze feedback after a coaching session?

[1896] The above is a description of the mode for carrying out the present invention. This system enables business people to receive professional coaching efficiently and effectively, and increases efficiency and effectiveness by automating the process.

[1897] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1898] Processing Steps

[1899] Step 1: Data accumulation

[1900] Subject: Server

[1901] input:

[1902] Audio, text, and image data from professional coaching sessions

[1903] Specific behavior:

[1904] The server records audio data of sessions conducted by professional business coaches and stores it in cloud storage (e.g., Amazon S3).

[1905] The server converts the stored voice data into text data using voice recognition technology (e.g., Google Speech-to-Text).

[1906] The server scans the professional coach's handwritten feedback notes and converts them into text data using optical character recognition technology (e.g., Tesseract OCR).

[1907] The server stores the converted text data in a database.

[1908] output:

[1909] Results of converting audio and image data into text data

[1910] Text data stored in a database

[1911] Step 2: Collect user information

[1912] Subject: Terminal

[1913] input:

[1914] User basic information (name, age, job title, industry, etc.)

[1915] User concerns and coaching needs

[1916] Specific behavior:

[1917] The terminal displays a form to collect basic information and occupational information from the user.

[1918] The terminal provides an interface for inputting the user's concerns and coaching needs.

[1919] The device transmits the collected information to the server in real time.

[1920] output:

[1921] User information and coaching needs

[1922] Information sent to the server for storage in the database

[1923] Step 3: Create a coaching plan

[1924] Subject: Server

[1925] input:

[1926] Information entered by the user

[1927] Past coaching data

[1928] Specific behavior:

[1929] The server retrieves the information received from the user from a database and uses AI algorithms (e.g., TensorFlow, PyTorch) to analyze the optimal coaching method.

[1930] The server generates a customized coaching plan based on the analysis results.

[1931] The server prepares the generated plan for serving to the user.

[1932] output:

[1933] Customized Coaching Plans

[1934] A concrete action plan provided to the user

[1935] Step 4: Conduct a coaching session

[1936] Subject: Terminal

[1937] input:

[1938] Coaching plan sent from the server

[1939] Specific behavior:

[1940] The terminal conducts a session in an interactive format with the user based on the generated coaching plan.

[1941] The device uses speech synthesis technology (e.g., Google Text-to-Speech, Amazon Polly) to provide advice and support via voice.

[1942] The device receives the user's questions and answers in real time and provides appropriate feedback.

[1943] output:

[1944] Coaching session content provided to users

[1945] Real-time feedback from users

[1946] Step 5: Gather and incorporate feedback

[1947] Subject: User, Server

[1948] input:

[1949] User Feedback

[1950] Collected session results

[1951] Specific behavior:

[1952] After a coaching session, the user fills in a feedback form and submits it to the server.

[1953] The server analyzes the submitted feedback and stores it in a database to improve the quality of the next session.

[1954] output:

[1955] User feedback data

[1956] Database updates to be reflected in the next coaching plan

[1957] (Application example 1)

[1958] 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."

[1959] Conventional coaching systems have not adequately addressed the efficiency of business coaching or the identification of effective coaching methods, making it difficult to quickly provide customized coaching plans for specific industries. There is a particular need for coaching systems that can appropriately address the challenges faced by online shopping site operators and staff. Conventional systems are unable to provide efficient coaching because they do not integrate processes such as collecting and analyzing coaching data, collecting user attribute information, generating customized coaching plans, conducting interactive coaching sessions with users, and collecting and incorporating feedback.

[1960] 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.

[1961] In this invention, the server includes means for collecting professional coaching data, means for analyzing the collected coaching data and identifying important topics and effective coaching methods, means for collecting user attribute information, means for generating a customized coaching plan based on the user's concerns and needs, means for conducting coaching sessions with the user based on the generated coaching plan, means for collecting and analyzing user feedback after the coaching session, means for storing the analysis results in a database and reflecting them in the next coaching plan, means for providing customized coaching to operators and staff of an online shopping site that runs on a smartphone, and means for generating coaching plans from prompt sentences based on the user's attributes and concerns using a generative AI model. This makes it possible to generate and implement customized coaching plans that can quickly and effectively address the unique challenges faced by operators and staff of an online shopping site.

[1962] "Professional Coaching Data" is data including recordings of coaching sessions and feedback provided by a business coach.

[1963] "Collecting coaching data" is the process of acquiring audio, text, and image data from professional coaching sessions.

[1964] "Analysis" refers to the process of examining collected data using machine learning algorithms and natural language processing techniques to identify important topics and effective coaching techniques.

[1965] "User attribute information" is basic information about the coachee, such as name, age, job title, and information about related industries.

[1966] A "customized coaching plan" is an individualized coaching strategy or action plan that is identified based on a user's concerns and needs.

[1967] A "coaching session" is an interactive activity in which a user receives advice and support based on a generated coaching plan.

[1968] "Gathering feedback" is the process of collecting opinions from users after a coaching session about the content of the session and their satisfaction with the session.

[1969] A "smartphone" is a mobile device that can connect to the Internet and install applications.

[1970] An "online shopping site" is a website that sells products and services over the Internet.

[1971] A "generative AI model" is an artificial intelligence algorithm that generates an appropriate coaching plan based on the user's attributes and concerns.

[1972] A "prompt sentence" is an input sentence provided to a generative AI model to generate a coaching plan.

[1973] This invention is a system that enables operators and staff of online shopping sites to receive professional coaching efficiently and effectively, and is specifically implemented as follows.

[1974] 1. Data accumulation

[1975] server

[1976] The server collects audio, text, and image data from coaching sessions conducted by professional business coaches and stores the data in a database. The collected data is analyzed using natural language processing and machine learning algorithms to identify important topics and effective coaching methods. This identified information is used to provide effective coaching to other users.

[1977] Examples:

[1978] The server stores recordings of the professional coach's sessions in cloud storage and analyzes them as text data using voice recognition technology.

[1979] The server converts the professional coach's feedback notes into a database using OCR technology and stores them.

[1980] 2. Collection of User Information

[1981] Terminal

[1982] The device provides an interface for collecting basic information and professional information (such as name, age, job title, and industry) from the user. Users can input this information through the device and communicate their concerns and coaching needs in detail.

[1983] Examples:

[1984] The terminal displays the question "What is your current job title?" to the user and transmits the answer to the server.

[1985] The device provides a form in which users can enter their concerns by category, and the contents are stored in a database.

[1986] 3. Create a coaching plan

[1987] server

[1988] The server generates a customized coaching plan based on the information entered by the user and past coaching data. Using a generative AI model, it selects the coaching method that best suits the user's attributes and concerns, and creates a specific action plan.

[1989] Examples:

[1990] Based on information such as "a customer support representative at an online shopping site is seeking advice on how to improve customer service," the server extracts effective methods of customer service from past data and creates a customized plan.

[1991] An example of a prompt sentence would be "User name: Taro Tanaka, Job title: Marketing manager, Concern: How to improve customer service," and based on that, the AI ​​model would generate an appropriate coaching plan.

[1992] 4. Conducting a coaching session

[1993] Terminal

[1994] The terminal conducts a coaching session in an interactive manner with the user based on the generated coaching plan, and provides advice and support to the user in voice and text format.

[1995] Examples:

[1996] The device asks the user, "Please tell us about specific situations that caused you stress at work today," and provides advice based on the user's response.

[1997] The device instructs the user to "set a goal for this week and review next week to see if you achieved it."

[1998] 5. Gather and incorporate feedback

[1999] User

[2000] After a coaching session, users fill out a feedback form to evaluate the session content and their satisfaction. This feedback is sent to the server and used to improve the quality of the next session.

[2001] Examples:

[2002] The user inputs feedback such as "This stress management advice was very helpful" and sends it to the server.

[2003] The server analyzes the feedback and stores it in a database to be reflected in future coaching plans.

[2004] Through the system of the present invention, operators and staff of online shopping sites can easily receive high-quality coaching, and by automating the process, efficiency and effectiveness can be increased.

[2005] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2006] Step 1:

[2007] Data accumulation

[2008] The server collects audio data, text data, and image data provided by professional business coaches. The collected data is stored in cloud storage. The audio data is converted into text data using speech recognition technology (e.g., Google Speech Recognition), and the text data is analyzed using natural language processing technology. The image data is converted into text data using OCR technology.

[2009] input:

[2010] Audio, text, and image data from professional business coaches

[2011] output:

[2012] Analyzed text and image data

[2013] Step 2:

[2014] Collection of User Information

[2015] The terminal provides an interface for collecting basic information and professional information (such as name, age, job title, and industry) entered by the user, which is then sent from the terminal to the server.

[2016] input:

[2017] User-supplied information such as name, age, job title, and industry affiliation

[2018] output:

[2019] User information sent to the server

[2020] Step 3:

[2021] Generate a coaching plan

[2022] The server uses a generative AI model based on the information entered by the user and past coaching data to generate a customized coaching plan. Prompt statements are input into the generative AI model to generate a coaching plan that is optimal for the user's attributes and concerns.

[2023] input:

[2024] User information, past coaching data, generative AI model

[2025] output:

[2026] Customized Coaching Plans

[2027] Step 4:

[2028] Conducting a coaching session

[2029] The device then conducts a coaching session interactively with the user based on the generated coaching plan, providing advice and support to the user in voice and text format and suggesting next actions based on the user's responses.

[2030] input:

[2031] Customized Coaching Plans

[2032] output:

[2033] User advice, support, and next steps

[2034] Step 5:

[2035] Gathering and implementing feedback

[2036] After a coaching session, users fill out a feedback form to evaluate the content of the session and their satisfaction. The feedback is sent to the server and analyzed. The analysis results are stored in a database and reflected in the next coaching plan.

[2037] input:

[2038] User Feedback

[2039] output:

[2040] Feedback data analyzed and stored in a database

[2041] Through these steps, online shopping site operators and staff can receive efficient and effective coaching.

[2042] 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.

[2043] The present invention is a system that enables businessmen to receive professional coaching efficiently and effectively, and is implemented through the following specific processes. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide the user with more personalized coaching.

[2044] 1. Data accumulation

[2045] Subject: Server

[2046] The server collects audio, text, and image data from coaching sessions conducted by professional coaches and stores them in a database. The collected data is analyzed using natural language processing and machine learning algorithms to identify important topics and effective coaching methods. This identified information is used to provide effective coaching to other users.

[2047] Examples:

[2048] The server stores recordings of the professional coach's sessions in cloud storage and analyzes them as text data using voice recognition technology.

[2049] The server converts the professional coach's feedback notes into a database using OCR technology and stores them.

[2050] 2. Collection of User Information

[2051] Subject: Device

[2052] The device provides an interface for collecting basic information and professional information (such as name, age, job title, and industry) from the user. Users can input this information through the device and communicate their concerns and coaching needs in detail.

[2053] Examples:

[2054] The terminal displays the question "What is your current job title?" to the user and transmits the answer to the server.

[2055] The device provides a form in which users can enter their concerns by category, and the contents are stored in a database.

[2056] 3. Create a coaching plan

[2057] Subject: Server

[2058] The server generates a customized coaching plan based on the information entered by the user and past coaching data. An AI algorithm selects the coaching method that best suits the user's attributes and concerns, and creates a specific action plan. In addition, an emotion engine collects the user's emotional data and adjusts the plan accordingly.

[2059] Examples:

[2060] Based on information such as "a user who is a sales manager is seeking advice on stress management," the server extracts effective stress management techniques from past data and creates a customized plan.

[2061] If the emotion engine identifies the user's emotional state as "fatigue," it will suggest additional relaxation techniques to the stress management plan.

[2062] 4. Conducting a coaching session

[2063] Subject: Device

[2064] The device conducts a coaching session interactively with the user based on the generated coaching plan. It provides advice and support to the user in voice and text format. The emotion engine monitors and responds to the user's emotional state in real time.

[2065] Examples:

[2066] The device asks the user, "Please tell us about specific situations that caused you stress at work today," and provides advice based on the user's response.

[2067] If the emotion engine recognizes that the user is feeling "anxious," it takes an approach that emphasizes encouragement and support within the session.

[2068] 5. Gather and incorporate feedback

[2069] Subject: User

[2070] After a coaching session, users fill out a feedback form to evaluate the session content and their satisfaction. This feedback is sent to the server and used to improve the quality of the next session.

[2071] Examples:

[2072] The user inputs feedback such as "This stress management advice was very helpful" and sends it to the server.

[2073] Subject: Server

[2074] The server analyzes the collected feedback and stores it as data to be used in future sessions, taking into account the analysis results of the emotion engine.

[2075] Examples:

[2076] The server analyzes the collected feedback and identifies patterns, such as "stress management advice was helpful," and incorporates them into the next coaching plan.

[2077] Data from the sentiment engine can tell you when a user has positive feelings about a particular action, and then reinforce that action and include it in your next plan.

[2078] In this way, the system of the present invention allows businessmen to easily receive high-quality coaching, and the addition of an emotion engine makes it possible to provide even more personalized coaching.

[2079] The processing flow will be explained below.

[2080] Program processing flow

[2081] 1. Data accumulation

[2082] Subject: Server

[2083] Step 1:

[2084] Data collection for professional coaching sessions

[2085] The server collects audio data, text data, and image data of coaching sessions by professional coaches.

[2086] Examples:

[2087] The server uploads the audio data from the recording device to cloud storage.

[2088] The server stores the feedback notes and log data received from emails and digital notes in a database.

[2089] Step 2:

[2090] Data analysis

[2091] The server analyzes the collected coaching data and identifies important topics and effective coaching methods.

[2092] Examples:

[2093] The server uses speech recognition algorithms to convert the audio data into text and then uses natural language processing (NLP) to classify it into categories.

[2094] The server generates metadata to identify existing coaching methods and evaluate their effectiveness.

[2095] Step 3:

[2096] Stored in a database

[2097] The server stores the analyzed results in a database for easy access later.

[2098] Examples:

[2099] The server enters the analysis results into specific fields in a database, making them searchable.

[2100] 2. Collection of User Information

[2101] Subject: Device

[2102] Step 4:

[2103] User Preferences

[2104] The terminal provides an interface for inputting basic information about the user (such as name, age, job title, industry, etc.).

[2105] Examples:

[2106] The terminal displays a form with fields such as "Please enter your name."

[2107] The information entered by the user is sent to the server.

[2108] Step 5:

[2109] Identifying the problem

[2110] The terminal displays a detailed questionnaire form to gather the user's specific concerns and coaching needs.

[2111] Examples:

[2112] The device asks the user, "What is the biggest business challenge you are currently facing?"

[2113] The terminal collects the user's answers and sends them to the server.

[2114] 3. Create a coaching plan

[2115] Subject: Server

[2116] Step 6:

[2117] Information Integration

[2118] The server integrates the collected user information with the accumulated coaching data to create a coaching plan that best suits the user's needs.

[2119] Examples:

[2120] The server links the attribute "sales manager" with the concern "stress management" and extracts the optimal method from past data.

[2121] Step 7:

[2122] Sentiment analysis with emotion engine

[2123] The server collects the user's emotion data using an emotion engine, which recognizes the user's emotion using voice analysis, facial expression recognition, and text analysis, and reflects the collected data in the coaching plan.

[2124] Examples:

[2125] The server analyzes the user's voice data, and the emotion engine detects "anxiety" and "stress."

[2126] The server incorporates the detected emotional information into a coaching plan, emphasizing relaxation techniques and positive feedback.

[2127] Step 8:

[2128] Final generation of coaching plan

[2129] The server then uses the integrated information to ultimately generate a specific coaching plan suited to the user.

[2130] Examples:

[2131] The server suggests specific actions such as "daily reflection time."

[2132] Generate additional actions based on your emotional state, such as "Try this relaxation technique today."

[2133] 4. Conducting a coaching session

[2134] Subject: Device

[2135] Step 9:

[2136] Starting a Session

[2137] The terminal starts a coaching session at a time set by the user, and the session proceeds interactively.

[2138] Examples:

[2139] The device sends a message to the user saying, "Good morning. Let's talk about your goals for today."

[2140] Step 10:

[2141] Session Progression

[2142] The device provides specific advice and support to the user based on the generated coaching plan, and the emotion engine monitors the user's emotions in real time and adapts the session content accordingly.

[2143] Examples:

[2144] The device asks, "Please tell us how stressed you felt at work yesterday," and suggests ways to deal with the situation based on the user's answer.

[2145] If the emotion engine detects the user's "anxiety," it takes an approach that emphasizes encouragement and support.

[2146] 5. Gather and incorporate feedback

[2147] Subject: User

[2148] Step 11:

[2149] Providing feedback

[2150] After the coaching session is over, the user fills out a feedback form to rate the session.

[2151] Examples:

[2152] The user enters feedback such as "I was satisfied with today's session."

[2153] Subject: Server

[2154] Step 12:

[2155] Analyzing and incorporating feedback

[2156] The server analyzes the user's feedback and stores it as data to be used in future sessions, taking into account the analysis results of the emotion engine.

[2157] Examples:

[2158] The server analyzes the collected feedback and identifies patterns such as "stress management advice was helpful."

[2159] Data from the sentiment engine can tell you when a user has positive feelings about a particular action, and then reinforce that action and include it in your next plan.

[2160] This series of steps allows users to receive consistent, personalized business coaching efficiently.

[2161] Example 2

[2162] 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."

[2163] Conventional coaching systems have not been able to fully realize the creation of professional coaching content or customized plans based on the user's individual needs, and have only been able to provide general coaching methods. Furthermore, they are unable to respond in real time taking into account the user's emotional state, making it difficult to provide optimal coaching to the user. As a result, user satisfaction and effectiveness have been limited.

[2164] 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.

[2165] In this invention, the server includes means for collecting professional coaching data, means for analyzing the collected coaching data and identifying important topics and effective coaching methods, means for collecting user attribute information, means for generating a coaching plan customized based on the user's concerns and needs, means for conducting a coaching session for the user based on the generated coaching plan, means for collecting and analyzing user feedback after the coaching session, means for collecting user emotional data in real time and adjusting the coaching plan, and means for storing the analysis results in a database and reflecting them in the next coaching plan. This makes it possible to provide an optimized coaching plan for the user and respond to the user's emotional state in real time.

[2166] "Professional coaching data" is a general term for data including audio data, text data, image data, etc. of coaching sessions provided by professional coaches.

[2167] "Analysis" is the process of processing collected data using statistical and machine learning techniques to identify important topics and effective coaching methods.

[2168] "User attribute information" is information that indicates the characteristics of a user, including information about the user's name, age, job title, and related industry.

[2169] A "customized coaching plan" is a coaching action plan that is optimized for a specific user based on the user's concerns and needs.

[2170] A "coaching session" is a process of providing guidance and advice to a user in an interactive format based on the generated coaching plan.

[2171] "Feedback" refers to the evaluation and impressions provided by the user after a coaching session, and is used to improve the quality of the next coaching plan.

[2172] "Collecting user emotional data in real time" refers to the process of using technologies such as voice recognition and emotion analysis to monitor the user's emotional state in real time during a coaching session.

[2173] A "database" is an information system for storing collected and analyzed data and feedback.

[2174] The present invention provides a system for receiving professional coaching that provides a customized coaching plan tailored to the individual needs of a user. This system is composed of elements including a server, a terminal, and a user.

[2175] Server Features

[2176] The server collects and analyzes coaching data provided by professional coaches and generates a customized coaching plan for each user. Specific hardware includes cloud storage (e.g., Amazon S3), and software includes voice recognition technology (e.g., Google Cloud Speech-to-Text), natural language processing AI algorithms (e.g., GPT-4), and emotion engines (e.g., IBM Watson Tone Analyzer).

[2177] The server uploads the sessions recorded by the professional coaches to cloud storage and collects the audio data from there. The collected audio data is converted into text data using voice recognition technology. Image data such as the professional coaches' feedback notes are also converted into text data using OCR technology and stored in a database.

[2178] Device Features

[2179] The device acts as an interface with the user, collecting information from the user and conducting coaching sessions. The user enters basic information, occupational information, and information about their worries and needs through the device, which is then sent to the server. The device then conducts the session in an interactive format with the user based on the generated coaching plan. The device also collects and analyzes the user's emotional data in real time and adjusts advice and guidance as needed.

[2180] User Roles

[2181] Users enter basic information and occupational information through their devices and provide details of their current concerns and needs. Based on the generated coaching plan, the user receives a coaching session. After the session, the user provides feedback, which is sent to the server and used to improve the quality of the next session.

[2182] Specific examples

[2183] The server inputs the prompt "The sales manager is seeking advice on stress management" into GPT-4, and has it generate a stress management coaching plan. If the emotion engine recognizes the user's emotional state as "fatigue," it will suggest adding relaxation techniques to the plan.

[2184] The device asks the user questions such as "What is your current job title?" and sends the answer to the server. If the user enters "Sales Manager," the server uses that information to generate a customized coaching plan.

[2185] During the session, the device asks, "Please tell us about specific situations that caused you stress at work today," and provides advice based on the user's input. If the emotion engine detects "anxiety" in real time, the device will display encouraging words such as, "It's important to take a break."

[2186] Example prompt sentence:

[2187] "Create a customized coaching plan for your sales manager who wants stress management advice. Also, include relaxation techniques if your users are feeling burned out by their work."

[2188] This system configuration makes it possible to provide coaching plans suited to the individual needs and emotional state of the user, thereby improving the quality of coaching.

[2189] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2190] Step 1:

[2191] Collecting professional coaching data

[2192] Subject: Server

[2193] The server collects audio data of coaching sessions recorded by professional coaches. The audio data is retrieved from cloud storage (e.g., Amazon S3). The retrieved audio data is converted into text data using speech recognition technology (e.g., Google Cloud Speech-to-Text). The converted text data is stored in a database. The input of this process is audio data, and the output is text data.

[2194] Specific behavior:

[2195] The server accesses Amazon S3 cloud storage and downloads the audio file.

[2196] Input the downloaded audio file into the Google Cloud Speech-to-Text API and convert it into text.

[2197] The converted text data is stored in a database.

[2198] Step 2:

[2199] Collection of User Information

[2200] Subject: Device

[2201] The terminal provides an interface for inputting basic information (such as name, age, and job title) and occupational information of the user. The user inputs this information and sends it from the terminal to the server. The input of this process is the basic information of the user, and the output is the user information sent to the server.

[2202] Specific behavior:

[2203] A form such as "Please enter your name" or "Please enter your age" will be displayed on the device screen.

[2204] The information entered by the user is sent to the server.

[2205] Step 3:

[2206] Collecting user concerns and needs

[2207] Subject: Device

[2208] The terminal provides a form for the user to input their concerns and coaching needs. The user inputs this information and sends it from the terminal to the server. The input of this process is the user's concerns and needs, and the output is the user information sent to the server.

[2209] Specific behavior:

[2210] The device screen displays the message "Please tell us what your current concerns are" and asks the user to select an item by category.

[2211] Information about the worries and needs selected by the user is sent to the server.

[2212] Step 4:

[2213] Generate a customized coaching plan

[2214] Subject: Server

[2215] The server generates a customized coaching plan using an AI algorithm (e.g., GPT-4) based on the user's input information and past coaching data. It then analyzes the user's emotional data using an emotion engine (e.g., IBM Watson Tone Analyzer) to adjust the coaching plan. The input of this process is the user's information and past coaching data, and the output is a customized coaching plan.

[2216] Specific behavior:

[2217] The server inputs the prompt "Sales manager wants advice on stress management" into GPT-4 and has it generate a customized plan.

[2218] The emotion engine analyzes the user's emotional state and adds suggestions such as relaxation techniques to the plan.

[2219] Step 5:

[2220] Conducting a coaching session

[2221] Subject: Device

[2222] The device interactively conducts a coaching session with the user based on the generated coaching plan. During the session, the device monitors the user's emotional state in real time and adjusts the plan as needed. The inputs to this process are the coaching plan and real-time emotional data, and the output is advice provided to the user.

[2223] Specific behavior:

[2224] The device asks the user, "Please tell us about a specific situation that caused you stress during work today."

[2225] Based on the user's answers, appropriate advice is provided.

[2226] If the emotion engine detects anxiety, it will display encouraging words such as "It's important to take breaks."

[2227] Step 6:

[2228] Gathering and implementing feedback

[2229] Subject: User and Server

[2230] The user provides feedback after a coaching session. The feedback is sent to the server and used to improve the quality of the next coaching plan. The server analyzes the collected feedback and incorporates it into the next coaching plan. The inputs to this process are the user's feedback and session data, and the output is an improved coaching plan.

[2231] Specific behavior:

[2232] The user inputs feedback into the terminal, such as "This stress management advice was very helpful."

[2233] The device sends the feedback to the server.

[2234] The server analyzes the feedback data and reflects patterns such as "the stress management advice was helpful" in the next plan.

[2235] (Application example 2)

[2236] 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."

[2237] Coaching for modern business people and store employees often involves a uniform approach, which means it is difficult to adequately address individual concerns and emotional states. Furthermore, it is difficult to provide coaching that responds to real-time emotional changes, which can result in ineffective guidance.

[2238] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2239] In this invention, the server includes means for collecting professional coaching data, means for analyzing the collected coaching data and identifying important topics and effective coaching methods, means for collecting user attribute information, means for generating a customized coaching plan based on the user's concerns and requests, means for conducting a coaching session with the user based on the generated coaching plan, means for collecting and analyzing user feedback after the coaching session, means for storing the analysis results in a database and reflecting them in the next coaching plan, and means for collecting user emotion data and adjusting the coaching plan in real time using an emotion engine. This enables effective and personalized coaching according to the user's individual concerns and emotional state.

[2240] "Professional instructional data" refers to educational or training information provided by a professional, and includes audio data, text data, and image data.

[2241] "Instructional Data" means educational or training information collected for a specific purpose, and includes audio data, text data, and image data.

[2242] "Key topics" refer to themes extracted from the analyzed data that have a significant impact on a particular situation or issue.

[2243] "Effective teaching methods" refer to educational or training techniques that have a proven track record of helping users achieve their goals.

[2244] "User demographic information" means basic data about an individual User, including name, age, job title, and industry-related information.

[2245] "User concerns and requests" refers to the problems the user is facing or the specific situations they wish to resolve.

[2246] "Customized instruction plan" refers to an educational or training program that is individually designed based on the user's demographic information and concerns or requests.

[2247] "Coaching Session" means an educational or training interactive program delivered to a User.

[2248] "Feedback" refers to the evaluation, opinions, and impressions provided by users after a coaching session.

[2249] "Emotional Data" refers to information used to measure and analyze a user's emotional state.

[2250] "Emotion engine" refers to the algorithms and systems that analyze users' emotional data in real time and adjust appropriate teaching plans.

[2251] The present invention is a system that enables users to receive professional guidance efficiently and effectively, and aims to provide personalized guidance that is tailored to the user's individual concerns and emotional state.

[2252] 1. Data accumulation

[2253] The server collects professional teaching data and stores it in a database. The teaching data includes audio data, text data, and image data. This data is analyzed using natural language processing and machine learning algorithms to identify important topics and effective teaching methods. This identified information is used to provide effective teaching to other users.

[2254] 2. Collection of User Information

[2255] The device provides an interface for collecting basic information and professional information (such as name, age, job title, and industry) from users. Users can input this information through the device and provide detailed information about their concerns and guidance needs.

[2256] 3. Generate lesson plans

[2257] The server generates a customized instruction plan based on the information entered by the user and past instruction data. An AI algorithm selects the instruction method that best suits the user's attributes and concerns, and creates a specific action plan. In addition, an emotion engine collects the user's emotional data and adjusts the plan accordingly.

[2258] 4. Conducting a mentoring session

[2259] The device conducts a teaching session with the user in an interactive format based on the generated teaching plan. It provides advice and support to the user in voice and text format. The emotion engine monitors and responds to the user's emotional state in real time.

[2260] 5. Gather and incorporate feedback

[2261] After a training session, users fill out a feedback form to evaluate the content of the session and their satisfaction. This feedback is sent to the server and used to improve the quality of the next session. The server analyzes the collected feedback and saves it as data to be reflected in future sessions. It also takes into account the analysis results of the emotion engine.

[2262] Hardware and software used

[2263] Server: Used to store and analyze data, generate teaching plans, and collect and incorporate feedback. Python's nltk and spaCy are used for natural language processing, and scikit-learn is used for machine learning algorithms.

[2264] Devices: Used to collect user information, conduct coaching sessions, and gather feedback. This includes smartphones, tablets, laptops, etc.

[2265] Emotion engine: Used to collect user emotion data and analyze it in real time. Emotion analysis uses emotion recognition API and TensorFlow.

[2266] Prompt Sentence Examples

[2267] "Please tell us about the most stressful experience you have had recently while dealing with a customer."

[2268] "Please tell me a bit more about your current position and your concerns."

[2269] "Was the content of the coaching session helpful? Please give us specific feedback."

[2270] In this way, the system of the present invention allows users to easily receive high-quality instruction, and by adding an emotion engine, it is possible to provide even more personalized instruction.

[2271] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2272] Step 1:

[2273] The server collects professional teaching data and stores it in a database. As input, it receives audio, text, and image data from teaching sessions. It analyzes this data using natural language processing techniques (e.g., Python's nltk or spaCy) to identify important topics and effective teaching methods. As output, the analysis results are stored in a database.

[2274] Step 2:

[2275] The terminal provides an interface to collect basic information and professional information about the user. Input includes name, age, job title, and industry affiliation, which are entered by the user on the terminal. This information is sent to the server and stored in a database. Specifically, the terminal displays an input form and allows the user to enter information.

[2276] Step 3:

[2277] The server collects users' concerns and guidance needs. The input includes specific concerns and requests that users enter on their devices. This information is also stored in the database. Specifically, the device provides a category-specific input form, allowing users to enter detailed concerns.

[2278] Step 4:

[2279] The server generates a customized teaching plan based on collected user information and past teaching data. The inputs include the user's attribute information, concerns and requests, and past teaching data. Using the generative AI model, it selects the optimal teaching method and creates a specific action plan. The output is the generated customized plan.

[2280] Step 5:

[2281] The device conducts a teaching session interactively with the user based on the generated teaching plan. The device takes the teaching plan as input. It provides advice and support to the user in voice or text format, and an emotion engine monitors the user's emotional state in real time. The device outputs instruction tailored to the user's emotional state.

[2282] Step 6:

[2283] Users provide feedback after a training session. Input includes ratings, opinions, and impressions that users enter on their devices. This feedback is sent to the server and stored in a database. Specifically, the device displays a feedback form for users to fill out.

[2284] Step 7:

[2285] The server analyzes the collected feedback and reflects it in the next teaching plan. The input is feedback data from users. This is analyzed and stored in a database. The analysis results are taken into consideration when generating the next teaching plan. Specifically, the server analyzes the feedback data using natural language processing technology and extracts important opinions and evaluations.

[2286] Step 8:

[2287] The server collects the user's emotional data and adjusts the teaching plan in real time using an emotion engine. The input is emotional information from the user's voice and text data. Based on this information, the server uses an emotion recognition API and TensorFlow to analyze the user's emotional state and adjust the teaching plan as needed. Specifically, the emotion engine analyzes the user's tone of voice and vocabulary to determine their emotional state.

[2288] 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.

[2289] 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.

[2290] 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 robot 414.

[2291] 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.

[2292] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2293] 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.

[2294] 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).

[2295] 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.

[2296] 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."

[2297] 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.

[2298] 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).

[2299] 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.

[2300] 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.

[2301] 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.

[2302] 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.

[2303] 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.

[2304] 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.

[2305] 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.

[2306] 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.

[2307] 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.

[2308] 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.

[2309] The following is further disclosed regarding the above embodiment.

[2310] (Claim 1)

[2311] A means of collecting professional coaching data;

[2312] A means to analyze the collected coaching data and identify important topics and effective coaching methods,

[2313] A means for collecting user attribute information;

[2314] means for generating a customized coaching plan based on the user's concerns and needs;

[2315] means for conducting a coaching session for the user based on the generated coaching plan;

[2316] a means for collecting and analyzing user feedback after a coaching session;

[2317] The analysis results will be stored in a database and reflected in the next coaching plan.

[2318] A system including:

[2319] (Claim 2)

[2320] 10. The system of claim 1, wherein the coaching data includes audio data, text data, and image data.

[2321] (Claim 3)

[2322] 2. The system of claim 1, wherein the user attribute information includes information about name, age, job title, and related industry.

[2323] "Example 1"

[2324] (Claim 1)

[2325] A means of collecting professional coaching data;

[2326] A means to analyze the collected coaching data and identify important topics and effective coaching methods,

[2327] A means for collecting user attribute information;

[2328] means for generating a customized coaching plan based on the user's concerns and needs;

[2329] means for conducting a coaching session for the user based on the generated coaching plan;

[2330] a means for collecting and analyzing user feedback after a coaching session;

[2331] The analysis results will be stored in a database and reflected in the next coaching plan.

[2332] means for converting the coaching data into text data using voice recognition technology and optical character recognition technology;

[2333] means for collecting user information via a user interface and storing the information in a database;

[2334] A means of analyzing user information and past coaching data using AI algorithms to generate an optimal coaching plan;

[2335] A system including:

[2336] (Claim 2)

[2337] 10. The system of claim 1, wherein the coaching data includes audio data, text data, and image data.

[2338] (Claim 3)

[2339] 2. The system of claim 1, wherein the user attribute information includes information about name, age, job title, and related industry.

[2340] "Application Example 1"

[2341] (Claim 1)

[2342] A means of collecting professional coaching data;

[2343] A means to analyze the collected coaching data and identify important topics and effective coaching methods,

[2344] A means for collecting user attribute information;

[2345] means for generating a customized coaching plan based on the user's concerns and needs;

[2346] means for conducting a coaching session for the user based on the generated coaching plan;

[2347] a means for collecting and analyzing user feedback after a coaching session;

[2348] The analysis results will be stored in a database and reflected in the next coaching plan.

[2349] It runs on smartphones and provides customized coaching to online shopping site operators and staff.

[2350] A means for generating a coaching plan from a prompt sentence based on the user's attributes and concerns using a generative AI model;

[2351] A system including:

[2352] (Claim 2)

[2353] 10. The system of claim 1, wherein the coaching data includes audio data, text data, and image data.

[2354] (Claim 3)

[2355] 2. The system of claim 1, wherein the user attribute information includes information about name, age, job title, and related industry.

[2356] "Example 2: Combining Emotion Engines"

[2357] (Claim 1)

[2358] A means of collecting professional coaching data;

[2359] A means to analyze the collected coaching data and identify important topics and effective coaching methods,

[2360] A means for collecting user attribute information;

[2361] means for generating a customized coaching plan based on the user's concerns and needs;

[2362] means for conducting a coaching session for the user based on the generated coaching plan;

[2363] a means for collecting and analyzing user feedback after a coaching session;

[2364] a means for collecting real-time user emotional data and adjusting coaching plans;

[2365] The analysis results will be stored in a database and reflected in the next coaching plan.

[2366] A system including:

[2367] (Claim 2)

[2368] 10. The system of claim 1, wherein the coaching data includes audio data, text data, and image data.

[2369] (Claim 3)

[2370] 2. The system of claim 1, wherein the user attribute information includes information about name, age, job title, and related industry.

[2371] "Application example 2 when combining emotion engines"

[2372] Rewritten claims

[2373] (Claim 1)

[2374] a means of collecting professional teaching data;

[2375] A means of analyzing the collected teaching data to identify important topics and effective teaching methods;

[2376] A means for collecting user attribute information;

[2377] A means for generating a customized teaching plan based on the user's concerns and requests;

[2378] means for conducting a training session for the user based on the generated training plan;

[2379] a means for collecting and analyzing user feedback after a coaching session;

[2380] The analysis results will be stored in a database and reflected in the next teaching plan.

[2381] a means for collecting user emotion data and adjusting instruction plans in real time using an emotion engine;

[2382] A system including:

[2383] (Claim 2)

[2384] 10. The system of claim 1, wherein the professional instruction data includes audio data, text data, and image data.

[2385] (Claim 3)

[2386] 2. The system of claim 1, wherein the user's attribute information includes name, age, job title, and information about related industries. [Explanation of symbols]

[2387] 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 of collecting professional coaching data; A means to analyze the collected coaching data and identify important topics and effective coaching methods, A means for collecting user attribute information; means for generating a customized coaching plan based on the user's concerns and needs; means for conducting a coaching session for the user based on the generated coaching plan; a means for collecting and analyzing user feedback after a coaching session; The analysis results will be stored in a database and reflected in the next coaching plan. A system including:

2. The system of claim 1 , wherein the coaching data includes audio data, text data, and image data.

3. The system according to claim 1 , wherein the user attribute information includes information about a name, an age, a job title, and an associated industry.

Images