System

Abstract

To find out problems of great value from essence of accumulated worries or complaints of a user, and develop a novel service solving the problem.SOLUTION: A system includes the means of: receiving inputs from users; generating questions on the basis of the received input; suggesting the generated question to the user; drawing out worries and complaints of the user to accumulate them; extracting a pattern and trend from the accumulated worries and complaints; using a generative AI model on the basis of an extraction result to generate a prompt sentence; based on the generated prompt sentence, using a software development tool to generate an application; and providing the user with the generated application.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Currently, there are many services that listen to users' worries and complaints, but these services simply listen, and it is difficult to extract and store the essence of users' worries and complaints. It is also difficult to find high-value issues from the essence of the accumulated worries and complaints and develop new services that solve those issues. [Means for solving the problem]

[0005] The present invention receives input from a user, generates probing questions based on the received input, and presents the generated questions to the user. This makes it possible to extract and store the essence of the user's worries and complaints. Furthermore, it is possible to find high-value issues from the essence of the accumulated worries and complaints, and develop new services that solve those issues. [Brief description of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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. 2 is a sequence diagram showing a process flow of the data processing system in the first embodiment of the first form example. [Figure 12] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the application example 1 of the first embodiment. [Figure 13]FIG. 11 is a sequence diagram showing a process flow of the data processing system in the second embodiment of the second embodiment. [Figure 14] FIG. 13 is a sequence diagram showing a process flow of the data processing system in application example 2 of embodiment 2. [Figure 15] FIG. 13 is a sequence diagram showing a process flow of a data processing system in a third embodiment of the present invention. [Figure 16] FIG. 13 is a sequence diagram showing a process flow of the data processing system in the third application example of the third embodiment. [Figure 17] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when combined with an emotion engine. [Figure 19] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system according to the embodiment 2 of the second embodiment in the case where an emotion engine is combined. [Figure 20] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when combined with an emotion engine. [Figure 21] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in the embodiment 3 of the form example 3 in the case where an emotion engine is combined. [Figure 22] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when combined with an emotion engine. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0011] 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.

[0012] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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 wide area network (WAN) and / or a local area network (LAN).

[0018] 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.

[0019] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. 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.

[0020] 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 (e.g., voice and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs voice according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0023] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

[0024] 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.

[0025] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 60. The reception output program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads out the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output processing 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] An embodiment of the present invention includes a voice recognition system and a text input interface as a means for receiving input from a user. This system receives the user's worries and complaints as text data. Next, a natural language processing (NLP) algorithm is used as a means for generating probing questions based on the received input. This algorithm extracts keywords and emotions from the user's input and generates questions based on them. The generated questions are presented to the user to elicit a response from the user. This allows the essence of the user's worries and complaints to be extracted and stored in a database.

[0029] "Example 2"

[0030] A data analysis algorithm is used to find high-value issues from the nature of the accumulated worries and complaints. This algorithm extracts patterns and trends from the accumulated data and identifies high-value issues based on them. For example, if many users complain about work-related stress, it can determine that a new service needs to be developed to solve this issue.

[0031] "Example 3"

[0032] Software development tools are used to develop new services that solve the problems that have been identified. These tools develop applications and web services to solve specific problems. For example, a relaxation app to reduce work stress or a project management tool to help manage tasks efficiently can be developed.

[0033] The process flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: Receive voice or text input from the user. In this step, the user inputs their concerns or complaints via voice or text.

[0036] Step 2: Run the received input through a natural language processing (NLP) algorithm to extract keywords and sentiment from the user's input. In this step, NLP algorithms are used to analyze the text data in order to understand the essence of the user's concerns and complaints.

[0037] Step 3: Generate probing questions based on the extracted keywords and sentiments and present them to the user. In this step, the generated questions are presented to the user to elicit a response.

[0038] Step 4: Receive user responses and store the essence of their concerns and complaints in a database. In this step, the user responses are stored in a database and accumulated for later analysis.

[0039] "Example 2"

[0040] Step 1: Run the accumulated data through a data analysis algorithm to extract patterns and trends. In this step, common patterns and trends are found in the accumulated data on users' concerns and complaints.

[0041] Step 2: Identify high-value issues based on extracted patterns and trends. In this step, the results of the data analysis are used to find high-value issues that users have in common.

[0042] "Example 3"

[0043] Step 1: Think of ideas for new services to solve the identified problems. In this step, you brainstorm ideas for new services to solve the identified problems.

[0044] Step 2: Realize the idea and develop a new service. In this step, software development tools are used to realize the idea and develop a new service.

[0045] Example 1

[0046] Next, a description will be given of Example 1 of the embodiment 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".

[0047] Conventional systems have had difficulty effectively collecting users' concerns and complaints and extracting their true essence. In addition, they were unable to generate appropriate questions based on user input and dig deeper, making it difficult to fully understand the true essence of users' concerns and complaints. Furthermore, it was difficult to effectively store collected data and find high-value issues.

[0048] 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.

[0049] In this invention, the server includes a means for receiving input from a user, a means for preprocessing the received input, a means for extracting keywords and emotions from the preprocessed input, a means for generating questions based on the extracted keywords and emotions, a means for presenting the generated questions to the user, a means for collecting the user's responses, and a means for extracting and storing the essence of the user's worries and complaints. This makes it possible to effectively collect the user's worries and complaints and extract their essence.

[0050] A "means for receiving input from a user" is an interface through which a user provides information to the system through voice or text.

[0051] "Means for preprocessing received input" refers to the process of converting text data received from a user into a format that is easy to parse by performing processes such as normalization and tokenization.

[0052] "Means for extracting keywords and sentiment from the preprocessed input" refers to a process of using natural language processing techniques to identify important words and sentiment from the preprocessed text data.

[0053] The "means for generating questions based on the extracted keywords and sentiment" refers to a process for automatically formulating questions to elicit further information from a user based on the identified keywords and sentiment.

[0054] The "means for presenting the generated question to the user" refers to an interface that displays the generated question on the user's terminal and enables the user to respond to it.

[0055] "Means of collecting user responses" refers to the process of incorporating responses entered by users into the system in response to questions posed.

[0056] "Means of extracting and storing the essence of users' worries and complaints" refers to the process of analyzing the information obtained from users' input and responses, and storing that essence in a database.

[0057] "Means of finding high-value issues" is the process of identifying particularly important problems or issues from accumulated data.

[0058] "Means of developing new services" refers to the process of designing and implementing new services and solutions to solve the problems identified.

[0059] This invention is a system that receives input from a user, generates questions based on the input, and extracts and accumulates the essence of the user's worries and complaints. This system receives input from a user using a voice recognition system and a text input interface. Specifically, a general voice recognition API is used for the voice recognition system, and an HTML form is used for the text input interface.

[0060] The server preprocesses the text data received from the user. This preprocessing involves normalizing the text (converting to lowercase, deleting unnecessary spaces, etc.) and tokenizing (dividing the text into words). From the preprocessed text data, the server uses natural language processing (NLP) techniques to extract keywords and sentiment. For this process, the Python libraries NLTK and spaCy are used.

[0061] Based on the extracted keywords and sentiment, the server generates questions using a generative AI model. The generative AI model uses an advanced natural language generation model such as GPT-3 (registered trademark). The generated questions are presented to the user's device. The user inputs an answer to the presented question. The server passes this answer back to the NLP algorithm to extract further keywords and sentiment.

[0062] Finally, the server extracts the essence of the user's worries and complaints and stores them in a database. A general relational database management system (RDBMS) is used for the database. For example, MySQL (registered trademark) or PostgreSQL is applied.

[0063] Examples

[0064] Example 1: User Input

[0065] The user speaks into the smartphone's microphone, "I've been busy at work lately and I'm feeling stressed," and the voice recognition system converts this into text.

[0066] Example 2: Generating a question

[0067] The server uses an NLP algorithm to extract the keywords "busy at work" and the emotion "stressed." It then uses a generative AI model (GPT-3) to generate the question "What specific tasks are you busy with?"

[0068] Example 3: User response

[0069] The user uses his / her PC keyboard to type, "I have a lot of meetings in particular, so it takes a long time to prepare."

[0070] Example 4: Accumulating data

[0071] The server stores the answer in a database and records the nature of the user's concern, e.g., by saving it as a new record in a "concerns" table in the database.

[0072] Examples of prompt statements

[0073] User input: Work has been busy and stressful lately.

[0074] Prompt for generative AI model: A user says, "I've been busy at work lately and it's stressful." What exactly are the tasks that are making them busy?

[0075] In this way, the system can dig deeper into the user's concerns and complaints and bring out their essence.

[0076] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0077] Step 1:

[0078] Users input their worries or complaints through a voice recognition system or text input interface. For example, a user speaks into the microphone of their smartphone, "I've been busy at work lately and I'm feeling stressed." The voice recognition system converts this into text and sends it to the server. The input is voice data or text data, and the output is text data.

[0079] Step 2:

[0080] The server preprocesses the received text data. Preprocessing involves normalizing the text (converting to lowercase, deleting unnecessary spaces, etc.) and tokenizing (dividing it into words). For example, the text "I've been busy at work lately and I'm getting stressed" is tokenized as "I've been busy at work lately and I'm getting stressed." The input is text data, and the output is the preprocessed text data.

[0081] Step 3:

[0082] The server extracts keywords and emotions from the preprocessed text data. For this process, it uses Python libraries NLTK and spaCy. For example, it extracts keywords such as "work," "busy," "stress," and "accumulating" and the emotion "accumulating stress." The input is the preprocessed text data, and the output is keywords and emotions.

[0083] Step 4:

[0084] The server generates questions based on the extracted keywords and sentiment. A generative AI model is used to generate these questions. For example, a generative AI model (GPT-3) is used to generate the question, "What specific tasks are you busy with?" The input is the keywords and sentiment, and the output is the generated question.

[0085] Step 5:

[0086] The server presents the generated question to the user. The question is displayed on the user's terminal. For example, the question may be, "What specific tasks are you busy with?" The input is the generated question, and the output is the question displayed on the user's terminal.

[0087] Step 6:

[0088] The user inputs an answer to the question presented. For example, the user uses the PC keyboard to type, "I have a lot of meetings, so it takes a lot of time to prepare." The input is the user's answer, and the output is text data.

[0089] Step 7:

[0090] The server passes the user's answer to the NLP algorithm again to extract further keywords and sentiments. For example, it extracts the keywords "meeting," "preparation," and "takes time" and the sentiment "takes time." The input is the user's answer, and the output is the keywords and sentiments.

[0091] Step 8:

[0092] The server extracts the essence of the user's worries and complaints and stores them in a database. A general relational database management system (RDBMS) is used for the database. For example, MySQL or PostgreSQL is used. The input is keywords and emotions, and the output is the data stored in the database.

[0093] (Application example 1)

[0094] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0095] In modern society, users have a wide range of security concerns and worries, but there is a lack of systems that provide specific measures and advice to properly resolve them. In addition, there is a challenge in that it is difficult to dig deep into users' concerns and clarify the essential problems.

[0096] 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.

[0097] In this invention, the server includes a means for receiving an input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, a means for converting the voice input into text using a voice recognition system, a means for generating questions using a natural language processing algorithm, a means for clarifying the user's specific problem based on the generated questions, and a means for providing appropriate measures and advice. This makes it possible to effectively resolve the user's worries and anxieties about security.

[0098] A "means for receiving input from a user" is an interface for receiving information input by a user via voice or text.

[0099] The "means for generating probing questions based on the received input" is an algorithm that analyzes the user's input and generates questions to elicit more detailed information.

[0100] The "means for presenting the generated question to the user" refers to an interface for displaying or audibly conveying the generated question to the user.

[0101] "A means for extracting and storing the essence of users' worries and complaints" is a system for extracting the essential problem from user input and storing it in a database.

[0102] "Means for converting voice input into text using a voice recognition system" refers to voice recognition technology for converting a user's voice input into text data.

[0103] "Means for generating questions using a natural language processing algorithm" refers to natural language processing technology for analyzing user input and generating appropriate questions.

[0104] The "means for clarifying a user's specific problem based on the generated questions" is a system for clarifying a user's specific problem or concern through the generated questions.

[0105] A "means for providing appropriate measures and advice" is a system for providing appropriate measures and advice for a user's specific problem.

[0106] A system for implementing this invention receives input from a user, generates probing questions based on that input, and presents these to the user to draw out the essence of the user's worries and complaints and provide appropriate solutions and advice.

[0107] Hardware and Software Used

[0108] Hardware: Microphone (for voice input), server, user device (smartphone, etc.)

[0109] software:

[0110] speech_recognition library (speech recognition)

[0111] NLP models (question generation) from the transformers library

[0112] System Configuration

[0113] 1. Speech recognition system: The user speaks through a microphone. The speech recognition system converts this speech into text data.

[0114] 2. Natural language processing algorithms: Analyze the converted text data and generate probing questions based on the user's input.

[0115] 3. Question Prompt Interface: Presents the generated question to the user and prompts for further input.

[0116] 4. Database: A database for extracting and storing the essence of users' concerns and complaints.

[0117] 5. Solution and advice provision system: Provides appropriate solutions and advice for users' specific problems.

[0118] Processing flow

[0119] 1. Acquire speech input: The user speaks into a microphone. The speech recognition system converts this speech into text.

[0120] 2. Question generation: Natural language processing algorithms analyze the text data and generate probing questions based on the user's input.

[0121] 3. Question prompting: Present the generated question to the user and prompt for further input.

[0122] 4. Data accumulation: The essence of users’ concerns and complaints is accumulated in a database.

[0123] 5. Providing countermeasures and advice: Providing appropriate countermeasures and advice to users based on accumulated data.

[0124] Examples

[0125] If a user says, "I've been worried about the security of my home lately," the system generates questions such as, "What specifically are you worried about?" and "Have you seen anything suspicious happen recently?" This helps clarify the user's specific problem and provides appropriate measures and advice.

[0126] Examples of prompt statements

[0127] User: I've been worried about the security of my home lately.

[0128] System: What specifically are you worried about?

[0129] User: I often hear noises in the middle of the night.

[0130] System: At what times of day is the noise most common?

[0131] In this way, it is possible to dig deeper into the user's concerns and propose appropriate security measures.

[0132] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0133] Step 1:

[0134] A user uses a microphone to input voice. The terminal acquires this voice data and sends it to a voice recognition system. The input is the user's voice data, and the output is text data. The voice recognition system analyzes the voice data and converts it into text data.

[0135] Step 2:

[0136] The server inputs the text data received from the speech recognition system into a natural language processing algorithm. The input is the text data and the output is the generated question. The natural language processing algorithm analyzes the text data and generates probing questions based on the user's input.

[0137] Step 3:

[0138] The server presents the generated question to the user. The input is the generated question and the output is the user's answer. Through a question presentation interface, the generated question is displayed to the user and prompts for further input.

[0139] Step 4:

[0140] The user inputs an answer to the question. The terminal acquires this answer and sends it to the server. The input is the user's answer, and the output is text data. In the case of voice input, the voice recognition system is used again to convert it to text.

[0141] Step 5:

[0142] The server stores the user's answers in a database. The input is the text data of the user's answers, and the output is the data stored in the database. The database is a system for extracting and storing the essence of the user's worries and complaints.

[0143] Step 6:

[0144] The server provides appropriate measures and advice to the user based on the accumulated data. The input is the data stored in the database, and the output is measures and advice. The solution and advice providing system generates appropriate measures and advice for the user's specific problem and presents them to the user.

[0145] In this way, it is possible to dig deeper into the user's concerns and propose appropriate security measures.

[0146] Example 2

[0147] Next, a second embodiment of the second embodiment will be described. 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."

[0148] In conventional systems, users' worries and complaints were simply accumulated, making it difficult to extract their true nature and identify high-value issues. In addition, there was a lack of concrete means to develop new services based on the identified issues. As a result, users' worries and complaints could not be effectively resolved.

[0149] 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.

[0150] In this invention, the server includes a means for receiving input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, a means for preprocessing the stored data, a means for analyzing the preprocessed data and extracting important keywords and phrases, a means for analyzing the frequency and co-occurrence relationships of the extracted keywords and phrases, and a means for identifying high-value issues based on the analysis results. This makes it possible to effectively extract the essence of the user's worries and complaints, identify high-value issues, and develop new services based on those issues.

[0151] "Means for receiving input from the user" refers to a function that provides an interface for users to input their concerns and complaints into the system.

[0152] "Means for generating probing questions based on received input" refers to a function that analyzes information entered by a user and automatically generates follow-up questions related to that information.

[0153] The "means for presenting the generated question to the user" is a function for displaying the generated question to the user and enabling the user to answer it.

[0154] "A means of extracting and storing the essence of the user's worries and complaints" refers to a function that analyzes the information entered by the user, extracts the essential parts, and stores them in a database.

[0155] The "means for preprocessing the accumulated data" refers to a function that performs operations such as deleting unnecessary characters and spaces and normalizing text in order to prepare the accumulated data in a format that is easy to analyze.

[0156] "Means for analyzing the preprocessed data and extracting important keywords and phrases" refers to a function for identifying important keywords and phrases from the preprocessed data using natural language processing technology.

[0157] The "means for analyzing the frequency and co-occurrence relationships of extracted keywords and phrases" is a function for statistically analyzing the frequency of occurrence of extracted keywords and phrases and the relationships in which they appear together.

[0158] "Means of identifying high-value issues based on analysis results" refers to a function that identifies particularly important issues that need to be resolved from among users' concerns and complaints based on the analysis results.

[0159] The present invention is a system for effectively collecting users' worries and complaints, extracting their essence, and identifying high-value issues. A specific embodiment of this system will be described below.

[0160] First, the server provides a means to receive input from users. This is achieved through a user interface such as a web interface or a mobile application. When a user inputs their worries or complaints, the data is sent to the server and stored in a database. The database is a common database management system such as MySQL or PostgreSQL.

[0161] The server then has the means to generate follow-up questions based on the input it receives, using natural language processing techniques such as Python libraries like NLTK and spaCy to parse the user's input and generate related follow-up questions.

[0162] The server then presents the generated questions to the user, who answers them and provides further details, a process that draws out the essence of the user's concerns and complaints.

[0163] The accumulated data is preprocessed by the server, which uses the Pandas library to read the data, remove unnecessary characters and spaces, normalize the text, and so on, to make the data easier to analyze.

[0164] The preprocessed data is then analyzed by the server, which uses NLTK and spaCy to tokenize the text data, tag parts of speech, and perform named entity recognition (NER) to extract important keywords and phrases from the text data.

[0165] The extracted keywords and phrases are analyzed by the server for frequency and co-occurrence. This analysis is performed using the Scikit-learn library. For example, if many users mention "work stress," it is confirmed that this is a frequent occurrence. Also, by analyzing co-occurrence, it is found that "work" and "stress" frequently appear together.

[0166] Finally, the server identifies high-value issues based on the analysis results. For example, it may determine that "work stress" is a common issue for many users. Based on this result, it may suggest that the development of a "stress management app" is a high-value solution.

[0167] As a concrete example, let's say you have accumulated the following user complaint data:

[0168] "Work has been so busy lately that it's stressful"

[0169] "My boss is strict and I feel pressured every day."

[0170] "I can't communicate well with my coworkers"

[0171] Example of a prompt to be input to a generative AI model:

[0172] Analyze your users' complaints data and identify common issues. Use the following data:

[0173] "Work has been so busy lately that it's stressful"

[0174] "My boss is strict and I feel pressured every day."

[0175] "I can't communicate well with my coworkers"

[0176] Using this prompt, the generative AI model can identify common issues from user complaint data and suggest valuable solutions.

[0177] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0178] Step 1:

[0179] The server receives input from the user. When the user inputs their worries or complaints through a web interface or a mobile application, the data is sent to the server. An example of input data is "Recently, I've been so busy at work that I'm getting stressed out." The server stores this data in a database. The input data is stored in text format.

[0180] Step 2:

[0181] The server generates probing questions based on the input it receives. Using natural language processing techniques, the server analyzes the user's input and generates related follow-up questions. For example, a question might be "Which parts of your job do you find particularly stressful?" The input data is the user's initial input, and the output data is the generated question.

[0182] Step 3:

[0183] The server presents the generated questions to the user, who answers them and provides further details. For example, if the user answers "my boss is demanding," the data is sent back to the server and stored in the database. The input data are the user's answers to the generated questions, and the output data are the further details.

[0184] Step 4:

[0185] The server preprocesses the accumulated data. Preprocessing includes reading the data using the Pandas library, deleting unnecessary characters and spaces, normalizing the text, etc. For example, it deletes unnecessary spaces from the text "My boss is demanding" and converts it to lowercase. The input data is the accumulated raw data, and the output data is the preprocessed clean data.

[0186] Step 5:

[0187] The server analyzes the preprocessed data and extracts important keywords and phrases. The analysis involves tokenizing the text data, tagging it with parts of speech, and performing named entity recognition (NER) using NLTK and spaCy. For example, keywords such as "boss," "demanding," and "strict" are extracted. The input data is the preprocessed text data, and the output data is the extracted keywords and phrases.

[0188] Step 6:

[0189] The server analyzes the frequency and co-occurrence of the extracted keywords and phrases. The Scikit-learn library is used for the analysis. For example, it is confirmed that "boss" and "strict" frequently appear together. The input data are the extracted keywords and phrases, and the output data are the analysis results of the frequency and co-occurrence.

[0190] Step 7:

[0191] The server identifies high-value issues based on the analysis results. For example, it is determined that "work stress" is a common issue shared by many users. Based on this result, it is proposed that the development of a "stress management app" is a high-value solution. The input data are the results of the analysis of frequency and co-occurrence relationships, and the output data are the identified high-value issues.

[0192] (Application example 2)

[0193] Next, a description will be given of Application Example 2 of Form 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."

[0194] In modern society, users have a wide range of worries and complaints in their daily lives and work. In order to effectively solve these worries and complaints, it is necessary to accurately grasp their true nature and take appropriate measures. However, conventional systems simply accumulate users' worries and complaints, making it difficult to extract their true nature and provide concrete solutions. There was also a lack of effective means to recommend the most suitable content to users. This resulted in the issue of being unable to improve user satisfaction.

[0195] 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.

[0196] In this invention, the server includes a means for receiving input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, a means for extracting patterns and trends from the stored data, and a means for recommending optimal content based on the extracted patterns and trends. This makes it possible to accurately grasp the essence of the user's worries and complaints and recommend appropriate content.

[0197] "Means for receiving input from the user" refers to a function that provides an interface for users to input their concerns and complaints into the system.

[0198] "Means for generating probing questions based on received input" refers to a function that analyzes information entered by a user and automatically generates more detailed questions based on that information.

[0199] The "means for presenting the generated question to the user" is a function for displaying the generated question to the user and enabling the user to answer it.

[0200] "A means of extracting and storing the essence of users' worries and complaints" refers to a function that analyzes the information provided by users, extracts its essence, and stores it in a database.

[0201] "Means for extracting patterns and trends from the accumulated data" refers to a function that executes an algorithm to analyze the accumulated data and find common patterns and trends.

[0202] "Means for recommending optimal content based on extracted patterns and trends" refers to a function that recommends optimal content (videos, music, articles, etc.) to users based on extracted patterns and trends.

[0203] The following system configuration will be described as an embodiment of the present invention.

[0204] System Configuration

[0205] This system consists of a user terminal, a server, and a database. The user terminal is a device such as a smartphone, tablet, or PC, and provides an interface for users to input their worries and complaints. The server plays a central role in receiving input from users, analyzing the data, and recommending appropriate content. The database is used to store user input data and analysis results.

[0206] Program Processing

[0207] The server performs the process in the following procedure.

[0208] 1. A way to receive input from the user

[0209] The server receives the text data of the user's worries and complaints sent from the user's device. This data is stored exactly as entered by the user.

[0210] 2. A way to generate probing questions based on the input received

[0211] The server analyzes the received text data and uses natural language processing technology to generate more detailed questions, using a generative AI model to automatically generate probing questions related to the user's input.

[0212] 3. A way to present the generated questions to the user

[0213] The generated questions are then sent to the user's device and presented to the user, who can then answer them to provide further detailed information.

[0214] 4. A means to extract and accumulate the essence of users' worries and complaints

[0215] The server that receives the user's responses analyzes the content and extracts the essence of the user's worries or complaints. This extracted information is stored in a database.

[0216] 5. A means of extracting patterns and trends from accumulated data

[0217] The server analyzes the accumulated data and runs algorithms to find common patterns and trends, specifically using machine learning algorithms such as TfidfVectorizer and KMeans clustering.

[0218] 6. A means to recommend the best content based on extracted patterns and trends

[0219] The server recommends the most suitable content (videos, music, articles, etc.) to the user based on the extracted patterns and trends. The recommendation results are sent to the user's terminal and displayed to the user.

[0220] Hardware and software used

[0221] Hardware: General purpose PC or server

[0222] Software: Python, pandas, scikit-learn, TfidfVectorizer, KMeans clustering

[0223] Examples

[0224] For example, if User A enters "I'm feeling stressed at work," the server receives this input and generates related probing questions. When User A answers the questions, the server analyzes the answers and extracts the true nature of the stress. It finds common patterns from the accumulated data and recommends "relaxing music" to User A.

[0225] Examples of prompt statements

[0226] "Please develop an algorithm that analyzes data on users' worries and complaints and recommends optimal stress relief content. Use the following data for clustering and content recommendation."

[0227] In this way, a system can be realized that provides optimal content based on the user's concerns and complaints.

[0228] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0229] Step 1:

[0230] The user terminal receives input from the user. The user inputs their worries or complaints in text format. This input data is sent to the server. An example of input data is "I'm feeling stressed at work."

[0231] Step 2:

[0232] The server analyzes the input data it receives. Specifically, it uses natural language processing technology to tokenize the text data and extract important keywords. Based on the results of this analysis, it generates more probing questions. Using a generative AI model, it automatically generates questions related to the user's input. For example, a question might be generated such as, "In what specific situations do you feel stressed?"

[0233] Step 3:

[0234] The server transmits the generated question to the user terminal. The user terminal presents the question to the user. The user inputs an answer to the presented question. This answer data is also transmitted to the server.

[0235] Step 4:

[0236] The server receives the response data from the user and analyzes it again. The response data is tokenized and important keywords and phrases are extracted. Based on the results of this analysis, the essence of the user's worries and complaints is revealed. For example, the essence that is extracted is "I feel stressed because of communication with my boss."

[0237] Step 5:

[0238] The server stores the extracted essential information in a database. The stored data is used for later analysis. The database stores user IDs, input data, analysis results, etc.

[0239] Step 6:

[0240] The server periodically analyzes the accumulated data to extract common patterns and trends. Specifically, it uses TfidfVectorizer to convert the text data into numerical vectors, and then uses KMeans clustering to classify the data into clusters. This analysis reveals common themes in users' concerns and complaints.

[0241] Step 7:

[0242] The server recommends the most suitable content to the user based on the extracted patterns and trends. For example, it recommends "relaxing music" to a user who is feeling stressed. The recommendation results are sent to the user's device and displayed to the user.

[0243] Step 8:

[0244] The user terminal displays the recommended content sent from the server to the user. The user can reduce worries and stress by using the recommended content. User feedback is also collected and sent to the server.

[0245] In this way, a system is realized that provides optimal content based on the user's concerns and complaints.

[0246] Example 3

[0247] Next, a third embodiment of the third embodiment will be described. 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."

[0248] Conventional systems simply accumulate users' worries and complaints, making it difficult to extract the essence of those issues and provide concrete solutions. In addition, there was a lack of means to effectively analyze the accumulated data and quickly develop new services to solve users' problems. This made it difficult to provide appropriate solutions that met users' needs.

[0249] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0250] In this invention, the server includes a means for receiving input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, a means for analyzing the stored data, a means for generating prompt sentences using a generative AI model based on the analysis results, a means for generating an application using a software development tool based on the generated prompt sentences, and a means for providing the generated application to the user. This makes it possible to effectively extract and analyze the essence of the user's worries and complaints and quickly provide a specific solution.

[0251] "Means for receiving input from the user" refers to a function that provides an interface for users to input issues or concerns into the system in text format.

[0252] "Means for generating probing questions based on received input" refers to a function that analyzes the information entered by the user and automatically generates additional questions to elicit more detailed information.

[0253] The "means for presenting the generated question to the user" is a function for displaying the generated question to the user and providing an interface for enabling the user to answer the question.

[0254] "A means of extracting and storing the essence of users' worries and complaints" refers to a function that analyzes the information provided by users, extracts important elements and essential problems from it, and stores them in a database.

[0255] "Means of analyzing accumulated data" refers to a function that provides algorithms and tools to analyze accumulated data on users' concerns and complaints, and find common patterns and important insights.

[0256] "Means for generating prompt sentences using a generative AI model based on analysis results" refers to a function that uses a generative AI model with analysis results as input to generate prompt sentences to suggest specific solutions to the user's problems.

[0257] "Means for generating an application using a software development tool based on the generated prompt text" refers to a function for automatically generating an application or web service for solving a user's problem using a software development tool based on the generated prompt text.

[0258] "Means for providing the generated application to the user" refers to a function for providing the generated application or web service to the user and carrying out distribution and installation procedures so that the user can use it.

[0259] The present invention relates to a system for developing new services to solve users' concerns and problems. A specific embodiment of this system will be described below.

[0260] System Overview

[0261] The system receives input from users, generates probing questions based on the input, and accumulates the essence of the user's worries and complaints. It analyzes the accumulated data and generates prompts using a generative AI model. Based on the generated prompts, it uses software development tools to generate an application and provides it to the user.

[0262] Hardware and Software Used

[0263] Hardware: Servers, user devices (PCs, smartphones, tablets, etc.)

[0264] Software: Natural language processing libraries (e.g., Python's NLTK library, Google® Cloud Natural Language API), generative AI models (e.g., OpenAI®'s GPT-4®), software development tools (e.g., Visual Studio Code, JetBrains IntelliJ IDEA, etc.)

[0265] Data processing and calculation

[0266] 1. Receiving input from the user

[0267] Users access the system's interface and enter the problem they want to solve into a text box, for example, "I want to reduce work stress."

[0268] 2. Analysis of input data

[0269] The server receives the task entered by the user and analyzes it using natural language processing (NLP) technology. For analysis, it uses Python's NLTK library and Google Cloud Natural Language API. The server analyzes the text "I want to reduce stress at work" and extracts keywords and important phrases.

[0270] 3. Generate prompts using a generative AI model

[0271] The server uses a generative AI model (e.g., OpenAI's GPT-4) based on the analysis results to generate an appropriate prompt sentence, which is intended to suggest a specific solution to the user's problem.

[0272] Examples of prompt statements

[0273] "Develop a relaxation app to help reduce work-related stress. The app should provide guided meditations, deep breathing exercises, and relaxation music to help users relax. It should also include a feature that allows users to record their stress levels and track their progress."

[0274] 4. Generate the application using software development tools

[0275] Based on the generated prompt text, the server uses a software development tool (e.g., Visual Studio Code or JetBrains IntelliJ IDEA) to generate an application or web service to solve the problem. Based on the prompt text, the server automatically generates the code for the relaxation app and builds the application by importing the necessary libraries and frameworks.

[0276] 5. Deliver the generated application to users

[0277] The user checks the application or web service generated by the server and provides feedback if necessary. The user accesses the system dashboard and clicks the download link for the generated application. The user downloads, installs, and actually uses the application.

[0278] In this way, it becomes possible to rapidly develop and provide new services that effectively solve the worries and problems of users. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0279] Step 1:

[0280] A user enters an assignment

[0281] How it works: A user accesses the system's interface and enters the problem they want to solve in a text box, for example, "I want to reduce stress at work."

[0282] Input: The text data of the assignment entered by the user

[0283] Output: The text data of the assignment that is sent to the server.

[0284] Step 2:

[0285] The server analyzes the problem

[0286] How it works: The server receives the tasks entered by the user and parses them using natural language processing (NLP) techniques, such as the NLTK library for Python and the Google Cloud Natural Language API.

[0287] Input: Text data of assignment received from user

[0288] Data processing: tokenizing text and extracting keywords and key phrases

[0289] Output: A list of extracted keywords or phrases.

[0290] Step 3:

[0291] The server generates a prompt using the generative AI model.

[0292] How it works: The server uses a generative AI model (e.g., OpenAI's GPT-4) to generate an appropriate prompt based on the analysis results.

[0293] Input: A list of extracted keywords or phrases

[0294] Data computation: Input keywords or phrases into the generative AI model to generate prompts

[0295] Output: The generated prompt statement

[0296] Step 4:

[0297] The server uses software development tools to generate the application.

[0298] How it works: Based on the generated prompt statements, the server uses a software development tool (e.g., Visual Studio Code or JetBrains IntelliJ IDEA) to generate an application or web service to solve the problem.

[0299] Input: Generated prompt text

[0300] Data processing: Automatically generate code based on prompt statements, import necessary libraries and frameworks, and build applications.

[0301] Output: The generated application code.

[0302] Step 5:

[0303] User Review of Generated Applications

[0304] How it works: The user reviews the application or web service generated by the server and provides feedback if necessary. The user accesses the system dashboard and clicks on the download link for the generated application. The user downloads, installs, and actually uses the application.

[0305] Input: The generated application code

[0306] Output: User feedback data

[0307] (Application example 3)

[0308] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0309] In modern factories, machine maintenance is extremely important to maintain productivity. However, it is not easy to detect machine abnormalities early and perform appropriate maintenance. In particular, if an abnormality is not dealt with promptly, it could have an impact on the entire production line. In addition, there is a lack of preventive maintenance proposals based on past maintenance history, making it difficult to prevent machine breakdowns. To solve these issues, a system is needed that can monitor the status of machines in real time and respond immediately when an abnormality occurs.

[0310] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0311] In this invention, the server includes a means for receiving input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, a means for monitoring the status of machines in a factory in real time, a means for issuing an alert when an abnormality is detected, a means for presenting an appropriate maintenance procedure, and a means for proposing preventive maintenance based on past maintenance history. This makes it possible to detect abnormalities in machines early and respond quickly. Furthermore, by proposing preventive maintenance based on past maintenance history, machine breakdowns can be prevented before they occur.

[0312] A "means for receiving input from a user" is an interface through which a user provides information to the system.

[0313] "Means for generating probing questions based on received input" refers to a function that automatically creates questions to elicit more detailed information based on the information received from the user.

[0314] The "means for presenting the generated question to the user" refers to an interface for displaying the question generated by the system to the user and requesting an answer.

[0315] "A means of extracting and storing the essence of users' worries and complaints" refers to a function that extracts the underlying problems and dissatisfaction from the information provided by the user and stores them in a database.

[0316] A "means for monitoring the status of machines in a factory in real time" is a system that constantly monitors the operating conditions and performance of machines running in a factory and collects data.

[0317] "Means for issuing an alert when an abnormality is detected" refers to a function that immediately issues a warning when an abnormality occurs in the operation of the machine.

[0318] A "means for presenting appropriate maintenance procedures" is a system that provides specific procedures for repair and maintenance when a machine abnormality is detected.

[0319] "A means for proposing preventive maintenance based on past maintenance history" is a function that analyzes records of past maintenance and proposes maintenance measures to prevent future breakdowns.

[0320] In order to implement the present invention, the following system configuration and program are required.

[0321] First, the server has a means for receiving input from the user, which can be an interface through which the user provides information to the system, such as a web form or a voice input system.

[0322] Next, the server has a means for generating probing questions based on the received input, which is a function of analyzing the information received from the user using a generative AI model and automatically creating questions to elicit further detailed information.

[0323] The generated question is displayed to the user through a means for presenting the question to the user. This means utilizes a user interface such as a web browser or a smartphone application to display the question to the user and request an answer.

[0324] The server also includes a means for extracting and storing the essence of the user's worries and complaints. This means is a function for extracting the fundamental problems and complaints from the information provided by the user and storing them in a database.

[0325] In addition, the server is equipped with a means for monitoring the status of machines in the factory in real time. This means is a system that constantly monitors the operating conditions and performance of machines operating in the factory and collects data. Specifically, the status of machines is monitored using sensors and IoT devices.

[0326] If an abnormality is detected, the server will immediately issue an alert using an alerting method such as email notification or push notification on a smartphone to notify the person in charge of the abnormality.

[0327] The server also includes a means for presenting appropriate maintenance procedures. This means is a system that provides specific procedures for repair and maintenance when a machine abnormality is detected. For example, it displays a procedure manual based on past maintenance history and expert knowledge.

[0328] Finally, the server is equipped with a means for proposing preventive maintenance based on past maintenance history. This means that it analyzes the records of past maintenance and proposes maintenance measures to prevent future breakdowns. This makes it possible to prevent machine breakdowns before they occur.

[0329] As a concrete example, we will assume a scenario in which five machines operating in a factory are monitored and maintenance is performed if an abnormality occurs. This system is implemented using Python and functions as a real-time monitoring system.

[0330] Examples of prompt sentences to be input to a generative AI model include the following:

[0331] "Please develop an application that monitors the status of machines in the factory in real time, issues an alert if an abnormality is detected, and presents appropriate maintenance procedures. Please also include a function that suggests preventive maintenance based on past maintenance history."

[0332] In this way, a system for efficiently performing maintenance on machines in a factory can be realized.

[0333] The flow of the specific process in the application example 3 will be described with reference to FIG.

[0334] Step 1:

[0335] The server receives input from the user.

[0336] Input: Information provided by the user (e.g., machine status, concerns, and complaints)

[0337] Output: Received user information

[0338] Specific operation: The server collects information from the user through a web form or a voice input system. For example, when a user reports a machine malfunction, the information is sent to the server.

[0339] Step 2:

[0340] The server generates further probing questions based on the input it receives.

[0341] Input: Information received from the user

[0342] Output: Generated probe questions

[0343] How it works: The server uses a generative AI model to analyze the user's input and automatically generate questions to elicit more detailed information, such as "What time did the machine malfunction occur?"

[0344] Step 3:

[0345] The server presents the generated questions to the user.

[0346] Input: Generated probing questions

[0347] Output: The question presented to the user.

[0348] Specific operation: The server displays the generated questions to the user through a web browser or smartphone application, and the user answers the questions.

[0349] Step 4:

[0350] The server extracts and stores the essence of users' worries and complaints.

[0351] Input: User's answer

[0352] Output: The essence of accumulated worries and complaints

[0353] Specific operation: The server analyzes the user's answers, extracts the underlying problems and dissatisfaction, and stores them in a database. For example, it accumulates essential problems such as "the machine requires frequent maintenance."

[0354] Step 5:

[0355] The server monitors the status of machines in the factory in real time.

[0356] Input: Machine operation data

[0357] Output: Real-time machine condition data

[0358] Specific operation: The server uses sensors and IoT devices to constantly monitor the operating status and performance of machines running in the factory and collect data.

[0359] Step 6:

[0360] The server issues an alert if an anomaly is detected.

[0361] Input: Real-time machine condition data

[0362] Output: Alert notification

[0363] Specific operation: The server analyzes the collected data, and if an abnormality is detected, it alerts the person in charge via email notification or smartphone push notification.

[0364] Step 7:

[0365] The server will suggest appropriate maintenance procedures.

[0366] Input: Detailed information about the anomaly

[0367] Output: Maintenance Procedure

[0368] Specific actions: The server provides specific procedures for repairs and maintenance based on past maintenance history and expert knowledge. For example, it may suggest a procedure such as "Replace part A."

[0369] Step 8:

[0370] The server makes preventive maintenance suggestions based on past maintenance history.

[0371] Input: Past maintenance history

[0372] Output: Preventive maintenance suggestions

[0373] Specific operation: The server analyzes the records of past maintenance and suggests maintenance measures to prevent future breakdowns. For example, it suggests that the next maintenance should be performed in three months.

[0374] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0375] "Example 1"

[0376] As one embodiment of the present invention, a system is provided that includes a means for receiving input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, and an emotion engine for recognizing the user's emotions. Specifically, when a user inputs worries or complaints by voice or text, the system receives the input and analyzes the user's words and emotions using a natural language processing (NLP) algorithm and the emotion engine. Based on the results of this analysis, the system generates questions to understand the essence of the user's worries and complaints more deeply and presents them to the user. When the system receives a user's response, it stores it in a database.

[0377] "Example 2"

[0378] In another embodiment of the present invention, a system is provided that includes a means for finding high-value issues from the nature of accumulated worries and complaints, and an emotion engine that recognizes the emotions of users. Specifically, the system analyzes the data of users' worries and complaints accumulated in a database using a data analysis algorithm and the emotion engine to find common patterns and trends. Based on the analysis results, the system identifies high-value issues that users have in common.

[0379] "Example 3"

[0380] As yet another embodiment of the present invention, a system is provided that includes a means for developing a new service to solve the identified problem and an emotion engine that recognizes the emotions of users. Specifically, the system brainstorms ideas for a new service to solve the identified problem, and uses a software development tool to materialize the ideas and develop the new service. For example, if the system identifies a problem that many users are suffering from work-related stress, it develops a new stress management service to solve the problem.

[0381] The process flow of each embodiment will be described below.

[0382] "Example 1"

[0383] Step 1: The user inputs their concerns or complaints via voice or text.

[0384] Step 2: The system takes this input and uses natural language processing (NLP) algorithms and a sentiment engine to analyze the user's words and sentiment.

[0385] Step 3: Based on the analysis results, the system generates questions to better understand the nature of the user's concerns and complaints and presents them to the user.

[0386] Step 4: After receiving the user's response, the system stores it in the database.

[0387] Step 1: The system analyzes the user's worries and complaints data accumulated in the database using data analysis algorithms and an emotion engine.

[0388] Step 2: Analyze to find common patterns and trends.

[0389] Step 3: Based on the analysis results, the system identifies high-value issues that users have in common.

[0390] "Example 3"

[0391] Step 1: The system brainstorms new service ideas to solve the identified problems.

[0392] Step 2: Use software development tools to materialize the idea and develop a new service.

[0393] Step 3: For example, if the system identifies an issue where many users are suffering from work-related stress, it will develop a new stress management service to resolve this issue.

[0394] Example 1

[0395] Next, a description will be given of Example 1 of the embodiment 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".

[0396] With conventional systems, it was difficult to effectively collect users' concerns and complaints and understand their essence. In addition, the process of generating appropriate questions in response to user input and storing the user's responses in a database was not automated, making it impossible to extract the essence of users' concerns and complaints. Furthermore, there was a lack of means to find high-value issues from the accumulated data and develop new services to solve them.

[0397] 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.

[0398] In this invention, the server includes a means for receiving input from a user, a means for analyzing the received input using a natural language processing algorithm, a means for generating questions that probe deeper based on the analysis results, a means for presenting the generated questions to the user, and a means for storing the user's responses in a database. This makes it possible to effectively collect the user's worries and complaints and understand their essence. It is also possible to find high-value issues from the stored data and develop new services that solve them.

[0399] "Means for receiving input from the user" refers to an interface through which the user can input their concerns or complaints via voice or text.

[0400] A "natural language processing algorithm" is an algorithm that analyzes text data received from a user and extracts keywords and emotions.

[0401] "Means of generating questions" refers to the process of generating questions to dig deeper into the user's concerns or complaints based on the analysis results.

[0402] A "means for presenting a question to a user" is an interface that displays the generated question to the user and allows the user to answer it.

[0403] "Means of storing user responses in a database" refers to the process of storing user responses or responses in a database for later analysis and use.

[0404] "Means of finding high-value issues" is the process for identifying important problems and issues from accumulated data.

[0405] "Means of developing new services" refers to the process of designing and developing new services and solutions to solve the problems identified.

[0406] The present invention is a system that receives input from a user, generates probing questions based on the input, and accumulates the user's responses in a database. A specific embodiment of this system will be described below.

[0407] Hardware and Software Configuration

[0408] A way to receive input from the user

[0409] Users input their worries and complaints using a voice recognition system and a text input interface. The voice recognition system uses a general voice recognition API, and the text input interface uses an HTML form.

[0410] Analyzing input data

[0411] The server passes the text data received from the user to a natural language processing (NLP) algorithm using the Python libraries NLTK and spaCy. The NLP algorithm extracts keywords and sentiment from the user's input.

[0412] Question Generation

[0413] The server generates questions to dig deeper into the user's concerns and complaints based on the extracted keywords and emotions. A generative AI model is used to generate these questions. Specifically, the following prompt sentences are input to the generative AI model:

[0414] User input: "Work has been busy and stressful lately."

[0415] Prompt: "User is talking about work stress. Generate questions to explore further."

[0416] Posing the Question

[0417] The server presents the generated questions to the user, who again inputs answers by voice or text, or, for example, displays the questions on a web page and allows the user to answer them.

[0418] Accumulation of data

[0419] The server stores the user's responses in a database. For this database, a general relational database such as MySQL or PostgreSQL is used.

[0420] Examples

[0421] For example, if a user enters, "I've been busy at work lately and I'm feeling stressed," the system will process it as follows:

[0422] 1. The user types, "I've been busy at work lately and I'm feeling stressed" through a text input interface.

[0423] 2. The server receives this text data and uses NLP algorithms (NLTK or spaCy) to extract keywords such as "work," "busy," and "stress."

[0424] 3. The server inputs the following prompt to the generative AI model:

[0425] User input: "Work has been busy and stressful lately."

[0426] Prompt: "User is talking about work stress. Generate questions to explore further."

[0427] 4. The generative AI model generates questions such as, "What specific tasks cause you stress?"

[0428] 5. The server presents this question to the user and receives the user's answer again.

[0429] 6. The server stores the user's answers in a database.

[0430] In this way, the system can extract the essence of users' worries and complaints and store them in a database. This data can later be used to identify high-value issues and develop new services to solve them.

[0431] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0432] Step 1:

[0433] The user inputs his / her worries or complaints using a voice recognition system or a text input interface. The input data is sent to the server. For example, the user inputs, "I've been busy at work lately and I'm stressed."

[0434] Input: User voice or text data

[0435] Output: Text data sent to the server

[0436] Step 2:

[0437] The server passes the received text data to a natural language processing (NLP) algorithm, using Python libraries such as NLTK and spaCy. The server extracts keywords and sentiment from the text data.

[0438] Input: Text data sent to the server

[0439] Output: Extracted keywords and sentiments

[0440] Step 3:

[0441] The server inputs a prompt sentence into the generative AI model based on the extracted keywords and emotions. The generative AI model generates questions to dig deeper into the user's worries and complaints. For example, the following prompt sentence is input into the generative AI model:

[0442] User input: "Work has been busy and stressful lately."

[0443] Prompt: "User is talking about work stress. Generate questions to explore further."

[0444] Input: Extracted keywords and sentiments

[0445] Output: Generated questions

[0446] Step 4:

[0447] The server presents the generated questions to the user, who then answers the questions by voice or text, or, for example, by displaying the questions on a web page and allowing the user to answer them.

[0448] Input: Generated Question

[0449] Output: The question presented to the user.

[0450] Step 5:

[0451] The server receives the user's answer and stores it in a database. For this database, a general relational database such as MySQL or PostgreSQL is used.

[0452] Input: User's answer

[0453] Output: Data stored in the database

[0454] In this way, the system can extract the essence of the user's worries and complaints and store them in a database. The accumulated data can later be used to identify high-value issues and develop new services to solve them.

[0455] (Application example 1)

[0456] Next, a description will be given of Application Example 1 of Form 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."

[0457] In modern society, users have a wide range of security-related concerns and anxieties. However, there are only a limited number of systems that can properly analyze these concerns and anxieties and propose specific countermeasures. In particular, there is a need for a system that can provide more effective security measures by generating in-depth questions that take the user's emotions into account and accumulating the user's responses in a database.

[0458] The identification process by the identification processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means. In this invention, the server includes a means for receiving an input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, a means for converting the user's voice into text using a voice recognition system, a means for analyzing the user's input and extracting emotions using a natural language processing algorithm, a means for generating appropriate questions based on the extracted emotions, and a means for storing the user's responses in a database. This makes it possible to effectively analyze the user's security-related worries and anxieties and propose specific measures.

[0459] A "means for receiving input from a user" is an interface for receiving information input by a user via voice or text.

[0460] The "means for generating probing questions based on the received input" is an algorithm that analyzes the input data received from the user and generates questions to elicit more detailed information.

[0461] The "means for presenting the generated question to the user" refers to an interface for displaying or audibly conveying the generated question to the user.

[0462] "A means of extracting and storing the essence of users' worries and complaints" is a system for extracting important information from user input and storing it in a database.

[0463] "Means of converting a user's voice into text using a voice recognition system" refers to technology for converting information input by a user through voice into text data.

[0464] "Means for analyzing user input and extracting emotions using a natural language processing algorithm" refers to an algorithm for analyzing a user's text input and extracting emotions and keywords from it.

[0465] The "means for generating appropriate questions based on extracted emotions" is a system for generating appropriate questions for a user based on extracted emotion information.

[0466] The "means for storing user responses in a database" refers to a system for storing the responses that users give to generated questions in a database.

[0467] As an embodiment of the present invention, the security consultation assistant system can be installed on a smartphone or smart glasses. This system allows users to input their security-related worries and anxieties by voice or text, analyzes them, and generates appropriate questions to present to the user.

[0468] Hardware and Software Used

[0469] Hardware: Smartphones, smart glasses

[0470] Software: Python, SpeechRecognition library, Transformers library, SQLite database

[0471] System Configuration

[0472] 1. Speech Recognition System: The SpeechRecognition library is used to convert the information input by the user through voice into text, so that the user's voice input is taken into the system as text data.

[0473] 2. Natural Language Processing Algorithms: To analyze the user's text input and extract sentiment and keywords, we use the Transformers library, which provides a detailed analysis of the user's input.

[0474] 3. Question generation system: Based on the extracted emotion information, appropriate questions are generated, allowing us to dig deeper into the user's concerns and worries.

[0475] 4. Database System: A SQLite database is used to store user inputs, generated questions, and user responses, which will then be used to store data for future recommendations.

[0476] Processing flow

[0477] 1. Receive user input: The user inputs their security concerns or fears via voice or text.

[0478] 2. Speech Recognition: For voice input, the SpeechRecognition library is used to convert speech to text.

[0479] 3. Natural Language Processing: The converted text data is analyzed using the Transformers library to extract sentiment and keywords.

[0480] 4. Question generation: Generate appropriate questions based on the extracted sentiment information.

[0481] 5. Ask a question: Present the generated question to the user to elicit further information.

[0482] 6. Database storage: User responses are stored in a SQLite database.

[0483] Examples

[0484] If a user says, "I've been worried about the security of my home lately," the system converts this into text and analyzes it with an NLP model. If the analysis results indicate a negative sentiment, the system generates a question for the user: "What specifically are you worried about?"

[0485] Examples of prompt statements

[0486] User: I've been worried about the security of my home lately.

[0487] System: What specifically are you worried about?

[0488] In this way, it is possible to dig deeper into the user's concerns and worries and provide appropriate advice.

[0489] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0490] Step 1:

[0491] The user inputs their security concerns or worries by voice or text. The input data is sent to the server through the interface of the smartphone or smart glasses. The input data is in the form of voice data or text data.

[0492] Step 2:

[0493] In the case of voice input, the server converts the voice data into text data using the SpeechRecognition library. Specifically, the server receives the voice data, and the voice recognition engine analyzes the voice waveform and generates the corresponding text. The output is text data.

[0494] Step 3:

[0495] The server analyzes the text data using the Transformers library. Specifically, the text data is input into an NLP model to extract emotions and keywords. The input is text data, and the output is emotion information and keywords.

[0496] Step 4:

[0497] The server generates appropriate questions based on the extracted emotion information. Specifically, if the emotion information is negative, it generates questions that will further dig into the user's anxiety. The input is emotion information and keywords, and the output is the generated question.

[0498] Step 5:

[0499] The server presents the generated question to the user. Specifically, the server displays the question on the display of a smartphone or smart glasses, or plays the question by voice. The input is the generated question, and the output is the question presented to the user.

[0500] Step 6:

[0501] The user inputs answers to the questions presented to them. The input data is then sent to the server again through the interface of the smartphone or smart glasses. The input data can be in the form of voice data or text data.

[0502] Step 7:

[0503] The server accumulates the user's answers in a database. Specifically, it saves the user's answers in a SQLite database. The input is the user's answer data, and the output is saving to the database.

[0504] In this way, it is possible to dig deeper into the user's concerns and worries and provide appropriate advice.

[0505] Example 2

[0506] Next, a second embodiment of the second embodiment will be described. 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."

[0507] In modern society, many users have various worries and complaints in their daily lives and work. However, it is difficult to understand the essence of these worries and complaints and identify high-value issues. Conventional systems are unable to accurately analyze users' emotions and find common patterns and trends, and as a result, are unable to provide useful information. Therefore, there is a need for a system that can draw out the essence of users' worries and complaints and identify high-value issues.

[0508] 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.

[0509] In this invention, the server includes means for receiving input from a user, means for generating probing questions based on the received input, means for presenting the generated questions to the user, means for extracting and storing the essence of the user's worries and complaints, means for executing a data analysis algorithm for analyzing the stored data, means for executing an emotion engine for analyzing the user's emotions, means for integrating the results of the data analysis algorithm and the emotion engine, means for identifying high-value issues from the integrated results, and means for generating the identified issues as a report. This makes it possible to accurately analyze the essence of the user's worries and complaints and identify high-value issues.

[0510] "Means for receiving input from a user" refers to a function that provides an interface for a user to input worries or complaints in text form and transmits that input to the system.

[0511] "Means for generating probing questions based on received input" refers to a function that analyzes the user's initial input and automatically generates follow-up questions to elicit more detailed information.

[0512] The "means for presenting the generated question to the user" is a function for displaying the generated additional question to the user and enabling the user to answer it.

[0513] "A means of extracting and storing the essence of users' worries and complaints" refers to a function that analyzes the answers obtained from users and stores the essential content in a database.

[0514] The "means for executing a data analysis algorithm for analyzing the stored data" refers to a function for executing an algorithm for extracting patterns and trends using the stored data.

[0515] "Means for executing an emotion engine to analyze user emotions" refers to a function for analyzing emotions from a user's text data and classifying them as either positive, negative, or neutral.

[0516] The "means for integrating the results of the data analysis algorithm and the emotion engine" is a function for integrating the analysis results of the data analysis algorithm and the emotion engine to generate a comprehensive analysis result.

[0517] "Means of identifying high-value issues from integrated results" refers to a function that identifies high-value issues that users commonly face based on integrated analysis results.

[0518] The "means for generating a report on the identified issues" is a function for generating a detailed report on the identified issues and outputting it in PDF format or web page format.

[0519] The present invention is a system for analyzing the worries and complaints of a user and identifying high-value issues. A specific embodiment of this system will be described below.

[0520] 1. System Configuration

[0521] The system consists of a terminal that receives user input, a server that analyzes the data, and a database that stores the data. The terminal provides an interface for users to input their worries and complaints. The server executes data analysis algorithms and an emotion engine, and integrates the analysis results. The database stores the user input data.

[0522] 2. Hardware and Software Used

[0523] The server is a computer equipped with a high-performance processor and large memory. Relational databases such as MySQL and PostgreSQL are used for the database. Python's Pandas and Scikit-learn are used for the data analysis algorithm. NLTK and Transformers, which are NLP (natural language processing) libraries, are used for the emotion engine.

[0524] 3. Data processing and calculation

[0525] The text data of worries and complaints entered by users through their devices is sent to a server. The server stores the received data in a database. The server then runs a data analysis algorithm to extract frequently occurring keywords and phrases from the text data. It also runs an emotion engine to analyze the user's emotions from the text data. The results of these analyses are integrated to find common patterns and trends.

[0526] 4. Specific Examples

[0527] For example, if a user responds to the prompt "Tell us about any recent complaints about work stress" by typing "I work a lot of overtime every day and I can't get rid of my fatigue," the device will send this text data to the server. The server will store this data in a database and analyze it using a data analysis algorithm and emotion engine. The analysis results will reveal that the keyword "work stress" appears frequently and that there are many negative emotions associated with it. The server will integrate these results and identify a high-value issue, "Many users are feeling work stress." Finally, the server will generate a report recommending the development of a new service to solve this issue.

[0528] In this way, the system can identify high-value issues from users' concerns and complaints and provide useful information to companies and service providers.

[0529] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0530] Program processing flow

[0531] Step 1: User enters their concerns or complaints

[0532] Users input their worries or complaints in text format through a dedicated application or web form. For example, they might input something like, "I've been busy at work lately and I'm feeling stressed."

[0533] Input: Text data of user's worries and complaints

[0534] Output: The text data entered

[0535] Step 2: The device sends the input data to the server

[0536] The terminal (user's smartphone or PC) sends the entered text data to the server. At this time, the data is encrypted before being sent.

[0537] Input: Text data entered by the user.

[0538] Output: Text data sent to the server

[0539] Step 3: The server stores the data in a database

[0540] The server stores the received text data in a database, which is a relational database.

[0541] Input: Text data sent to the server

[0542] Output: Text data stored in the database

[0543] Step 4: The server runs the data analysis algorithms

[0544] The server runs data analysis algorithms using Python's Pandas and Scikit-learn to extract frequently occurring keywords and phrases from the text data and find patterns and trends.

[0545] Input: Text data stored in a database

[0546] Output: Extracted keywords, phrases, patterns and trends

[0547] Step 5: The server runs the emotion engine

[0548] The server runs an emotion engine using NLP libraries such as NLTK and Transformers, which analyzes user emotions from text data and classifies them as positive, negative, or neutral.

[0549] Input: Text data stored in a database

[0550] Output: Parsed sentiment data (positive, negative, neutral)

[0551] Step 6: The server consolidates the analysis results

[0552] The server combines the results of the data analysis algorithm and the emotion engine. For example, if the keyword "work stress" appears frequently and has a high proportion of negative emotions, it will combine these into a single analysis result.

[0553] Input: Extracted keywords and phrases, parsed sentiment data

[0554] Output: Integrated analysis results

[0555] Step 7: Identify high-value issues for your server

[0556] The server identifies high-value issues from the integrated analysis results. For example, it identifies an issue such as "Many users are feeling stressed at work."

[0557] Input: Integrated analysis results

[0558] Output: High-value issues identified

[0559] Step 8: Generate a report of the issues the server identifies

[0560] The server generates a report of the identified issues, which includes an overview of the issues, detailed analysis results, recommended measures, etc. The report is output in PDF format or web page format.

[0561] Input: Identified high-value issues

[0562] Output: Generated report (in PDF and / or web page format)

[0563] (Application example 2)

[0564] Next, a description will be given of Application Example 2 of Form 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."

[0565] In traditional brick-and-mortar stores, there was a lack of effective ways to collect and analyze customer feedback, which often delayed improvements to store operations and the development of new services. It was also difficult to accurately recognize customer emotions and find common patterns and trends. This meant that customer satisfaction and the efficiency of store operations were not fully achieved.

[0566] 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.

[0567] In this invention, the server includes a means for receiving input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, a means for analyzing the stored data with a data analysis algorithm, a means for recognizing the user's emotions using an emotion engine, a means for finding common patterns and trends based on the analysis results, and a means for proposing improvements to store operations and new services based on the patterns and trends found. This makes it possible to effectively collect and analyze customer feedback and quickly develop improvements to store operations and new services.

[0568] "Means for receiving input from the user" refers to a function that provides an interface for users to input feedback and opinions into the system.

[0569] "Means for generating probing questions based on received input" refers to a function that analyzes initial input from a user and automatically generates follow-up questions to elicit more detailed information.

[0570] The "means for presenting the generated question to the user" is a function for displaying the generated question to the user and enabling the user to answer it.

[0571] "A means of extracting and storing the essence of users' concerns and complaints" refers to a function that analyzes user feedback, extracts important information and common issues from it, and stores them in a database.

[0572] "Means for analyzing accumulated data using a data analysis algorithm" refers to a function that uses accumulated feedback data to perform data analysis to find patterns and trends.

[0573] The "means for recognizing a user's emotions using an emotion engine" is a function for analyzing the emotions contained in the user's feedback and recognizing the type and intensity of those emotions.

[0574] "Means of finding common patterns and trends based on analysis results" refers to a function that integrates the results of data analysis and emotion recognition to identify common patterns and trends in user feedback.

[0575] "Means for proposing improvements to store operations and new services based on identified patterns and trends" is a function for proposing improvements to store operations and the development of new services based on identified patterns and trends.

[0576] The system for implementing the present invention provides a series of functions for collecting and analyzing feedback from users. A specific embodiment of the system will be described below.

[0577] System Configuration

[0578] The system consists of a user terminal, a server, a database, and an analysis engine. The user terminal is a device such as a smartphone or tablet, and provides an interface for users to input feedback. The server receives input from users, stores it in the database, and analyzes the data using the analysis engine.

[0579] Hardware and Software Used

[0580] User devices: Smartphones, tablets

[0581] Server: A server with a powerful processor and large memory capacity

[0582] Database: Relational database such as MySQL, PostgreSQL, etc.

[0583] Analysis engine: Data analysis algorithms using programming languages ​​such as Python and R

[0584] Sentiment engines: Natural language processing libraries such as TextBlob and NLTK

[0585] Data processing and calculation

[0586] The server receives the feedback data sent from the user's device and stores it in a database. It then uses a data analysis algorithm to analyze the accumulated data and extract common patterns and trends. It uses an emotion engine to analyze the emotions contained in the feedback and recognize the type and intensity of those emotions. Based on the analysis results, it finds common patterns and trends and suggests improvements to store operations and new services.

[0587] Examples

[0588] For example, if a certain brick-and-mortar store receives a lot of feedback that its staff are slow to respond, the system will analyze the feedback and suggest specific measures to improve staff response speed. These suggestions are made possible by using data analysis algorithms and an emotion engine to extract common patterns and trends from the feedback data.

[0589] Examples of prompt statements

[0590] "Develop an application that collects customer feedback from brick-and-mortar stores, analyzes it using data analysis algorithms and sentiment engines, and proposes improvements to store operations and new services."

[0591] In this way, the system can effectively collect and analyze customer feedback, enabling it to quickly improve store operations and develop new services.

[0592] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0593] Step 1:

[0594] The user inputs feedback using a terminal such as a smartphone or a tablet. The input feedback is transmitted from the terminal to a server. The input data includes the feedback in text format.

[0595] Step 2:

[0596] The server receives the feedback data sent from the terminal and stores it in a database. The input data is the feedback in text format, and the output is the feedback data stored in the database.

[0597] Step 3:

[0598] The server analyzes the feedback data stored in the database with data analysis algorithms, specifically, extracting common patterns and trends from the feedback data using programming languages ​​such as Python and R. The input data is the feedback data stored in the database, and the output is the extracted patterns and trends.

[0599] Step 4:

[0600] The server uses an emotion engine to analyze the emotions contained in the feedback. Specifically, it uses natural language processing libraries such as TextBlob and NLTK to recognize the type and intensity of the emotion in the feedback. The input data is the feedback text, and the output is the type and intensity of the recognized emotion.

[0601] Step 5:

[0602] The server integrates the results of the data analysis algorithms and the emotion engine to find common patterns and trends. The input data are the extracted patterns and trends, as well as the type and intensity of the recognized emotions, and the output is the integrated analysis results.

[0603] Step 6:

[0604] The server proposes improvements to store operations and new services based on the integrated analysis results. Specifically, it proposes which parts of store operations should be improved or what new services should be introduced based on the analysis results. The input data are the integrated analysis results, and the output is the proposed improvements and new services.

[0605] In this way, the system can effectively collect and analyze customer feedback, enabling it to quickly improve store operations and develop new services.

[0606] Example 3

[0607] Next, a third embodiment of the third embodiment will be described. 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."

[0608] Conventional systems could extract users' concerns and complaints, but they lacked the process to develop specific services based on those concerns. They also lacked the means to recognize users' emotions, collect feedback, and improve services. This made it difficult to provide services that met users' needs.

[0609] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0610] In this invention, the server includes means for receiving input from a user, means for generating probing questions based on the received input, means for presenting the generated questions to the user, means for extracting and storing the essence of the user's worries and complaints, means for finding high-value issues from the essence of the accumulated worries and complaints, means for generating ideas for solving the issues found, means for developing the generated ideas into specific services, means for recognizing the user's emotions, and means for collecting user feedback and improving the service. This makes it possible to rapidly develop and provide specific services that meet the needs of users.

[0611] "Means for receiving input from the user" refers to a function that provides an interface for users to input their issues and concerns into the system.

[0612] "Means for generating probing questions based on received input" refers to a function that automatically generates questions to elicit more detailed information based on the initial input information received from the user.

[0613] The "means for presenting the generated question to the user" is a function for displaying the generated question to the user and enabling the user to answer it.

[0614] "Means of extracting and storing the essence of the user's worries and complaints" refers to a function that extracts the essential problems and worries from the information provided by the user and stores them in a database, etc.

[0615] "A means of finding high-value issues from the essence of accumulated worries and complaints" is a function that analyzes accumulated data and identifies issues that are high-value to solve.

[0616] The "means for generating ideas for solving the identified problems" is a function for generating solution ideas for the identified problems.

[0617] "Means for developing the generated ideas into concrete services" refers to a function for developing concrete applications or web services based on the generated ideas.

[0618] "Means of recognizing user emotions" refers to a function for analyzing and recognizing emotions from the user's facial expressions, voice, etc.

[0619] "Means of collecting user feedback and improving the service" refers to a function for collecting feedback from users, identifying areas for improvement to the service based on that feedback, and incorporating that into the next version.

[0620] The present invention relates to a system for developing new services to solve users' concerns and problems. A specific embodiment of this system will be described below.

[0621] System Configuration

[0622] Hardware and Software

[0623] The system is implemented using the following hardware and software.

[0624] Server: Use a server with a powerful processor and large memory capacity. For example, you can use a cloud-based server.

[0625] Terminal: A device that allows a user to input information, such as a computer, smartphone, or tablet.

[0626] Software: Visual Studio and Eclipse are used as software development tools, and IBM Watson (registered trademark) and Microsoft (registered trademark) Azure (registered trademark) Emotion API are used as emotion engines.

[0627] Program Processing

[0628] Entering an assignment

[0629] The user inputs the problem they want to solve into the system via a terminal. For example, they can input a problem such as "I want to reduce stress at work."

[0630] Idea generation

[0631] The server receives the task input by the user and inputs a prompt sentence into the generative AI model. An example of a prompt sentence is "Please propose an idea for a new service to reduce work stress." The generative AI model generates multiple ideas based on the prompt sentence. For example, it suggests ideas such as "relaxation apps" and "stress management tools."

[0632] Making ideas a reality

[0633] The server selects the best ideas from the generated ideas and develops them into concrete applications or web services using software development tools (e.g. Visual Studio, Eclipse). Specifically, the server performs the following steps:

[0634] Creating a Project: Start Visual Studio and create a new project.

[0635] Feature implementation: For a relaxation app, implement music playback and guided meditation features.

[0636] Testing and debugging: Test the developed application and fix any bugs.

[0637] Using the Emotion Engine

[0638] The server uses an emotion engine (e.g. IBM Watson, Microsoft Azure Emotion API) to recognize the user's emotions. When the user uses the application, the emotion engine analyzes the user's emotions from their facial expressions and voice. Specifically, the server performs the following steps:

[0639] Emotion data collection: Collect emotion data through the camera and microphone while the user is using the application.

[0640] Sentiment analysis: The collected data is sent to the sentiment engine to analyze the user's sentiment.

[0641] Feedback generation: Based on the analysis results, feedback is generated according to the user's sentiment.

[0642] Providing services and collecting feedback

[0643] The server provides the developed service to the user. The user uses the service through the terminal and provides feedback. Specifically, the server performs the following steps:

[0644] Deploying services: Deploy the developed application on a cloud server and make it accessible to users.

[0645] Feedback collection: When you use the Service, we collect emotional data collected through our emotion engine and directly entered feedback.

[0646] Service Improvement: Based on the feedback collected, we identify areas for improvement in our service and incorporate them into the development of the next version.

[0647] The above is a specific embodiment of this system. This system makes it possible to rapidly develop and provide specific services that meet the needs of users. The flow of the specific process in the third embodiment will be described with reference to FIG.

[0648] Step 1:

[0649] Entering an assignment

[0650] A user inputs a problem to be solved into the system through a terminal. For example, the user inputs a problem such as "I want to reduce work stress." The input problem is sent to the server.

[0651] Input: User's problem (e.g., "I want to reduce stress at work")

[0652] Output: The issue data sent to the server.

[0653] Step 2:

[0654] Idea generation

[0655] The server inputs a prompt sentence into the generative AI model based on the task data received from the user. An example of a prompt sentence is "Please propose an idea for a new service to reduce work stress." The generative AI model generates multiple ideas based on the prompt sentence.

[0656] Input: User problem data, prompt (e.g., "Please suggest new service ideas to reduce work stress.")

[0657] Output: Generated ideas (e.g. "Relaxation app", "Stress management tool")

[0658] Step 3:

[0659] Making ideas a reality

[0660] The server selects the best ideas from the generated ideas and develops them into concrete applications or web services using software development tools (e.g. Visual Studio, Eclipse). Specifically, the server performs the following steps:

[0661] Creating a Project: Start Visual Studio and create a new project.

[0662] Feature implementation: For a relaxation app, implement music playback and guided meditation features.

[0663] Testing and debugging: Test the developed application and fix any bugs.

[0664] Input: Generated idea (e.g. "Relaxation app")

[0665] Output: Developed application (e.g. "Relaxation App")

[0666] Step 4:

[0667] Using the Emotion Engine

[0668] The server uses an emotion engine (e.g. IBM Watson, Microsoft Azure Emotion API) to recognize the user's emotions. When the user uses the application, the emotion engine analyzes the user's emotions from their facial expressions and voice. Specifically, the server performs the following steps:

[0669] Emotion data collection: Emotion data is collected through the camera and microphone while the user is using the application.

[0670] Sentiment analysis: The collected data is sent to the sentiment engine to analyze the user's sentiment.

[0671] Feedback generation: Based on the analysis results, feedback is generated according to the user's emotions.

[0672] Input: User emotion data (e.g. facial expressions, voice)

[0673] Output: Sentiment analysis results, feedback

[0674] Step 5:

[0675] Providing services and collecting feedback

[0676] The server provides the developed service to the user. The user uses the service through the terminal and provides feedback. Specifically, the server performs the following steps:

[0677] Deploying services: Deploy the developed application on a cloud server and make it accessible to users.

[0678] Feedback collection: When users use the service, we collect emotional data collected through the emotion engine and directly entered feedback.

[0679] Service Improvement: Based on the feedback collected, we identify areas for improvement in our service and incorporate them into the development of the next version.

[0680] Input: User feedback, emotion data

[0681] Output: Improved service

[0682] (Application example 3)

[0683] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0684] In modern brick-and-mortar stores, there is a demand for providing personalized services according to customers' emotions and needs. However, conventional systems have the challenge of being unable to recognize customers' emotions in real time and suggest optimal products and services based on those emotions. This has resulted in insufficient improvements to customer experience.

[0685] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0686] In this invention, the server includes a means for receiving input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, a means for recognizing the user's emotions, and a means for proposing optimal products and services based on the recognized emotions. This makes it possible to propose personalized products and services based on the customer's emotions.

[0687] A "means for receiving input from a user" is an interface through which a user provides information to the system.

[0688] "Means for generating probing questions based on received input" refers to a function that analyzes information received from a user and automatically generates questions to elicit more detailed information.

[0689] The "means for presenting the generated question to the user" refers to an interface for displaying the question generated by the system to the user and prompting the user to answer.

[0690] "Means of extracting and storing the essence of users' worries and complaints" refers to a function that extracts the essential problems and dissatisfactions from the information provided by users and stores them in a database.

[0691] "Means of recognizing the user's emotions" refers to technology that analyzes the user's emotions from their facial expressions, voice, etc., and grasps their state in real time.

[0692] The "means for suggesting optimal products and services based on recognized emotions" refers to an algorithm for selecting and suggesting optimal products and services based on the user's emotional state.

[0693] The system for implementing the present invention includes a series of means for recognizing a user's emotion and proposing optimal products and services based on the emotion. A specific embodiment of the system will be described below.

[0694] Hardware and Software Used

[0695] Hardware

[0696] Smart glasses: Equipped with a camera to capture the customer's face.

[0697] Head-mounted display: Equipped with a display to capture the customer's face and display information.

[0698] software

[0699] Emotion recognition engine: Analyzes emotions from captured images using, for example, the Microsoft Azure Emotion API.

[0700] Software development tools: Use Visual Studio and Unity to develop the entire system program.

[0701] Data processing and calculation

[0702] emotion recognition

[0703] 1. Capture: Capture the customer's face using a camera mounted on smart glasses or a head-mounted display.

[0704] 2. Analysis: The captured image is sent to the emotion recognition engine to analyze the emotions in real time.

[0705] 3. Data acquisition: Acquire emotion data (e.g., happiness, sadness, surprise, anger, etc.).

[0706] Data Processing

[0707] 1. Evaluate: Evaluate the customer’s current emotional state based on the captured emotion data.

[0708] 2. Suggestion generation: Generate a list of products or services depending on the emotional state.

[0709] suggestion

[0710] 1. Display: Display emotion-based product and service suggestions on the screen of smart glasses or head-mounted displays.

[0711] 2. Provide More Information: If the customer is interested, provide them with more information and instructions on how to purchase.

[0712] Examples

[0713] For example, if a customer is feeling stressed, they can be offered relaxation-related products (aromatherapy oils, massage chairs, etc.) or services (directions to a relaxation room).

[0714] Examples of prompt statements

[0715] "If a customer is feeling stressed, develop an application that suggests relaxation-related products and services. The hardware used will be smart glasses, and the Microsoft Azure Emotion API will be used as the emotion recognition engine. Specific suggestions will include offering aroma oils, massage chairs, and relaxation rooms."

[0716] In this way, personalized product and service recommendations can be made based on customer emotions.

[0717] The flow of the specific process in the application example 3 will be described with reference to FIG.

[0718] Step 1:

[0719] Capture

[0720] The terminal (smart glasses or head-mounted display) captures the customer's face with a camera. The input is the customer's face image, and the output is the captured image data. This image data is used for subsequent emotion recognition processing.

[0721] Step 2:

[0722] Emotion analysis

[0723] The server sends the captured image data to an emotion recognition engine (e.g., Microsoft Azure Emotion API). The input is the image data, and the output is the analyzed emotion data (e.g., happiness, sadness, surprise, anger, etc.). The emotion recognition engine analyzes the image data and generates a score for each emotion.

[0724] Step 3:

[0725] Emotional assessment

[0726] The server evaluates the customer's current emotional state based on the acquired emotional data. The input is the emotional data, and the output is the evaluated emotional state (e.g., stressed, relaxed, etc.). The server analyzes the scores of the emotional data and identifies the strongest emotion.

[0727] Step 4:

[0728] Proposal generation

[0729] The server generates a list of optimal products and services according to the evaluated emotional state. The input is the evaluated emotional state, and the output is a suggestion list. The server searches a database for relevant products and services based on the emotional state and creates the list.

[0730] Step 5:

[0731] Suggestion display

[0732] The terminal displays the generated suggestion list on the display of the smart glasses or head-mounted display. The input is the suggestion list, and the output is the suggestion information displayed on the display. The terminal displays the suggestion contents so that the user can visually confirm them.

[0733] Step 6:

[0734] Provide detailed information

[0735] The server provides detailed information and purchase procedure guidance for the proposals that the user is interested in. The input is the user's selection information, and the output is detailed information and purchase procedure guidance. The server retrieves the detailed information from the database based on the user's selection and sends it to the terminal.

[0736] In this way, the system can make personalized product and service recommendations based on customer sentiment.

[0737] The specific processing unit 290 transmits the result of the specific processing to the smart device 14.

[0738] In the device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0739] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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.

[0740] Another example of generative AI is Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0741] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0742] [Second embodiment]

[0743] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0744] 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.

[0745] 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 wide area network (WAN) and / or a local area network (LAN).

[0746] 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.

[0747] 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 the voice according to instructions from the processor 46.

[0748] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0749] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0750] Fig. 4 shows an example of 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.

[0751] 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.

[0752] 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.

[0753] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0754] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0755] "Example 1"

[0756] An embodiment of the present invention includes a voice recognition system and a text input interface as a means for receiving input from a user. This system receives the user's worries and complaints as text data. Next, a natural language processing (NLP) algorithm is used as a means for generating probing questions based on the received input. This algorithm extracts keywords and emotions from the user's input and generates questions based on them. The generated questions are presented to the user to elicit a response from the user. This allows the essence of the user's worries and complaints to be extracted and stored in a database.

[0757] "Example 2"

[0758] A data analysis algorithm is used to find high-value issues from the nature of the accumulated worries and complaints. This algorithm extracts patterns and trends from the accumulated data and identifies high-value issues based on them. For example, if many users complain about work-related stress, it can determine that a new service needs to be developed to solve this issue.

[0759] "Example 3"

[0760] Software development tools are used to develop new services that solve the problems that have been identified. These tools develop applications and web services to solve specific problems. For example, a relaxation app to reduce work stress or a project management tool to help manage tasks efficiently can be developed.

[0761] The process flow of each embodiment will be described below.

[0762] "Example 1"

[0763] Step 1: Receive voice or text input from the user. In this step, the user inputs their concerns or complaints through voice or text.

[0764] Step 2: Run the received input through a natural language processing (NLP) algorithm to extract keywords and sentiment from the user's input. In this step, NLP algorithms are used to analyze the text data in order to understand the essence of the user's concerns and complaints.

[0765] Step 3: Generate probing questions based on the extracted keywords and sentiments and present them to the user. In this step, the generated questions are presented to the user to elicit a response.

[0766] Step 4: Receive user responses and store the essence of their concerns and complaints in a database. In this step, the user responses are stored in a database and accumulated for later analysis.

[0767] "Example 2"

[0768] Step 1: Run the accumulated data through a data analysis algorithm to extract patterns and trends. In this step, common patterns and trends are found in the accumulated data on users' concerns and complaints.

[0769] Step 2: Identify high-value issues based on extracted patterns and trends. In this step, the results of the data analysis are used to find high-value issues that users have in common.

[0770] "Example 3"

[0771] Step 1: Think of ideas for new services to solve the identified problems. In this step, you brainstorm ideas for new services to solve the identified problems.

[0772] Step 2: Realize the idea and develop a new service. In this step, software development tools are used to realize the idea and develop a new service.

[0773] Example 1

[0774] Next, a description will be given of Example 1 of Form 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".

[0775] Conventional systems have had difficulty effectively collecting users' concerns and complaints and extracting their true essence. In addition, they were unable to generate appropriate questions based on user input and dig deeper, making it difficult to fully understand the true essence of users' concerns and complaints. Furthermore, it was difficult to effectively store collected data and find high-value issues.

[0776] 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.

[0777] In this invention, the server includes a means for receiving input from a user, a means for preprocessing the received input, a means for extracting keywords and emotions from the preprocessed input, a means for generating questions based on the extracted keywords and emotions, a means for presenting the generated questions to the user, a means for collecting the user's responses, and a means for extracting and storing the essence of the user's worries and complaints. This makes it possible to effectively collect the user's worries and complaints and extract their essence.

[0778] A "means for receiving input from a user" is an interface through which a user provides information to the system through voice or text.

[0779] "Means for preprocessing received input" refers to the process of converting text data received from a user into a format that is easy to parse by performing processes such as normalization and tokenization.

[0780] "Means for extracting keywords and sentiment from the preprocessed input" refers to a process of using natural language processing techniques to identify important words and sentiment from the preprocessed text data.

[0781] The "means for generating questions based on the extracted keywords and sentiment" refers to a process for automatically formulating questions to elicit further information from a user based on the identified keywords and sentiment.

[0782] The "means for presenting the generated question to the user" refers to an interface that displays the generated question on the user's terminal and enables the user to respond to it.

[0783] "Means of collecting user responses" refers to the process of incorporating responses entered by users into the system in response to questions posed.

[0784] "Means of extracting and storing the essence of users' worries and complaints" refers to the process of analyzing the information obtained from users' input and responses, and storing that essence in a database.

[0785] "Means of finding high-value issues" is the process of identifying particularly important problems or issues from accumulated data.

[0786] "Means of developing new services" refers to the process of designing and implementing new services and solutions to solve the problems identified.

[0787] This invention is a system that receives input from a user, generates questions based on the input, and extracts and accumulates the essence of the user's worries and complaints. This system receives input from a user using a voice recognition system and a text input interface. Specifically, a general voice recognition API is used for the voice recognition system, and an HTML form is used for the text input interface.

[0788] The server preprocesses the text data received from the user. This preprocessing involves normalizing the text (converting to lowercase, deleting unnecessary spaces, etc.) and tokenizing (dividing the text into words). From the preprocessed text data, the server uses natural language processing (NLP) techniques to extract keywords and sentiment. For this process, the Python libraries NLTK and spaCy are used.

[0789] Based on the extracted keywords and sentiment, the server generates questions using a generative AI model. The generative AI model uses an advanced natural language generation model such as GPT-3. The generated questions are presented to the user's device. The user enters an answer to the presented question. The server passes this answer back to the NLP algorithm to extract further keywords and sentiment.

[0790] Finally, the server extracts the essence of the user's worries and complaints and stores them in a database. The database uses a general relational database management system (RDBMS), such as MySQL or PostgreSQL.

[0791] Examples

[0792] Example 1: User Input

[0793] The user speaks into the smartphone's microphone, "I've been busy at work lately and I'm feeling stressed," and the voice recognition system converts this into text.

[0794] Example 2: Generating a question

[0795] The server uses an NLP algorithm to extract the keywords "busy at work" and the emotion "stressed." It then uses a generative AI model (GPT-3) to generate the question "What specific tasks are you busy with?"

[0796] Example 3: User response

[0797] The user uses his / her PC keyboard to type, "I have a lot of meetings in particular, so it takes a long time to prepare."

[0798] Example 4: Accumulating data

[0799] The server stores the answer in a database and records the nature of the user's concern, e.g., by saving it as a new record in a "concerns" table in the database.

[0800] Examples of prompt statements

[0801] User input: Work has been busy and stressful lately.

[0802] Prompt for generative AI model: A user says, "I've been busy at work lately and it's stressful." What exactly are the tasks that are making them busy?

[0803] In this way, the system can dig deeper into the user's concerns and complaints and bring out their essence.

[0804] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0805] Step 1:

[0806] Users input their worries or complaints through a voice recognition system or text input interface. For example, a user speaks into the microphone of their smartphone, "I've been busy at work lately and I'm feeling stressed." The voice recognition system converts this into text and sends it to the server. The input is voice data or text data, and the output is text data.

[0807] Step 2:

[0808] The server preprocesses the received text data. Preprocessing involves normalizing the text (converting to lowercase, deleting unnecessary spaces, etc.) and tokenizing (dividing it into words). For example, the text "I've been busy at work lately and I'm getting stressed" is tokenized as "I've been busy at work lately and I'm getting stressed." The input is text data, and the output is the preprocessed text data.

[0809] Step 3:

[0810] The server extracts keywords and emotions from the preprocessed text data. For this process, it uses Python libraries NLTK and spaCy. For example, it extracts keywords such as "work," "busy," "stress," and "accumulating" and the emotion "accumulating stress." The input is the preprocessed text data, and the output is keywords and emotions.

[0811] Step 4:

[0812] The server generates questions based on the extracted keywords and sentiment. A generative AI model is used to generate these questions. For example, a generative AI model (GPT-3) is used to generate the question, "What specific tasks are you busy with?" The input is the keywords and sentiment, and the output is the generated question.

[0813] Step 5:

[0814] The server presents the generated question to the user. The question is displayed on the user's terminal. For example, the question may be, "What specific tasks are you busy with?" The input is the generated question, and the output is the question displayed on the user's terminal.

[0815] Step 6:

[0816] The user inputs an answer to the question presented. For example, the user uses the PC keyboard to type, "I have a lot of meetings, so it takes a lot of time to prepare." The input is the user's answer, and the output is text data.

[0817] Step 7:

[0818] The server passes the user's answer to the NLP algorithm again to extract further keywords and sentiments. For example, it extracts the keywords "meeting," "preparation," and "takes time" and the sentiment "takes time." The input is the user's answer, and the output is the keywords and sentiments.

[0819] Step 8:

[0820] The server extracts the essence of the user's worries and complaints and stores them in a database. A general relational database management system (RDBMS) is used for the database. For example, MySQL or PostgreSQL is used. The input is keywords and emotions, and the output is the data stored in the database.

[0821] (Application example 1)

[0822] Next, a description will be given of Application Example 1 of Form 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".

[0823] In modern society, users have a wide range of security concerns and worries, but there is a lack of systems that provide specific measures and advice to properly resolve them. In addition, there is a challenge in that it is difficult to dig deep into users' concerns and clarify the essential problems.

[0824] 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.

[0825] In this invention, the server includes a means for receiving an input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, a means for converting the voice input into text using a voice recognition system, a means for generating questions using a natural language processing algorithm, a means for clarifying the user's specific problem based on the generated questions, and a means for providing appropriate measures and advice. This makes it possible to effectively resolve the user's worries and anxieties about security.

[0826] A "means for receiving input from a user" is an interface for receiving information input by a user via voice or text.

[0827] The "means for generating probing questions based on the received input" is an algorithm that analyzes the user's input and generates questions to elicit more detailed information.

[0828] The "means for presenting the generated question to the user" refers to an interface for displaying or audibly conveying the generated question to the user.

[0829] "A means for extracting and storing the essence of users' worries and complaints" is a system for extracting the essential problem from user input and storing it in a database.

[0830] "Means for converting voice input into text using a voice recognition system" refers to voice recognition technology for converting a user's voice input into text data.

[0831] "Means for generating questions using a natural language processing algorithm" refers to natural language processing technology for analyzing user input and generating appropriate questions.

[0832] The "means for clarifying a user's specific problem based on the generated questions" is a system for clarifying a user's specific problem or concern through the generated questions.

[0833] A "means for providing appropriate measures and advice" is a system for providing appropriate measures and advice for a user's specific problem.

[0834] A system for implementing this invention receives input from a user, generates probing questions based on that input, and presents these to the user to draw out the essence of the user's worries and complaints and provide appropriate solutions and advice.

[0835] Hardware and Software Used

[0836] Hardware: Microphone (for voice input), server, user device (smartphone, etc.)

[0837] software:

[0838] speech_recognition library (speech recognition)

[0839] NLP models (question generation) from the transformers library

[0840] System Configuration

[0841] 1. Speech recognition system: The user speaks through a microphone. The speech recognition system converts this speech into text data.

[0842] 2. Natural language processing algorithms: Analyze the converted text data and generate probing questions based on the user's input.

[0843] 3. Question Prompt Interface: Presents the generated question to the user and prompts for further input.

[0844] 4. Database: A database for extracting and storing the essence of users' concerns and complaints.

[0845] 5. Solution and advice provision system: Provides appropriate solutions and advice for users' specific problems.

[0846] Processing flow

[0847] 1. Acquire speech input: The user speaks into a microphone. The speech recognition system converts this speech into text.

[0848] 2. Question generation: Natural language processing algorithms analyze the text data and generate probing questions based on the user's input.

[0849] 3. Question prompting: Present the generated question to the user and prompt for further input.

[0850] 4. Data accumulation: The essence of users’ concerns and complaints is accumulated in a database.

[0851] 5. Providing countermeasures and advice: Providing appropriate countermeasures and advice to users based on accumulated data.

[0852] Examples

[0853] If a user says, "I've been worried about the security of my home lately," the system generates questions such as, "What specifically are you worried about?" and "Have you seen anything suspicious happen recently?" This helps clarify the user's specific problem and provides appropriate measures and advice.

[0854] Examples of prompt statements

[0855] User: I've been worried about the security of my home lately.

[0856] System: What specifically are you worried about?

[0857] User: I often hear noises in the middle of the night.

[0858] System: At what times of day is the noise most common?

[0859] In this way, it is possible to dig deeper into the user's concerns and propose appropriate security measures.

[0860] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0861] Step 1:

[0862] A user uses a microphone to input voice. The terminal acquires this voice data and sends it to a voice recognition system. The input is the user's voice data, and the output is text data. The voice recognition system analyzes the voice data and converts it into text data.

[0863] Step 2:

[0864] The server inputs the text data received from the speech recognition system into a natural language processing algorithm. The input is the text data and the output is the generated question. The natural language processing algorithm analyzes the text data and generates probing questions based on the user's input.

[0865] Step 3:

[0866] The server presents the generated question to the user. The input is the generated question and the output is the user's answer. Through a question presentation interface, the generated question is displayed to the user and prompts for further input.

[0867] Step 4:

[0868] The user inputs an answer to the question. The terminal acquires this answer and sends it to the server. The input is the user's answer, and the output is text data. In the case of voice input, the voice recognition system is used again to convert it to text.

[0869] Step 5:

[0870] The server stores the user's answers in a database. The input is the text data of the user's answers, and the output is the data stored in the database. The database is a system for extracting and storing the essence of the user's worries and complaints.

[0871] Step 6:

[0872] The server provides appropriate measures and advice to the user based on the accumulated data. The input is the data stored in the database, and the output is measures and advice. The solution and advice providing system generates appropriate measures and advice for the user's specific problem and presents them to the user.

[0873] In this way, it is possible to dig deeper into the user's concerns and propose appropriate security measures.

[0874] Example 2

[0875] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0876] In conventional systems, users' worries and complaints were simply accumulated, making it difficult to extract their true nature and identify high-value issues. In addition, there was a lack of concrete means to develop new services based on the identified issues. As a result, users' worries and complaints could not be effectively resolved.

[0877] 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.

[0878] In this invention, the server includes a means for receiving input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, a means for preprocessing the stored data, a means for analyzing the preprocessed data and extracting important keywords and phrases, a means for analyzing the frequency and co-occurrence relationships of the extracted keywords and phrases, and a means for identifying high-value issues based on the analysis results. This makes it possible to effectively extract the essence of the user's worries and complaints, identify high-value issues, and develop new services based on those issues.

[0879] "Means for receiving input from the user" refers to a function that provides an interface for users to input their concerns and complaints into the system.

[0880] "Means for generating probing questions based on received input" refers to a function that analyzes information entered by a user and automatically generates follow-up questions related to that information.

[0881] The "means for presenting the generated question to the user" is a function for displaying the generated question to the user and enabling the user to answer it.

[0882] "A means of extracting and storing the essence of the user's worries and complaints" refers to a function that analyzes the information entered by the user, extracts the essential parts, and stores them in a database.

[0883] The "means for preprocessing the accumulated data" refers to a function that performs operations such as deleting unnecessary characters and spaces and normalizing text in order to prepare the accumulated data in a format that is easy to analyze.

[0884] "Means for analyzing the preprocessed data and extracting important keywords and phrases" refers to a function for identifying important keywords and phrases from the preprocessed data using natural language processing technology.

[0885] The "means for analyzing the frequency and co-occurrence relationships of extracted keywords and phrases" is a function for statistically analyzing the frequency of occurrence of extracted keywords and phrases and the relationships in which they appear together.

[0886] "Means of identifying high-value issues based on analysis results" refers to a function that identifies particularly important issues that need to be resolved from among users' concerns and complaints based on the analysis results.

[0887] The present invention is a system for effectively collecting users' worries and complaints, extracting their essence, and identifying high-value issues. A specific embodiment of this system will be described below.

[0888] First, the server provides a means to receive input from users. This is achieved through a user interface such as a web interface or a mobile application. When a user inputs their worries or complaints, the data is sent to the server and stored in a database. The database is a common database management system such as MySQL or PostgreSQL.

[0889] The server then has the means to generate follow-up questions based on the input it receives, using natural language processing techniques such as Python libraries like NLTK and spaCy to parse the user's input and generate related follow-up questions.

[0890] The server then presents the generated questions to the user, who answers them and provides further details, a process that draws out the essence of the user's concerns and complaints.

[0891] The accumulated data is preprocessed by the server, which uses the Pandas library to read the data, remove unnecessary characters and spaces, normalize the text, and so on, to make the data easier to analyze.

[0892] The preprocessed data is then analyzed by the server, which uses NLTK and spaCy to tokenize the text data, tag parts of speech, and perform named entity recognition (NER) to extract important keywords and phrases from the text data.

[0893] The extracted keywords and phrases are analyzed by the server for frequency and co-occurrence. This analysis is performed using the Scikit-learn library. For example, if many users mention "work stress," it is confirmed that this is a frequent occurrence. Also, by analyzing co-occurrence, it is found that "work" and "stress" frequently appear together.

[0894] Finally, the server identifies high-value issues based on the analysis results. For example, it may determine that "work stress" is a common issue for many users. Based on this result, it may suggest that the development of a "stress management app" is a high-value solution.

[0895] As a concrete example, let's say you have accumulated the following user complaint data:

[0896] "Work has been so busy lately that it's stressful"

[0897] "My boss is strict and I feel pressured every day."

[0898] "I can't communicate well with my coworkers"

[0899] Example of a prompt to be input to a generative AI model:

[0900] Analyze your users' complaints data and identify common issues. Use the following data:

[0901] "Work has been so busy lately that it's stressful"

[0902] "My boss is strict and I feel pressured every day."

[0903] "I can't communicate well with my coworkers"

[0904] Using this prompt, the generative AI model can identify common issues from user complaint data and suggest valuable solutions.

[0905] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0906] Step 1:

[0907] The server receives input from the user. When the user inputs their worries or complaints through a web interface or a mobile application, the data is sent to the server. An example of input data is "Recently, I've been so busy at work that I'm getting stressed out." The server stores this data in a database. The input data is stored in text format.

[0908] Step 2:

[0909] The server generates probing questions based on the input it receives. Using natural language processing techniques, the server analyzes the user's input and generates related follow-up questions. For example, a question might be "Which parts of your job do you find particularly stressful?" The input data is the user's initial input, and the output data is the generated question.

[0910] Step 3:

[0911] The server presents the generated questions to the user, who answers them and provides further details. For example, if the user answers "my boss is demanding," the data is sent back to the server and stored in the database. The input data are the user's answers to the generated questions, and the output data are the further details.

[0912] Step 4:

[0913] The server preprocesses the accumulated data. Preprocessing includes reading the data using the Pandas library, deleting unnecessary characters and spaces, normalizing the text, etc. For example, it deletes unnecessary spaces from the text "My boss is demanding" and converts it to lowercase. The input data is the accumulated raw data, and the output data is the preprocessed clean data.

[0914] Step 5:

[0915] The server analyzes the preprocessed data and extracts important keywords and phrases. The analysis involves tokenizing the text data, tagging it with parts of speech, and performing named entity recognition (NER) using NLTK and spaCy. For example, keywords such as "boss," "demanding," and "strict" are extracted. The input data is the preprocessed text data, and the output data is the extracted keywords and phrases.

[0916] Step 6:

[0917] The server analyzes the frequency and co-occurrence of the extracted keywords and phrases. The Scikit-learn library is used for the analysis. For example, it is confirmed that "boss" and "strict" frequently appear together. The input data are the extracted keywords and phrases, and the output data are the analysis results of the frequency and co-occurrence.

[0918] Step 7:

[0919] The server identifies high-value issues based on the analysis results. For example, it is determined that "work stress" is a common issue shared by many users. Based on this result, it is proposed that the development of a "stress management app" is a high-value solution. The input data are the results of the analysis of frequency and co-occurrence relationships, and the output data are the identified high-value issues.

[0920] (Application example 2)

[0921] Next, a description will be given of Application Example 2 of Form 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".

[0922] In modern society, users have a wide range of worries and complaints in their daily lives and work. In order to effectively solve these worries and complaints, it is necessary to accurately grasp their true nature and take appropriate measures. However, conventional systems simply accumulate users' worries and complaints, making it difficult to extract their true nature and provide concrete solutions. There was also a lack of effective means to recommend the most suitable content to users. This resulted in the issue of being unable to improve user satisfaction.

[0923] 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.

[0924] In this invention, the server includes a means for receiving input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, a means for extracting patterns and trends from the stored data, and a means for recommending optimal content based on the extracted patterns and trends. This makes it possible to accurately grasp the essence of the user's worries and complaints and recommend appropriate content.

[0925] "Means for receiving input from the user" refers to a function that provides an interface for users to input their concerns and complaints into the system.

[0926] "Means for generating probing questions based on received input" refers to a function that analyzes information entered by a user and automatically generates more detailed questions based on that information.

[0927] The "means for presenting the generated question to the user" is a function for displaying the generated question to the user and enabling the user to answer it.

[0928] "A means of extracting and storing the essence of users' worries and complaints" refers to a function that analyzes the information provided by users, extracts its essence, and stores it in a database.

[0929] "Means for extracting patterns and trends from the accumulated data" refers to a function that executes an algorithm to analyze the accumulated data and find common patterns and trends.

[0930] "Means for recommending optimal content based on extracted patterns and trends" refers to a function that recommends optimal content (videos, music, articles, etc.) to users based on extracted patterns and trends.

[0931] The following system configuration will be described as an embodiment of the present invention.

[0932] System Configuration

[0933] This system consists of a user terminal, a server, and a database. The user terminal is a device such as a smartphone, tablet, or PC, and provides an interface for users to input their worries and complaints. The server plays a central role in receiving input from users, analyzing the data, and recommending appropriate content. The database is used to store user input data and analysis results.

[0934] Program Processing

[0935] The server performs the process in the following procedure.

[0936] 1. A way to receive input from the user

[0937] The server receives the text data of the user's worries and complaints sent from the user's device. This data is stored exactly as entered by the user.

[0938] 2. A way to generate probing questions based on the input received

[0939] The server analyzes the received text data and uses natural language processing technology to generate more detailed questions, using a generative AI model to automatically generate probing questions related to the user's input.

[0940] 3. A way to present the generated questions to the user

[0941] The generated questions are then sent to the user's device and presented to the user, who can then answer them to provide further detailed information.

[0942] 4. A means to extract and accumulate the essence of users' worries and complaints

[0943] The server that receives the user's responses analyzes the content and extracts the essence of the user's worries or complaints. This extracted information is stored in a database.

[0944] 5. A means of extracting patterns and trends from accumulated data

[0945] The server analyzes the accumulated data and runs algorithms to find common patterns and trends, specifically using machine learning algorithms such as TfidfVectorizer and KMeans clustering.

[0946] 6. A means to recommend the best content based on extracted patterns and trends

[0947] The server recommends the most suitable content (videos, music, articles, etc.) to the user based on the extracted patterns and trends. The recommendation results are sent to the user's terminal and displayed to the user.

[0948] Hardware and software used

[0949] Hardware: General purpose PC or server

[0950] Software: Python, pandas, scikit-learn, TfidfVectorizer, KMeans clustering

[0951] Examples

[0952] For example, if User A enters "I'm feeling stressed at work," the server receives this input and generates related probing questions. When User A answers the questions, the server analyzes the answers and extracts the true nature of the stress. It finds common patterns from the accumulated data and recommends "relaxing music" to User A.

[0953] Examples of prompt statements

[0954] "Please develop an algorithm that analyzes data on users' worries and complaints and recommends optimal stress relief content. Use the following data for clustering and content recommendation."

[0955] In this way, a system can be realized that provides optimal content based on the user's concerns and complaints.

[0956] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0957] Step 1:

[0958] The user terminal receives input from the user. The user inputs their worries or complaints in text format. This input data is sent to the server. An example of input data is "I'm feeling stressed at work."

[0959] Step 2:

[0960] The server analyzes the input data it receives. Specifically, it uses natural language processing technology to tokenize the text data and extract important keywords. Based on the results of this analysis, it generates more probing questions. Using a generative AI model, it automatically generates questions related to the user's input. For example, a question might be generated such as, "In what specific situations do you feel stressed?"

[0961] Step 3:

[0962] The server transmits the generated question to the user terminal. The user terminal presents the question to the user. The user inputs an answer to the presented question. This answer data is also transmitted to the server.

[0963] Step 4:

[0964] The server receives the response data from the user and analyzes it again. The response data is tokenized and important keywords and phrases are extracted. Based on the results of this analysis, the essence of the user's worries and complaints is revealed. For example, the essence that is extracted is "I feel stressed because of communication with my boss."

[0965] Step 5:

[0966] The server stores the extracted essential information in a database. The stored data is used for later analysis. The database stores user IDs, input data, analysis results, etc.

[0967] Step 6:

[0968] The server periodically analyzes the accumulated data to extract common patterns and trends. Specifically, it uses TfidfVectorizer to convert the text data into numerical vectors, and then uses KMeans clustering to classify the data into clusters. This analysis reveals common themes in users' concerns and complaints.

[0969] Step 7:

[0970] The server recommends the most suitable content to the user based on the extracted patterns and trends. For example, it recommends "relaxing music" to a user who is feeling stressed. The recommendation results are sent to the user's device and displayed to the user.

[0971] Step 8:

[0972] The user terminal displays the recommended content sent from the server to the user. The user can reduce worries and stress by using the recommended content. User feedback is also collected and sent to the server.

[0973] In this way, a system is realized that provides optimal content based on the user's concerns and complaints.

[0974] Example 3

[0975] Next, a third embodiment of the third embodiment will be described. 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".

[0976] Conventional systems simply accumulate users' worries and complaints, making it difficult to extract the essence of those issues and provide concrete solutions. In addition, there was a lack of means to effectively analyze the accumulated data and quickly develop new services to solve users' problems. This made it difficult to provide appropriate solutions that met users' needs.

[0977] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0978] In this invention, the server includes a means for receiving input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, a means for analyzing the stored data, a means for generating prompt sentences using a generative AI model based on the analysis results, a means for generating an application using a software development tool based on the generated prompt sentences, and a means for providing the generated application to the user. This makes it possible to effectively extract and analyze the essence of the user's worries and complaints and quickly provide a specific solution.

[0979] "Means for receiving input from the user" refers to a function that provides an interface for users to input issues or concerns into the system in text format.

[0980] "Means for generating probing questions based on received input" refers to a function that analyzes the information entered by the user and automatically generates additional questions to elicit more detailed information.

[0981] The "means for presenting the generated question to the user" is a function for displaying the generated question to the user and providing an interface for enabling the user to answer the question.

[0982] "A means of extracting and storing the essence of users' worries and complaints" refers to a function that analyzes the information provided by users, extracts important elements and essential problems from it, and stores them in a database.

[0983] "Means of analyzing accumulated data" refers to a function that provides algorithms and tools to analyze accumulated data on users' concerns and complaints, and find common patterns and important insights.

[0984] "Means for generating prompt sentences using a generative AI model based on analysis results" refers to a function that uses a generative AI model with analysis results as input to generate prompt sentences to suggest specific solutions to the user's problems.

[0985] "Means for generating an application using a software development tool based on the generated prompt text" refers to a function for automatically generating an application or web service for solving a user's problem using a software development tool based on the generated prompt text.

[0986] "Means for providing the generated application to the user" refers to a function for providing the generated application or web service to the user and carrying out distribution and installation procedures so that the user can use it.

[0987] The present invention relates to a system for developing new services to solve users' concerns and problems. A specific embodiment of this system will be described below.

[0988] System Overview

[0989] The system receives input from users, generates probing questions based on the input, and accumulates the essence of the user's worries and complaints. It analyzes the accumulated data and generates prompts using a generative AI model. Based on the generated prompts, it uses software development tools to generate an application and provides it to the user.

[0990] Hardware and Software Used

[0991] Hardware: Servers, user devices (PCs, smartphones, tablets, etc.)

[0992] Software: Natural language processing libraries (e.g., Python's NLTK library, Google Cloud Natural Language API), generative AI models (e.g., OpenAI's GPT-4), software development tools (e.g., Visual Studio Code, JetBrains IntelliJ IDEA, etc.)

[0993] Data processing and calculation

[0994] 1. Receiving input from the user

[0995] Users access the system's interface and enter the problem they want to solve into a text box, for example, "I want to reduce work stress."

[0996] 2. Analysis of input data

[0997] The server receives the task entered by the user and analyzes it using natural language processing (NLP) technology. For analysis, it uses Python's NLTK library and Google Cloud Natural Language API. The server analyzes the text "I want to reduce stress at work" and extracts keywords and important phrases.

[0998] 3. Generate prompts using a generative AI model

[0999] The server uses a generative AI model (e.g., OpenAI's GPT-4) based on the analysis results to generate an appropriate prompt sentence, which is intended to suggest a specific solution to the user's problem.

[1000] Examples of prompt statements

[1001] "Develop a relaxation app to help reduce work-related stress. The app should provide guided meditations, deep breathing exercises, and relaxation music to help users relax. It should also include a feature that allows users to record their stress levels and track their progress."

[1002] 4. Generate the application using software development tools

[1003] Based on the generated prompt text, the server uses a software development tool (e.g., Visual Studio Code or JetBrains IntelliJ IDEA) to generate an application or web service to solve the problem. Based on the prompt text, the server automatically generates the code for the relaxation app and builds the application by importing the necessary libraries and frameworks.

[1004] 5. Deliver the generated application to users

[1005] The user checks the application or web service generated by the server and provides feedback if necessary. The user accesses the system dashboard and clicks the download link for the generated application. The user downloads, installs, and actually uses the application.

[1006] In this way, it becomes possible to rapidly develop and provide new services that effectively solve the worries and problems of users. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1007] Step 1:

[1008] A user enters an assignment

[1009] How it works: A user accesses the system's interface and enters the problem they want to solve in a text box, for example, "I want to reduce stress at work."

[1010] Input: The text data of the assignment entered by the user

[1011] Output: The text data of the assignment that is sent to the server.

[1012] Step 2:

[1013] The server analyzes the problem

[1014] How it works: The server receives the tasks entered by the user and parses them using natural language processing (NLP) techniques, such as the NLTK library for Python and the Google Cloud Natural Language API.

[1015] Input: Text data of assignment received from user

[1016] Data processing: tokenizing text and extracting keywords and key phrases

[1017] Output: A list of extracted keywords or phrases.

[1018] Step 3:

[1019] The server generates a prompt using the generative AI model.

[1020] How it works: The server uses a generative AI model (e.g., OpenAI's GPT-4) to generate an appropriate prompt based on the analysis results.

[1021] Input: A list of extracted keywords or phrases

[1022] Data computation: Input keywords or phrases into the generative AI model to generate prompts

[1023] Output: The generated prompt statement

[1024] Step 4:

[1025] The server uses software development tools to generate the application.

[1026] How it works: Based on the generated prompt statements, the server uses a software development tool (e.g., Visual Studio Code or JetBrains IntelliJ IDEA) to generate an application or web service to solve the problem.

[1027] Input: Generated prompt text

[1028] Data processing: Automatically generate code based on prompt statements, import necessary libraries and frameworks, and build applications.

[1029] Output: The generated application code.

[1030] Step 5:

[1031] User Review of Generated Applications

[1032] How it works: The user reviews the application or web service generated by the server and provides feedback if necessary. The user accesses the system dashboard and clicks on the download link for the generated application. The user downloads, installs, and actually uses the application.

[1033] Input: The generated application code

[1034] Output: User feedback data

[1035] (Application example 3)

[1036] Next, a description will be given of Application Example 3 of Form Example 3. 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".

[1037] In modern factories, machine maintenance is extremely important to maintain productivity. However, it is not easy to detect machine abnormalities early and perform appropriate maintenance. In particular, if an abnormality is not dealt with promptly, it could have an impact on the entire production line. In addition, there is a lack of preventive maintenance proposals based on past maintenance history, making it difficult to prevent machine breakdowns. To solve these issues, a system is needed that can monitor the status of machines in real time and respond immediately when an abnormality occurs.

[1038] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1039] In this invention, the server includes a means for receiving input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, a means for monitoring the status of machines in a factory in real time, a means for issuing an alert when an abnormality is detected, a means for presenting an appropriate maintenance procedure, and a means for proposing preventive maintenance based on past maintenance history. This makes it possible to detect abnormalities in machines early and respond quickly. Furthermore, by proposing preventive maintenance based on past maintenance history, machine breakdowns can be prevented before they occur.

[1040] A "means for receiving input from a user" is an interface through which a user provides information to the system.

[1041] "Means for generating probing questions based on received input" refers to a function that automatically creates questions to elicit more detailed information based on the information received from the user.

[1042] The "means for presenting the generated question to the user" refers to an interface for displaying the question generated by the system to the user and requesting an answer.

[1043] "A means of extracting and storing the essence of users' worries and complaints" refers to a function that extracts the underlying problems and dissatisfaction from the information provided by the user and stores them in a database.

[1044] A "means for monitoring the status of machines in a factory in real time" is a system that constantly monitors the operating conditions and performance of machines running in a factory and collects data.

[1045] "Means for issuing an alert when an abnormality is detected" refers to a function that immediately issues a warning when an abnormality occurs in the operation of the machine.

[1046] A "means for presenting appropriate maintenance procedures" is a system that provides specific procedures for repair and maintenance when a machine abnormality is detected.

[1047] "A means for proposing preventive maintenance based on past maintenance history" is a function that analyzes records of past maintenance and proposes maintenance measures to prevent future breakdowns.

[1048] In order to implement the present invention, the following system configuration and program are required.

[1049] First, the server has a means for receiving input from the user, which can be an interface through which the user provides information to the system, such as a web form or a voice input system.

[1050] Next, the server has a means for generating probing questions based on the received input, which is a function of using a generative AI model to analyze the information received from the user and automatically create questions to elicit further detailed information.

[1051] The generated question is displayed to the user through a means for presenting the question to the user. This means utilizes a user interface such as a web browser or a smartphone application to display the question to the user and request an answer.

[1052] The server also includes a means for extracting and storing the essence of the user's worries and complaints. This means is a function for extracting the fundamental problems and complaints from the information provided by the user and storing them in a database.

[1053] In addition, the server is equipped with a means for monitoring the status of machines in the factory in real time. This means is a system that constantly monitors the operating conditions and performance of machines operating in the factory and collects data. Specifically, the status of machines is monitored using sensors and IoT devices.

[1054] If an abnormality is detected, the server will immediately issue an alert using an alerting method such as email notification or push notification on a smartphone to notify the person in charge of the abnormality.

[1055] The server also includes a means for presenting appropriate maintenance procedures. This means is a system that provides specific procedures for repair and maintenance when a machine abnormality is detected. For example, it displays a procedure manual based on past maintenance history and expert knowledge.

[1056] Finally, the server is equipped with a means for proposing preventive maintenance based on past maintenance history. This means is a function that analyzes the records of past maintenance and proposes maintenance measures to prevent future breakdowns. This makes it possible to prevent machine breakdowns before they occur.

[1057] As a concrete example, we will assume a scenario in which five machines operating in a factory are monitored and maintenance is performed if an abnormality occurs. This system is implemented using Python and functions as a real-time monitoring system.

[1058] Examples of prompt sentences to be input to a generative AI model include the following:

[1059] "Please develop an application that monitors the status of machines in the factory in real time, issues an alert if an abnormality is detected, and presents appropriate maintenance procedures. Please also include a function that suggests preventive maintenance based on past maintenance history."

[1060] In this way, a system for efficiently performing maintenance on machines in a factory can be realized.

[1061] The flow of the specific process in the application example 3 will be described with reference to FIG.

[1062] Step 1:

[1063] The server receives input from the user.

[1064] Input: Information provided by the user (e.g., machine status, concerns, and complaints)

[1065] Output: Received user information

[1066] Specific operation: The server collects information from the user through a web form or a voice input system. For example, when a user reports a machine malfunction, the information is sent to the server.

[1067] Step 2:

[1068] The server generates further probing questions based on the input it receives.

[1069] Input: Information received from the user

[1070] Output: Generated probe questions

[1071] How it works: The server uses a generative AI model to analyze the user's input and automatically generate questions to elicit more detailed information, such as "What time did the machine malfunction occur?"

[1072] Step 3:

[1073] The server presents the generated questions to the user.

[1074] Input: Generated probing questions

[1075] Output: The question presented to the user.

[1076] Specific operation: The server displays the generated questions to the user through a web browser or smartphone application, and the user answers the questions.

[1077] Step 4:

[1078] The server extracts and stores the essence of users' worries and complaints.

[1079] Input: User's answer

[1080] Output: The essence of accumulated worries and complaints

[1081] Specific operation: The server analyzes the user's answers, extracts the underlying problems and dissatisfaction, and stores them in a database. For example, it accumulates essential problems such as "the machine requires frequent maintenance."

[1082] Step 5:

[1083] The server monitors the status of machines in the factory in real time.

[1084] Input: Machine operation data

[1085] Output: Real-time machine condition data

[1086] Specific operation: The server uses sensors and IoT devices to constantly monitor the operating status and performance of machines running in the factory and collect data.

[1087] Step 6:

[1088] The server issues an alert if an anomaly is detected.

[1089] Input: Real-time machine condition data

[1090] Output: Alert notification

[1091] Specific operation: The server analyzes the collected data, and if an abnormality is detected, it alerts the person in charge via email notification or smartphone push notification.

[1092] Step 7:

[1093] The server will suggest appropriate maintenance procedures.

[1094] Input: Detailed information about the anomaly

[1095] Output: Maintenance Procedure

[1096] Specific actions: The server provides specific procedures for repairs and maintenance based on past maintenance history and expert knowledge. For example, it may suggest a procedure such as "Replace part A."

[1097] Step 8:

[1098] The server makes preventive maintenance suggestions based on past maintenance history.

[1099] Input: Past maintenance history

[1100] Output: Preventive maintenance suggestions

[1101] Specific operation: The server analyzes the records of past maintenance and suggests maintenance measures to prevent future breakdowns. For example, it suggests that the next maintenance should be performed in three months.

[1102] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[1103] "Example 1"

[1104] As one embodiment of the present invention, a system is provided that includes a means for receiving input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, and an emotion engine for recognizing the user's emotions. Specifically, when a user inputs worries or complaints by voice or text, the system receives the input and analyzes the user's words and emotions using a natural language processing (NLP) algorithm and the emotion engine. Based on the results of this analysis, the system generates questions to understand the essence of the user's worries and complaints more deeply and presents them to the user. When the system receives a user's response, it stores it in a database.

[1105] "Example 2"

[1106] In another embodiment of the present invention, a system is provided that includes a means for finding high-value issues from the nature of accumulated worries and complaints, and an emotion engine that recognizes the emotions of users. Specifically, the system analyzes the data of users' worries and complaints accumulated in a database using a data analysis algorithm and the emotion engine to find common patterns and trends. Based on the analysis results, the system identifies high-value issues that users have in common.

[1107] "Example 3"

[1108] As yet another embodiment of the present invention, a system is provided that includes a means for developing a new service to solve the identified problem and an emotion engine that recognizes the emotions of users. Specifically, the system brainstorms ideas for a new service to solve the identified problem, and uses a software development tool to materialize the ideas and develop the new service. For example, if the system identifies a problem that many users are suffering from work-related stress, it develops a new stress management service to solve the problem.

[1109] The process flow of each embodiment will be described below.

[1110] "Example 1"

[1111] Step 1: The user inputs their concerns or complaints via voice or text.

[1112] Step 2: The system takes this input and uses natural language processing (NLP) algorithms and a sentiment engine to analyze the user's words and sentiment.

[1113] Step 3: Based on the analysis results, the system generates questions to better understand the nature of the user's concerns and complaints and presents them to the user.

[1114] Step 4: Once the user's response is received, the system stores it in a database.

[1115] "Example 2"

[1116] Step 1: The system analyzes the user's worries and complaints data accumulated in the database using data analysis algorithms and an emotion engine.

[1117] Step 2: Analyze to find common patterns and trends.

[1118] Step 3: Based on the analysis results, the system identifies high-value issues that users have in common.

[1119] "Example 3"

[1120] Step 1: The system brainstorms new service ideas to solve the identified problems.

[1121] Step 2: Use software development tools to materialize the idea and develop a new service.

[1122] Step 3: For example, if the system identifies an issue where many users are suffering from work-related stress, it will develop a new stress management service to resolve this issue.

[1123] Example 1

[1124] Next, a description will be given of Example 1 of Form 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".

[1125] With conventional systems, it was difficult to effectively collect users' concerns and complaints and understand their essence. In addition, the process of generating appropriate questions in response to user input and storing the user's responses in a database was not automated, making it impossible to extract the essence of users' concerns and complaints. Furthermore, there was a lack of means to find high-value issues from the accumulated data and develop new services to solve them.

[1126] 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.

[1127] In this invention, the server includes a means for receiving input from a user, a means for analyzing the received input using a natural language processing algorithm, a means for generating questions that probe deeper based on the analysis results, a means for presenting the generated questions to the user, and a means for storing the user's responses in a database. This makes it possible to effectively collect the user's worries and complaints and understand their essence. It is also possible to find high-value issues from the stored data and develop new services that solve them.

[1128] "Means for receiving input from the user" refers to an interface through which the user can input their concerns or complaints via voice or text.

[1129] A "natural language processing algorithm" is an algorithm that analyzes text data received from a user and extracts keywords and emotions.

[1130] "Means of generating questions" refers to the process of generating questions to dig deeper into the user's concerns or complaints based on the analysis results.

[1131] A "means for presenting a question to a user" is an interface that displays the generated question to the user and allows the user to answer it.

[1132] "Means of storing user responses in a database" refers to the process of storing user responses or responses in a database for later analysis and use.

[1133] "Means of finding high-value issues" is the process for identifying important problems and issues from accumulated data.

[1134] "Means of developing new services" refers to the process of designing and developing new services and solutions to solve the problems identified.

[1135] The present invention is a system that receives input from a user, generates probing questions based on the input, and accumulates the user's responses in a database. A specific embodiment of this system will be described below.

[1136] Hardware and Software Configuration

[1137] A way to receive input from the user

[1138] Users input their worries and complaints using a voice recognition system and a text input interface. The voice recognition system uses a general voice recognition API, and the text input interface uses an HTML form.

[1139] Analyzing input data

[1140] The server passes the text data received from the user to a natural language processing (NLP) algorithm using the Python libraries NLTK and spaCy. The NLP algorithm extracts keywords and sentiment from the user's input.

[1141] Question Generation

[1142] The server generates questions to dig deeper into the user's concerns and complaints based on the extracted keywords and emotions. A generative AI model is used to generate these questions. Specifically, the following prompt sentences are input to the generative AI model:

[1143] User input: "Work has been busy and stressful lately."

[1144] Prompt: "User is talking about work stress. Generate questions to explore further."

[1145] Posing the Question

[1146] The server presents the generated questions to the user, who again inputs answers by voice or text, or, for example, displays the questions on a web page and allows the user to answer them.

[1147] Accumulation of data

[1148] The server stores the user's responses in a database. For this database, a general relational database such as MySQL or PostgreSQL is used.

[1149] Examples

[1150] For example, if a user enters, "I've been busy at work lately and I'm feeling stressed," the system will process it as follows:

[1151] 1. The user types, "I've been busy at work lately and I'm feeling stressed" through a text input interface.

[1152] 2. The server receives this text data and uses NLP algorithms (NLTK or spaCy) to extract keywords such as "work," "busy," and "stress."

[1153] 3. The server inputs the following prompt to the generative AI model:

[1154] User input: "Work has been busy and stressful lately."

[1155] Prompt: "User is talking about work stress. Generate questions to explore further."

[1156] 4. The generative AI model generates questions such as, "What specific tasks cause you stress?"

[1157] 5. The server presents this question to the user and receives the user's answer again.

[1158] 6. The server stores the user's answers in a database.

[1159] In this way, the system can extract the essence of users' worries and complaints and store them in a database. This data can later be used to identify high-value issues and develop new services to solve them.

[1160] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1161] Step 1:

[1162] The user inputs his / her worries or complaints using a voice recognition system or a text input interface. The input data is sent to the server. For example, the user inputs, "I've been busy at work lately and I'm stressed."

[1163] Input: User voice or text data

[1164] Output: Text data sent to the server

[1165] Step 2:

[1166] The server passes the received text data to a natural language processing (NLP) algorithm, using Python libraries such as NLTK and spaCy. The server extracts keywords and sentiment from the text data.

[1167] Input: Text data sent to the server

[1168] Output: Extracted keywords and sentiments

[1169] Step 3:

[1170] The server inputs a prompt sentence into the generative AI model based on the extracted keywords and emotions. The generative AI model generates questions to dig deeper into the user's worries and complaints. For example, the following prompt sentence is input into the generative AI model:

[1171] User input: "Work has been busy and stressful lately."

[1172] Prompt: "User is talking about work stress. Generate questions to explore further."

[1173] Input: Extracted keywords and sentiments

[1174] Output: Generated questions

[1175] Step 4:

[1176] The server presents the generated questions to the user, who then answers the questions by voice or text, or, for example, by displaying the questions on a web page and allowing the user to answer them.

[1177] Input: Generated Question

[1178] Output: The question presented to the user.

[1179] Step 5:

[1180] The server receives the user's answer and stores it in a database. For this database, a general relational database such as MySQL or PostgreSQL is used.

[1181] Input: User's answer

[1182] Output: Data stored in the database

[1183] In this way, the system can extract the essence of the user's worries and complaints and store them in a database. The accumulated data can later be used to identify high-value issues and develop new services to solve them.

[1184] (Application example 1)

[1185] Next, a description will be given of Application Example 1 of Form 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".

[1186] In modern society, users have a wide range of security-related concerns and anxieties. However, there are only a limited number of systems that can properly analyze these concerns and anxieties and propose specific countermeasures. In particular, there is a need for a system that can provide more effective security measures by generating in-depth questions that take the user's emotions into account and accumulating the user's responses in a database.

[1187] The identification process by the identification processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means. In this invention, the server includes a means for receiving an input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, a means for converting the user's voice into text using a voice recognition system, a means for analyzing the user's input and extracting emotions using a natural language processing algorithm, a means for generating appropriate questions based on the extracted emotions, and a means for storing the user's responses in a database. This makes it possible to effectively analyze the user's security-related worries and anxieties and propose specific measures.

[1188] A "means for receiving input from a user" is an interface for receiving information input by a user via voice or text.

[1189] The "means for generating probing questions based on the received input" is an algorithm that analyzes the input data received from the user and generates questions to elicit more detailed information.

[1190] The "means for presenting the generated question to the user" refers to an interface for displaying or audibly conveying the generated question to the user.

[1191] "A means of extracting and storing the essence of users' worries and complaints" is a system for extracting important information from user input and storing it in a database.

[1192] "Means of converting a user's voice into text using a voice recognition system" refers to technology for converting information input by a user through voice into text data.

[1193] "Means for analyzing user input and extracting emotions using a natural language processing algorithm" refers to an algorithm for analyzing a user's text input and extracting emotions and keywords from it.

[1194] The "means for generating appropriate questions based on extracted emotions" is a system for generating appropriate questions for a user based on extracted emotion information.

[1195] The "means for storing user responses in a database" refers to a system for storing the responses that users give to generated questions in a database.

[1196] As an embodiment of the present invention, the security consultation assistant system can be installed on a smartphone or smart glasses. This system allows users to input their security-related worries and anxieties by voice or text, analyzes them, and generates appropriate questions to present to the user.

[1197] Hardware and Software Used

[1198] Hardware: Smartphones, smart glasses

[1199] Software: Python, SpeechRecognition library, Transformers library, SQLite database

[1200] System Configuration

[1201] 1. Speech Recognition System: The SpeechRecognition library is used to convert the information input by the user through voice into text, so that the user's voice input is taken into the system as text data.

[1202] 2. Natural Language Processing Algorithms: To analyze the user's text input and extract sentiment and keywords, we use the Transformers library, which provides a detailed analysis of the user's input.

[1203] 3. Question generation system: Based on the extracted emotion information, appropriate questions are generated, allowing us to dig deeper into the user's concerns and worries.

[1204] 4. Database System: A SQLite database is used to store user inputs, generated questions, and user responses, which will then be used to store data for future recommendations.

[1205] Processing flow

[1206] 1. Receive user input: The user inputs their security concerns or fears via voice or text.

[1207] 2. Speech Recognition: For voice input, the SpeechRecognition library is used to convert speech to text.

[1208] 3. Natural Language Processing: The converted text data is analyzed using the Transformers library to extract sentiment and keywords.

[1209] 4. Question generation: Generate appropriate questions based on the extracted sentiment information.

[1210] 5. Ask a question: Present the generated question to the user to elicit further information.

[1211] 6. Database storage: User responses are stored in a SQLite database.

[1212] Examples

[1213] If a user says, "I've been worried about the security of my home lately," the system converts this into text and analyzes it with an NLP model. If the analysis results indicate a negative sentiment, the system generates a question for the user: "What specifically are you worried about?"

[1214] Examples of prompt statements

[1215] User: I've been worried about the security of my home lately.

[1216] System: What specifically are you worried about?

[1217] In this way, it is possible to dig deeper into the user's concerns and worries and provide appropriate advice.

[1218] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1219] Step 1:

[1220] The user inputs their security concerns or worries by voice or text. The input data is sent to the server through the interface of the smartphone or smart glasses. The input data is in the form of voice data or text data.

[1221] Step 2:

[1222] In the case of voice input, the server converts the voice data into text data using the SpeechRecognition library. Specifically, the server receives the voice data, and the voice recognition engine analyzes the voice waveform and generates the corresponding text. The output is text data.

[1223] Step 3:

[1224] The server analyzes the text data using the Transformers library. Specifically, the text data is input into an NLP model to extract emotions and keywords. The input is text data, and the output is emotion information and keywords.

[1225] Step 4:

[1226] The server generates appropriate questions based on the extracted emotion information. Specifically, if the emotion information is negative, it generates questions that will further dig into the user's anxiety. The input is emotion information and keywords, and the output is the generated questions.

[1227] Step 5:

[1228] The server presents the generated question to the user. Specifically, the server displays the question on the display of a smartphone or smart glasses, or plays the question by voice. The input is the generated question, and the output is the question presented to the user.

[1229] Step 6:

[1230] The user inputs answers to the questions presented to them. The input data is then sent to the server via the interface of the smartphone or smart glasses. The input data can be in the form of voice data or text data.

[1231] Step 7:

[1232] The server accumulates the user's answers in a database. Specifically, it saves the user's answers in a SQLite database. The input is the user's answer data, and the output is saving to the database.

[1233] In this way, it is possible to dig deeper into the user's concerns and worries and provide appropriate advice.

[1234] Example 2

[1235] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[1236] In modern society, many users have various worries and complaints in their daily lives and work. However, it is difficult to understand the essence of these worries and complaints and identify high-value issues. Conventional systems are unable to accurately analyze users' emotions and find common patterns and trends, and as a result, are unable to provide useful information. Therefore, there is a need for a system that can draw out the essence of users' worries and complaints and identify high-value issues.

[1237] 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.

[1238] In this invention, the server includes means for receiving input from a user, means for generating probing questions based on the received input, means for presenting the generated questions to the user, means for extracting and storing the essence of the user's worries and complaints, means for executing a data analysis algorithm for analyzing the stored data, means for executing an emotion engine for analyzing the user's emotions, means for integrating the results of the data analysis algorithm and the emotion engine, means for identifying high-value issues from the integrated results, and means for generating the identified issues as a report. This makes it possible to accurately analyze the essence of the user's worries and complaints and identify high-value issues.

[1239] "Means for receiving input from a user" refers to a function that provides an interface for a user to input worries or complaints in text form and transmits that input to the system.

[1240] "Means for generating probing questions based on received input" refers to a function that analyzes the user's initial input and automatically generates follow-up questions to elicit more detailed information.

[1241] The "means for presenting the generated question to the user" is a function for displaying the generated additional question to the user and enabling the user to answer it.

[1242] "A means of extracting and storing the essence of users' worries and complaints" refers to a function that analyzes the answers obtained from users and stores the essential content in a database.

[1243] The "means for executing a data analysis algorithm for analyzing the stored data" refers to a function for executing an algorithm for extracting patterns and trends using the stored data.

[1244] "Means for executing an emotion engine to analyze user emotions" refers to a function for analyzing emotions from a user's text data and classifying them as either positive, negative, or neutral.

[1245] The "means for integrating the results of the data analysis algorithm and the emotion engine" is a function for integrating the analysis results of the data analysis algorithm and the emotion engine to generate a comprehensive analysis result.

[1246] "Means of identifying high-value issues from integrated results" refers to a function that identifies high-value issues that users commonly face based on integrated analysis results.

[1247] The "means for generating a report on the identified issues" is a function for generating a detailed report on the identified issues and outputting it in PDF format or web page format.

[1248] The present invention is a system for analyzing the worries and complaints of a user and identifying high-value issues. A specific embodiment of this system will be described below.

[1249] 1. System Configuration

[1250] The system consists of a terminal that receives user input, a server that analyzes the data, and a database that stores the data. The terminal provides an interface for users to input their worries and complaints. The server executes data analysis algorithms and an emotion engine, and integrates the analysis results. The database stores the user input data.

[1251] 2. Hardware and Software Used

[1252] The server is a computer equipped with a high-performance processor and large memory. Relational databases such as MySQL and PostgreSQL are used for the database. Python's Pandas and Scikit-learn are used for the data analysis algorithm. NLTK and Transformers, which are NLP (natural language processing) libraries, are used for the emotion engine.

[1253] 3. Data processing and calculation

[1254] The text data of worries and complaints entered by users through their devices is sent to a server. The server stores the received data in a database. The server then runs a data analysis algorithm to extract frequently occurring keywords and phrases from the text data. It also runs an emotion engine to analyze the user's emotions from the text data. The results of these analyses are integrated to find common patterns and trends.

[1255] 4. Specific Examples

[1256] For example, if a user responds to the prompt "Tell us about any recent complaints about work stress" by typing "I work a lot of overtime every day and I can't get rid of my fatigue," the device will send this text data to the server. The server will store this data in a database and analyze it using a data analysis algorithm and emotion engine. The analysis results will reveal that the keyword "work stress" appears frequently and that there are many negative emotions associated with it. The server will integrate these results and identify a high-value issue, "Many users are feeling work stress." Finally, the server will generate a report recommending the development of a new service to solve this issue.

[1257] In this way, the system can identify high-value issues from users' concerns and complaints and provide useful information to companies and service providers.

[1258] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1259] Program processing flow

[1260] Step 1: User enters their concerns or complaints

[1261] Users input their worries or complaints in text format through a dedicated application or web form. For example, they might input something like, "I've been busy at work lately and I'm feeling stressed."

[1262] Input: Text data of user's worries and complaints

[1263] Output: The text data entered

[1264] Step 2: The device sends the input data to the server

[1265] The terminal (user's smartphone or PC) sends the entered text data to the server. At this time, the data is encrypted before being sent.

[1266] Input: Text data entered by the user.

[1267] Output: Text data sent to the server

[1268] Step 3: The server stores the data in a database

[1269] The server stores the received text data in a database, which is a relational database.

[1270] Input: Text data sent to the server

[1271] Output: Text data stored in the database

[1272] Step 4: The server runs the data analysis algorithms

[1273] The server runs data analysis algorithms using Python's Pandas and Scikit-learn to extract frequently occurring keywords and phrases from the text data and find patterns and trends.

[1274] Input: Text data stored in a database

[1275] Output: Extracted keywords, phrases, patterns and trends

[1276] Step 5: The server runs the emotion engine

[1277] The server runs an emotion engine using NLP libraries such as NLTK and Transformers, which analyzes user emotions from text data and classifies them as positive, negative, or neutral.

[1278] Input: Text data stored in a database

[1279] Output: Parsed sentiment data (positive, negative, neutral)

[1280] Step 6: The server consolidates the analysis results

[1281] The server combines the results of the data analysis algorithm and the emotion engine. For example, if the keyword "work stress" appears frequently and has a high proportion of negative emotions, it will combine these into a single analysis result.

[1282] Input: Extracted keywords and phrases, parsed sentiment data

[1283] Output: Integrated analysis results

[1284] Step 7: Identify high-value issues for your server

[1285] The server identifies high-value issues from the integrated analysis results. For example, it identifies an issue such as "Many users are feeling stressed at work."

[1286] Input: Integrated analysis results

[1287] Output: High-value issues identified

[1288] Step 8: Generate a report of the issues the server identifies

[1289] The server generates a report of the identified issues, which includes an overview of the issues, detailed analysis results, recommended measures, etc. The report is output in PDF format or web page format.

[1290] Input: Identified high-value issues

[1291] Output: Generated report (in PDF and / or web page format)

[1292] (Application example 2)

[1293] Next, a description will be given of Application Example 2 of Form 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".

[1294] In traditional brick-and-mortar stores, there was a lack of effective ways to collect and analyze customer feedback, which often delayed improvements to store operations and the development of new services. It was also difficult to accurately recognize customer emotions and find common patterns and trends. This meant that customer satisfaction and the efficiency of store operations were not fully achieved.

[1295] 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.

[1296] In this invention, the server includes a means for receiving input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, a means for analyzing the stored data with a data analysis algorithm, a means for recognizing the user's emotions using an emotion engine, a means for finding common patterns and trends based on the analysis results, and a means for proposing improvements to store operations and new services based on the patterns and trends found. This makes it possible to effectively collect and analyze customer feedback and quickly develop improvements to store operations and new services.

[1297] "Means for receiving input from the user" refers to a function that provides an interface for users to input feedback and opinions into the system.

[1298] "Means for generating probing questions based on received input" refers to a function that analyzes initial input from a user and automatically generates follow-up questions to elicit more detailed information.

[1299] The "means for presenting the generated question to the user" is a function for displaying the generated question to the user and enabling the user to answer it.

[1300] "A means of extracting and storing the essence of users' concerns and complaints" refers to a function that analyzes user feedback, extracts important information and common issues from it, and stores them in a database.

[1301] "Means for analyzing accumulated data using a data analysis algorithm" refers to a function that uses accumulated feedback data to perform data analysis to find patterns and trends.

[1302] The "means for recognizing a user's emotions using an emotion engine" is a function for analyzing the emotions contained in the user's feedback and recognizing the type and intensity of those emotions.

[1303] "Means of finding common patterns and trends based on analysis results" refers to a function that integrates the results of data analysis and emotion recognition to identify common patterns and trends in user feedback.

[1304] "Means for proposing improvements to store operations and new services based on identified patterns and trends" is a function for proposing improvements to store operations and the development of new services based on identified patterns and trends.

[1305] The system for implementing the present invention provides a series of functions for collecting and analyzing feedback from users. A specific embodiment of the system will be described below.

[1306] System Configuration

[1307] The system consists of a user terminal, a server, a database, and an analysis engine. The user terminal is a device such as a smartphone or tablet, and provides an interface for users to input feedback. The server receives input from users, stores it in the database, and analyzes the data using the analysis engine.

[1308] Hardware and Software Used

[1309] User devices: Smartphones, tablets

[1310] Server: A server with a powerful processor and large memory capacity

[1311] Database: Relational database such as MySQL, PostgreSQL, etc.

[1312] Analysis engine: Data analysis algorithms using programming languages ​​such as Python and R

[1313] Sentiment engines: Natural language processing libraries such as TextBlob and NLTK

[1314] Data processing and calculation

[1315] The server receives the feedback data sent from the user's device and stores it in a database. It then uses a data analysis algorithm to analyze the accumulated data and extract common patterns and trends. It uses an emotion engine to analyze the emotions contained in the feedback and recognize the type and intensity of those emotions. Based on the analysis results, it finds common patterns and trends and suggests improvements to store operations and new services.

[1316] Examples

[1317] For example, if a certain brick-and-mortar store receives a lot of feedback that its staff are slow to respond, the system will analyze the feedback and suggest specific measures to improve staff response speed. These suggestions are made possible by using data analysis algorithms and an emotion engine to extract common patterns and trends from the feedback data.

[1318] Examples of prompt statements

[1319] "Develop an application that collects customer feedback from brick-and-mortar stores, analyzes it using data analysis algorithms and sentiment engines, and proposes improvements to store operations and new services."

[1320] In this way, the system can effectively collect and analyze customer feedback, enabling it to quickly improve store operations and develop new services.

[1321] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1322] Step 1:

[1323] The user inputs feedback using a terminal such as a smartphone or a tablet. The input feedback is transmitted from the terminal to a server. The input data includes the feedback in text format.

[1324] Step 2:

[1325] The server receives the feedback data sent from the terminal and stores it in a database. The input data is the feedback in text format, and the output is the feedback data stored in the database.

[1326] Step 3:

[1327] The server analyzes the feedback data stored in the database with data analysis algorithms, specifically, extracting common patterns and trends from the feedback data using programming languages ​​such as Python and R. The input data is the feedback data stored in the database, and the output is the extracted patterns and trends.

[1328] Step 4:

[1329] The server uses an emotion engine to analyze the emotions contained in the feedback. Specifically, it uses natural language processing libraries such as TextBlob and NLTK to recognize the type and intensity of the emotion in the feedback. The input data is the feedback text, and the output is the type and intensity of the recognized emotion.

[1330] Step 5:

[1331] The server integrates the results of the data analysis algorithms and the emotion engine to find common patterns and trends. The input data are the extracted patterns and trends, as well as the type and intensity of the recognized emotions, and the output is the integrated analysis results.

[1332] Step 6:

[1333] The server proposes improvements to store operations and new services based on the integrated analysis results. Specifically, it proposes which parts of store operations should be improved or what new services should be introduced based on the analysis results. The input data are the integrated analysis results, and the output is the proposed improvements and new services.

[1334] In this way, the system can effectively collect and analyze customer feedback, enabling it to quickly improve store operations and develop new services.

[1335] Example 3

[1336] Next, a third embodiment of the third embodiment will be described. 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".

[1337] Conventional systems could extract users' concerns and complaints, but they lacked the process to develop specific services based on those concerns. They also lacked the means to recognize users' emotions, collect feedback, and improve services. This made it difficult to provide services that met users' needs.

[1338] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1339] In this invention, the server includes means for receiving input from a user, means for generating probing questions based on the received input, means for presenting the generated questions to the user, means for extracting and storing the essence of the user's worries and complaints, means for finding high-value issues from the essence of the accumulated worries and complaints, means for generating ideas for solving the issues found, means for developing the generated ideas into specific services, means for recognizing the user's emotions, and means for collecting user feedback and improving the service. This makes it possible to rapidly develop and provide specific services that meet the needs of users.

[1340] "Means for receiving input from the user" refers to a function that provides an interface for users to input their issues and concerns into the system.

[1341] "Means for generating probing questions based on received input" refers to a function that automatically generates questions to elicit more detailed information based on the initial input information received from the user.

[1342] The "means for presenting the generated question to the user" is a function for displaying the generated question to the user and enabling the user to answer it.

[1343] "Means of extracting and storing the essence of users' worries and complaints" refers to a function that extracts the essential problems and worries from the information provided by the user and stores them in a database, etc.

[1344] "A means of finding high-value issues from the essence of accumulated worries and complaints" is a function that analyzes accumulated data and identifies issues that are high-value to solve.

[1345] The "means for generating ideas for solving the identified problems" is a function for generating solution ideas for the identified problems.

[1346] "Means for developing the generated ideas into concrete services" refers to a function for developing concrete applications or web services based on the generated ideas.

[1347] "Means of recognizing user emotions" refers to a function for analyzing and recognizing emotions from the user's facial expressions, voice, etc.

[1348] "Means of collecting user feedback and improving the service" refers to a function for collecting feedback from users, identifying areas for improvement to the service based on that feedback, and incorporating that into the next version.

[1349] The present invention relates to a system for developing new services to solve users' concerns and problems. A specific embodiment of this system will be described below.

[1350] System Configuration

[1351] Hardware and Software

[1352] The system is implemented using the following hardware and software.

[1353] Server: Use a server with a powerful processor and large memory capacity. For example, you can use a cloud-based server.

[1354] Terminal: A device that allows a user to input information, such as a computer, smartphone, or tablet.

[1355] Software: Visual Studio and Eclipse are used as software development tools, and IBM Watson and Microsoft Azure Emotion API are used as emotion engines.

[1356] Program Processing

[1357] Entering an assignment

[1358] The user inputs the problem they want to solve into the system via a terminal. For example, they can input a problem such as "I want to reduce stress at work."

[1359] Idea generation

[1360] The server receives the task input by the user and inputs a prompt sentence into the generative AI model. An example of a prompt sentence is "Please propose an idea for a new service to reduce work stress." The generative AI model generates multiple ideas based on the prompt sentence. For example, it suggests ideas such as "relaxation apps" and "stress management tools."

[1361] Making ideas a reality

[1362] The server selects the best ideas from the generated ideas and develops them into concrete applications or web services using software development tools (e.g. Visual Studio, Eclipse). Specifically, the server performs the following steps:

[1363] Creating a Project: Start Visual Studio and create a new project.

[1364] Feature implementation: For a relaxation app, implement music playback and guided meditation features.

[1365] Testing and debugging: Test the developed application and fix any bugs.

[1366] Using the Emotion Engine

[1367] The server uses an emotion engine (e.g. IBM Watson, Microsoft Azure Emotion API) to recognize the user's emotions. When the user uses the application, the emotion engine analyzes the user's emotions from their facial expressions and voice. Specifically, the server performs the following steps:

[1368] Emotion data collection: Collect emotion data through the camera and microphone while the user is using the application.

[1369] Sentiment analysis: The collected data is sent to the sentiment engine to analyze the user's sentiment.

[1370] Feedback generation: Based on the analysis results, feedback is generated according to the user's sentiment.

[1371] Providing services and collecting feedback

[1372] The server provides the developed service to the user. The user uses the service through the terminal and provides feedback. Specifically, the server performs the following steps:

[1373] Deploying services: Deploy the developed application on a cloud server and make it accessible to users.

[1374] Feedback collection: When you use the Service, we collect emotional data collected through our emotion engine and directly entered feedback.

[1375] Service Improvement: Based on the feedback collected, we identify areas for improvement in our service and incorporate them into the development of the next version.

[1376] The above is a specific embodiment of this system. This system makes it possible to rapidly develop and provide specific services that meet the needs of users. The flow of the specific process in the third embodiment will be described with reference to FIG.

[1377] Step 1:

[1378] Entering an assignment

[1379] A user inputs a problem to be solved into the system through a terminal. For example, the user inputs a problem such as "I want to reduce work stress." The input problem is sent to the server.

[1380] Input: User's problem (e.g., "I want to reduce stress at work")

[1381] Output: The issue data sent to the server.

[1382] Step 2:

[1383] Idea generation

[1384] The server inputs a prompt sentence into the generative AI model based on the task data received from the user. An example of a prompt sentence is "Please propose an idea for a new service to reduce work stress." The generative AI model generates multiple ideas based on the prompt sentence.

[1385] Input: User problem data, prompt (e.g., "Please suggest new service ideas to reduce work stress.")

[1386] Output: Generated ideas (e.g. "Relaxation app", "Stress management tool")

[1387] Step 3:

[1388] Making ideas a reality

[1389] The server selects the best ideas from the generated ideas and develops them into concrete applications or web services using software development tools (e.g. Visual Studio, Eclipse). Specifically, the server performs the following steps:

[1390] Creating a Project: Start Visual Studio and create a new project.

[1391] Feature implementation: For a relaxation app, implement music playback and guided meditation features.

[1392] Testing and debugging: Test the developed application and fix any bugs.

[1393] Input: Generated idea (e.g. "Relaxation app")

[1394] Output: Developed application (e.g. "Relaxation App")

[1395] Step 4:

[1396] Using the Emotion Engine

[1397] The server uses an emotion engine (e.g. IBM Watson, Microsoft Azure Emotion API) to recognize the user's emotions. When the user uses the application, the emotion engine analyzes the user's emotions from their facial expressions and voice. Specifically, the server performs the following steps:

[1398] Emotion data collection: Emotion data is collected through the camera and microphone while the user is using the application.

[1399] Sentiment analysis: The collected data is sent to the sentiment engine to analyze the user's sentiment.

[1400] Feedback generation: Based on the analysis results, feedback is generated according to the user's emotions.

[1401] Input: User emotion data (e.g. facial expressions, voice)

[1402] Output: Sentiment analysis results, feedback

[1403] Step 5:

[1404] Providing services and collecting feedback

[1405] The server provides the developed service to the user. The user uses the service through the terminal and provides feedback. Specifically, the server performs the following steps:

[1406] Deploying services: Deploy the developed application on a cloud server and make it accessible to users.

[1407] Feedback collection: When users use the service, we collect emotional data collected through the emotion engine and directly entered feedback.

[1408] Service Improvement: Based on the feedback collected, we identify areas for improvement in our service and incorporate them into the development of the next version.

[1409] Input: User feedback, emotion data

[1410] Output: Improved service

[1411] (Application example 3)

[1412] Next, a description will be given of Application Example 3 of Form Example 3. 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".

[1413] In modern brick-and-mortar stores, there is a demand for providing personalized services according to customers' emotions and needs. However, conventional systems have the challenge of being unable to recognize customers' emotions in real time and suggest optimal products and services based on those emotions. This has resulted in insufficient improvements to customer experience.

[1414] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1415] In this invention, the server includes a means for receiving input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, a means for recognizing the user's emotions, and a means for proposing optimal products and services based on the recognized emotions. This makes it possible to propose personalized products and services based on the customer's emotions.

[1416] A "means for receiving input from a user" is an interface through which a user provides information to the system.

[1417] "Means for generating probing questions based on received input" refers to a function that analyzes information received from a user and automatically generates questions to elicit more detailed information.

[1418] The "means for presenting the generated question to the user" refers to an interface for displaying the question generated by the system to the user and prompting the user to answer.

[1419] "Means of extracting and storing the essence of users' worries and complaints" refers to a function that extracts the essential problems and dissatisfactions from the information provided by users and stores them in a database.

[1420] "Means of recognizing the user's emotions" refers to technology that analyzes the user's emotions from their facial expressions, voice, etc., and grasps their state in real time.

[1421] The "means for suggesting optimal products and services based on recognized emotions" refers to an algorithm for selecting and suggesting optimal products and services based on the user's emotional state.

[1422] The system for implementing the present invention includes a series of means for recognizing a user's emotion and proposing optimal products and services based on the emotion. A specific embodiment of the system will be described below.

[1423] Hardware and Software Used

[1424] Hardware

[1425] Smart glasses: Equipped with a camera to capture the customer's face.

[1426] Head-mounted display: Equipped with a display to capture the customer's face and display information.

[1427] software

[1428] Emotion recognition engine: Analyzes emotions from captured images using, for example, the Microsoft Azure Emotion API.

[1429] Software development tools: Use Visual Studio and Unity to develop the entire system program.

[1430] Data processing and calculation

[1431] emotion recognition

[1432] 1. Capture: Capture the customer's face using a camera mounted on smart glasses or a head-mounted display.

[1433] 2. Analysis: The captured image is sent to the emotion recognition engine to analyze the emotions in real time.

[1434] 3. Data acquisition: Acquire emotion data (e.g., happiness, sadness, surprise, anger, etc.).

[1435] Data Processing

[1436] 1. Evaluate: Evaluate the customer’s current emotional state based on the captured emotion data.

[1437] 2. Suggestion generation: Generate a list of products or services depending on the emotional state.

[1438] suggestion

[1439] 1. Display: Display emotion-based product and service suggestions on the screen of smart glasses or head-mounted displays.

[1440] 2. Provide More Information: If the customer is interested, provide them with more information and instructions on how to purchase.

[1441] Examples

[1442] For example, if a customer is feeling stressed, they can be offered relaxation-related products (aromatherapy oils, massage chairs, etc.) or services (directions to a relaxation room).

[1443] Examples of prompt statements

[1444] "If a customer is feeling stressed, develop an application that suggests relaxation-related products and services. The hardware used will be smart glasses, and the Microsoft Azure Emotion API will be used as the emotion recognition engine. Specific suggestions will include offering aroma oils, massage chairs, and relaxation rooms."

[1445] In this way, personalized product and service recommendations can be made based on customer emotions.

[1446] The flow of the specific process in the application example 3 will be described with reference to FIG.

[1447] Step 1:

[1448] Capture

[1449] The terminal (smart glasses or head-mounted display) captures the customer's face with a camera. The input is the customer's face image, and the output is the captured image data. This image data is used for subsequent emotion recognition processing.

[1450] Step 2:

[1451] Emotion analysis

[1452] The server sends the captured image data to an emotion recognition engine (e.g., Microsoft Azure Emotion API). The input is the image data, and the output is the analyzed emotion data (e.g., happiness, sadness, surprise, anger, etc.). The emotion recognition engine analyzes the image data and generates a score for each emotion.

[1453] Step 3:

[1454] Emotional assessment

[1455] The server evaluates the customer's current emotional state based on the acquired emotional data. The input is the emotional data, and the output is the evaluated emotional state (e.g., stressed, relaxed, etc.). The server analyzes the scores of the emotional data and identifies the strongest emotion.

[1456] Step 4:

[1457] Proposal generation

[1458] The server generates a list of optimal products and services according to the evaluated emotional state. The input is the evaluated emotional state, and the output is a suggestion list. The server searches a database for relevant products and services based on the emotional state and creates the list.

[1459] Step 5:

[1460] Suggestion display

[1461] The terminal displays the generated suggestion list on the display of the smart glasses or head-mounted display. The input is the suggestion list, and the output is the suggestion information displayed on the display. The terminal displays the suggestion contents so that the user can visually confirm them.

[1462] Step 6:

[1463] Provide detailed information

[1464] The server provides detailed information and purchase procedure guidance for the proposals that the user is interested in. The input is the user's selection information, and the output is detailed information and purchase procedure guidance. The server retrieves the detailed information from the database based on the user's selection and sends it to the terminal.

[1465] In this way, the system can make personalized product and service recommendations based on customer sentiment.

[1466] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[1467] 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> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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.

[1468] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1469] 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 smart glasses 214.

[1470] [Third embodiment]

[1471] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1472] 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.

[1473] 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 wide area network (WAN) and / or a local area network (LAN).

[1474] 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.

[1475] 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 the voice according to instructions from the processor 46.

[1476] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[1477] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[1478] Fig. 6 shows an example of 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.

[1479] 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.

[1480] 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.

[1481] In the headset type terminal 314, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1482] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1483] "Example 1"

[1484] An embodiment of the present invention includes a voice recognition system and a text input interface as a means for receiving input from a user. This system receives the user's worries and complaints as text data. Next, a natural language processing (NLP) algorithm is used as a means for generating probing questions based on the received input. This algorithm extracts keywords and emotions from the user's input and generates questions based on them. The generated questions are presented to the user to elicit a response from the user. This allows the essence of the user's worries and complaints to be extracted and stored in a database.

[1485] "Example 2"

[1486] A data analysis algorithm is used to find high-value issues from the nature of the accumulated worries and complaints. This algorithm extracts patterns and trends from the accumulated data and identifies high-value issues based on them. For example, if many users complain about work-related stress, it can determine that a new service needs to be developed to solve this issue.

[1487] "Example 3"

[1488] Software development tools are used to develop new services that solve the problems that have been identified. These tools develop applications and web services to solve specific problems. For example, a relaxation app to reduce work stress or a project management tool to help manage tasks efficiently can be developed.

[1489] The process flow of each embodiment will be described below.

[1490] "Example 1"

[1491] Step 1: Receive voice or text input from the user. In this step, the user inputs their concerns or complaints through voice or text.

[1492] Step 2: Run the received input through a natural language processing (NLP) algorithm to extract keywords and sentiment from the user's input. In this step, NLP algorithms are used to analyze the text data in order to understand the essence of the user's concerns and complaints.

[1493] Step 3: Generate probing questions based on the extracted keywords and sentiments and present them to the user. In this step, the generated questions are presented to the user to elicit a response.

[1494] Step 4: Receive user responses and store the essence of their concerns and complaints in a database. In this step, the user responses are stored in a database and accumulated for later analysis.

[1495] "Example 2"

[1496] Step 1: Run the accumulated data through a data analysis algorithm to extract patterns and trends. In this step, common patterns and trends are found in the accumulated data on users' concerns and complaints.

[1497] Step 2: Identify high-value issues based on extracted patterns and trends. In this step, the results of the data analysis are used to find high-value issues that users have in common.

[1498] "Example 3"

[1499] Step 1: Think of ideas for new services to solve the identified problems. In this step, you brainstorm ideas for new services to solve the identified problems.

[1500] Step 2: Realize the idea and develop a new service. In this step, software development tools are used to realize the idea and develop a new service.

[1501] Example 1

[1502] Next, a description will be given of Example 1 of the embodiment 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".

[1503] Conventional systems have had difficulty effectively collecting users' concerns and complaints and extracting their true essence. In addition, they were unable to generate appropriate questions based on user input and dig deeper, making it difficult to fully understand the true essence of users' concerns and complaints. Furthermore, it was difficult to effectively store collected data and find high-value issues.

[1504] 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.

[1505] In this invention, the server includes a means for receiving input from a user, a means for preprocessing the received input, a means for extracting keywords and emotions from the preprocessed input, a means for generating questions based on the extracted keywords and emotions, a means for presenting the generated questions to the user, a means for collecting the user's responses, and a means for extracting and storing the essence of the user's worries and complaints. This makes it possible to effectively collect the user's worries and complaints and extract their essence.

[1506] A "means for receiving input from a user" is an interface through which a user provides information to the system through voice or text.

[1507] "Means for preprocessing received input" refers to the process of converting text data received from a user into a format that is easy to parse by performing processes such as normalization and tokenization.

[1508] "Means for extracting keywords and sentiment from the preprocessed input" refers to a process of using natural language processing techniques to identify important words and sentiment from the preprocessed text data.

[1509] The "means for generating questions based on the extracted keywords and sentiment" refers to a process for automatically formulating questions to elicit further information from a user based on the identified keywords and sentiment.

[1510] The "means for presenting the generated question to the user" refers to an interface that displays the generated question on the user's terminal and enables the user to respond to it.

[1511] "Means of collecting user responses" refers to the process of incorporating responses entered by users into the system in response to questions posed.

[1512] "Means of extracting and storing the essence of users' worries and complaints" refers to the process of analyzing the information obtained from users' input and responses, and storing that essence in a database.

[1513] "Means of finding high-value issues" is the process of identifying particularly important problems or issues from accumulated data.

[1514] "Means of developing new services" refers to the process of designing and implementing new services and solutions to solve the problems identified.

[1515] This invention is a system that receives input from a user, generates questions based on the input, and extracts and accumulates the essence of the user's worries and complaints. This system receives input from a user using a voice recognition system and a text input interface. Specifically, a general voice recognition API is used for the voice recognition system, and an HTML form is used for the text input interface.

[1516] The server preprocesses the text data received from the user. This preprocessing involves normalizing the text (converting to lowercase, deleting unnecessary spaces, etc.) and tokenizing (dividing the text into words). From the preprocessed text data, the server uses natural language processing (NLP) techniques to extract keywords and sentiment. For this process, the Python libraries NLTK and spaCy are used.

[1517] Based on the extracted keywords and sentiment, the server generates questions using a generative AI model. The generative AI model uses an advanced natural language generation model such as GPT-3. The generated questions are presented to the user's device. The user enters an answer to the presented question. The server passes this answer back to the NLP algorithm to extract further keywords and sentiment.

[1518] Finally, the server extracts the essence of the user's worries and complaints and stores them in a database. The database uses a general relational database management system (RDBMS), such as MySQL or PostgreSQL.

[1519] Examples

[1520] Example 1: User Input

[1521] The user speaks into the smartphone's microphone, "I've been busy at work lately and I'm feeling stressed," and the voice recognition system converts this into text.

[1522] Example 2: Generating a question

[1523] The server uses an NLP algorithm to extract the keywords "busy at work" and the emotion "stressed." It then uses a generative AI model (GPT-3) to generate the question "What specific tasks are you busy with?"

[1524] Example 3: User response

[1525] The user uses his / her PC keyboard to type, "I have a lot of meetings in particular, so it takes a long time to prepare."

[1526] Example 4: Accumulating data

[1527] The server stores the answer in a database and records the nature of the user's concern, e.g., by saving it as a new record in a "concerns" table in the database.

[1528] Examples of prompt statements

[1529] User input: Work has been busy and stressful lately.

[1530] Prompt for generative AI model: A user says, "I've been busy at work lately and it's stressful." What exactly are the tasks that are making them busy?

[1531] In this way, the system can dig deeper into the user's concerns and complaints and bring out their essence.

[1532] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1533] Step 1:

[1534] Users input their worries or complaints through a voice recognition system or text input interface. For example, a user speaks into the microphone of their smartphone, "I've been busy at work lately and I'm feeling stressed." The voice recognition system converts this into text and sends it to the server. The input is voice data or text data, and the output is text data.

[1535] Step 2:

[1536] The server preprocesses the received text data. Preprocessing involves normalizing the text (converting to lowercase, deleting unnecessary spaces, etc.) and tokenizing (dividing it into words). For example, the text "I've been busy at work lately and I'm getting stressed" is tokenized as "I've been busy at work lately and I'm getting stressed." The input is text data, and the output is the preprocessed text data.

[1537] Step 3:

[1538] The server extracts keywords and emotions from the preprocessed text data. For this process, it uses Python libraries NLTK and spaCy. For example, it extracts keywords such as "work," "busy," "stress," and "accumulating" and the emotion "accumulating stress." The input is the preprocessed text data, and the output is keywords and emotions.

[1539] Step 4:

[1540] The server generates questions based on the extracted keywords and sentiment. A generative AI model is used to generate these questions. For example, a generative AI model (GPT-3) is used to generate the question, "What specific tasks are you busy with?" The input is the keywords and sentiment, and the output is the generated question.

[1541] Step 5:

[1542] The server presents the generated question to the user. The question is displayed on the user's terminal. For example, the question may be, "What specific tasks are you busy with?" The input is the generated question, and the output is the question displayed on the user's terminal.

[1543] Step 6:

[1544] The user inputs an answer to the question presented. For example, the user uses the PC keyboard to type, "I have a lot of meetings, so it takes a lot of time to prepare." The input is the user's answer, and the output is text data.

[1545] Step 7:

[1546] The server passes the user's answer to the NLP algorithm again to extract further keywords and sentiments. For example, it extracts the keywords "meeting," "preparation," and "takes time" and the sentiment "takes time." The input is the user's answer, and the output is the keywords and sentiments.

[1547] Step 8:

[1548] The server extracts the essence of the user's worries and complaints and stores them in a database. A general relational database management system (RDBMS) is used for the database. For example, MySQL or PostgreSQL is used. The input is keywords and emotions, and the output is the data stored in the database.

[1549] (Application example 1)

[1550] Next, a description will be given of Application Example 1 of Form 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".

[1551] In modern society, users have a wide range of security concerns and worries, but there is a lack of systems that provide specific measures and advice to properly resolve them. In addition, there is a challenge in that it is difficult to dig deep into users' concerns and clarify the essential problems.

[1552] 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.

[1553] In this invention, the server includes a means for receiving an input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, a means for converting the voice input into text using a voice recognition system, a means for generating questions using a natural language processing algorithm, a means for clarifying the user's specific problem based on the generated questions, and a means for providing appropriate measures and advice. This makes it possible to effectively resolve the user's worries and anxieties about security.

[1554] A "means for receiving input from a user" is an interface for receiving information input by a user via voice or text.

[1555] The "means for generating probing questions based on the received input" is an algorithm that analyzes the user's input and generates questions to elicit more detailed information.

[1556] The "means for presenting the generated question to the user" refers to an interface for displaying or audibly conveying the generated question to the user.

[1557] "A means for extracting and storing the essence of users' worries and complaints" is a system for extracting the essential problem from user input and storing it in a database.

[1558] "Means for converting voice input into text using a voice recognition system" refers to voice recognition technology for converting a user's voice input into text data.

[1559] "Means for generating questions using a natural language processing algorithm" refers to natural language processing technology for analyzing user input and generating appropriate questions.

[1560] The "means for clarifying a user's specific problem based on the generated questions" is a system for clarifying a user's specific problem or concern through the generated questions.

[1561] A "means for providing appropriate measures and advice" is a system for providing appropriate measures and advice for a user's specific problem.

[1562] A system for implementing this invention receives input from a user, generates probing questions based on that input, and presents these to the user to draw out the essence of the user's worries and complaints and provide appropriate solutions and advice.

[1563] Hardware and Software Used

[1564] Hardware: Microphone (for voice input), server, user device (smartphone, etc.)

[1565] software:

[1566] speech_recognition library (speech recognition)

[1567] NLP models (question generation) from the transformers library

[1568] System Configuration

[1569] 1. Speech recognition system: The user speaks through a microphone. The speech recognition system converts this speech into text data.

[1570] 2. Natural language processing algorithms: Analyze the converted text data and generate probing questions based on the user's input.

[1571] 3. Question Prompt Interface: Presents the generated question to the user and prompts for further input.

[1572] 4. Database: A database for extracting and storing the essence of users' concerns and complaints.

[1573] 5. Solution and advice provision system: Provides appropriate solutions and advice for users' specific problems.

[1574] Processing flow

[1575] 1. Acquire speech input: The user speaks into a microphone. The speech recognition system converts this speech into text.

[1576] 2. Question generation: Natural language processing algorithms analyze the text data and generate probing questions based on the user's input.

[1577] 3. Question prompting: Present the generated question to the user and prompt for further input.

[1578] 4. Data accumulation: The essence of users’ concerns and complaints is accumulated in a database.

[1579] 5. Providing countermeasures and advice: Providing appropriate countermeasures and advice to users based on accumulated data.

[1580] Examples

[1581] If a user says, "I've been worried about the security of my home lately," the system generates questions such as, "What specifically are you worried about?" and "Have you seen anything suspicious happen recently?" This helps clarify the user's specific problem and provides appropriate measures and advice.

[1582] Examples of prompt statements

[1583] User: I've been worried about the security of my home lately.

[1584] System: What specifically are you worried about?

[1585] User: I often hear noises in the middle of the night.

[1586] System: At what times of day is the noise most common?

[1587] In this way, it is possible to dig deeper into the user's concerns and propose appropriate security measures.

[1588] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1589] Step 1:

[1590] A user uses a microphone to input voice. The terminal acquires this voice data and sends it to a voice recognition system. The input is the user's voice data, and the output is text data. The voice recognition system analyzes the voice data and converts it into text data.

[1591] Step 2:

[1592] The server inputs the text data received from the speech recognition system into a natural language processing algorithm. The input is the text data and the output is the generated question. The natural language processing algorithm analyzes the text data and generates probing questions based on the user's input.

[1593] Step 3:

[1594] The server presents the generated question to the user. The input is the generated question and the output is the user's answer. Through a question presentation interface, the generated question is displayed to the user and prompts for further input.

[1595] Step 4:

[1596] The user inputs an answer to the question. The terminal acquires this answer and sends it to the server. The input is the user's answer, and the output is text data. In the case of voice input, the voice recognition system is used again to convert it to text.

[1597] Step 5:

[1598] The server stores the user's answers in a database. The input is the text data of the user's answers, and the output is the data stored in the database. The database is a system for extracting and storing the essence of the user's worries and complaints.

[1599] Step 6:

[1600] The server provides appropriate measures and advice to the user based on the accumulated data. The input is the data stored in the database, and the output is measures and advice. The solution and advice providing system generates appropriate measures and advice for the user's specific problem and presents them to the user.

[1601] In this way, it is possible to dig deeper into the user's concerns and propose appropriate security measures.

[1602] Example 2

[1603] Next, a second embodiment of the second form will be described. 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".

[1604] In conventional systems, users' worries and complaints were simply accumulated, making it difficult to extract their true nature and identify high-value issues. In addition, there was a lack of concrete means to develop new services based on the identified issues. As a result, users' worries and complaints could not be effectively resolved.

[1605] 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.

[1606] In this invention, the server includes a means for receiving input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, a means for preprocessing the stored data, a means for analyzing the preprocessed data and extracting important keywords and phrases, a means for analyzing the frequency and co-occurrence relationships of the extracted keywords and phrases, and a means for identifying high-value issues based on the analysis results. This makes it possible to effectively extract the essence of the user's worries and complaints, identify high-value issues, and develop new services based on those issues.

[1607] "Means for receiving input from the user" refers to a function that provides an interface for users to input their concerns and complaints into the system.

[1608] "Means for generating probing questions based on received input" refers to a function that analyzes information entered by a user and automatically generates follow-up questions related to that information.

[1609] The "means for presenting the generated question to the user" is a function for displaying the generated question to the user and enabling the user to answer it.

[1610] "A means of extracting and storing the essence of the user's worries and complaints" refers to a function that analyzes the information entered by the user, extracts the essential parts, and stores them in a database.

[1611] The "means for preprocessing the accumulated data" refers to a function that performs operations such as deleting unnecessary characters and spaces and normalizing text in order to prepare the accumulated data in a format that is easy to analyze.

[1612] "Means for analyzing the preprocessed data and extracting important keywords and phrases" refers to a function for identifying important keywords and phrases from the preprocessed data using natural language processing technology.

[1613] The "means for analyzing the frequency and co-occurrence relationships of extracted keywords and phrases" is a function for statistically analyzing the frequency of occurrence of extracted keywords and phrases and the relationships in which they appear together.

[1614] "Means of identifying high-value issues based on analysis results" refers to a function that identifies particularly important issues that need to be resolved from among users' concerns and complaints based on the analysis results.

[1615] The present invention is a system for effectively collecting users' worries and complaints, extracting their essence, and identifying high-value issues. A specific embodiment of this system will be described below.

[1616] First, the server provides a means to receive input from users. This is achieved through a user interface such as a web interface or a mobile application. When a user inputs their worries or complaints, the data is sent to the server and stored in a database. The database is a common database management system such as MySQL or PostgreSQL.

[1617] The server then has the means to generate follow-up questions based on the input it receives, using natural language processing techniques such as Python libraries like NLTK and spaCy to parse the user's input and generate related follow-up questions.

[1618] The server then presents the generated questions to the user, who answers them and provides further details, a process that draws out the essence of the user's concerns and complaints.

[1619] The accumulated data is preprocessed by the server, which uses the Pandas library to read the data, remove unnecessary characters and spaces, normalize the text, and so on, to make the data easier to analyze.

[1620] The preprocessed data is then analyzed by the server, which uses NLTK and spaCy to tokenize the text data, tag parts of speech, and perform named entity recognition (NER) to extract important keywords and phrases from the text data.

[1621] The extracted keywords and phrases are analyzed by the server for frequency and co-occurrence. This analysis is performed using the Scikit-learn library. For example, if many users mention "work stress," it is confirmed that this is a frequent occurrence. Also, by analyzing co-occurrence, it is found that "work" and "stress" frequently appear together.

[1622] Finally, the server identifies high-value issues based on the analysis results. For example, it may determine that "work stress" is a common issue for many users. Based on this result, it may suggest that the development of a "stress management app" is a high-value solution.

[1623] As a concrete example, let's say you have accumulated the following user complaint data:

[1624] "Work has been so busy lately that it's stressful"

[1625] "My boss is strict and I feel pressured every day."

[1626] "I can't communicate well with my coworkers"

[1627] Example of a prompt to be input to a generative AI model:

[1628] Analyze your users' complaints data and identify common issues. Use the following data:

[1629] "Work has been so busy lately that it's stressful"

[1630] "My boss is strict and I feel pressured every day."

[1631] "I can't communicate well with my coworkers"

[1632] Using this prompt, the generative AI model can identify common issues from user complaint data and suggest valuable solutions.

[1633] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1634] Step 1:

[1635] The server receives input from the user. When the user inputs their worries or complaints through a web interface or a mobile application, the data is sent to the server. An example of input data is "Recently, I've been so busy at work that I'm getting stressed out." The server stores this data in a database. The input data is stored in text format.

[1636] Step 2:

[1637] The server generates probing questions based on the input it receives. Using natural language processing techniques, the server analyzes the user's input and generates related follow-up questions. For example, a question might be "Which parts of your job do you find particularly stressful?" The input data is the user's initial input, and the output data is the generated question.

[1638] Step 3:

[1639] The server presents the generated questions to the user, who answers them and provides further details. For example, if the user answers "my boss is demanding," the data is sent back to the server and stored in the database. The input data are the user's answers to the generated questions, and the output data are the further details.

[1640] Step 4:

[1641] The server preprocesses the accumulated data. Preprocessing includes reading the data using the Pandas library, deleting unnecessary characters and spaces, normalizing the text, etc. For example, it deletes unnecessary spaces from the text "My boss is demanding" and converts it to lowercase. The input data is the accumulated raw data, and the output data is the preprocessed clean data.

[1642] Step 5:

[1643] The server analyzes the preprocessed data and extracts important keywords and phrases. The analysis involves tokenizing the text data, tagging it with parts of speech, and performing named entity recognition (NER) using NLTK and spaCy. For example, keywords such as "boss," "demanding," and "strict" are extracted. The input data is the preprocessed text data, and the output data is the extracted keywords and phrases.

[1644] Step 6:

[1645] The server analyzes the frequency and co-occurrence of the extracted keywords and phrases. The Scikit-learn library is used for the analysis. For example, it is confirmed that "boss" and "strict" frequently appear together. The input data are the extracted keywords and phrases, and the output data are the analysis results of the frequency and co-occurrence.

[1646] Step 7:

[1647] The server identifies high-value issues based on the analysis results. For example, it is determined that "work stress" is a common issue shared by many users. Based on this result, it is proposed that the development of a "stress management app" is a high-value solution. The input data are the results of the analysis of frequency and co-occurrence relationships, and the output data are the identified high-value issues.

[1648] (Application example 2)

[1649] Next, a description will be given of Application Example 2 of Form 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".

[1650] In modern society, users have a wide range of worries and complaints in their daily lives and work. In order to effectively solve these worries and complaints, it is necessary to accurately grasp their true nature and take appropriate measures. However, conventional systems simply accumulate users' worries and complaints, making it difficult to extract their true nature and provide concrete solutions. There was also a lack of effective means to recommend the most suitable content to users. This resulted in the issue of being unable to improve user satisfaction.

[1651] 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.

[1652] In this invention, the server includes a means for receiving input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, a means for extracting patterns and trends from the stored data, and a means for recommending optimal content based on the extracted patterns and trends. This makes it possible to accurately grasp the essence of the user's worries and complaints and recommend appropriate content.

[1653] "Means for receiving input from the user" refers to a function that provides an interface for users to input their concerns and complaints into the system.

[1654] "Means for generating probing questions based on received input" refers to a function that analyzes information entered by a user and automatically generates more detailed questions based on that information.

[1655] The "means for presenting the generated question to the user" is a function for displaying the generated question to the user and enabling the user to answer it.

[1656] "A means of extracting and storing the essence of users' worries and complaints" refers to a function that analyzes the information provided by users, extracts its essence, and stores it in a database.

[1657] "Means for extracting patterns and trends from the accumulated data" refers to a function that executes an algorithm to analyze the accumulated data and find common patterns and trends.

[1658] "Means for recommending optimal content based on extracted patterns and trends" refers to a function that recommends optimal content (videos, music, articles, etc.) to users based on extracted patterns and trends.

[1659] The following system configuration will be described as an embodiment of the present invention.

[1660] System Configuration

[1661] This system consists of a user terminal, a server, and a database. The user terminal is a device such as a smartphone, tablet, or PC, and provides an interface for users to input their worries and complaints. The server plays a central role in receiving input from users, analyzing the data, and recommending appropriate content. The database is used to store user input data and analysis results.

[1662] Program Processing

[1663] The server performs the process in the following procedure.

[1664] 1. A way to receive input from the user

[1665] The server receives the text data of the user's worries and complaints sent from the user's device. This data is stored exactly as entered by the user.

[1666] 2. A way to generate probing questions based on the input received

[1667] The server analyzes the received text data and uses natural language processing technology to generate more detailed questions, using a generative AI model to automatically generate probing questions related to the user's input.

[1668] 3. A way to present the generated questions to the user

[1669] The generated questions are then sent to the user's device and presented to the user, who can then answer them to provide further detailed information.

[1670] 4. A means to extract and accumulate the essence of users' worries and complaints

[1671] The server that receives the user's responses analyzes the content and extracts the essence of the user's worries or complaints. This extracted information is stored in a database.

[1672] 5. A means of extracting patterns and trends from accumulated data

[1673] The server analyzes the accumulated data and runs algorithms to find common patterns and trends, specifically using machine learning algorithms such as TfidfVectorizer and KMeans clustering.

[1674] 6. A means to recommend the best content based on extracted patterns and trends

[1675] The server recommends the most suitable content (videos, music, articles, etc.) to the user based on the extracted patterns and trends. The recommendation results are sent to the user's terminal and displayed to the user.

[1676] Hardware and software used

[1677] Hardware: General purpose PC or server

[1678] Software: Python, pandas, scikit-learn, TfidfVectorizer, KMeans clustering

[1679] Examples

[1680] For example, if User A enters "I'm feeling stressed at work," the server receives this input and generates related probing questions. When User A answers the questions, the server analyzes the answers and extracts the true nature of the stress. It finds common patterns from the accumulated data and recommends "relaxing music" to User A.

[1681] Examples of prompt statements

[1682] "Please develop an algorithm that analyzes data on users' worries and complaints and recommends optimal stress relief content. Use the following data for clustering and content recommendation."

[1683] In this way, a system can be realized that provides optimal content based on the user's concerns and complaints.

[1684] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1685] Step 1:

[1686] The user terminal receives input from the user. The user inputs their worries or complaints in text format. This input data is sent to the server. An example of input data is "I'm feeling stressed at work."

[1687] Step 2:

[1688] The server analyzes the input data it receives. Specifically, it uses natural language processing technology to tokenize the text data and extract important keywords. Based on the results of this analysis, it generates more probing questions. Using a generative AI model, it automatically generates questions related to the user's input. For example, a question might be generated such as, "In what specific situations do you feel stressed?"

[1689] Step 3:

[1690] The server transmits the generated question to the user terminal. The user terminal presents the question to the user. The user inputs an answer to the presented question. This answer data is also transmitted to the server.

[1691] Step 4:

[1692] The server receives the response data from the user and analyzes it again. The response data is tokenized and important keywords and phrases are extracted. Based on the results of this analysis, the essence of the user's worries and complaints is revealed. For example, the essence that is extracted is "I feel stressed because of communication with my boss."

[1693] Step 5:

[1694] The server stores the extracted essential information in a database. The stored data is used for later analysis. The database stores user IDs, input data, analysis results, etc.

[1695] Step 6:

[1696] The server periodically analyzes the accumulated data to extract common patterns and trends. Specifically, it uses TfidfVectorizer to convert the text data into numerical vectors, and then uses KMeans clustering to classify the data into clusters. This analysis reveals common themes in users' concerns and complaints.

[1697] Step 7:

[1698] The server recommends the most suitable content to the user based on the extracted patterns and trends. For example, it recommends "relaxing music" to a user who is feeling stressed. The recommendation results are sent to the user's device and displayed to the user.

[1699] Step 8:

[1700] The user terminal displays the recommended content sent from the server to the user. The user can reduce worries and stress by using the recommended content. User feedback is also collected and sent to the server.

[1701] In this way, a system is realized that provides optimal content based on the user's concerns and complaints.

[1702] Example 3

[1703] Next, a third embodiment of the third embodiment will be described. 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".

[1704] Conventional systems simply accumulate users' worries and complaints, making it difficult to extract the essence of those issues and provide concrete solutions. In addition, there was a lack of means to effectively analyze the accumulated data and quickly develop new services to solve users' problems. This made it difficult to provide appropriate solutions that met users' needs.

[1705] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1706] In this invention, the server includes a means for receiving input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, a means for analyzing the stored data, a means for generating prompt sentences using a generative AI model based on the analysis results, a means for generating an application using a software development tool based on the generated prompt sentences, and a means for providing the generated application to the user. This makes it possible to effectively extract and analyze the essence of the user's worries and complaints and quickly provide a specific solution.

[1707] "Means for receiving input from the user" refers to a function that provides an interface for users to input issues or concerns into the system in text format.

[1708] "Means for generating probing questions based on received input" refers to a function that analyzes the information entered by the user and automatically generates additional questions to elicit more detailed information.

[1709] The "means for presenting the generated question to the user" is a function for displaying the generated question to the user and providing an interface for enabling the user to answer the question.

[1710] "A means of extracting and storing the essence of users' worries and complaints" refers to a function that analyzes the information provided by users, extracts important elements and essential problems from it, and stores them in a database.

[1711] "Means of analyzing accumulated data" refers to a function that provides algorithms and tools to analyze accumulated data on users' concerns and complaints, and find common patterns and important insights.

[1712] "Means for generating prompt sentences using a generative AI model based on analysis results" refers to a function that uses a generative AI model with analysis results as input to generate prompt sentences to suggest specific solutions to the user's problems.

[1713] "Means for generating an application using a software development tool based on the generated prompt text" refers to a function for automatically generating an application or web service for solving a user's problem using a software development tool based on the generated prompt text.

[1714] "Means for providing the generated application to the user" refers to a function for providing the generated application or web service to the user and carrying out distribution and installation procedures so that the user can use it.

[1715] The present invention relates to a system for developing new services to solve users' concerns and problems. A specific embodiment of this system will be described below.

[1716] System Overview

[1717] The system receives input from users, generates probing questions based on the input, and accumulates the essence of the user's worries and complaints. It analyzes the accumulated data and generates prompts using a generative AI model. Based on the generated prompts, it uses software development tools to generate an application and provides it to the user.

[1718] Hardware and Software Used

[1719] Hardware: Servers, user devices (PCs, smartphones, tablets, etc.)

[1720] Software: Natural language processing libraries (e.g., Python's NLTK library, Google Cloud Natural Language API), generative AI models (e.g., OpenAI's GPT-4), software development tools (e.g., Visual Studio Code, JetBrains IntelliJ IDEA, etc.)

[1721] Data processing and calculation

[1722] 1. Receiving input from the user

[1723] Users access the system's interface and enter the problem they want to solve into a text box, for example, "I want to reduce work stress."

[1724] 2. Analysis of input data

[1725] The server receives the task entered by the user and analyzes it using natural language processing (NLP) technology. For analysis, it uses Python's NLTK library and Google Cloud Natural Language API. The server analyzes the text "I want to reduce stress at work" and extracts keywords and important phrases.

[1726] 3. Generate prompts using a generative AI model

[1727] The server uses a generative AI model (e.g., OpenAI's GPT-4) based on the analysis results to generate an appropriate prompt sentence, which is intended to suggest a specific solution to the user's problem.

[1728] Examples of prompt statements

[1729] "Develop a relaxation app to help reduce work-related stress. The app should provide guided meditations, deep breathing exercises, and relaxation music to help users relax. It should also include a feature that allows users to record their stress levels and track their progress."

[1730] 4. Generate the application using software development tools

[1731] Based on the generated prompt text, the server uses a software development tool (e.g., Visual Studio Code or JetBrains IntelliJ IDEA) to generate an application or web service to solve the problem. Based on the prompt text, the server automatically generates the code for the relaxation app and builds the application by importing the necessary libraries and frameworks.

[1732] 5. Deliver the generated application to users

[1733] The user checks the application or web service generated by the server and provides feedback if necessary. The user accesses the system dashboard and clicks the download link for the generated application. The user downloads, installs, and actually uses the application.

[1734] In this way, it becomes possible to rapidly develop and provide new services that effectively solve the worries and problems of users. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1735] Step 1:

[1736] A user enters an assignment

[1737] How it works: A user accesses the system's interface and enters the problem they want to solve in a text box, for example, "I want to reduce stress at work."

[1738] Input: The text data of the assignment entered by the user

[1739] Output: The text data of the assignment that is sent to the server.

[1740] Step 2:

[1741] The server analyzes the problem

[1742] How it works: The server receives the tasks entered by the user and parses them using natural language processing (NLP) techniques, such as the NLTK library for Python and the Google Cloud Natural Language API.

[1743] Input: Text data of assignment received from user

[1744] Data processing: tokenizing text and extracting keywords and key phrases

[1745] Output: A list of extracted keywords or phrases.

[1746] Step 3:

[1747] The server generates a prompt using the generative AI model.

[1748] How it works: The server uses a generative AI model (e.g., OpenAI's GPT-4) to generate an appropriate prompt based on the analysis results.

[1749] Input: A list of extracted keywords or phrases

[1750] Data computation: Input keywords or phrases into the generative AI model to generate prompts

[1751] Output: The generated prompt statement

[1752] Step 4:

[1753] The server uses software development tools to generate the application.

[1754] How it works: Based on the generated prompt statements, the server uses a software development tool (e.g., Visual Studio Code or JetBrains IntelliJ IDEA) to generate an application or web service to solve the problem.

[1755] Input: Generated prompt text

[1756] Data processing: Automatically generate code based on prompt statements, import necessary libraries and frameworks, and build applications.

[1757] Output: The generated application code.

[1758] Step 5:

[1759] User Review of Generated Applications

[1760] How it works: The user reviews the application or web service generated by the server and provides feedback if necessary. The user accesses the system dashboard and clicks on the download link for the generated application. The user downloads, installs, and actually uses the application.

[1761] Input: The generated application code

[1762] Output: User feedback data

[1763] (Application example 3)

[1764] Next, a description will be given of Application Example 3 of Form Example 3. 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".

[1765] In modern factories, machine maintenance is extremely important to maintain productivity. However, it is not easy to detect machine abnormalities early and perform appropriate maintenance. In particular, if an abnormality is not dealt with promptly, it could have an impact on the entire production line. In addition, there is a lack of preventive maintenance proposals based on past maintenance history, making it difficult to prevent machine breakdowns. To solve these issues, a system is needed that can monitor the status of machines in real time and respond immediately when an abnormality occurs.

[1766] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1767] In this invention, the server includes a means for receiving input from a user, a means for generating probing questions based on the received input, a means for presenting the generated questions to the user, a means for extracting and storing the essence of the user's worries and complaints, a means for monitoring the status of machines in a factory in real time, a means for issuing an alert when an abnormality is detected, a means for presenting an appropriate maintenance procedure, and a means for proposing preventive maintenance based on past maintenance history. This makes it possible to detect abnormalities in machines early and respond quickly. Furthermore, by proposing preventive maintenance based on past maintenance history, machine breakdowns can be prevented before they occur.

[1768] A "means for receiving input from a user" is an interface through which a user provides information to the system.

[1769] "Means for generating probing questions based on received input" refers to a function that automatically creates questions to elicit more detailed information based on the information received from the user.

[1770] The "means for presenting the generated question to the user" refers to an interface for displaying the question generated by the system to the user and requesting an answer.

[1771] "A means of extracting and storing the essence of users' worries and complaints" refers to a function that extracts the underlying problems and dissatisfaction from the information provided by the user and stores them in a database.

[1772] A "means for monitoring the status of machines in a factory in real time" is a system that constantly monitors the operating conditions and performance of machines running in a factory and collects data.

[1773] "Means for issuing an alert when an abnormality is detected" refers to a function that immediately issues a warning when an abnormality occurs in the operation of the machine.

[1774] A "means for presenting appropriate maintenance procedures" is a system that provides specific procedures for repair and maintenance when a machine abnormality is detected.

[1775] "A means for proposing preventive maintenance based on past maintenance history" is a function that analyzes records of past maintenance and proposes maintenance measures to prevent future breakdowns.

[1776] In order to implement the present invention, the following system configuration and program are required.

[1777] First, the server has a means for receiving input from the user, which can be an interface through which the user provides information to the system, such as a web form or a voice input system.

[1778] Next, the s...

Claims

1. A means for receiving input from a user; and means for generating a query based on the received input; means for presenting the generated question to the user; A means for extracting and storing the worries and complaints of the user; means for extracting patterns and trends from said accumulated worries and complaints; A means for generating a prompt sentence using a generative AI model based on the result of the extraction; A means for generating an application using a software development tool based on the generated prompt text; The system further comprises means for providing the generated application to the user.

2. A means for receiving input from a user; and means for generating a query based on the received input; means for presenting the generated question to a user; A means for extracting and storing the worries and complaints of the user; A means of monitoring the status of machines in the factory in real time; means for issuing an alert when an abnormality is detected in the machine; means for presenting a maintenance procedure for the machine; A system that includes a means for making preventive maintenance suggestions based on past maintenance history.

3. A means for receiving input from a user; and means for generating a query based on the received input; a means for presenting the generated questions to a user; A means for extracting and storing the essence of the user's worries and complaints; A means for analyzing the accumulated worries and complaints using a data analysis algorithm; means for recognizing emotions of the user using an emotion engine; means for finding common patterns and trends based on the results of said analysis; The system includes a means for suggesting improvements to store operations and new services based on the patterns and trends identified.

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