System

Abstract

To provide a system.SOLUTION: A system includes: first means for collecting feedback and opinions from a customer; second means for generating a prompt sentence for outputting needs and feeling of the customer on the basis of the feedback and opinions from the customer; third means for inputting the generated prompt sentence into a generative AI model to generate needs and feeling of the customer; and fourth means for transmitting the generated needs and feeling of the customer to a terminal of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Planning, design, and development staff in business divisions face challenges in understanding the "true needs of customers" when formulating business strategies and measures. [Means for solving the problem]

[0005] Generative AI is used to analyze the voice of the customer (VoC) and provide insights to business divisions' planning, design, and development staff based on the analysis results. Furthermore, by utilizing the insights provided across multiple channels and groups, customer satisfaction is improved and competitiveness is strengthened. [Brief explanation 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. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 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. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

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

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

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

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

[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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[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 WAN (Wide Area Network) and / or a LAN (Local Area Network).

[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, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0023] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[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 process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0027] "Example 1"

[0028] One embodiment of the present invention is a voice-of-customer (VoC) analysis system that utilizes generative AI. This system collects customer feedback and opinions and inputs them into the generative AI. The generative AI uses natural language processing (NLP) technology to extract and analyze customer needs and emotions from the collected data. The analysis results are provided to planning, design, and development staff in the business division to support their decision-making.

[0029] "Example 2"

[0030] Furthermore, the insights provided are utilized across multiple channels and groups. Specifically, they are shared by different departments within the company, such as the planning department, design department, and development department, and each department plans and implements measures based on customer needs. This makes it possible to improve customer satisfaction and strengthen competitiveness.

[0031] "Example 3"

[0032] As a concrete example, consider the case where a company uses this system when developing a new product. First, the planning department uses generative AI to analyze customer feedback and understand the customer needs necessary for developing a new product. Next, the design and development departments decide on the product's design and functionality based on those needs. Each department then shares information across multiple channels and the group to develop products consistently. This enables product development based on the true needs of customers, which is expected to improve customer satisfaction and strengthen competitiveness.

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

[0034] "Example 1"

[0035] Step 1: Collect customer feedback and opinions. This data is referred to as Voice of Customer (VoC).

[0036] Step 2: Input the collected VoC data into a generative AI, which uses natural language processing (NLP) techniques to extract customer needs and emotions from the data.

[0037] Step 3: Provide the results of the generative AI analysis to the planning, design, and development staff in the business division to support their decision-making.

[0038] "Example 2"

[0039] Step 1: The analysis results from generative AI are shared across multiple channels and groups. Specifically, the analysis results are shared among different departments within the company, such as the planning department, design department, and development department.

[0040] Step 2: Each department will use the shared insights to plan and implement measures that address customer needs, thereby improving customer satisfaction and strengthening competitiveness.

[0041] "Example 3"

[0042] Step 1: The planning department uses generative AI to analyze customer feedback and understand customer needs necessary for developing new products.

[0043] Step 2: The design and development departments decide on the product design and functions based on the needs provided by the planning department.

[0044] Step 3: Each department shares information across multiple channels and the group to develop products consistently. This enables product development based on real customer needs, which is expected to improve customer satisfaction and strengthen competitiveness.

[0045] Example 1

[0046] 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 device 14 will be referred to as a "terminal."

[0047] With conventional Voice of Customer (VoC) analysis systems, it was difficult to efficiently collect and accurately analyze customer feedback and opinions. Furthermore, there was a lack of appropriate means to provide the analysis results to business division planning, design, and development personnel to support their decision-making. Furthermore, it was difficult to utilize the insights provided across multiple channels and groups, which meant that improvements in customer satisfaction and strengthened competitiveness were not fully achieved.

[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 means for collecting customer feedback and opinions, means for preprocessing the collected data, means for inputting the preprocessed data into a generative AI model, means for extracting customer needs and emotions using the generative AI model, means for analyzing the extracted needs and emotions, means for providing the analysis results to planning, design, and development personnel in the business division, and means for utilizing the provided insights across multiple channels and groups. This enables efficient collection and accurate analysis of customer feedback and opinions. Furthermore, by providing the analysis results to business division personnel and supporting their decision-making, improved customer satisfaction and stronger competitiveness can be achieved.

[0050] "Means for collecting customer feedback and opinions" refers to methods and devices for collecting information such as opinions, impressions, and evaluations provided by customers.

[0051] "Means for pre-processing collected data" refers to methods or devices for converting collected raw data into a format that is easier to analyze, and specifically includes text cleaning, tokenization, removal of stop words, etc.

[0052] A "means for inputting preprocessed data into a generative AI model" is a method or apparatus for appropriately supplying preprocessed data to a generative AI model.

[0053] "Means for extracting customer needs and emotions using generative AI models" refers to methods or devices that utilize generative AI models to identify and extract needs and emotions from customer feedback.

[0054] "Means for analyzing extracted needs and emotions" refers to methods or devices for further analyzing the needs and emotions extracted by the generative AI model and obtaining specific insights.

[0055] "Means for providing analysis results to business division planning, design, and development personnel" refers to methods and devices for communicating the analysis results to business division personnel and supporting their decision-making.

[0056] "Means for utilizing the provided insights across multiple channels and groups" refers to methods and devices for sharing the obtained insights across multiple channels and groups and utilizing them in a unified manner.

[0057] This invention is a system for analyzing voice of customer (VoC) using generative AI. Specific embodiments of this system are described below.

[0058] System configuration

[0059] 1. Data Collection

[0060] Users collect customer feedback and opinions through online forms, emails, social media comments, etc.

[0061] The device sends the collected feedback to a server. Specifically, the application on the device sends data to the server through an API.

[0062] 2. Data Preprocessing

[0063] The server preprocesses the received data. Specifically, it performs the following processes:

[0064] Text cleaning: remove unnecessary characters and noise, for example, remove HTML tags and special characters.

[0065] Tokenization: Splitting text into words and phrases. For example, splitting "product delivery is slow" into "product", "of", "delivery", "is", and "slow".

[0066] Stop word removal: Remove meaningless words (e.g., "no" and "ga").

[0067] Software used: Python (registered trademark), NLTK (Natural Language Toolkit) (registered trademark)

[0068] 3. Input to generative AI

[0069] The server inputs the preprocessed data into the generative AI model, specifically by sending the preprocessed text as a prompt to the generative AI.

[0070] Generative AI model used: A general generative AI model (e.g., GPT-4 (registered trademark))

[0071] 4. Data Analysis

[0072] Generative AI uses natural language processing (NLP) technology to extract customer needs and emotions. For example, it can extract the need for "delayed delivery" and the emotion of "dissatisfaction."

[0073] The server receives the output from the generative AI and organizes the analysis results, specifically categorizing the extracted needs and emotions.

[0074] 5. Providing results

[0075] The server provides the analysis results to the planning, design, and development staff in the business division. Specifically, it generates the analysis results in report format and sends them to the staff by email.

[0076] The terminal displays the results to the person in charge, for example, by displaying the analysis results in graphs and charts on a dashboard.

[0077] Specific examples

[0078] For example, the following shows how an online shop processes feedback from a customer that "product delivery is slow."

[0079] 1. Data Collection

[0080] Users collect feedback about "slow delivery of product" through an online form.

[0081] The terminal sends this feedback to the server.

[0082] 2. Data Preprocessing

[0083] The server receives the text "Slow delivery of product" and cleans it, e.g. removes HTML tags and special characters.

[0084] The server tokenizes the text and splits it into "product," "delivery," and "slow."

[0085] The server removes the stop words "no" and "ga."

[0086] 3. Input to generative AI

[0087] The server sends the preprocessed text "Product delivery is slow" as a prompt to the generative AI model.

[0088] 4. Data Analysis

[0089] Generative AI extracts the need for "delayed delivery" and the emotion of "dissatisfaction."

[0090] The server organizes this information and generates an analysis result called "dissatisfaction with delivery delays."

[0091] 5. Providing results

[0092] The server generates the analysis results in the form of a report and sends it to the business department manager via email.

[0093] The terminal displays the results to the person in charge, and the analysis results, such as "dissatisfaction with delivery delays," are displayed in a graph on the dashboard.

[0094] Prompt Sentence Examples

[0095] An example of a prompt sentence to be input to the generative AI model is as follows:

[0096] Customer feedback: "Product delivery is slow"

[0097] Extract customer needs and feelings from this feedback.

[0098] In this way, the system efficiently analyzes the voice of the customer (VoC) and supports decision-making in business divisions.

[0099] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0100] Step 1:

[0101] Data collection

[0102] Users collect customer feedback and opinions through online forms, emails, social media comments, etc.

[0103] Input: Customer feedback and opinions (e.g., "Product delivery was slow")

[0104] The device sends the collected feedback to a server. Specifically, the application on the device sends data to the server through an API.

[0105] Output: Feedback data sent to the server

[0106] Step 2:

[0107] Data Preprocessing

[0108] The server preprocesses the received data. Specifically, it performs the following processes:

[0109] Text cleaning: remove unnecessary characters and noise, for example, remove HTML tags and special characters.

[0110] Tokenization: Splitting text into words and phrases. For example, splitting "product delivery is slow" into "product", "of", "delivery", "is", and "slow".

[0111] Stop word removal: Remove meaningless words (e.g., "no" and "ga").

[0112] Input: Feedback data sent to the server (e.g., "Product delivery was slow")

[0113] Output: Preprocessed text data (e.g., "Product delivery is slow")

[0114] Step 3:

[0115] Input to generative AI

[0116] The server inputs the preprocessed data into the generative AI model, specifically by sending the preprocessed text as a prompt to the generative AI.

[0117] Input: Preprocessed text data (e.g., "Product delivery is slow")

[0118] Output: The prompt input to the generative AI model

[0119] Step 4:

[0120] Data analysis

[0121] Generative AI uses natural language processing (NLP) technology to extract customer needs and emotions. For example, it can extract the need for "delayed delivery" and the emotion of "dissatisfaction."

[0122] Input: A prompt sentence entered into the generative AI model (e.g., "Product delivery is slow")

[0123] Output: Extracted needs and emotions (e.g., "Delayed delivery" or "Dissatisfied")

[0124] Step 5:

[0125] Providing results

[0126] The server receives the output from the generative AI and organizes the analysis results, specifically categorizing the extracted needs and emotions.

[0127] Input: Extracted needs and emotions (e.g., "Delayed delivery" or "Dissatisfied")

[0128] Output: Organized analysis results (e.g., "Dissatisfaction with delayed deliveries")

[0129] The server provides the analysis results to the planning, design, and development staff in the business division. Specifically, it generates the analysis results in report format and sends them to the staff by email.

[0130] The terminal displays the results to the person in charge, for example, by displaying the analysis results in graphs and charts on a dashboard.

[0131] Input: Organized analysis results (e.g., "Dissatisfaction with delayed deliveries")

[0132] Output: Reports and dashboards provided to the person in charge

[0133] In this way, the system efficiently analyzes the voice of the customer (VoC) and supports decision-making in business divisions.

[0134] (Application example 1)

[0135] Next, a description will be given of Application Example 1 of Embodiment 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."

[0136] Conventional Voice of Customer (VoC) analysis systems struggled to effectively collect and analyze customer feedback and reviews and provide them to business division planning, design, and development staff. Furthermore, there was a lack of a way to present the analysis results in a visually understandable manner, limiting the use of insights. This made it difficult to understand customers' true needs and reflect them in business strategies and policy planning. Furthermore, improvements in customer satisfaction and competitiveness were not fully achieved.

[0137] 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.

[0138] In this invention, the server includes: means for analyzing voice of customer (VoC) using generative AI; means for providing insights to business division planning, design, and development personnel based on the analysis results; means for utilizing the provided insights across multiple channels and groups; means for collecting customer reviews and feedback and analyzing them using generative AI; and means for visualizing the analysis results in graphs and charts and providing them to business division personnel. This enables customer feedback to be effectively collected and analyzed and presented in a visually easy-to-understand format. This allows the true needs of customers to be understood and reflected in business strategies and policy planning, thereby improving customer satisfaction and strengthening competitiveness.

[0139] "Generative AI" is a type of artificial intelligence that uses natural language processing technology to generate, analyze, and understand text data.

[0140] "Voice of Customer (VoC)" refers to text data such as customer feedback, reviews, and opinions.

[0141] The "analysis results" are data containing needs, emotions, and insights extracted by generative AI by analyzing customer feedback.

[0142] A "business division" refers to a department within a company that is responsible for planning, design, development, and other tasks.

[0143] "Insights" refers to the insights and knowledge gained by analyzing customer feedback, and is information that is useful for formulating business strategies and measures.

[0144] "Multi-channel, cross-group" refers to the sharing and utilization of information across multiple channels and departments.

[0145] A "review" refers to an evaluation or opinion that a customer gives about a product or service.

[0146] "Feedback" refers to information such as opinions, impressions, and areas for improvement provided by customers.

[0147] "Graphs and charts" refer to diagrams that visually represent data and are used to display analytical results in an easy-to-understand manner.

[0148] "Visualization" refers to displaying data or information visually to make it easier to understand.

[0149] A system for implementing the present invention is configured as follows.

[0150] Data collection

[0151] The server uses web scraping tools (e.g., BeautifulSoup®, Scrapy®) or APIs to collect customer reviews and feedback, which is then stored in a database as Voice of Customer (VoC).

[0152] Data Preprocessing

[0153] The server cleans the collected data and performs preprocessing such as tokenizing the text data, removing stop words, and stemming, using software such as Python, Pandas (registered trademark), and NLTK.

[0154] Generative AI analysis

[0155] The server inputs the preprocessed data into a generative AI model (e.g., GPT-4 (registered trademark), BERT (registered trademark)) to extract customer needs and emotions. This allows for detailed analysis of customer feedback.

[0156] Visualizing the results

[0157] The server visualizes the analysis results in graphs and charts and provides them to business department personnel. This process uses software such as Dash (registered trademark) and Plotly, allowing business personnel to obtain insights in a visually easy-to-understand format.

[0158] Specific examples

[0159] For example, the following prompt sentence is input into the generative AI model:

[0160] Example prompt sentence:

[0161] Analyze the following customer reviews and extract key needs and emotions. Review: 'This product is very easy to use, but the battery life is poor. I like the design, but I wish it was a little lighter.'

[0162] An example output of a generative AI model is as follows:

[0163] Example output of a generative AI model:

[0164] "Primary needs: Improved battery life, lighter weight

[0165] Sentiments: Positive (easy to use, good design), Negative (poor battery life)

[0166] In this way, customer feedback can be collected and analyzed effectively and presented in a visually easy-to-understand format, allowing customers to understand their true needs and reflect them in business strategies and policy planning, thereby improving customer satisfaction and strengthening competitiveness.

[0167] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0168] Step 1:

[0169] The server collects customer reviews and feedback. Specifically, it uses web scraping tools (e.g., BeautifulSoup, Scrapy) and APIs to store the Voice of Customer (VoC) in a database. The input is customer reviews and feedback, and the output is text data stored in the database.

[0170] Step 2:

[0171] The server cleans the collected data. Specifically, it uses software such as Python, Pandas, and NLTK to perform preprocessing such as tokenizing the text data, removing stop words, and stemming. The input is the text data stored in the database, and the output is the preprocessed text data.

[0172] Step 3:

[0173] The server inputs the preprocessed data into a generative AI model (e.g., GPT-4, BERT) to extract customer needs and emotions. Specifically, it inputs a prompt into the generative AI model and analyzes the text output by the model. The input is the preprocessed text data and the prompt, and the output is the analysis results that include customer needs and emotions.

[0174] Step 4:

[0175] The server visualizes the analysis results in graphs and charts. Specifically, it uses software such as Dash or Plotly to visually display the analysis results. The input is the analysis results, including customer needs and sentiment, and the output is graphs and charts.

[0176] Step 5:

[0177] The terminal provides visualized analysis results to business department personnel. Specifically, the personnel can view the analysis results via a web browser or dedicated application. The input is graphs and charts, and the output is insights that the personnel can view.

[0178] Step 6:

[0179] Users can then use the provided insights to develop business strategies and measures. Specifically, they can use the visualized data as a reference to improve product development and marketing strategies. The input is the insights viewed by the person in charge, and the output is improved business strategies and measures.

[0180] Example 2

[0181] Next, a description will be given of 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."

[0182] In conventional systems for analyzing Voice of Customer (VoC), the process from collecting data to analyzing it and providing insights was inconsistent, making it difficult for each department to quickly plan and implement measures based on customer needs. Also, insights were not visualized or shared properly, and information sharing between departments was often not smooth. This led to issues such as insufficient improvement of customer satisfaction and strengthening of competitiveness.

[0183] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a data collection means, a data preprocessing means, a means for analyzing voice of customer (VoC) using a generative AI model, a means for providing insights to planning, design, and development personnel in the business department based on the analysis results, a means for utilizing the provided insights across multiple channels and groups, a means for visualizing the insights, and a means for sharing the insights with each department. This allows the processes from data collection to analysis, providing, visualizing, and sharing insights to be performed consistently, enabling each department to quickly plan and implement measures based on customer needs.

[0184] "Data collection methods" are methods used to collect customer feedback and market research data.

[0185] "Data preprocessing means" refers to means for preprocessing collected data, such as cleaning and normalizing the data, to prepare it in a format suitable for analysis.

[0186] A "generative AI model" is an artificial intelligence model that uses natural language processing to analyze data and generate insights.

[0187] "Means for analyzing voice of customer (VoC)" refers to means for analyzing customer feedback and opinions using generative AI models.

[0188] "Means of providing insights" refers to the means of providing useful information and suggestions to the planning, design, and development staff of the business division based on the results of the analysis.

[0189] "Means for multi-channel, group-wide utilization" refers to the means for sharing and utilizing the insights provided across different departments and channels within the company.

[0190] "Means for visualizing insights" refers to means for displaying analytical results in visual formats such as graphs and charts to make them easier to understand.

[0191] "Means for sharing insights with each department" refers to means for distributing the generated insights to each department within the company and sharing the information.

[0192] This invention is a system that utilizes a generative AI model to analyze voice of customer (VoC) and provide insights to each department within a company. Specific embodiments of this system are described below.

[0193] 1. Data Collection

[0194] The server collects customer feedback and market research data, specifically using tools such as Google Analytics and Salesforce to obtain website visit data, customer purchase history, survey results, etc.

[0195] 2. Data Preprocessing

[0196] The server preprocesses the collected data by using the Python Pandas library to clean and normalize the data and prepare it for analysis, for example by imputing missing values, removing outliers, and encoding categorical variables.

[0197] 3. Data analysis

[0198] The server inputs the preprocessed data into a generative AI model to analyze customer needs and trends. For example, OpenAI's (registered trademark) GPT-4 is used as the generative AI model. The server inputs the preprocessed data into the GPT-4 model to generate analysis results in natural language.

[0199] 4. Insight generation

[0200] The server generates insights to provide to each department based on the analysis results obtained from the generative AI model. The insights include specific action plans and areas for improvement. The server visualizes the generated analysis results using visualization tools such as Power BI (registered trademark) and Tableau (registered trademark), providing the insights in a format that is easy for each department to understand.

[0201] 5. Insight sharing

[0202] The server shares the generated insights with each department within the company (planning department, design department, development department, etc.). The server distributes the insights to each department via email or internal chat tools (such as Slack (registered trademark) or MICROSOFT (registered trademark) TEAMS (registered trademark)). The server also provides a dashboard for sharing insights so that each department can access them in real time.

[0203] Specific examples

[0204] When a user enters the prompt "Based on the latest customer feedback, what are the key trends for next product development?", the server does the following:

[0205] 1. Data collection: The server uses the Google Analytics API to obtain website visit data and the Salesforce API to collect customer purchase history and survey results.

[0206] 2. Data Preprocessing: The server uses the Pandas library to clean and normalize the data.

[0207] 3. Data analysis: The server inputs the preprocessed data into the GPT-4 model to analyze "which product features are most highly rated based on recent customer feedback."

[0208] 4. Insight generation: Based on the analysis results, the server visualizes them using Power BI (registered trademark) and generates the insight that "improvements to user interfaces and revamped designs are important trends."

[0209] 5. Insight sharing: The server distributes the generated insights to each department via Slack and provides a dashboard that can be accessed in real time.

[0210] In this way, each department can plan and implement measures based on the insights provided, improving customer satisfaction and strengthening competitiveness.

[0211] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0212] Step 1: Data collection

[0213] The server collects customer feedback and market research data. Specifically, it uses tools such as Google Analytics and Salesforce to obtain website visit data, customer purchase history, survey results, etc. The input is raw data obtained through the API, and the output is a set of collected raw data.

[0214] Specific behavior:

[0215] The server periodically retrieves website visit data using the Google Analytics API, and also uses the Salesforce API to collect customer purchase history and survey results.

[0216] Step 2: Data Preprocessing

[0217] The server preprocesses the collected data by using the Python Pandas library to clean and normalize the data and prepare it for analysis. The input is the raw data collected, and the output is a preprocessed, clean dataset.

[0218] Specific behavior:

[0219] The server uses the Pandas library to impute missing values, remove outliers, normalize data, and encode categorical variables.

[0220] Step 3: Data analysis

[0221] The server inputs the preprocessed data into a generative AI model to analyze customer needs and trends. The generative AI model used is, for example, OpenAI's GPT-4. The input is a preprocessed, clean dataset, and the output is the analysis results from the generative AI model.

[0222] Specific behavior:

[0223] The server then inputs the preprocessed data into the GPT-4 model to generate natural language analysis results, such as "Which product features are most popular based on recent customer feedback?"

[0224] Step 4: Insight generation

[0225] The server generates insights to provide to each department based on the analysis results obtained from the generative AI model. The insights include specific measures and improvements. The input is the analysis results from the generative AI model, and the output is visualized insights.

[0226] Specific behavior:

[0227] The server visualizes the generated analytical results using visualization tools such as Power BI and Tableau, providing insights in a format that is easy for each department to understand.

[0228] Step 5: Share your insights

[0229] The server shares the generated insights with each department within the company (planning department, design department, development department, etc.). The input is the visualized insights, and the output is the insights distributed to each department.

[0230] Specific behavior:

[0231] The server distributes insights to each department via email or internal chat tools (such as Slack or Microsoft Teams), and also provides a dashboard for sharing insights so that each department can access them in real time.

[0232] (Application example 2)

[0233] 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."

[0234] With the previous system, it was difficult for different departments within a company to effectively share insights to plan and implement measures based on customer needs. Furthermore, there was a lack of means for each department within a factory to plan and implement measures based on market trends in real time. This meant that improvements in customer satisfaction and strengthening of competitiveness were not fully achieved.

[0235] 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.

[0236] In this invention, the server includes: means for analyzing voice of customer (VoC) using generative AI; means for providing insights to planning, design, and development personnel in business divisions based on the analysis results; means for utilizing the provided insights across multiple channels and groups; means for each division within the factory to plan and implement measures based on customer needs and market trends in real time; means for a robot to automatically collect data and generate insights using a generative AI model; and means for notifying each division of the insights via a smartphone, tablet, or AR smart glasses. This enables different divisions within a company to effectively share insights, enabling each division within the factory to plan and implement measures based on market trends in real time.

[0237] "Generative AI" is a system that uses artificial intelligence technology to generate data and perform analysis and predictions.

[0238] "Voice of the Customer (VoC)" refers to feedback, opinions, requests, etc. from customers.

[0239] "Insights" refers to insights and findings gained from data analysis.

[0240] "Multi-channel, cross-group" refers to the sharing and utilization of information across multiple channels and departments.

[0241] "Each department within the factory" refers to different departments within the factory, such as production control, quality control, and maintenance.

[0242] "Real-time" refers to data and information being processed immediately and available for immediate use.

[0243] "Market trends" refers to market changes, trends, customer needs, and other movements.

[0244] A "robot" refers to a mechanical device that performs work automatically.

[0245] A "generative AI model" refers to a model that uses generative AI to generate data and perform analysis and predictions.

[0246] A "smartphone" refers to a mobile phone that can connect to the Internet and use applications.

[0247] A "tablet" refers to a portable computer with a touchscreen.

[0248] "AR smart glasses" refers to a glasses-type device that displays information using augmented reality technology.

[0249] A system for carrying out this invention is configured as follows: The server includes means for analyzing voice of the customer (VoC) using generative AI, means for providing insights to planning, design, and development personnel in business divisions based on the analysis results, means for utilizing the provided insights across multiple channels and the group, means for each division within a factory to plan and implement measures based on customer needs and market trends in real time, means for a robot to automatically collect data and generate insights using a generative AI model, and means for notifying each division of the insights via a smartphone, tablet, or AR smart glasses.

[0250] Hardware and software used

[0251] Hardware: Sensors in factories, smartphones, tablets, AR smart glasses, robots

[0252] Software: Python, generative AI models (e.g., OpenAI's GPT-3 (registered trademark))

[0253] Data processing and calculation

[0254] 1. Data collection: Robots collect data such as production rates, defect rates, scheduled maintenance dates, and customer feedback from sensors and APIs in the factory, enabling real-time data acquisition.

[0255] 2. Generating insights using a generative AI model: The collected data is input into a generative AI model to generate insights. The generative AI model can be, for example, OpenAI's GPT-3.

[0256] 3. Sharing insights: Generated insights are shared with each department via smartphone, tablet, or AR smart glasses, allowing each department to plan and implement measures based on market trends in real time.

[0257] Specific examples

[0258] For example, the production management department can adjust production schedules based on the generated insights, the quality control department can plan quality improvement measures based on defect rate data, and the maintenance department can implement preventive maintenance based on scheduled maintenance dates.

[0259] Prompt Sentence Examples

[0260] Production rate: 100%, Defect rate: 2%, Maintenance scheduled date: 2023-10-15, Customer feedback: Positive

[0261] By inputting this prompt into a generative AI model, the insights needed by each department can be generated.

[0262] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0263] Step 1:

[0264] The server collects data from sensors and APIs in the factory. Specifically, it acquires data such as production rates, defect rates, scheduled maintenance dates, and customer feedback. The input is data from sensors and APIs, and the output is the collected data. This makes it possible to acquire data in real time.

[0265] Step 2:

[0266] The server inputs the collected data into the generative AI model. Specifically, it converts the collected data into a prompt sentence format and sends it to the generative AI model (e.g., OpenAI's GPT-3). The input is the collected data, and the output is data in the prompt sentence format. This provides the basic data for the generative AI model to generate insights.

[0267] Step 3:

[0268] The server receives insights from the generative AI model. Specifically, it obtains the insights generated by the generative AI model based on prompt statements. The input is data in the form of prompt statements, and the output is the generated insights. This allows each department to obtain the insights they need.

[0269] Step 4:

[0270] The server notifies each department of the generated insights via smartphones, tablets, or AR smart glasses. Specifically, the insights are sent to each department's device and notified. The input is the generated insight, and the output is the insight displayed on each department's device. This enables each department to plan and implement measures based on market trends in real time.

[0271] Step 5:

[0272] Users (those in charge of each department) plan and implement measures based on the notified insights. Specifically, the production control department adjusts production schedules, the quality control department plans quality improvement measures, and the maintenance department implements preventive maintenance. The input is the notified insights, and the output is the measures implemented by each department. This leads to improved customer satisfaction and stronger competitiveness.

[0273] Example 3

[0274] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0275] With conventional systems for analyzing Voice of Customer (VoC), it was difficult to accurately grasp customer needs and develop products based on them. Furthermore, there was insufficient information sharing between departments, making consistent product development difficult. This meant that improvements in customer satisfaction and strengthened competitiveness were not being fully achieved.

[0276] 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.

[0277] In this invention, the server includes: means for analyzing Voice of Customer (VoC) using generative AI; means for providing insights to planning, design, and development personnel in business divisions based on the analysis results; means for utilizing the provided insights across multiple channels and groups; means for inputting prompts to the generative AI model; means for storing generated responses in a database; means for analyzing the stored responses; means for creating design proposals based on customer needs; means for determining technical specifications based on the design proposals; means for providing a platform for sharing information generated by each division; and means for updating the shared information in real time and notifying each division. This enables accurate understanding of customer needs and consistent product development based on those needs, thereby improving customer satisfaction and strengthening competitiveness.

[0278] "Generative AI" is a system that generates text and data using artificial intelligence technology.

[0279] "Voice of Customer (VoC)" is information such as feedback, opinions, and requests collected from customers.

[0280] "Insights" are insights and knowledge gained from data analysis, and are information that is useful for formulating business strategies and measures.

[0281] "Multi-channel, cross-group" refers to sharing information across multiple communication channels and departments.

[0282] A "prompt" is a question or instruction text that is input to a generative AI.

[0283] A "database" is a system for storing and managing data in an organized manner.

[0284] A "design proposal" is a specific blueprint or concept regarding the appearance and function of a product.

[0285] "Technical specifications" are detailed technical requirements and standards for the design and manufacture of a product.

[0286] A "platform" is a system that serves as the foundation for each department to share information and work together.

[0287] "Real-time updates" means that information is updated immediately and always reflects the latest status.

[0288] This invention is a system that uses generative AI to analyze the voice of the customer (VoC), provide insights to each business division, and utilize them across multiple channels and groups. A specific embodiment of this system is shown below.

[0289] First, the user (planning department) uses a device (PC or tablet) to input a prompt into the generative AI model. Examples of prompts include "What features do you want in a new smartphone?" or "What is the most important feature for your next smartphone purchase?"

[0290] The server then uses a generative AI model (e.g., a text generation system using artificial intelligence technology) to generate an answer to the input prompt. The generated answer may include specific needs, such as "long battery life," "good camera image quality," and "waterproof."

[0291] The server stores the generated answers in a database (e.g., a system for systematically storing and managing data), which is used for later analysis.

[0292] The user (the planning department) retrieves data from the database to analyze the saved responses, for example, by using a BI tool (business intelligence tool) to visualize and analyze customer needs.

[0293] Next, the user (design department) creates a design proposal based on the customer's needs. To create the design proposal, they use design software such as Adobe Creative Cloud (registered trademark). They create a specific design and upload the file to the server.

[0294] The user (development department) determines the technical specifications based on the design proposal. To determine the technical specifications, CAD software such as AutoCAD (registered trademark) or SolidWorks (registered trademark) is used. The technical details are designed and the files are saved on the server.

[0295] The server provides a platform for sharing information generated by each department. Collaboration tools such as Microsoft Teams and Slack are used for the platform. This allows each department to share information in real time, enabling consistent product development.

[0296] Finally, the server updates the shared information in real time and notifies each department, allowing each department to proceed with their work based on the latest information.

[0297] In this way, it becomes possible to accurately grasp customer needs and develop consistent products based on them, thereby improving customer satisfaction and strengthening competitiveness. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0298] Step 1:

[0299] The user (planning department) uses a device (PC or tablet) to input a prompt to the generative AI model. Specifically, they log in to a dedicated interface and input a prompt such as, "What features do you want in a new smartphone?" The input prompt is sent to the server.

[0300] Step 2:

[0301] The server uses a generative AI model to generate an answer to the input prompt. The generative AI model analyzes the prompt and generates an answer based on related data. For example, it outputs specific needs such as "long battery life," "good camera image quality," and "waterproof." The generated answer is stored on the server.

[0302] Step 3:

[0303] The server stores the generated answers in a database. Specifically, the server uses a database management system (e.g., MySQL (registered trademark) or PostgreSQL (registered trademark)) to store the generated answers in an appropriate format. The stored data is used for later analysis.

[0304] Step 4:

[0305] The user (planning department) retrieves data from the database to analyze the saved responses. Specifically, they use a BI tool (e.g., Tableau or Power BI) to access the database and visualize customer needs. Based on the visualized data, the user analyzes customer needs and gains insights.

[0306] Step 5:

[0307] The user (design department) creates a design proposal based on the customer's needs. Specifically, they use design software such as Adobe Creative Cloud to create a design that reflects the customer's needs. The created design proposal is uploaded to the server.

[0308] Step 6:

[0309] The user (development department) determines the technical specifications based on the design proposal. Specifically, they use CAD software such as AutoCAD or SolidWorks to design the technical details based on the design proposal. The designed technical specifications are saved on the server.

[0310] Step 7:

[0311] The server provides a platform for sharing information generated by each department. Specifically, it provides an interface for sharing information using collaboration tools such as Microsoft Teams and Slack. Each department uses this platform to share information.

[0312] Step 8:

[0313] The server updates the shared information in real time and notifies each department. Specifically, each time the information is updated, a notification is sent to each department. This allows each department to proceed with their work based on the latest information.

[0314] In this way, it becomes possible to accurately grasp customer needs and develop consistent products based on those needs, thereby improving customer satisfaction and strengthening competitiveness.

[0315] (Application example 3)

[0316] 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."

[0317] With conventional systems for analyzing Voice of the Customer (VoC), it was difficult to accurately grasp customer needs and quickly reflect them in product development. Furthermore, information sharing between departments was insufficient, resulting in inconsistent product development. This meant that improvements in customer satisfaction and strengthened competitiveness were not fully achieved.

[0318] 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.

[0319] In this invention, the server includes: a means for analyzing voice of customer (VoC) using generative AI; a means for providing insights to planning, design, and development personnel in business divisions based on the analysis results; a means for utilizing the provided insights across multiple channels and groups; a means for collecting customer voice; a means for sharing the analysis results by the generative AI with each division in real time; and a means for automatically generating prompts to be input into the generative AI model. This enables accurate understanding of customer needs and rapid reflection of these needs in product development. Furthermore, smooth information sharing between divisions enables consistent product development, which is expected to improve customer satisfaction and strengthen competitiveness.

[0320] "Generative AI" is a type of artificial intelligence, a technology that generates new information and content based on large amounts of data.

[0321] "Voice of Customer (VoC)" refers to customer feedback, reviews, opinions, and other customer impressions and requests regarding products and services.

[0322] "Insights" refers to insights and understanding gained by analyzing data and information, and plays an especially important role in business strategy and policy planning.

[0323] "Multi-channel, group-wide" refers to sharing information and promoting collaboration across multiple channels and departments.

[0324] A "prompt" is an instruction or question input to a generative AI, and serves as the basis for the AI ​​to respond or generate appropriate responses.

[0325] "Real-time" refers to data and information being processed immediately and provided without delay.

[0326] "Product development" refers to the process of planning, designing, and manufacturing new products and services, which must reflect customer needs and market trends.

[0327] The system for carrying out this invention mainly comprises a server and a user terminal. The server includes means for analyzing voice of customer (VoC) using generative AI, means for providing insights to planning, design, and development personnel in business divisions based on the analysis results, means for utilizing the provided insights across multiple channels and across the group, means for collecting customer voice, means for sharing the analysis results by the generative AI with each division in real time, and means for automatically generating prompt sentences to be input into the generative AI model.

[0328] Hardware and software used

[0329] Hardware: Servers, user devices (smartphones, tablets, PCs, etc.)

[0330] Software: Python, OpenAI API, Requests library

[0331] Data processing and calculation

[0332] 1. Collecting customer feedback: Customer reviews and feedback are collected from the user's device via the shopping site's API. The server receives this data and stores it in a database.

[0333] 2. Analysis by generative AI: The server inputs the collected data into generative AI (e.g., OpenAI API) to extract customer needs. Specifically, it analyzes customer feedback and identifies key needs and problems.

[0334] 3. Providing insights: Based on the analysis results, the server provides insights to the planning, design, and development staff of the business division. These insights are useful information for formulating business strategies and measures.

[0335] 4. Real-time information sharing: The server shares the analysis results of generative AI with each department in real time, allowing each department to respond quickly and ensure consistent product development.

[0336] 5. Automatic generation of prompts: The server automatically generates prompts to be input to the generative AI model, improving the analytical accuracy of the generative AI and yielding more accurate insights.

[0337] Specific examples

[0338] For example, if an online shopping site collects customer feedback such as "This product has a great design, but the battery life is poor," the server can input this data into a generative AI to generate a prompt like this:

[0339] "Analyze the following customer testimonials and extract their key needs:\n\nThis product has a great design, but the battery life is poor."

[0340] The generative AI analyzes this prompt and provides the insight, "Customers appreciate the design, but are dissatisfied with the battery life." This insight is shared in real time with the planning, design, and development departments, allowing each department to respond quickly.

[0341] This will enable us to accurately understand customer needs and quickly reflect them in product development.In addition, information will be shared smoothly between departments, realizing consistent product development, which is expected to improve customer satisfaction and strengthen competitiveness.

[0342] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0343] Step 1:

[0344] Collecting customer feedback

[0345] The user device collects customer reviews and feedback through the API of the online shopping site. As input, it uses the API endpoint and authentication information of the online shopping site. As output, it obtains customer testimonials (reviews and feedback) in JSON format. The server receives this data and stores it in a database.

[0346] Step 2:

[0347] Generative AI analysis

[0348] The server inputs the collected customer feedback into the generative AI. The text data of the customer feedback is used as input. The server generates a prompt sentence for the generative AI model (for example, OpenAI's API) and inputs this. As output, the generative AI returns text data that extracts the customer's needs and problems.

[0349] Step 3:

[0350] Providing insights

[0351] The server provides insights to the planning, design, and development staff in the business division based on the analysis results obtained from the generative AI. The analysis results of the generative AI are used as input. As output, insights are generated in text format and notified to each staff member. Specifically, insights are provided via email or a dedicated dashboard.

[0352] Step 4:

[0353] Real-time information sharing

[0354] The server shares the analysis results of the generative AI with each department in real time. The analysis results and insights of the generative AI are used as input. As output, information is shared with each department in real time. Specifically, information is shared through chat tools and project management tools.

[0355] Step 5:

[0356] Automatic prompt generation

[0357] The server automatically generates prompts to be input into the generative AI model. Text data of customer testimonials is used as input. A prompt is generated as output. Specifically, the following prompt is generated: "Analyze the following customer testimonials and extract their main needs.\n\nThis product has a great design, but the battery life is poor."

[0358] Through these steps, it becomes possible to accurately grasp customer needs and quickly reflect them in product development.In addition, information is shared smoothly between departments, which allows for consistent product development, which is expected to improve customer satisfaction and strengthen competitiveness.

[0359] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0360] "Example 1"

[0361] One embodiment of the present invention is a system that uses a generative AI incorporating an emotion engine. This system collects voice of the customer (VoC) and inputs that data into the generative AI. The generative AI uses natural language processing (NLP) technology to extract customer needs and emotions from the data. In particular, the emotion engine recognizes and analyzes customer emotions. The analysis results are provided to business division planning, design, and development staff to support their decision-making. Specifically, the system understands customers' emotions and expectations toward a product and reflects this information in product development.

[0362] "Example 2"

[0363] Another embodiment of the present invention is the use of an emotion engine to share information across multiple channels and groups. Specifically, the analysis results of generative AI are shared across multiple channels and groups. This allows different departments within a company, such as the planning department, design department, and development department, to share the analysis results and plan and implement measures based on customer emotions.

[0364] "Example 3"

[0365] Another embodiment of the present invention involves the use of an emotion engine to develop business strategies. Specifically, the results of emotion analysis by the emotion engine are utilized in business strategy and policy planning. For example, the expectations and anxieties customers have about a new product can be grasped and reflected in the development and marketing strategy of the new product. This allows for an understanding of the true needs of customers, which can lead to improved customer satisfaction and stronger competitiveness.

[0366] The processing flow of each embodiment will be described below.

[0367] "Example 1"

[0368] Step 1: Collect Voice of the Customer (VoC).

[0369] Step 2: Input the collected VoC data into the generative AI.

[0370] Step 3: Generative AI uses natural language processing (NLP) techniques to extract customer needs and sentiment from the data.

[0371] Step 4: The emotion engine recognizes the customer's emotions and analyzes them.

[0372] Step 5: The analysis results are provided to the business division's planning, design and development staff to support their decision-making.

[0373] "Example 2"

[0374] Step 1: Share the analysis results from generative AI across multiple channels and groups.

[0375] Step 2: The analysis results are shared with different departments within the company, such as the planning, design, and development departments.

[0376] Step 3: Each department plans and implements measures based on customer sentiment.

[0377] "Example 3"

[0378] Step 1: Use the results of sentiment analysis by the sentiment engine to develop business strategies and measures.

[0379] Step 2: Understand customers' expectations and concerns about the new product.

[0380] Step 3: Reflect this in new product development and marketing strategies.

[0381] Example 1

[0382] 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 device 14 will be referred to as a "terminal."

[0383] Traditional Voice of Customer (VoC) analysis systems made it difficult to effectively collect and analyze customer feedback and opinions. They also failed to accurately extract customer needs and sentiment from the collected data and provide insights that could be used in business decision-making. Furthermore, there was a lack of a way to utilize the insights provided across multiple channels and groups, preventing the company from fully achieving its goals of improving customer satisfaction and strengthening its competitiveness.

[0384] 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.

[0385] In this invention, the server includes means for collecting customer feedback and opinions, means for inputting the collected data into a generative AI model, means for the generative AI model to extract customer needs and emotions from the data using natural language processing technology, means for analyzing the extracted information, means for providing the analysis results to planning, design, and development personnel in the business division, and means for utilizing the provided insights across multiple channels and groups. This enables effective collection and accurate analysis of customer feedback and opinions, and provides insights to support business division decision-making. Furthermore, utilizing the provided insights across multiple channels and groups can improve customer satisfaction and strengthen competitiveness.

[0386] "Customer feedback and opinions" refers to information such as ratings, comments, requests, and complaints provided by customers regarding products and services.

[0387] A "generative AI model" is an algorithm or system that uses artificial intelligence techniques to generate, analyze, and process data.

[0388] "Natural language processing technology" is a technology that allows computers to understand, interpret, and generate human language.

[0389] "Customer needs and emotions" are the requirements and expectations that customers have about products and services, as well as their emotional responses to them.

[0390] "Analysis results" are information about customer needs and emotions that is output by the generative AI model based on collected data.

[0391] "Business planning, design and development staff" are people in a company who are responsible for planning, designing and developing products and services.

[0392] "Insights" are insights and understanding gained from data analysis, and are information that supports decision-making.

[0393] "Multi-channel, cross-group" refers to the sharing and utilization of information and resources across multiple different channels and departments.

[0394] This invention is a system that uses a generative AI model to analyze voice of customer (VoC) and provide insights to business department planning, design, and development personnel. The system collects customer feedback and opinions and inputs them into the generative AI model. The generative AI model uses natural language processing technology to extract and analyze customer needs and emotions from the collected data. The analysis results are provided to business department personnel to support their decision-making.

[0395] Hardware and software used

[0396] Hardware: Servers, terminals

[0397] Software: Generative AI models (e.g., GPT-4), natural language processing (NLP) libraries (e.g., spaCy, NLTK)

[0398] Explanation of program processing

[0399] Data collection

[0400] Users collect customer feedback and opinions, for example through survey forms or customer support chat logs.

[0401] The device temporarily stores the collected data, for example, in the device's local storage or cloud storage.

[0402] Data Entry

[0403] The device sends the collected data to the server, specifically by uploading the data to the server using an HTTP request.

[0404] The server stores the received data in a database, such as MySQL or PostgreSQL.

[0405] Data Processing

[0406] The server retrieves the collected data from the database and uses SQL queries to extract the required data.

[0407] The server inputs the acquired data into the generative AI model. Specifically, it sends the data to the generative AI model using an API request.

[0408] Generative AI models use natural language processing techniques to extract customer needs and sentiment from data, for example using sentiment analysis algorithms to categorize positive, negative, and neutral sentiment.

[0409] The generative AI model returns the extracted information to the server, which returns the analysis results as an API response.

[0410] Providing analysis results

[0411] The server stores the analysis results received from the generative AI model in a database, and inserts the analysis results using SQL queries.

[0412] The server provides the analysis results to the planning, design, and development staff in the business divisions through a web application that displays the results in the form of dashboards and reports.

[0413] The person in charge checks the dashboard and reports and makes decisions, such as considering design improvements or new product plans.

[0414] Specific examples

[0415] For example, if customer feedback on a product is something like, "This product is easy to use, but the design is a bit old-fashioned," the generative AI model processes it as follows:

[0416] 1. A user fills in a survey form saying, "This product is easy to use, but the design is a bit old-fashioned."

[0417] 2. The device stores this feedback in local storage and prepares to send it to the server.

[0418] 3. The device sends the feedback data to the server using an HTTP POST request.

[0419] 4. The server stores the received data in a MySQL database.

[0420] 5. The server executes the SQL query "SELECT FROM feedbacks WHERE processed = 0" to retrieve the unprocessed feedback data.

[0421] 6. The server sends the acquired data to the generative AI model as an API request, for example, using the following prompt:

[0422] Customer feedback: "This product is easy to use, but the design is a bit outdated."

[0423] Prompt: "Extract customer needs and emotions from this feedback."

[0424] 7. The generative AI model returns the analysis result: "Positive: Easy to use, Negative: Outdated design."

[0425] 8. The server stores the results of this analysis in a database.

[0426] 9. The server executes the SQL query "SELECT FROM analysis_results WHERE feedback_id = 123" to retrieve the analysis results for the specific feedback.

[0427] 10. The server displays the analysis results on a dashboard. For example, it displays information such as "Customers are satisfied with the ease of use, but dissatisfied with the design."

[0428] 11. The person in charge will use this information to consider improving the design.

[0429] The above is a specific embodiment of a voice of customer (VoC) analysis system that utilizes generative AI.

[0430] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0431] Step 1:

[0432] Data collection

[0433] Users collect customer feedback and opinions, for example through survey forms or customer support chat logs.

[0434] Input: Customer feedback and opinions (e.g., "This product is easy to use, but the design is a bit outdated")

[0435] Output: Collected feedback data

[0436] Specific behavior: The user enters feedback into the survey form and presses the submit button.

[0437] Step 2:

[0438] Data Entry

[0439] The device sends the collected data to the server, specifically by uploading the data to the server using an HTTP request.

[0440] Input: Collected feedback data

[0441] Output: Feedback data sent to the server

[0442] Specific operation: The device sends feedback data to the server using an HTTP POST request.

[0443] Step 3:

[0444] Data storage

[0445] The server stores the received data in a database, such as MySQL or PostgreSQL.

[0446] Input: Feedback data sent to the server

[0447] Output: Feedback data stored in a database

[0448] Specific operation: The server saves the received data in a MySQL database using an INSERT query.

[0449] Step 4:

[0450] Data Acquisition

[0451] The server retrieves the collected data from the database and uses SQL queries to extract the required data.

[0452] Input: Feedback data stored in the database

[0453] Output: The obtained feedback data

[0454] Specific operation: The server executes the SQL query "SELECT FROM feedbacks WHERE processed = 0" to retrieve unprocessed feedback data.

[0455] Step 5:

[0456] Data Processing

[0457] The server inputs the acquired data into the generative AI model. Specifically, it sends the data to the generative AI model using an API request.

[0458] Input: Retrieved feedback data

[0459] Output: Data sent to the generative AI model

[0460] What happens: The server sends feedback data to the generative AI model using an API request, for example using a prompt like this:

[0461] Customer feedback: "This product is easy to use, but the design is a bit outdated."

[0462] Prompt: "Extract customer needs and emotions from this feedback."

[0463] Step 6:

[0464] Data analysis

[0465] Generative AI models use natural language processing techniques to extract customer needs and sentiment from data, for example using sentiment analysis algorithms to categorize positive, negative, and neutral sentiment.

[0466] Input: Data sent to the generative AI model

[0467] Output: Extracted customer needs and emotions

[0468] Specific behavior: The generative AI model returns the analysis result: "Positive: Easy to use, Negative: Outdated design."

[0469] Step 7:

[0470] Saving analysis results

[0471] The server stores the analysis results received from the generative AI model in a database, and inserts the analysis results using SQL queries.

[0472] Input: Extracted customer needs and emotions

[0473] Output: Analysis results stored in a database

[0474] What happens: The server uses an INSERT query to save the analysis results to the database.

[0475] Step 8:

[0476] Providing analysis results

[0477] The server provides the analysis results to the planning, design, and development staff in the business divisions through a web application that displays the results in the form of dashboards and reports.

[0478] Input: Analysis results stored in the database

[0479] Output: Analysis results provided to the person in charge

[0480] Specific operation: The server executes the SQL query "SELECT FROM analysis_results WHERE feedback_id = 123" to retrieve the analysis results for the specific feedback and display them in a dashboard.

[0481] Step 9:

[0482] decision making

[0483] The person in charge checks the dashboard and reports and makes decisions, such as considering design improvements or new product plans.

[0484] Input: Analysis results provided to the person in charge

[0485] Output: Improved designs and new product plans

[0486] Specific actions: Based on the information in the dashboard, the person in charge considers design improvements and develops a specific action plan.

[0487] (Application example 1)

[0488] Next, a description will be given of Application Example 1 of Embodiment 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."

[0489] Conventional Voice of Customer (VoC) analysis systems have difficulty analyzing customer feedback in real time and extracting emotions and needs. Furthermore, the extracted data cannot be used quickly to improve product development and marketing strategies, which can hinder efforts to improve customer satisfaction and strengthen competitiveness.

[0490] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for analyzing voice of customer (VoC) using generative AI, means for providing insights to planning, design, and development personnel in the business division based on the analysis results, means for utilizing the provided insights across multiple channels and groups, means for analyzing customer feedback in real time and extracting emotions and needs, and means for utilizing the extracted data to improve product development and marketing strategies. This makes it possible to quickly and accurately analyze customer feedback and reflect it in product development and marketing strategies.

[0491] "Generative AI" is artificial intelligence that uses natural language processing technology to analyze text data and generate new information and insights.

[0492] "Voice of the Customer (VoC)" is text data such as feedback, opinions, and impressions from customers.

[0493] "Means of analysis" refers to methods and techniques for analyzing collected data and extracting customer needs and emotions.

[0494] "Insights" are insights and knowledge gained through data analysis, and are information that supports decision-making in business departments.

[0495] "Multi-channel, cross-group" refers to the sharing and utilization of information across multiple channels and departments.

[0496] "Means of real-time analysis" refers to technologies and methods that analyze data as it is input and output results immediately.

[0497] "Means for extracting emotions and needs" refers to techniques and methods for identifying and extracting customer emotions and specific requests from text data.

[0498] "Means to improve product development and marketing strategies" are methods and techniques for developing new products and reviewing sales strategies based on the analysis results.

[0499] A system for implementing this invention includes means for analyzing voice of customer (VoC) using generative AI, means for providing insights to planning, design, and development personnel in business divisions based on the analysis results, means for utilizing the provided insights across multiple channels and groups, means for analyzing customer feedback in real time and extracting emotions and needs, and means for utilizing the extracted data to improve product development and marketing strategies.

[0500] System Program

[0501] This system is realized using the following hardware and software.

[0502] Hardware: Smartphones, servers

[0503] Software: Python, OpenAI API, TextBlob (registered trademark)

[0504] Processing Description

[0505] Collecting and analyzing customer feedback

[0506] The server receives customer feedback text sent from smartphones. The received feedback text is first subjected to sentiment analysis using TextBlob, which outputs a score for the text's positive, negative, or neutral sentiment.

[0507] Needs extraction using generative AI

[0508] Next, the server uses OpenAI's generative AI (GPT-3) to extract customer needs from the feedback text. Specifically, the feedback text is input to the generative AI as a prompt, and the server outputs the customer needs in text format.

[0509] Providing and leveraging insights

[0510] The extracted data on emotions and needs is provided as insights to planning, design, and development staff in the business divisions. This allows them to understand the true needs of customers and helps improve product development and marketing strategies. The insights provided are used across multiple channels and groups to support rapid decision-making.

[0511] Specific examples

[0512] For example, if a customer posts feedback such as, "This product is very easy to use, but the battery life is poor. I hope that this will be improved," the server analyzes this feedback in real time and extracts the need to "improve battery life" along with an emotional score.

[0513] Prompt Sentence Examples

[0514] Customer feedback: This product is very easy to use, but the battery life is poor. I hope they improve it.

[0515] Extract customer needs from this feedback.

[0516] In this way, customer feedback can be analyzed quickly and accurately and reflected in product development and marketing strategies.

[0517] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0518] Step 1:

[0519] A user inputs and sends feedback text using a smartphone. The input feedback text is sent to a server. The input data is text data including customer opinions and impressions.

[0520] Step 2:

[0521] The server performs sentiment analysis on the received feedback text using TextBlob. The input data is the feedback text, and the output data is a positive, negative, or neutral sentiment score. Specifically, TextBlob analyzes the text and outputs the sentiment polarity (positive, negative, neutral) and the sentiment intensity as a score.

[0522] Step 3:

[0523] The server stores the results of the sentiment analysis and then uses OpenAI's generative AI (GPT-3) to extract customer needs from the feedback text. The input data is the feedback text and sentiment score, and the output data is text indicating the customer needs. Specifically, the feedback text is input as a prompt to the generative AI, which then outputs the customer needs in text format.

[0524] Step 4:

[0525] The server provides the extracted sentiment scores and customer needs as insights to the planning, design, and development staff in the business division. The input data is the sentiment scores and text indicating the customer needs, and the output data is the insights provided to the staff. Specifically, the insights are provided through the staff's dashboard and notification system.

[0526] Step 5:

[0527] Business department staff use the insights provided to improve product development and marketing strategies. The input data is the insights provided, and the output data is an improved product development plan or marketing strategy. Specifically, the staff analyzes the insights, plans specific improvement measures, and puts them into action.

[0528] Step 6:

[0529] The server monitors the results of the improvement measures and forms a feedback loop. The input data is the execution result data, and the output data is data that will be useful for the next feedback analysis. Specifically, the execution results are saved in a database and referenced during the next feedback analysis.

[0530] Example 2

[0531] Next, a description will be given of 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."

[0532] With the previous system, it was difficult to effectively analyze the voice of the customer (VoC) and provide appropriate insights to each department within the company. Furthermore, there was a lack of a mechanism for utilizing the provided insights across multiple channels and groups, making it difficult to grasp the true needs of customers. This meant that improvements in customer satisfaction and strengthening of competitiveness were not fully achieved.

[0533] 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.

[0534] In this invention, the server includes a data collection means, a data preprocessing means, a means for analyzing data using a generative AI model, a means for generating insights based on the analysis results, a means for providing the generated insights to planning, design, and development personnel in the business divisions, and a means for utilizing the provided insights across multiple channels and groups. This enables effective analysis of customer feedback, providing appropriate insights to each division within the company, and enabling utilization across multiple channels and groups. This improves customer satisfaction and strengthens competitiveness.

[0535] "Data collection means" refers to means for collecting customer data from various data sources inside and outside the company.

[0536] The "data preprocessing means" refers to a means for preprocessing the collected data, such as cleaning, normalizing, and imputing missing values.

[0537] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and generate insights.

[0538] "Means for analyzing data" refers to the use of generative AI models to analyze pre-processed data and understand customer sentiment and trends.

[0539] "Means for generating insights" are means for proposing measures based on customer needs and emotions based on the results of data analysis.

[0540] "Means for providing insights" refers to the means for providing the generated insights to each department within the company (planning department, design department, development department, etc.).

[0541] "Means for multi-channel, group-wide utilization" refers to a means by which the insights provided can be shared among different departments within a company, and each department can then plan and implement measures.

[0542] MODE FOR CARRYING OUT THE INVENTION

[0543] This invention is a system that uses a generative AI model to analyze voice of customer (VoC) and provide insights to each department within a company. A specific embodiment of this system will be described below.

[0544] 1. Program Generation

[0545] The server generates a program using a generative AI model. For example, OpenAI's GPT-4 is used as this generative AI model. The server implements the program using a programming language such as Python or Java (registered trademark).

[0546] 2. Program Processing

[0547] The server processes and calculates data using the following hardware and software:

[0548] Hardware: A server equipped with a high-performance CPU and GPU

[0549] Software: Python, Pandas, Scikit-learn, TENSORFLOW (registered trademark), Keras (registered trademark), Google Analytics, Tableau

[0550] The server processes the data as follows:

[0551] 1. Data collection: The server obtains data from the customer feedback system through API and uses Google Analytics to analyze website behavior logs.

[0552] 2. Data preprocessing: The server cleans the data using Python's Pandas library and applies a machine learning model to impute missing values.

[0553] 3. Data Analysis: The server performs sentiment analysis on the text data using a generative AI model (e.g., GPT-4) and analyzes time series data for trend analysis.

[0554] 4. Insight generation: The server uses a generative AI model to describe the analysis results in natural language and compiles the insights in report format.

[0555] 5. Insight sharing: The server shares insights via email or internal chat tools (e.g., Slack) and uses BI tools (e.g., Tableau) to display the insights in a dashboard.

[0556] 3. Specific Examples

[0557] To illustrate, consider the following scenario:

[0558] The user (a planning department employee) inputs a prompt statement to the server: "Analyze customer feedback from the past year and tell us what new features and design trends customers are looking for."

[0559] The server uses a generative AI model to analyze customer feedback and generate insights such as, "Customers are particularly interested in the ease of use and design of smartphone apps."

[0560] The server shares this insight with the planning, design, and development departments.

[0561] Each department will then use this insight to plan and implement new measures.

[0562] In this way, the server uses generative AI models to provide insights for each department within a company to plan and implement measures based on customer needs and emotions, thereby improving customer satisfaction and strengthening competitiveness.

[0563] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0564] Step 1: Data collection

[0565] The server collects customer data from various internal and external data sources, such as retrieving data from customer feedback systems via APIs and using Google Analytics to analyze website behavior logs.

[0566] Input: Customer feedback, purchase history, website activity logs, social media comments

[0567] Output: Raw data collected

[0568] Step 2: Data Preprocessing

[0569] The server preprocesses the collected data, specifically cleaning it using Python's Pandas library and applying machine learning models to impute missing values.

[0570] Input: Raw data collected

[0571] Output: Preprocessed data

[0572] Step 3: Data analysis

[0573] The server analyzes the preprocessed data using generative AI models (e.g., GPT-4) to perform sentiment analysis on the text data and analyze time series data for trend analysis.

[0574] Input: Preprocessed data

[0575] Output: Analysis results (customer sentiment and trends)

[0576] Step 4: Insight generation

[0577] The server generates insights based on the results of the data analysis. Specifically, it uses a generative AI model to describe the analysis results in natural language and compiles the insights in report format.

[0578] Input: Analysis results

[0579] Output: Generated insights (in report format)

[0580] Step 5: Share your insights

[0581] The server shares the generated insights with each department within the company via email or internal chat tools (e.g., Slack), and uses BI tools (e.g., Tableau) to display the insights on a dashboard.

[0582] Input: Generated insights

[0583] Output: Insights shared across departments

[0584] Step 6: Departmental policy planning and implementation

[0585] Each department (planning, design, development, etc.) will plan and implement measures based on the shared insights, such as planning new products based on customer needs, improving designs, and adding features.

[0586] Input: Shared Insights

[0587] Output: Planned measures, implemented measures

[0588] (Application example 2)

[0589] 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."

[0590] While conventional customer support systems could analyze Voice of Customer (VoC), it was difficult to grasp customer sentiment in real time and respond based on that. Furthermore, information sharing between different business divisions was insufficient, preventing the company from fully improving customer satisfaction and strengthening its competitiveness.

[0591] 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.

[0592] In this invention, the server includes: a means for analyzing voice of customer (VoC) using generative AI; a means for providing insights to planning, design, and development personnel in business divisions based on the analysis results; a means for utilizing the provided insights across multiple channels and groups; a means for analyzing emotions from customer facial expressions and voice in real time; a means for providing the analysis results to store staff; and a means for the staff to respond based on the customer's emotions. This enables the system to grasp customer emotions in real time and respond accordingly. It also promotes information sharing between different business divisions, improving customer satisfaction and strengthening competitiveness.

[0593] "Generative AI" is an artificial intelligence technology that generates new information and patterns based on data.

[0594] "Voice of the Customer (VoC)" refers to information such as feedback, opinions, and requests from customers.

[0595] "Insights" refers to insights and findings gained from data analysis.

[0596] "Multi-channel, cross-group" refers to the sharing and utilization of information across multiple channels and departments.

[0597] "Customer facial expression" refers to the emotions and reactions that can be read from the customer's facial expression.

[0598] "Means for analyzing emotions from voice in real time" refers to technology that analyzes voice data and identifies emotions on the spot.

[0599] "Brick-and-mortar staff" refers to employees who deal with customers in physical stores.

[0600] "Means for staff to respond based on customer emotions" refers to methods and tools that allow staff to understand customer emotions and provide services and responses accordingly.

[0601] A system for implementing the present invention has the following configuration.

[0602] First, the server uses generative AI to analyze the voice of the customer (VoC). Specifically, it analyzes data collected from customers, such as feedback, opinions, and requests, to identify their emotions and needs. This analysis uses natural language processing technology and machine learning algorithms.

[0603] The server then provides insights based on the analysis results to the planning, design, and development staff in each business division. This allows each division to plan and implement measures based on customer sentiment and needs. The insights provided are used across multiple channels and groups. This promotes information sharing between different divisions and enables unified customer responses.

[0604] Furthermore, the server provides a means to analyze emotions from customers' facial expressions and voice in real time. Specifically, cameras and microphones are installed on smartphones and tablets used by store staff, and customers' facial expressions and voices are captured through these devices. The captured data is sent to the server and analyzed in real time by a generative AI model.

[0605] The analysis results are provided to store staff. Staff can check the customer's emotions on their smartphone or tablet screens and respond accordingly. For example, if a customer is recognized as "happy," the staff can say, "You seem to be in a good mood today! Is there anything in particular I can help you with?" Or, if a customer asks a question about a product and the analysis reveals "anxiety," the staff can explain, "Don't worry, this product is of very high quality."

[0606] The following hardware and software are used to implement this system:

[0607] Hardware: Camera (for video capture), microphone (for audio input)

[0608] Software: OpenCV (registered trademark) (for video processing), Keras (for emotion recognition models), SpeechRecognition (for voice recognition), Transformers (for emotion analysis)

[0609] Examples of specific prompts include the following:

[0610] "We want to develop an application that analyzes emotions from customers' facial expressions and voices and provides the results to staff in real time. Specifically, it would have a function that uses a camera and microphone to recognize customers' emotions and notify staff of the results. What technology and methods should we use?"

[0611] This allows for real-time understanding of customer sentiment and the ability to respond accordingly, facilitating information sharing across different business departments, improving customer satisfaction and strengthening competitiveness.

[0612] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0613] Step 1:

[0614] The server collects Voice of Customer (VoC) data. Specifically, it collects text data such as customer feedback, opinions, and requests. The input is the Voice of Customer (VoC) data, and the output is the collected text data.

[0615] Step 2:

[0616] The server inputs the collected voice of the customer (VoC) data into a generative AI model to analyze emotions and needs. Specifically, it uses natural language processing technology to analyze the text data and identify customer emotions and needs. The input is the collected text data, and the output is information on emotions and needs as the analysis results.

[0617] Step 3:

[0618] The server provides insights to the planning, design, and development staff in the business divisions based on the analysis results. Specifically, the analysis results are notified to each division and provided as reference information for policy planning and design improvements. The input is the analysis results, and the output is the provided insight information.

[0619] Step 4:

[0620] The server utilizes the provided insights across multiple channels and groups. Specifically, it promotes information sharing between different departments and realizes unified customer responses. The input is the provided insight information, and the output is the shared insight information.

[0621] Step 5:

[0622] The device (smartphone or tablet) captures the customer's facial expression and voice. Specifically, it uses a camera and microphone to collect the customer's facial expression and voice data. The input is the customer's facial expression and voice, and the output is the captured video and voice data.

[0623] Step 6:

[0624] The device transmits the captured facial expression and audio data to the server. Specifically, the data is uploaded to the server via a network. The input is the captured video and audio data, and the output is the data transmitted to the server.

[0625] Step 7:

[0626] The server inputs the received facial and voice data into a generative AI model to analyze emotions in real time. Specifically, it analyzes the data using video processing and voice recognition technologies to identify the customer's emotions. The input is the received video and voice data, and the output is the real-time emotion analysis results.

[0627] Step 8:

[0628] The server sends the real-time emotion analysis results to the terminal. Specifically, it notifies the terminal of the analysis results via the network. The input is the real-time emotion analysis results, and the output is the analysis results sent to the terminal.

[0629] Step 9:

[0630] The terminal then displays the received emotion analysis results to the store staff. Specifically, the customer's emotions are displayed on the screen of a smartphone or tablet so that the staff can check them. The input is the received analysis results, and the output is the displayed emotion information.

[0631] Step 10:

[0632] The user (store staff) responds to customers based on the displayed emotional information. Specifically, they provide services and responses that correspond to the customer's emotions. The input is the displayed emotional information, and the output is an appropriate response to the customer.

[0633] Example 3

[0634] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0635] With the previous system, it was difficult to effectively analyze customer feedback and reflect it in product development and business strategies. There was also insufficient information sharing between departments, making consistent product development difficult. Furthermore, there was a lack of means to analyze customer sentiment and utilize it in business strategies, which meant that improvements in customer satisfaction and competitiveness were not fully achieved.

[0636] 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.

[0637] In this invention, the server includes: a means for analyzing customer feedback using generative artificial intelligence; a means for providing insights to business division planning, design, and development personnel based on the analysis results; a means for utilizing the provided insights across multiple channels and groups; a means for analyzing customer sentiment using an emotion engine and utilizing the results in business strategy and policy planning; and a means for sharing information between divisions in real time to promote consistent product development. This enables effective analysis of customer feedback, strengthening information sharing between divisions, and reflecting customer sentiment in business strategies. As a result, improved customer satisfaction and stronger competitiveness are achieved.

[0638] "Generative AI" is an AI technology that uses natural language processing and machine learning techniques to generate and analyze text data.

[0639] "Voice of the customer" refers to information such as feedback, opinions, and requests provided by customers.

[0640] "Insights" are insights and knowledge gained from data analysis, and are information useful for business strategy and product development.

[0641] "Multi-channel, cross-group" refers to the sharing and utilization of information across multiple communication channels and departments.

[0642] An "emotion engine" is a technology for analyzing emotions from text data and is used to understand customer emotions.

[0643] "Real-time" refers to data and information being processed and shared instantly, meaning that it is reflected immediately without delay.

[0644] "Product development" is the process of planning, designing, and manufacturing new products, and is carried out in response to customer needs and market demands.

[0645] A "business strategy" is a long-term plan or policy that a company formulates to maintain and improve its competitiveness.

[0646] "Customer satisfaction" is an indicator that shows the degree of satisfaction that customers feel with products and services.

[0647] "Competitiveness" refers to a company's ability to compete with other companies in the marketplace and is an element that gives it an advantage.

[0648] This invention is a system that utilizes generative artificial intelligence to analyze customer feedback and plan product development and business strategies based on the analysis results. Specific embodiments of this system are described below.

[0649] 1. Collecting and analyzing customer feedback

[0650] The device collects customer feedback data from the planning department, specifically survey results, social media comments, customer support logs, and more.

[0651] The server inputs the collected data into a generative AI model, which may use natural language processing technology (e.g., GPT-4).

[0652] The server uses a generative AI model to analyze customer needs, specifically by parsing text data and extracting important keywords and trends.

[0653] 2. Deciding on product design and functionality

[0654] The terminal receives the analysis results from the server and shares them with the design and development departments.

[0655] Users (design and development personnel) decide on the product's design and functionality based on shared needs. Specifically, they use CAD software and prototyping tools to create designs and define functional requirements.

[0656] The user uploads the final design and functionality to the server.

[0657] 3. Information sharing and consistent product development

[0658] The server provides a platform for sharing information across multiple channels and groups between departments, using communication tools such as Slack and Microsoft Teams.

[0659] Users (people from each department) share information in real time through the platform and carry out consistent product development. Specifically, prototype designs created by the design department are shared with the development department and feedback is received.

[0660] The device manages progress based on the shared information and assigns tasks and adjusts schedules as needed.

[0661] 4. Business strategy planning using emotion engines

[0662] The server analyzes the customer's emotions using an emotion engine, which uses, for example, emotion analysis technology (e.g., IBM Watson (registered trademark) Tone Analyzer).

[0663] The device provides the results of sentiment analysis to the marketing department for use in developing business strategies and initiatives.

[0664] The user (a marketing department employee) uses the results of the sentiment analysis to develop a marketing strategy for the new product, specifically, to plan an advertising campaign and promotional activities.

[0665] Examples of specific examples and prompts

[0666] As a concrete example, a company uses this system when developing a new smartphone. The planning department extracts "longer battery life" and "improved camera performance" as important needs from customer feedback. The design and development departments decide on the design and functions of the new smartphone based on these needs, and share information to carry out consistent product development. The marketing department then uses the analysis results of the emotion engine to implement an advertising campaign to heighten customer expectations.

[0667] Example prompt sentence:

[0668] Enter your customer feedback data. Example: "The new product has a simple design and is easy to use. I would be happy if the battery life was longer."

[0669]

[0670] Enter a prompt to analyze customer emotions using the emotion engine and reflect it in your business strategy. Example: "Analyze the expectations and concerns customers have about our new product and reflect that in your marketing strategy for the new product."

[0671] The flow of the identification process in the third embodiment will be described with reference to FIG.

[0672] Step 1:

[0673] The terminal collects customer feedback data from the planning department, specifically survey results, social media comments, customer support logs, etc. It receives customer feedback data as input and sends this data to the server as output.

[0674] Step 2:

[0675] The server inputs the collected data into a generative AI model. This generative AI model uses, for example, a model that uses natural language processing technology (e.g., GPT-4). It receives customer feedback data as input, processes the data by analyzing the text data, and extracts important keywords and trends. It generates analysis results as output.

[0676] Step 3:

[0677] The server receives the analysis results from the generative AI model as input and sends them to the device as output.

[0678] Step 4:

[0679] The terminal shares the analysis results received from the server with the design and development departments. It receives the analysis results from the server as input and sends them to the design and development departments as output.

[0680] Step 5:

[0681] Users (design and development staff) decide on the product's design and functionality based on shared needs. Specifically, they create designs using CAD software and prototyping tools and define functional requirements. They receive the analysis results as input, process the data, and decide on the design and functionality. As output, they upload the decided design and functionality to the server.

[0682] Step 6:

[0683] The server provides a platform for sharing information across multiple channels and groups between departments. This platform uses communication tools (e.g., Slack or Microsoft Teams). It receives design and functionality information as input and shares it with each department as output.

[0684] Step 7:

[0685] Users (people in each department) share information in real time through the platform and carry out consistent product development. Specifically, the design department shares prototype designs with the development department and receives feedback. As input, the shared information is received, and as data processing, feedback is provided and necessary corrections are made. As output, the corrected design and functions are uploaded to the server.

[0686] Step 8:

[0687] The device manages the progress based on the shared information, and allocates tasks and adjusts the schedule as necessary. It receives the shared information as input, processes the data, and manages the progress, allocates tasks, and adjusts the schedule. It outputs the updated progress and task information to the server.

[0688] Step 9:

[0689] The server analyzes customer emotions using an emotion engine. This emotion engine uses emotion analysis technology (e.g., IBM Watson's Tone Analyzer). It receives customer feedback data as input, processes the data, and analyzes emotions. It generates emotion analysis results as output.

[0690] Step 10:

[0691] The terminal provides the sentiment analysis results to the marketing department to utilize them in business strategy and policy planning. It receives the sentiment analysis results as input and sends them to the marketing department as output.

[0692] Step 11:

[0693] The user (a marketing department employee) plans a marketing strategy for a new product based on the results of the sentiment analysis. Specifically, they plan an advertising campaign and promotional activities. They receive the sentiment analysis results as input, process the data, and plan a marketing strategy. As output, they upload the planned marketing strategy to the server.

[0694] (Application example 3)

[0695] 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."

[0696] Conventional systems for analyzing Voice of Customer (VoC) have had the problem of being unable to fully grasp customer emotions, making it difficult to accurately grasp customers' true needs. Furthermore, there was a lack of means to reflect the results of customer emotion analysis in business strategies and policy planning, which meant that improvements in customer satisfaction and strengthened competitiveness were not fully achieved.

[0697] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes: means for analyzing voice of customer (VoC) using generative AI; means for providing insights to planning, design, and development personnel in business departments based on the analysis results; means for utilizing the provided insights across multiple channels and groups; means for analyzing customer emotions using an emotion engine; means for reflecting the results of the customer emotion analysis in business strategy and policy planning; and means for collecting customer reviews using an application installed on a smartphone and analyzing them using a generative AI model and an emotion engine. This enables accurate understanding of customer needs, improving customer satisfaction, and strengthening competitiveness.

[0698] "Generative AI" is a type of artificial intelligence, a technology that generates new information and content based on data.

[0699] "Voice of Customer (VoC)" refers to feedback such as opinions, impressions, and requests that customers have about products and services.

[0700] "Insights" refers to insights and understanding gained by analyzing data and information, and is particularly useful for formulating business strategies and policies.

[0701] "Multi-channel, group-wide" refers to sharing information across multiple different channels and departments and utilizing it in an integrated manner.

[0702] An "emotion engine" is a technology that analyzes human emotions from data such as text and voice, and estimates emotional states.

[0703] A "business strategy" refers to the plans and policies that a company formulates to achieve its long-term goals.

[0704] "Policy planning" refers to the process of formulating specific action plans and measures to achieve specific objectives.

[0705] A "smartphone" is a type of mobile phone that is a multi-function device that can connect to the Internet and use applications.

[0706] "Application" means a software program designed to provide a particular function or service.

[0707] "Customer reviews" refer to ratings and comments made by customers about products and services.

[0708] A "generative AI model" is a specific implementation of generative AI, and refers to algorithms and programs for generating new information and content based on data.

[0709] The following system configuration will be described as an embodiment of the present invention.

[0710] System Configuration

[0711] The system includes a server, smartphone, generative AI model, and emotion engine. The server uses generative AI to analyze the voice of the customer (VoC) and provides insights to business division planning, design, and development staff based on the analysis results. The provided insights are then utilized across multiple channels and groups. The emotion engine is also used to analyze customer emotions, and the results of the customer emotion analysis are reflected in business strategies and policy planning.

[0712] Program processing explanation

[0713] Hardware and software used

[0714] Hardware: Servers, smartphones

[0715] Software: Python, OpenAI API, TextBlob

[0716] Data processing and calculation

[0717] 1. Data collection: Customer reviews are collected using an application installed on a smartphone. Users enter reviews using their smartphones and send them to the server.

[0718] 2. Analysis using a generative AI model: The server uses the OpenAI API to extract customer needs from collected customer reviews. Specifically, it uses the following prompt sentence:

[0719] Analyze the following reviews to identify customer needs.

[0720] This product is very easy to use.

[0721] The design is great, but the functionality is a bit lacking.

[0722] I think the price is too high.

[0723] I can't wait for the new version.

[0724] 3. Sentiment Engine Analysis: The server analyzes the sentiment of customer reviews using TextBlob, and calculates the positivity and subjectivity of each review.

[0725] 4. Providing insights: Based on the analysis results of the generative AI model and emotion engine, the server provides insights to the planning, design, and development staff in the business division, allowing them to understand the true needs of customers and reflect them in business strategies and policy planning.

[0726] Specific examples

[0727] For example, consider the following customer reviews collected:

[0728] "This product is very easy to use." -> Positivity: 0.8, Subjectivity: 0.6

[0729] "The design is great, but the features are a bit lacking." -> Positivity: 0.2, Subjectivity: 0.7

[0730] The server analyzes these reviews using a generative AI model and an emotion engine to extract customer needs and emotions. For example, it can obtain insights such as, "Ease of use is important, but additional features are desired." Based on these insights, business divisions can develop new products and formulate marketing strategies.

[0731] In this way, a system is provided that can analyze customer opinions and emotions and use them in product development and marketing strategies.

[0732] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0733] Step 1:

[0734] A user launches the application using a smartphone and inputs a customer review. The input review is sent from the smartphone to a server. The input data is the customer review in text format. The output data is the customer review sent to the server.

[0735] Step 2:

[0736] The server inputs the received customer reviews into the generative AI model. The generative AI model uses OpenAI's API to extract customer needs from the customer reviews. The input data is the customer reviews in text format. The output data is text that indicates the customer needs. Specifically, the following prompt sentence is input into the generative AI model:

[0737] Analyze the following reviews to identify customer needs.

[0738] This product is very easy to use.

[0739] The design is great, but the functionality is a bit lacking.

[0740] I think the price is too high.

[0741] I can't wait for the new version.

[0742] Step 3:

[0743] The server stores the customer needs obtained from the generative AI model and then analyzes the sentiment of the customer reviews using a sentiment engine. The sentiment engine uses TextBlob to calculate the positivity and subjectivity of each review. The input data are customer reviews in text format. The output data are numbers indicating the positivity and subjectivity of each review. Specifically, sentiment analysis is performed using TextBlob as follows:

[0744] Review: "This product is very easy to use." -> Positivity: 0.8, Subjectivity: 0.6

[0745] Review: "Great design, but lacks functionality." -> Positivity: 0.2, Subjectivity: 0.7

[0746] Step 4:

[0747] The server integrates the analysis results obtained from the generative AI model and the emotion engine, and provides insights to the business division's planning, design, and development staff. The input data is text indicating customer needs and numerical values ​​indicating the positivity and subjectivity of each review. The output data is a report showing the integrated insights. Specifically, the server generates the following insights:

[0748] Insight: "Ease of use is important, but additional features are desired."

[0749] Step 5:

[0750] Based on the insights provided by the server, business department staff will formulate business strategies and measures. The input data is a report showing the insights provided by the server. The output data is a new product development plan and marketing strategy. Specifically, the staff will formulate measures such as the following:

[0751] Solution: "Add new product features, maintain ease of use, and set the price appropriately."

[0752] In this way, a system is provided that can analyze customer opinions and emotions and use them in product development and marketing strategies.

[0753] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0754] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0755] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0756] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0757] [Second embodiment]

[0758] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0759] 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.

[0760] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0761] 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.

[0762] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0763] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0764] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0765] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0770] "Example 1"

[0771] One embodiment of the present invention is a voice-of-customer (VoC) analysis system that utilizes generative AI. This system collects customer feedback and opinions and inputs them into the generative AI. The generative AI uses natural language processing (NLP) technology to extract and analyze customer needs and emotions from the collected data. The analysis results are provided to planning, design, and development staff in the business division to support their decision-making.

[0772] "Example 2"

[0773] Furthermore, the insights provided are utilized across multiple channels and groups. Specifically, they are shared by different departments within the company, such as the planning department, design department, and development department, and each department plans and implements measures based on customer needs. This makes it possible to improve customer satisfaction and strengthen competitiveness.

[0774] "Example 3"

[0775] As a concrete example, consider the case where a company uses this system when developing a new product. First, the planning department uses generative AI to analyze customer feedback and understand the customer needs necessary for developing a new product. Next, the design and development departments decide on the product's design and functionality based on those needs. Each department then shares information across multiple channels and the group to develop products consistently. This enables product development based on the true needs of customers, which is expected to improve customer satisfaction and strengthen competitiveness.

[0776] The processing flow of each embodiment will be described below.

[0777] "Example 1"

[0778] Step 1: Collect customer feedback and opinions. This data is referred to as Voice of Customer (VoC).

[0779] Step 2: Input the collected VoC data into a generative AI, which uses natural language processing (NLP) techniques to extract customer needs and emotions from the data.

[0780] Step 3: Provide the results of the generative AI analysis to the planning, design, and development staff in the business division to support their decision-making.

[0781] "Example 2"

[0782] Step 1: The analysis results from generative AI are shared across multiple channels and groups. Specifically, the analysis results are shared among different departments within the company, such as the planning department, design department, and development department.

[0783] Step 2: Each department will use the shared insights to plan and implement measures that address customer needs, thereby improving customer satisfaction and strengthening competitiveness.

[0784] "Example 3"

[0785] Step 1: The planning department uses generative AI to analyze customer feedback and understand customer needs necessary for developing new products.

[0786] Step 2: The design and development departments decide on the product design and functions based on the needs provided by the planning department.

[0787] Step 3: Each department shares information across multiple channels and the group to develop products consistently. This enables product development based on real customer needs, which is expected to improve customer satisfaction and strengthen competitiveness.

[0788] Example 1

[0789] 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."

[0790] With conventional Voice of Customer (VoC) analysis systems, it was difficult to efficiently collect and accurately analyze customer feedback and opinions. Furthermore, there was a lack of appropriate means to provide the analysis results to business division planning, design, and development personnel to support their decision-making. Furthermore, it was difficult to utilize the insights provided across multiple channels and groups, which meant that improvements in customer satisfaction and strengthened competitiveness were not fully achieved.

[0791] 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.

[0792] In this invention, the server includes means for collecting customer feedback and opinions, means for preprocessing the collected data, means for inputting the preprocessed data into a generative AI model, means for extracting customer needs and emotions using the generative AI model, means for analyzing the extracted needs and emotions, means for providing the analysis results to planning, design, and development personnel in the business division, and means for utilizing the provided insights across multiple channels and groups. This enables efficient collection and accurate analysis of customer feedback and opinions. Furthermore, by providing the analysis results to business division personnel and supporting their decision-making, improved customer satisfaction and stronger competitiveness can be achieved.

[0793] "Means for collecting customer feedback and opinions" refers to methods and devices for collecting information such as opinions, impressions, and evaluations provided by customers.

[0794] "Means for pre-processing collected data" refers to methods or devices for converting collected raw data into a format that is easier to analyze, and specifically includes text cleaning, tokenization, removal of stop words, etc.

[0795] A "means for inputting preprocessed data into a generative AI model" is a method or apparatus for appropriately supplying preprocessed data to a generative AI model.

[0796] "Means for extracting customer needs and emotions using generative AI models" refers to methods or devices that utilize generative AI models to identify and extract needs and emotions from customer feedback.

[0797] "Means for analyzing extracted needs and emotions" refers to methods or devices for further analyzing the needs and emotions extracted by the generative AI model and obtaining specific insights.

[0798] "Means for providing analysis results to business division planning, design, and development personnel" refers to methods and devices for communicating the analysis results to business division personnel and supporting their decision-making.

[0799] "Means for utilizing the provided insights across multiple channels and groups" refers to methods and devices for sharing the obtained insights across multiple channels and groups and utilizing them in a unified manner.

[0800] This invention is a system for analyzing voice of customer (VoC) using generative AI. Specific embodiments of this system are described below.

[0801] System configuration

[0802] 1. Data Collection

[0803] Users collect customer feedback and opinions through online forms, emails, social media comments, etc.

[0804] The device sends the collected feedback to a server. Specifically, the application on the device sends data to the server through an API.

[0805] 2. Data Preprocessing

[0806] The server preprocesses the received data. Specifically, it performs the following processes:

[0807] Text cleaning: remove unnecessary characters and noise, for example, remove HTML tags and special characters.

[0808] Tokenization: Splitting text into words and phrases. For example, splitting "product delivery is slow" into "product", "of", "delivery", "is", and "slow".

[0809] Stop word removal: Remove meaningless words (e.g., "no" and "ga").

[0810] Software used: Python, NLTK (Natural Language Toolkit)

[0811] 3. Input to generative AI

[0812] The server inputs the preprocessed data into the generative AI model, specifically by sending the preprocessed text as a prompt to the generative AI.

[0813] Generative AI model used: A general generative AI model (e.g., GPT-4)

[0814] 4. Data Analysis

[0815] Generative AI uses natural language processing (NLP) technology to extract customer needs and emotions. For example, it can extract the need for "delayed delivery" and the emotion of "dissatisfaction."

[0816] The server receives the output from the generative AI and organizes the analysis results, specifically categorizing the extracted needs and emotions.

[0817] 5. Providing results

[0818] The server provides the analysis results to the planning, design, and development staff in the business division. Specifically, it generates the analysis results in report format and sends them to the staff by email.

[0819] The terminal displays the results to the person in charge, for example, by displaying the analysis results in graphs and charts on a dashboard.

[0820] Specific examples

[0821] For example, the following shows how an online shop processes feedback from a customer that "product delivery is slow."

[0822] 1. Data Collection

[0823] Users collect feedback about "slow delivery of product" through an online form.

[0824] The terminal sends this feedback to the server.

[0825] 2. Data Preprocessing

[0826] The server receives the text "Slow delivery of product" and cleans it, e.g. removes HTML tags and special characters.

[0827] The server tokenizes the text and splits it into "product," "delivery," and "slow."

[0828] The server removes the stop words "no" and "ga."

[0829] 3. Input to generative AI

[0830] The server sends the preprocessed text "Product delivery is slow" as a prompt to the generative AI model.

[0831] 4. Data Analysis

[0832] Generative AI extracts the need for "delayed delivery" and the emotion of "dissatisfaction."

[0833] The server organizes this information and generates an analysis result called "dissatisfaction with delivery delays."

[0834] 5. Providing results

[0835] The server generates the analysis results in the form of a report and sends it to the business department manager via email.

[0836] The terminal displays the results to the person in charge, and the analysis results, such as "dissatisfaction with delivery delays," are displayed in a graph on the dashboard.

[0837] Prompt Sentence Examples

[0838] An example of a prompt sentence to be input to the generative AI model is as follows:

[0839] Customer feedback: "Product delivery is slow"

[0840] Extract customer needs and feelings from this feedback.

[0841] In this way, the system efficiently analyzes the voice of the customer (VoC) and supports decision-making in business divisions.

[0842] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0843] Step 1:

[0844] Data collection

[0845] Users collect customer feedback and opinions through online forms, emails, social media comments, etc.

[0846] Input: Customer feedback and opinions (e.g., "Product delivery was slow")

[0847] The device sends the collected feedback to a server. Specifically, the application on the device sends data to the server through an API.

[0848] Output: Feedback data sent to the server

[0849] Step 2:

[0850] Data Preprocessing

[0851] The server preprocesses the received data. Specifically, it performs the following processes:

[0852] Text cleaning: remove unnecessary characters and noise, for example, remove HTML tags and special characters.

[0853] Tokenization: Splitting text into words and phrases. For example, splitting "product delivery is slow" into "product", "of", "delivery", "is", and "slow".

[0854] Stop word removal: Remove meaningless words (e.g., "no" and "ga").

[0855] Input: Feedback data sent to the server (e.g., "Product delivery was slow")

[0856] Output: Preprocessed text data (e.g., "Product delivery is slow")

[0857] Step 3:

[0858] Input to generative AI

[0859] The server inputs the preprocessed data into the generative AI model, specifically by sending the preprocessed text as a prompt to the generative AI.

[0860] Input: Preprocessed text data (e.g., "Product delivery is slow")

[0861] Output: The prompt input to the generative AI model

[0862] Step 4:

[0863] Data analysis

[0864] Generative AI uses natural language processing (NLP) technology to extract customer needs and emotions. For example, it can extract the need for "delayed delivery" and the emotion of "dissatisfaction."

[0865] Input: A prompt sentence entered into the generative AI model (e.g., "Product delivery is slow")

[0866] Output: Extracted needs and emotions (e.g., "Delayed delivery" or "Dissatisfied")

[0867] Step 5:

[0868] Providing results

[0869] The server receives the output from the generative AI and organizes the analysis results, specifically categorizing the extracted needs and emotions.

[0870] Input: Extracted needs and emotions (e.g., "Delayed delivery" or "Dissatisfied")

[0871] Output: Organized analysis results (e.g., "Dissatisfaction with delayed deliveries")

[0872] The server provides the analysis results to the planning, design, and development staff in the business division. Specifically, it generates the analysis results in report format and sends them to the staff by email.

[0873] The terminal displays the results to the person in charge, for example, by displaying the analysis results in graphs and charts on a dashboard.

[0874] Input: Organized analysis results (e.g., "Dissatisfaction with delayed deliveries")

[0875] Output: Reports and dashboards provided to the person in charge

[0876] In this way, the system efficiently analyzes the voice of the customer (VoC) and supports decision-making in business divisions.

[0877] (Application example 1)

[0878] 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."

[0879] Conventional Voice of Customer (VoC) analysis systems struggled to effectively collect and analyze customer feedback and reviews and provide them to business division planning, design, and development staff. Furthermore, there was a lack of a way to present the analysis results in a visually understandable manner, limiting the use of insights. This made it difficult to understand customers' true needs and reflect them in business strategies and policy planning. Furthermore, improvements in customer satisfaction and competitiveness were not fully achieved.

[0880] 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.

[0881] In this invention, the server includes: means for analyzing voice of customer (VoC) using generative AI; means for providing insights to business division planning, design, and development personnel based on the analysis results; means for utilizing the provided insights across multiple channels and groups; means for collecting customer reviews and feedback and analyzing them using generative AI; and means for visualizing the analysis results in graphs and charts and providing them to business division personnel. This enables customer feedback to be effectively collected and analyzed and presented in a visually easy-to-understand format. This allows the true needs of customers to be understood and reflected in business strategies and policy planning, thereby improving customer satisfaction and strengthening competitiveness.

[0882] "Generative AI" is a type of artificial intelligence that uses natural language processing technology to generate, analyze, and understand text data.

[0883] "Voice of Customer (VoC)" refers to text data such as customer feedback, reviews, and opinions.

[0884] The "analysis results" are data containing needs, emotions, and insights extracted by generative AI by analyzing customer feedback.

[0885] A "business division" refers to a department within a company that is responsible for planning, design, development, and other tasks.

[0886] "Insights" refers to the insights and knowledge gained by analyzing customer feedback, and is information that is useful for formulating business strategies and measures.

[0887] "Multi-channel, cross-group" refers to the sharing and utilization of information across multiple channels and departments.

[0888] A "review" refers to an evaluation or opinion that a customer gives about a product or service.

[0889] "Feedback" refers to information such as opinions, impressions, and areas for improvement provided by customers.

[0890] "Graphs and charts" refer to diagrams that visually represent data and are used to display analytical results in an easy-to-understand manner.

[0891] "Visualization" refers to displaying data or information visually to make it easier to understand.

[0892] A system for implementing the present invention is configured as follows.

[0893] Data collection

[0894] The server uses web scraping tools (e.g. BeautifulSoup, Scrapy) or APIs to collect customer reviews and feedback, which is then stored in a database as Voice of Customer (VoC).

[0895] Data Preprocessing

[0896] The server cleans the collected data and performs preprocessing such as tokenizing the text data, removing stop words, and stemming, using software such as Python, Pandas, and NLTK.

[0897] Generative AI analysis

[0898] The server inputs the preprocessed data into a generative AI model (e.g., GPT-4, BERT) to extract customer needs and emotions, enabling detailed analysis of customer feedback.

[0899] Visualizing the results

[0900] The server then visualizes the analysis results in graphs and charts and provides them to business unit personnel using software such as Dash and Plotly, allowing business personnel to gain insights in a visually easy-to-understand format.

[0901] Specific examples

[0902] For example, the following prompt sentence is input into the generative AI model:

[0903] Example prompt sentence:

[0904] Analyze the following customer reviews and extract key needs and emotions. Review: 'This product is very easy to use, but the battery life is poor. I like the design, but I wish it was a little lighter.'

[0905] An example output of a generative AI model is as follows:

[0906] Example output of a generative AI model:

[0907] "Primary needs: Improved battery life, lighter weight

[0908] Sentiments: Positive (easy to use, good design), Negative (poor battery life)

[0909] In this way, customer feedback can be collected and analyzed effectively and presented in a visually easy-to-understand format, allowing customers to understand their true needs and reflect them in business strategies and policy planning, thereby improving customer satisfaction and strengthening competitiveness.

[0910] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0911] Step 1:

[0912] The server collects customer reviews and feedback. Specifically, it uses web scraping tools (e.g., BeautifulSoup, Scrapy) and APIs to store the Voice of Customer (VoC) in a database. The input is customer reviews and feedback, and the output is text data stored in the database.

[0913] Step 2:

[0914] The server cleans the collected data. Specifically, it uses software such as Python, Pandas, and NLTK to perform preprocessing such as tokenizing the text data, removing stop words, and stemming. The input is the text data stored in the database, and the output is the preprocessed text data.

[0915] Step 3:

[0916] The server inputs the preprocessed data into a generative AI model (e.g., GPT-4, BERT) to extract customer needs and emotions. Specifically, it inputs a prompt into the generative AI model and analyzes the text output by the model. The input is the preprocessed text data and the prompt, and the output is the analysis results that include customer needs and emotions.

[0917] Step 4:

[0918] The server visualizes the analysis results in graphs and charts. Specifically, it uses software such as Dash or Plotly to visually display the analysis results. The input is the analysis results, including customer needs and sentiment, and the output is graphs and charts.

[0919] Step 5:

[0920] The terminal provides visualized analysis results to business department personnel. Specifically, the personnel can view the analysis results via a web browser or dedicated application. The input is graphs and charts, and the output is insights that the personnel can view.

[0921] Step 6:

[0922] Users can then use the provided insights to develop business strategies and measures. Specifically, they can use the visualized data as a reference to improve product development and marketing strategies. The input is the insights viewed by the person in charge, and the output is improved business strategies and measures.

[0923] Example 2

[0924] Next, a description will be given of 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."

[0925] In conventional systems for analyzing Voice of Customer (VoC), the process from collecting data to analyzing it and providing insights was inconsistent, making it difficult for each department to quickly plan and implement measures based on customer needs. Also, insights were not visualized or shared properly, and information sharing between departments was often not smooth. This led to issues such as insufficient improvement of customer satisfaction and strengthening of competitiveness.

[0926] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a data collection means, a data preprocessing means, a means for analyzing voice of customer (VoC) using a generative AI model, a means for providing insights to planning, design, and development personnel in the business department based on the analysis results, a means for utilizing the provided insights across multiple channels and groups, a means for visualizing the insights, and a means for sharing the insights with each department. This allows the processes from data collection to analysis, providing, visualizing, and sharing insights to be performed consistently, enabling each department to quickly plan and implement measures based on customer needs.

[0927] "Data collection methods" are methods used to collect customer feedback and market research data.

[0928] "Data preprocessing means" refers to means for preprocessing collected data, such as cleaning and normalizing the data, to prepare it in a format suitable for analysis.

[0929] A "generative AI model" is an artificial intelligence model that uses natural language processing to analyze data and generate insights.

[0930] "Means for analyzing voice of customer (VoC)" refers to means for analyzing customer feedback and opinions using generative AI models.

[0931] "Means of providing insights" refers to the means of providing useful information and suggestions to the planning, design, and development staff of the business division based on the results of the analysis.

[0932] "Means for multi-channel, group-wide utilization" refers to the means for sharing and utilizing the insights provided across different departments and channels within the company.

[0933] "Means for visualizing insights" refers to means for displaying analytical results in visual formats such as graphs and charts to make them easier to understand.

[0934] "Means for sharing insights with each department" refers to means for distributing the generated insights to each department within the company and sharing the information.

[0935] This invention is a system that utilizes a generative AI model to analyze voice of customer (VoC) and provide insights to each department within a company. Specific embodiments of this system are described below.

[0936] 1. Data Collection

[0937] The server collects customer feedback and market research data, such as website visit data, customer purchase history, and survey results, using tools like Google Analytics and Salesforce.

[0938] 2. Data Preprocessing

[0939] The server preprocesses the collected data by using the Python Pandas library to clean and normalize the data and prepare it for analysis, for example by imputing missing values, removing outliers, and encoding categorical variables.

[0940] 3. Data analysis

[0941] The server inputs the preprocessed data into a generative AI model to analyze customer needs and trends. For example, OpenAI's GPT-4 is used as the generative AI model. The server inputs the preprocessed data into the GPT-4 model and generates analysis results in natural language.

[0942] 4. Insight generation

[0943] The server generates insights to provide to each department based on the analysis results obtained from the generative AI model. The insights include specific action plans and areas for improvement. The server visualizes the generated analysis results using visualization tools such as Power BI and Tableau, providing the insights in a format that is easy for each department to understand.

[0944] 5. Insight sharing

[0945] The server shares the generated insights with each department within the company (planning, design, development, etc.). The server distributes the insights to each department via email or internal chat tools (such as Slack or Microsoft Teams). It also provides a dashboard for sharing insights so that each department can access them in real time.

[0946] Specific examples

[0947] When a user enters the prompt "Based on the latest customer feedback, what are the key trends for next product development?", the server does the following:

[0948] 1. Data collection: The server uses the Google Analytics API to obtain website visit data and the Salesforce API to collect customer purchase history and survey results.

[0949] 2. Data Preprocessing: The server uses the Pandas library to clean and normalize the data.

[0950] 3. Data analysis: The server inputs the preprocessed data into the GPT-4 model to analyze "which product features are most highly rated based on recent customer feedback."

[0951] 4. Insight generation: Based on the analysis results, the server visualizes them using Power BI and generates the insight that "improving user interfaces and revamping designs are important trends."

[0952] 5. Insight sharing: The server distributes the generated insights to each department via Slack and provides a dashboard that can be accessed in real time.

[0953] In this way, each department can plan and implement measures based on the insights provided, improving customer satisfaction and strengthening competitiveness.

[0954] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0955] Step 1: Data collection

[0956] The server collects customer feedback and market research data. Specifically, it uses tools such as Google Analytics and Salesforce to obtain website visit data, customer purchase history, survey results, etc. The input is raw data obtained through the API, and the output is a set of collected raw data.

[0957] Specific behavior:

[0958] The server periodically retrieves website visit data using the Google Analytics API, and also uses the Salesforce API to collect customer purchase history and survey results.

[0959] Step 2: Data Preprocessing

[0960] The server preprocesses the collected data by using the Python Pandas library to clean and normalize the data and prepare it for analysis. The input is the raw data collected, and the output is a preprocessed, clean dataset.

[0961] Specific behavior:

[0962] The server uses the Pandas library to impute missing values, remove outliers, normalize data, and encode categorical variables.

[0963] Step 3: Data analysis

[0964] The server inputs the preprocessed data into a generative AI model to analyze customer needs and trends. The generative AI model used is, for example, OpenAI's GPT-4. The input is a preprocessed, clean dataset, and the output is the analysis results from the generative AI model.

[0965] Specific behavior:

[0966] The server then inputs the preprocessed data into the GPT-4 model to generate natural language analysis results, such as "Which product features are most popular based on recent customer feedback?"

[0967] Step 4: Insight generation

[0968] The server generates insights to provide to each department based on the analysis results obtained from the generative AI model. The insights include specific measures and improvements. The input is the analysis results from the generative AI model, and the output is visualized insights.

[0969] Specific behavior:

[0970] The server visualizes the generated analytical results using visualization tools such as Power BI and Tableau, providing insights in a format that is easy for each department to understand.

[0971] Step 5: Share your insights

[0972] The server shares the generated insights with each department within the company (planning department, design department, development department, etc.). The input is the visualized insights, and the output is the insights distributed to each department.

[0973] Specific behavior:

[0974] The server distributes insights to each department via email or internal chat tools (such as Slack or Microsoft Teams), and also provides a dashboard for sharing insights so that each department can access them in real time.

[0975] (Application example 2)

[0976] 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."

[0977] With the previous system, it was difficult for different departments within a company to effectively share insights to plan and implement measures based on customer needs. Furthermore, there was a lack of means for each department within a factory to plan and implement measures based on market trends in real time. This meant that improvements in customer satisfaction and strengthening of competitiveness were not fully achieved.

[0978] 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.

[0979] In this invention, the server includes: means for analyzing voice of customer (VoC) using generative AI; means for providing insights to planning, design, and development personnel in business divisions based on the analysis results; means for utilizing the provided insights across multiple channels and groups; means for each division within the factory to plan and implement measures based on customer needs and market trends in real time; means for a robot to automatically collect data and generate insights using a generative AI model; and means for notifying each division of the insights via a smartphone, tablet, or AR smart glasses. This enables different divisions within a company to effectively share insights, enabling each division within the factory to plan and implement measures based on market trends in real time.

[0980] "Generative AI" is a system that uses artificial intelligence technology to generate data and perform analysis and predictions.

[0981] "Voice of the Customer (VoC)" refers to feedback, opinions, requests, etc. from customers.

[0982] "Insights" refers to insights and findings gained from data analysis.

[0983] "Multi-channel, cross-group" refers to the sharing and utilization of information across multiple channels and departments.

[0984] "Each department within the factory" refers to different departments within the factory, such as production control, quality control, and maintenance.

[0985] "Real-time" refers to data and information being processed immediately and available for immediate use.

[0986] "Market trends" refers to market changes, trends, customer needs, and other movements.

[0987] A "robot" refers to a mechanical device that performs work automatically.

[0988] A "generative AI model" refers to a model that uses generative AI to generate data and perform analysis and predictions.

[0989] A "smartphone" refers to a mobile phone that can connect to the Internet and use applications.

[0990] A "tablet" refers to a portable computer with a touchscreen.

[0991] "AR smart glasses" refers to a glasses-type device that displays information using augmented reality technology.

[0992] A system for carrying out this invention is configured as follows: The server includes means for analyzing voice of the customer (VoC) using generative AI, means for providing insights to planning, design, and development personnel in business divisions based on the analysis results, means for utilizing the provided insights across multiple channels and the group, means for each division within a factory to plan and implement measures based on customer needs and market trends in real time, means for a robot to automatically collect data and generate insights using a generative AI model, and means for notifying each division of the insights via a smartphone, tablet, or AR smart glasses.

[0993] Hardware and software used

[0994] Hardware: Sensors in factories, smartphones, tablets, AR smart glasses, robots

[0995] Software: Python, generative AI models (e.g., OpenAI's GPT-3)

[0996] Data processing and calculation

[0997] 1. Data collection: Robots collect data such as production rates, defect rates, scheduled maintenance dates, and customer feedback from sensors and APIs in the factory, enabling real-time data acquisition.

[0998] 2. Generating insights using a generative AI model: The collected data is input into a generative AI model to generate insights. The generative AI model can be, for example, OpenAI's GPT-3.

[0999] 3. Sharing insights: Generated insights are shared with each department via smartphone, tablet, or AR smart glasses, allowing each department to plan and implement measures based on market trends in real time.

[1000] Specific examples

[1001] For example, the production management department can adjust production schedules based on the generated insights, the quality control department can plan quality improvement measures based on defect rate data, and the maintenance department can implement preventive maintenance based on scheduled maintenance dates.

[1002] Prompt Sentence Examples

[1003] Production rate: 100%, Defect rate: 2%, Maintenance scheduled date: 2023-10-15, Customer feedback: Positive

[1004] By inputting this prompt into a generative AI model, the insights needed by each department can be generated.

[1005] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1006] Step 1:

[1007] The server collects data from sensors and APIs in the factory. Specifically, it acquires data such as production rates, defect rates, scheduled maintenance dates, and customer feedback. The input is data from sensors and APIs, and the output is the collected data. This makes it possible to acquire data in real time.

[1008] Step 2:

[1009] The server inputs the collected data into the generative AI model. Specifically, it converts the collected data into a prompt sentence format and sends it to the generative AI model (e.g., OpenAI's GPT-3). The input is the collected data, and the output is data in the prompt sentence format. This provides the basic data for the generative AI model to generate insights.

[1010] Step 3:

[1011] The server receives insights from the generative AI model. Specifically, it obtains the insights generated by the generative AI model based on prompt statements. The input is data in the form of prompt statements, and the output is the generated insights. This allows each department to obtain the insights they need.

[1012] Step 4:

[1013] The server notifies each department of the generated insights via smartphones, tablets, or AR smart glasses. Specifically, the insights are sent to each department's device and notified. The input is the generated insight, and the output is the insight displayed on each department's device. This enables each department to plan and implement measures based on market trends in real time.

[1014] Step 5:

[1015] Users (those in charge of each department) plan and implement measures based on the notified insights. Specifically, the production control department adjusts production schedules, the quality control department plans quality improvement measures, and the maintenance department implements preventive maintenance. The input is the notified insights, and the output is the measures implemented by each department. This leads to improved customer satisfaction and stronger competitiveness.

[1016] Example 3

[1017] Next, a description will be given of 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."

[1018] With conventional systems for analyzing Voice of Customer (VoC), it was difficult to accurately grasp customer needs and develop products based on them. Furthermore, there was insufficient information sharing between departments, making consistent product development difficult. This meant that improvements in customer satisfaction and strengthened competitiveness were not being fully achieved.

[1019] 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.

[1020] In this invention, the server includes: means for analyzing Voice of Customer (VoC) using generative AI; means for providing insights to planning, design, and development personnel in business divisions based on the analysis results; means for utilizing the provided insights across multiple channels and groups; means for inputting prompts to the generative AI model; means for storing generated responses in a database; means for analyzing the stored responses; means for creating design proposals based on customer needs; means for determining technical specifications based on the design proposals; means for providing a platform for sharing information generated by each division; and means for updating the shared information in real time and notifying each division. This enables accurate understanding of customer needs and consistent product development based on those needs, thereby improving customer satisfaction and strengthening competitiveness.

[1021] "Generative AI" is a system that generates text and data using artificial intelligence technology.

[1022] "Voice of Customer (VoC)" is information such as feedback, opinions, and requests collected from customers.

[1023] "Insights" are insights and knowledge gained from data analysis, and are information that is useful for formulating business strategies and measures.

[1024] "Multi-channel, cross-group" refers to sharing information across multiple communication channels and departments.

[1025] A "prompt" is a question or instruction text that is input to a generative AI.

[1026] A "database" is a system for storing and managing data in an organized manner.

[1027] A "design proposal" is a specific blueprint or concept regarding the appearance and function of a product.

[1028] "Technical specifications" are detailed technical requirements and standards for the design and manufacture of a product.

[1029] A "platform" is a system that serves as the foundation for each department to share information and work together.

[1030] "Real-time updates" means that information is updated immediately and always reflects the latest status.

[1031] This invention is a system that uses generative AI to analyze the voice of the customer (VoC), provide insights to each business division, and utilize them across multiple channels and groups. A specific embodiment of this system is shown below.

[1032] First, the user (planning department) uses a device (PC or tablet) to input a prompt into the generative AI model. Examples of prompts include "What features do you want in a new smartphone?" or "What is the most important feature for your next smartphone purchase?"

[1033] The server then uses a generative AI model (e.g., a text generation system using artificial intelligence technology) to generate an answer to the input prompt. The generated answer may include specific needs, such as "long battery life," "good camera image quality," and "waterproof."

[1034] The server stores the generated answers in a database (e.g., a system for systematically storing and managing data), which is used for later analysis.

[1035] The user (the planning department) retrieves data from the database to analyze the saved responses, for example, by using a BI tool (business intelligence tool) to visualize and analyze customer needs.

[1036] Next, the user (design department) creates a design proposal based on the customer's needs. To create the design proposal, they use design software such as Adobe Creative Cloud. They create a specific design and upload the file to the server.

[1037] The user (development department) determines the technical specifications based on the design proposal. To determine the technical specifications, they use CAD software such as AutoCAD or SolidWorks. They design the technical details and save the files on the server.

[1038] The server provides a platform for sharing information generated by each department. Collaboration tools such as Microsoft Teams and Slack are used for the platform. This allows each department to share information in real time, enabling consistent product development.

[1039] Finally, the server updates the shared information in real time and notifies each department, allowing each department to proceed with their work based on the latest information.

[1040] In this way, it becomes possible to accurately grasp customer needs and develop consistent products based on them, thereby improving customer satisfaction and strengthening competitiveness. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1041] Step 1:

[1042] The user (planning department) uses a device (PC or tablet) to input a prompt to the generative AI model. Specifically, they log in to a dedicated interface and input a prompt such as, "What features do you want in a new smartphone?" The input prompt is sent to the server.

[1043] Step 2:

[1044] The server uses a generative AI model to generate an answer to the input prompt. The generative AI model analyzes the prompt and generates an answer based on related data. For example, it outputs specific needs such as "long battery life," "good camera image quality," and "waterproof." The generated answer is stored on the server.

[1045] Step 3:

[1046] The server stores the generated answers in a database. Specifically, it uses a database management system (e.g., MySQL or PostgreSQL) to store the generated answers in an appropriate format. The stored data is used for later analysis.

[1047] Step 4:

[1048] The user (planning department) retrieves data from the database to analyze the saved responses. Specifically, they use a BI tool (e.g., Tableau or Power BI) to access the database and visualize customer needs. Based on the visualized data, the user analyzes customer needs and gains insights.

[1049] Step 5:

[1050] The user (design department) creates a design proposal based on the customer's needs. Specifically, they use design software such as Adobe Creative Cloud to create a design that reflects the customer's needs. The created design proposal is uploaded to the server.

[1051] Step 6:

[1052] The user (development department) determines the technical specifications based on the design proposal. Specifically, they use CAD software such as AutoCAD or SolidWorks to design the technical details based on the design proposal. The designed technical specifications are saved on the server.

[1053] Step 7:

[1054] The server provides a platform for sharing information generated by each department. Specifically, it provides an interface for sharing information using collaboration tools such as Microsoft Teams and Slack. Each department uses this platform to share information.

[1055] Step 8:

[1056] The server updates the shared information in real time and notifies each department. Specifically, each time the information is updated, a notification is sent to each department. This allows each department to proceed with their work based on the latest information.

[1057] In this way, it becomes possible to accurately grasp customer needs and develop consistent products based on those needs, thereby improving customer satisfaction and strengthening competitiveness.

[1058] (Application example 3)

[1059] 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."

[1060] With conventional systems for analyzing Voice of the Customer (VoC), it was difficult to accurately grasp customer needs and quickly reflect them in product development. Furthermore, information sharing between departments was insufficient, resulting in inconsistent product development. This meant that improvements in customer satisfaction and strengthened competitiveness were not fully achieved.

[1061] 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.

[1062] In this invention, the server includes: a means for analyzing voice of customer (VoC) using generative AI; a means for providing insights to planning, design, and development personnel in business divisions based on the analysis results; a means for utilizing the provided insights across multiple channels and groups; a means for collecting customer voice; a means for sharing the analysis results by the generative AI with each division in real time; and a means for automatically generating prompts to be input into the generative AI model. This enables accurate understanding of customer needs and rapid reflection of these needs in product development. Furthermore, smooth information sharing between divisions enables consistent product development, which is expected to improve customer satisfaction and strengthen competitiveness.

[1063] "Generative AI" is a type of artificial intelligence, a technology that generates new information and content based on large amounts of data.

[1064] "Voice of Customer (VoC)" refers to customer feedback, reviews, opinions, and other customer impressions and requests regarding products and services.

[1065] "Insights" refers to insights and understanding gained by analyzing data and information, and plays an especially important role in business strategy and policy planning.

[1066] "Multi-channel, group-wide" refers to sharing information and promoting collaboration across multiple channels and departments.

[1067] A "prompt" is an instruction or question input to a generative AI, and serves as the basis for the AI ​​to respond or generate appropriate responses.

[1068] "Real-time" refers to data and information being processed immediately and provided without delay.

[1069] "Product development" refers to the process of planning, designing, and manufacturing new products and services, which must reflect customer needs and market trends.

[1070] The system for carrying out this invention mainly comprises a server and a user terminal. The server includes means for analyzing voice of customer (VoC) using generative AI, means for providing insights to planning, design, and development personnel in business divisions based on the analysis results, means for utilizing the provided insights across multiple channels and across the group, means for collecting customer voice, means for sharing the analysis results by the generative AI with each division in real time, and means for automatically generating prompt sentences to be input into the generative AI model.

[1071] Hardware and software used

[1072] Hardware: Servers, user devices (smartphones, tablets, PCs, etc.)

[1073] Software: Python, OpenAI API, Requests library

[1074] Data processing and calculation

[1075] 1. Collecting customer feedback: Customer reviews and feedback are collected from the user's device via the shopping site's API. The server receives this data and stores it in a database.

[1076] 2. Analysis by generative AI: The server inputs the collected data into generative AI (e.g., OpenAI API) to extract customer needs. Specifically, it analyzes customer feedback and identifies key needs and problems.

[1077] 3. Providing insights: Based on the analysis results, the server provides insights to the planning, design, and development staff of the business division. These insights are useful information for formulating business strategies and measures.

[1078] 4. Real-time information sharing: The server shares the analysis results of generative AI with each department in real time, allowing each department to respond quickly and ensure consistent product development.

[1079] 5. Automatic generation of prompts: The server automatically generates prompts to be input to the generative AI model, improving the analytical accuracy of the generative AI and yielding more accurate insights.

[1080] Specific examples

[1081] For example, if an online shopping site collects customer feedback such as "This product has a great design, but the battery life is poor," the server can input this data into a generative AI to generate a prompt like this:

[1082] "Analyze the following customer testimonials and extract their key needs:\n\nThis product has a great design, but the battery life is poor."

[1083] The generative AI analyzes this prompt and provides the insight, "Customers appreciate the design, but are dissatisfied with the battery life." This insight is shared in real time with the planning, design, and development departments, allowing each department to respond quickly.

[1084] This will enable us to accurately understand customer needs and quickly reflect them in product development.In addition, information will be shared smoothly between departments, realizing consistent product development, which is expected to improve customer satisfaction and strengthen competitiveness.

[1085] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1086] Step 1:

[1087] Collecting customer feedback

[1088] The user device collects customer reviews and feedback through the API of the online shopping site. As input, it uses the API endpoint and authentication information of the online shopping site. As output, it obtains customer testimonials (reviews and feedback) in JSON format. The server receives this data and stores it in a database.

[1089] Step 2:

[1090] Generative AI analysis

[1091] The server inputs the collected customer feedback into the generative AI. The text data of the customer feedback is used as input. The server generates a prompt sentence for the generative AI model (for example, OpenAI's API) and inputs this. As output, the generative AI returns text data that extracts the customer's needs and problems.

[1092] Step 3:

[1093] Providing insights

[1094] The server provides insights to the planning, design, and development staff in the business division based on the analysis results obtained from the generative AI. The analysis results of the generative AI are used as input. As output, insights are generated in text format and notified to each staff member. Specifically, insights are provided via email or a dedicated dashboard.

[1095] Step 4:

[1096] Real-time information sharing

[1097] The server shares the analysis results of the generative AI with each department in real time. The analysis results and insights of the generative AI are used as input. As output, information is shared with each department in real time. Specifically, information is shared through chat tools and project management tools.

[1098] Step 5:

[1099] Automatic prompt generation

[1100] The server automatically generates prompts to be input into the generative AI model. Text data of customer testimonials is used as input. A prompt is generated as output. Specifically, the following prompt is generated: "Analyze the following customer testimonials and extract their main needs.\n\nThis product has a great design, but the battery life is poor."

[1101] Through these steps, it becomes possible to accurately grasp customer needs and quickly reflect them in product development.In addition, information is shared smoothly between departments, which allows for consistent product development, which is expected to improve customer satisfaction and strengthen competitiveness.

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

[1103] "Example 1"

[1104] One embodiment of the present invention is a system that uses a generative AI incorporating an emotion engine. This system collects voice of the customer (VoC) and inputs that data into the generative AI. The generative AI uses natural language processing (NLP) technology to extract customer needs and emotions from the data. In particular, the emotion engine recognizes and analyzes customer emotions. The analysis results are provided to business division planning, design, and development staff to support their decision-making. Specifically, the system understands customers' emotions and expectations toward a product and reflects this information in product development.

[1105] "Example 2"

[1106] Another embodiment of the present invention is the use of an emotion engine to share information across multiple channels and groups. Specifically, the analysis results of generative AI are shared across multiple channels and groups. This allows different departments within a company, such as the planning department, design department, and development department, to share the analysis results and plan and implement measures based on customer emotions.

[1107] "Example 3"

[1108] Another embodiment of the present invention involves the use of an emotion engine to develop business strategies. Specifically, the results of emotion analysis by the emotion engine are utilized in business strategy and policy planning. For example, the expectations and anxieties customers have about a new product can be grasped and reflected in the development and marketing strategy of the new product. This allows for an understanding of the true needs of customers, which can lead to improved customer satisfaction and stronger competitiveness.

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

[1110] "Example 1"

[1111] Step 1: Collect Voice of the Customer (VoC).

[1112] Step 2: Input the collected VoC data into the generative AI.

[1113] Step 3: Generative AI uses natural language processing (NLP) techniques to extract customer needs and sentiment from the data.

[1114] Step 4: The emotion engine recognizes the customer's emotions and analyzes them.

[1115] Step 5: The analysis results are provided to the business division's planning, design and development staff to support their decision-making.

[1116] "Example 2"

[1117] Step 1: Share the analysis results from generative AI across multiple channels and groups.

[1118] Step 2: The analysis results are shared with different departments within the company, such as the planning, design, and development departments.

[1119] Step 3: Each department plans and implements measures based on customer sentiment.

[1120] "Example 3"

[1121] Step 1: Use the results of sentiment analysis by the sentiment engine to develop business strategies and measures.

[1122] Step 2: Understand customers' expectations and concerns about the new product.

[1123] Step 3: Reflect this in new product development and marketing strategies.

[1124] Example 1

[1125] 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."

[1126] Traditional Voice of Customer (VoC) analysis systems made it difficult to effectively collect and analyze customer feedback and opinions. They also failed to accurately extract customer needs and sentiment from the collected data and provide insights that could be used in business decision-making. Furthermore, there was a lack of a way to utilize the insights provided across multiple channels and groups, preventing the company from fully achieving its goals of improving customer satisfaction and strengthening its competitiveness.

[1127] 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.

[1128] In this invention, the server includes means for collecting customer feedback and opinions, means for inputting the collected data into a generative AI model, means for the generative AI model to extract customer needs and emotions from the data using natural language processing technology, means for analyzing the extracted information, means for providing the analysis results to planning, design, and development personnel in the business division, and means for utilizing the provided insights across multiple channels and groups. This enables effective collection and accurate analysis of customer feedback and opinions, and provides insights to support business division decision-making. Furthermore, utilizing the provided insights across multiple channels and groups can improve customer satisfaction and strengthen competitiveness.

[1129] "Customer feedback and opinions" refers to information such as ratings, comments, requests, and complaints provided by customers regarding products and services.

[1130] A "generative AI model" is an algorithm or system that uses artificial intelligence techniques to generate, analyze, and process data.

[1131] "Natural language processing technology" is a technology that allows computers to understand, interpret, and generate human language.

[1132] "Customer needs and emotions" are the requirements and expectations that customers have about products and services, as well as their emotional responses to them.

[1133] "Analysis results" are information about customer needs and emotions that is output by the generative AI model based on collected data.

[1134] "Business planning, design and development staff" are people in a company who are responsible for planning, designing and developing products and services.

[1135] "Insights" are insights and understanding gained from data analysis, and are information that supports decision-making.

[1136] "Multi-channel, cross-group" refers to the sharing and utilization of information and resources across multiple different channels and departments.

[1137] This invention is a system that uses a generative AI model to analyze voice of customer (VoC) and provide insights to business department planning, design, and development personnel. The system collects customer feedback and opinions and inputs them into the generative AI model. The generative AI model uses natural language processing technology to extract and analyze customer needs and emotions from the collected data. The analysis results are provided to business department personnel to support their decision-making.

[1138] Hardware and software used

[1139] Hardware: Servers, terminals

[1140] Software: Generative AI models (e.g., GPT-4), natural language processing (NLP) libraries (e.g., spaCy, NLTK)

[1141] Explanation of program processing

[1142] Data collection

[1143] Users collect customer feedback and opinions, for example through survey forms or customer support chat logs.

[1144] The device temporarily stores the collected data, for example, in the device's local storage or cloud storage.

[1145] Data Entry

[1146] The device sends the collected data to the server, specifically by uploading the data to the server using an HTTP request.

[1147] The server stores the received data in a database, such as MySQL or PostgreSQL.

[1148] Data Processing

[1149] The server retrieves the collected data from the database and uses SQL queries to extract the required data.

[1150] The server inputs the acquired data into the generative AI model. Specifically, it sends the data to the generative AI model using an API request.

[1151] Generative AI models use natural language processing techniques to extract customer needs and sentiment from data, for example using sentiment analysis algorithms to categorize positive, negative, and neutral sentiment.

[1152] The generative AI model returns the extracted information to the server, which returns the analysis results as an API response.

[1153] Providing analysis results

[1154] The server stores the analysis results received from the generative AI model in a database, and inserts the analysis results using SQL queries.

[1155] The server provides the analysis results to the planning, design, and development staff in the business divisions through a web application that displays the results in the form of dashboards and reports.

[1156] The person in charge checks the dashboard and reports and makes decisions, such as considering design improvements or new product plans.

[1157] Specific examples

[1158] For example, if customer feedback on a product is something like, "This product is easy to use, but the design is a bit old-fashioned," the generative AI model processes it as follows:

[1159] 1. A user fills in a survey form saying, "This product is easy to use, but the design is a bit old-fashioned."

[1160] 2. The device stores this feedback in local storage and prepares to send it to the server.

[1161] 3. The device sends the feedback data to the server using an HTTP POST request.

[1162] 4. The server stores the received data in a MySQL database.

[1163] 5. The server executes the SQL query "SELECT FROM feedbacks WHERE processed = 0" to retrieve the unprocessed feedback data.

[1164] 6. The server sends the acquired data to the generative AI model as an API request, for example, using the following prompt:

[1165] Customer feedback: "This product is easy to use, but the design is a bit outdated."

[1166] Prompt: "Extract customer needs and emotions from this feedback."

[1167] 7. The generative AI model returns the analysis result: "Positive: Easy to use, Negative: Outdated design."

[1168] 8. The server stores the results of this analysis in a database.

[1169] 9. The server executes the SQL query "SELECT FROM analysis_results WHERE feedback_id = 123" to retrieve the analysis results for the specific feedback.

[1170] 10. The server displays the analysis results on a dashboard. For example, it displays information such as "Customers are satisfied with the ease of use, but dissatisfied with the design."

[1171] 11. The person in charge will use this information to consider improving the design.

[1172] The above is a specific embodiment of a voice of customer (VoC) analysis system that utilizes generative AI.

[1173] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1174] Step 1:

[1175] Data collection

[1176] Users collect customer feedback and opinions, for example through survey forms or customer support chat logs.

[1177] Input: Customer feedback and opinions (e.g., "This product is easy to use, but the design is a bit outdated")

[1178] Output: Collected feedback data

[1179] Specific behavior: The user enters feedback into the survey form and presses the submit button.

[1180] Step 2:

[1181] Data Entry

[1182] The device sends the collected data to the server, specifically by uploading the data to the server using an HTTP request.

[1183] Input: Collected feedback data

[1184] Output: Feedback data sent to the server

[1185] Specific operation: The device sends feedback data to the server using an HTTP POST request.

[1186] Step 3:

[1187] Data storage

[1188] The server stores the received data in a database, such as MySQL or PostgreSQL.

[1189] Input: Feedback data sent to the server

[1190] Output: Feedback data stored in a database

[1191] Specific operation: The server saves the received data in a MySQL database using an INSERT query.

[1192] Step 4:

[1193] Data Acquisition

[1194] The server retrieves the collected data from the database and uses SQL queries to extract the required data.

[1195] Input: Feedback data stored in the database

[1196] Output: The obtained feedback data

[1197] Specific operation: The server executes the SQL query "SELECT FROM feedbacks WHERE processed = 0" to retrieve unprocessed feedback data.

[1198] Step 5:

[1199] Data Processing

[1200] The server inputs the acquired data into the generative AI model. Specifically, it sends the data to the generative AI model using an API request.

[1201] Input: Retrieved feedback data

[1202] Output: Data sent to the generative AI model

[1203] What happens: The server sends feedback data to the generative AI model using an API request, for example using a prompt like this:

[1204] Customer feedback: "This product is easy to use, but the design is a bit outdated."

[1205] Prompt: "Extract customer needs and emotions from this feedback."

[1206] Step 6:

[1207] Data analysis

[1208] Generative AI models use natural language processing techniques to extract customer needs and sentiment from data, for example using sentiment analysis algorithms to categorize positive, negative, and neutral sentiment.

[1209] Input: Data sent to the generative AI model

[1210] Output: Extracted customer needs and emotions

[1211] Specific behavior: The generative AI model returns the analysis result: "Positive: Easy to use, Negative: Outdated design."

[1212] Step 7:

[1213] Saving analysis results

[1214] The server stores the analysis results received from the generative AI model in a database, and inserts the analysis results using SQL queries.

[1215] Input: Extracted customer needs and emotions

[1216] Output: Analysis results stored in a database

[1217] What happens: The server uses an INSERT query to save the analysis results to the database.

[1218] Step 8:

[1219] Providing analysis results

[1220] The server provides the analysis results to the planning, design, and development staff in the business divisions through a web application that displays the results in the form of dashboards and reports.

[1221] Input: Analysis results stored in the database

[1222] Output: Analysis results provided to the person in charge

[1223] Specific operation: The server executes the SQL query "SELECT FROM analysis_results WHERE feedback_id = 123" to retrieve the analysis results for the specific feedback and display them in a dashboard.

[1224] Step 9:

[1225] decision making

[1226] The person in charge checks the dashboard and reports and makes decisions, such as considering design improvements or new product plans.

[1227] Input: Analysis results provided to the person in charge

[1228] Output: Improved designs and new product plans

[1229] Specific actions: Based on the information in the dashboard, the person in charge considers design improvements and develops a specific action plan.

[1230] (Application example 1)

[1231] 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."

[1232] Conventional Voice of Customer (VoC) analysis systems have difficulty analyzing customer feedback in real time and extracting emotions and needs. Furthermore, the extracted data cannot be used quickly to improve product development and marketing strategies, which can hinder efforts to improve customer satisfaction and strengthen competitiveness.

[1233] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for analyzing voice of customer (VoC) using generative AI, means for providing insights to planning, design, and development personnel in the business division based on the analysis results, means for utilizing the provided insights across multiple channels and groups, means for analyzing customer feedback in real time and extracting emotions and needs, and means for utilizing the extracted data to improve product development and marketing strategies. This makes it possible to quickly and accurately analyze customer feedback and reflect it in product development and marketing strategies.

[1234] "Generative AI" is artificial intelligence that uses natural language processing technology to analyze text data and generate new information and insights.

[1235] "Voice of the Customer (VoC)" is text data such as feedback, opinions, and impressions from customers.

[1236] "Means of analysis" refers to methods and techniques for analyzing collected data and extracting customer needs and emotions.

[1237] "Insights" are insights and knowledge gained through data analysis, and are information that supports decision-making in business departments.

[1238] "Multi-channel, cross-group" refers to the sharing and utilization of information across multiple channels and departments.

[1239] "Means of real-time analysis" refers to technologies and methods that analyze data as it is input and output results immediately.

[1240] "Means for extracting emotions and needs" refers to techniques and methods for identifying and extracting customer emotions and specific requests from text data.

[1241] "Means to improve product development and marketing strategies" are methods and techniques for developing new products and reviewing sales strategies based on the analysis results.

[1242] A system for implementing this invention includes means for analyzing voice of customer (VoC) using generative AI, means for providing insights to planning, design, and development personnel in business divisions based on the analysis results, means for utilizing the provided insights across multiple channels and groups, means for analyzing customer feedback in real time and extracting emotions and needs, and means for utilizing the extracted data to improve product development and marketing strategies.

[1243] System Program

[1244] This system is realized using the following hardware and software.

[1245] Hardware: Smartphones, servers

[1246] Software: Python, OpenAI API, TextBlob

[1247] Processing Description

[1248] Collecting and analyzing customer feedback

[1249] The server receives customer feedback text sent from smartphones. The received feedback text is first subjected to sentiment analysis using TextBlob, which outputs a score for the text's positive, negative, or neutral sentiment.

[1250] Needs extraction using generative AI

[1251] Next, the server uses OpenAI's generative AI (GPT-3) to extract customer needs from the feedback text. Specifically, the feedback text is input to the generative AI as a prompt, and the server outputs the customer needs in text format.

[1252] Providing and leveraging insights

[1253] The extracted data on emotions and needs is provided as insights to planning, design, and development staff in the business divisions. This allows them to understand the true needs of customers and helps improve product development and marketing strategies. The insights provided are used across multiple channels and groups to support rapid decision-making.

[1254] Specific examples

[1255] For example, if a customer posts feedback such as, "This product is very easy to use, but the battery life is poor. I hope that this will be improved," the server analyzes this feedback in real time and extracts the need to "improve battery life" along with an emotional score.

[1256] Prompt Sentence Examples

[1257] Customer feedback: This product is very easy to use, but the battery life is poor. I hope they improve it.

[1258] Extract customer needs from this feedback.

[1259] In this way, customer feedback can be analyzed quickly and accurately and reflected in product development and marketing strategies.

[1260] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1261] Step 1:

[1262] A user inputs and sends feedback text using a smartphone. The input feedback text is sent to a server. The input data is text data including customer opinions and impressions.

[1263] Step 2:

[1264] The server performs sentiment analysis on the received feedback text using TextBlob. The input data is the feedback text, and the output data is a positive, negative, or neutral sentiment score. Specifically, TextBlob analyzes the text and outputs the sentiment polarity (positive, negative, neutral) and the sentiment intensity as a score.

[1265] Step 3:

[1266] The server stores the results of the sentiment analysis and then uses OpenAI's generative AI (GPT-3) to extract customer needs from the feedback text. The input data is the feedback text and sentiment score, and the output data is text indicating the customer needs. Specifically, the feedback text is input as a prompt to the generative AI, which then outputs the customer needs in text format.

[1267] Step 4:

[1268] The server provides the extracted sentiment scores and customer needs as insights to the planning, design, and development staff in the business division. The input data is the sentiment scores and text indicating the customer needs, and the output data is the insights provided to the staff. Specifically, the insights are provided through the staff's dashboard and notification system.

[1269] Step 5:

[1270] Business department staff use the insights provided to improve product development and marketing strategies. The input data is the insights provided, and the output data is an improved product development plan or marketing strategy. Specifically, the staff analyzes the insights, plans specific improvement measures, and puts them into action.

[1271] Step 6:

[1272] The server monitors the results of the improvement measures and forms a feedback loop. The input data is the execution result data, and the output data is data that will be useful for the next feedback analysis. Specifically, the execution results are saved in a database and referenced during the next feedback analysis.

[1273] Example 2

[1274] Next, a description will be given of 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."

[1275] With the previous system, it was difficult to effectively analyze the voice of the customer (VoC) and provide appropriate insights to each department within the company. Furthermore, there was a lack of a mechanism for utilizing the provided insights across multiple channels and groups, making it difficult to grasp the true needs of customers. This meant that improvements in customer satisfaction and strengthening of competitiveness were not fully achieved.

[1276] 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.

[1277] In this invention, the server includes a data collection means, a data preprocessing means, a means for analyzing data using a generative AI model, a means for generating insights based on the analysis results, a means for providing the generated insights to planning, design, and development personnel in the business divisions, and a means for utilizing the provided insights across multiple channels and groups. This enables effective analysis of customer feedback, providing appropriate insights to each division within the company, and enabling utilization across multiple channels and groups. This improves customer satisfaction and strengthens competitiveness.

[1278] "Data collection means" refers to means for collecting customer data from various data sources inside and outside the company.

[1279] The "data preprocessing means" refers to a means for preprocessing the collected data, such as cleaning, normalizing, and imputing missing values.

[1280] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and generate insights.

[1281] "Means for analyzing data" refers to the use of generative AI models to analyze pre-processed data and understand customer sentiment and trends.

[1282] "Means for generating insights" are means for proposing measures based on customer needs and emotions based on the results of data analysis.

[1283] "Means for providing insights" refers to the means for providing the generated insights to each department within the company (planning department, design department, development department, etc.).

[1284] "Means for multi-channel, group-wide utilization" refers to a means by which the insights provided can be shared among different departments within a company, and each department can then plan and implement measures.

[1285] MODE FOR CARRYING OUT THE INVENTION

[1286] This invention is a system that uses a generative AI model to analyze voice of customer (VoC) and provide insights to each department within a company. A specific embodiment of this system will be described below.

[1287] 1. Program Generation

[1288] The server generates programs using a generative AI model, such as OpenAI's GPT-4, and implements the programs using a programming language such as Python or Java.

[1289] 2. Program Processing

[1290] The server processes and calculates data using the following hardware and software:

[1291] Hardware: A server equipped with a high-performance CPU and GPU

[1292] Software: Python, Pandas, Scikit-learn, TensorFlow, Keras, Google Analytics, Tableau

[1293] The server processes the data as follows:

[1294] 1. Data collection: The server obtains data from the customer feedback system through API and uses Google Analytics to analyze website behavior logs.

[1295] 2. Data preprocessing: The server cleans the data using Python's Pandas library and applies a machine learning model to impute missing values.

[1296] 3. Data Analysis: The server performs sentiment analysis on the text data using a generative AI model (e.g., GPT-4) and analyzes time series data for trend analysis.

[1297] 4. Insight generation: The server uses a generative AI model to describe the analysis results in natural language and compiles the insights in report format.

[1298] 5. Insight sharing: The server shares insights via email or internal chat tools (e.g., Slack) and uses BI tools (e.g., Tableau) to display the insights in a dashboard.

[1299] 3. Specific Examples

[1300] To illustrate, consider the following scenario:

[1301] The user (a planning department employee) inputs a prompt statement to the server: "Analyze customer feedback from the past year and tell us what new features and design trends customers are looking for."

[1302] The server uses a generative AI model to analyze customer feedback and generate insights such as, "Customers are particularly interested in the ease of use and design of smartphone apps."

[1303] The server shares this insight with the planning, design, and development departments.

[1304] Each department will then use this insight to plan and implement new measures.

[1305] In this way, the server uses generative AI models to provide insights for each department within a company to plan and implement measures based on customer needs and emotions, thereby improving customer satisfaction and strengthening competitiveness.

[1306] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1307] Step 1: Data collection

[1308] The server collects customer data from various internal and external data sources, such as retrieving data from customer feedback systems via APIs and using Google Analytics to analyze website behavior logs.

[1309] Input: Customer feedback, purchase history, website activity logs, social media comments

[1310] Output: Raw data collected

[1311] Step 2: Data Preprocessing

[1312] The server preprocesses the collected data, specifically cleaning it using Python's Pandas library and applying machine learning models to impute missing values.

[1313] Input: Raw data collected

[1314] Output: Preprocessed data

[1315] Step 3: Data analysis

[1316] The server analyzes the preprocessed data using generative AI models (e.g., GPT-4) to perform sentiment analysis on the text data and analyze time series data for trend analysis.

[1317] Input: Preprocessed data

[1318] Output: Analysis results (customer sentiment and trends)

[1319] Step 4: Insight generation

[1320] The server generates insights based on the results of the data analysis. Specifically, it uses a generative AI model to describe the analysis results in natural language and compiles the insights in report format.

[1321] Input: Analysis results

[1322] Output: Generated insights (in report format)

[1323] Step 5: Share your insights

[1324] The server shares the generated insights with each department within the company via email or internal chat tools (e.g., Slack), and uses BI tools (e.g., Tableau) to display the insights on a dashboard.

[1325] Input: Generated insights

[1326] Output: Insights shared across departments

[1327] Step 6: Departmental policy planning and implementation

[1328] Each department (planning, design, development, etc.) will plan and implement measures based on the shared insights, such as planning new products based on customer needs, improving designs, and adding features.

[1329] Input: Shared Insights

[1330] Output: Planned measures, implemented measures

[1331] (Application example 2)

[1332] 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."

[1333] While conventional customer support systems could analyze Voice of Customer (VoC), it was difficult to grasp customer sentiment in real time and respond based on that. Furthermore, information sharing between different business divisions was insufficient, preventing the company from fully improving customer satisfaction and strengthening its competitiveness.

[1334] 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.

[1335] In this invention, the server includes: a means for analyzing voice of customer (VoC) using generative AI; a means for providing insights to planning, design, and development personnel in business divisions based on the analysis results; a means for utilizing the provided insights across multiple channels and groups; a means for analyzing emotions from customer facial expressions and voice in real time; a means for providing the analysis results to store staff; and a means for the staff to respond based on the customer's emotions. This enables the system to grasp customer emotions in real time and respond accordingly. It also promotes information sharing between different business divisions, improving customer satisfaction and strengthening competitiveness.

[1336] "Generative AI" is an artificial intelligence technology that generates new information and patterns based on data.

[1337] "Voice of the Customer (VoC)" refers to information such as feedback, opinions, and requests from customers.

[1338] "Insights" refers to insights and findings gained from data analysis.

[1339] "Multi-channel, cross-group" refers to the sharing and utilization of information across multiple channels and departments.

[1340] "Customer facial expression" refers to the emotions and reactions that can be read from the customer's facial expression.

[1341] "Means for analyzing emotions from voice in real time" refers to technology that analyzes voice data and identifies emotions on the spot.

[1342] "Brick-and-mortar staff" refers to employees who deal with customers in physical stores.

[1343] "Means for staff to respond based on customer emotions" refers to methods and tools that allow staff to understand customer emotions and provide services and responses accordingly.

[1344] A system for implementing the present invention has the following configuration.

[1345] First, the server uses generative AI to analyze the voice of the customer (VoC). Specifically, it analyzes data collected from customers, such as feedback, opinions, and requests, to identify their emotions and needs. This analysis uses natural language processing technology and machine learning algorithms.

[1346] The server then provides insights based on the analysis results to the planning, design, and development staff in each business division. This allows each division to plan and implement measures based on customer sentiment and needs. The insights provided are used across multiple channels and groups. This promotes information sharing between different divisions and enables unified customer responses.

[1347] Furthermore, the server provides a means to analyze emotions from customers' facial expressions and voice in real time. Specifically, cameras and microphones are installed on smartphones and tablets used by store staff, and customers' facial expressions and voices are captured through these devices. The captured data is sent to the server and analyzed in real time by a generative AI model.

[1348] The analysis results are provided to store staff. Staff can check the customer's emotions on their smartphone or tablet screens and respond accordingly. For example, if a customer is recognized as "happy," the staff can say, "You seem to be in a good mood today! Is there anything in particular I can help you with?" Or, if a customer asks a question about a product and the analysis reveals "anxiety," the staff can explain, "Don't worry, this product is of very high quality."

[1349] The following hardware and software are used to implement this system:

[1350] Hardware: Camera (for video capture), microphone (for audio input)

[1351] Software: OpenCV (for video processing), Keras (for emotion recognition models), SpeechRecognition (for voice recognition), Transformers (for emotion analysis)

[1352] Examples of specific prompts include the following:

[1353] "We want to develop an application that analyzes emotions from customers' facial expressions and voices and provides the results to staff in real time. Specifically, it would have a function that uses a camera and microphone to recognize customers' emotions and notify staff of the results. What technology and methods should we use?"

[1354] This allows for real-time understanding of customer sentiment and the ability to respond accordingly, facilitating information sharing across different business departments, improving customer satisfaction and strengthening competitiveness.

[1355] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1356] Step 1:

[1357] The server collects Voice of Customer (VoC) data. Specifically, it collects text data such as customer feedback, opinions, and requests. The input is the Voice of Customer (VoC) data, and the output is the collected text data.

[1358] Step 2:

[1359] The server inputs the collected voice of the customer (VoC) data into a generative AI model to analyze emotions and needs. Specifically, it uses natural language processing technology to analyze the text data and identify customer emotions and needs. The input is the collected text data, and the output is information on emotions and needs as the analysis results.

[1360] Step 3:

[1361] The server provides insights to the planning, design, and development staff in the business divisions based on the analysis results. Specifically, the analysis results are notified to each division and provided as reference information for policy planning and design improvements. The input is the analysis results, and the output is the provided insight information.

[1362] Step 4:

[1363] The server utilizes the provided insights across multiple channels and groups. Specifically, it promotes information sharing between different departments and realizes unified customer responses. The input is the provided insight information, and the output is the shared insight information.

[1364] Step 5:

[1365] The device (smartphone or tablet) captures the customer's facial expression and voice. Specifically, it uses a camera and microphone to collect the customer's facial expression and voice data. The input is the customer's facial expression and voice, and the output is the captured video and voice data.

[1366] Step 6:

[1367] The device transmits the captured facial expression and audio data to the server. Specifically, the data is uploaded to the server via a network. The input is the captured video and audio data, and the output is the data transmitted to the server.

[1368] Step 7:

[1369] The server inputs the received facial and voice data into a generative AI model to analyze emotions in real time. Specifically, it analyzes the data using video processing and voice recognition technologies to identify the customer's emotions. The input is the received video and voice data, and the output is the real-time emotion analysis results.

[1370] Step 8:

[1371] The server sends the real-time emotion analysis results to the terminal. Specifically, it notifies the terminal of the analysis results via the network. The input is the real-time emotion analysis results, and the output is the analysis results sent to the terminal.

[1372] Step 9:

[1373] The terminal then displays the received emotion analysis results to the store staff. Specifically, the customer's emotions are displayed on the screen of a smartphone or tablet so that the staff can check them. The input is the received analysis results, and the output is the displayed emotion information.

[1374] Step 10:

[1375] The user (store staff) responds to customers based on the displayed emotional information. Specifically, they provide services and responses that correspond to the customer's emotions. The input is the displayed emotional information, and the output is an appropriate response to the customer.

[1376] Example 3

[1377] Next, a description will be given of 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."

[1378] With the previous system, it was difficult to effectively analyze customer feedback and reflect it in product development and business strategies. There was also insufficient information sharing between departments, making consistent product development difficult. Furthermore, there was a lack of means to analyze customer sentiment and utilize it in business strategies, which meant that improvements in customer satisfaction and competitiveness were not fully achieved.

[1379] 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.

[1380] In this invention, the server includes: a means for analyzing customer feedback using generative artificial intelligence; a means for providing insights to business division planning, design, and development personnel based on the analysis results; a means for utilizing the provided insights across multiple channels and groups; a means for analyzing customer sentiment using an emotion engine and utilizing the results in business strategy and policy planning; and a means for sharing information between divisions in real time to promote consistent product development. This enables effective analysis of customer feedback, strengthening information sharing between divisions, and reflecting customer sentiment in business strategies. As a result, improved customer satisfaction and stronger competitiveness are achieved.

[1381] "Generative AI" is an AI technology that uses natural language processing and machine learning techniques to generate and analyze text data.

[1382] "Voice of the customer" refers to information such as feedback, opinions, and requests provided by customers.

[1383] "Insights" are insights and knowledge gained from data analysis, and are information useful for business strategy and product development.

[1384] "Multi-channel, cross-group" refers to the sharing and utilization of information across multiple communication channels and departments.

[1385] An "emotion engine" is a technology for analyzing emotions from text data and is used to understand customer emotions.

[1386] "Real-time" refers to data and information being processed and shared instantly, meaning that it is reflected immediately without delay.

[1387] "Product development" is the process of planning, designing, and manufacturing new products, and is carried out in response to customer needs and market demands.

[1388] A "business strategy" is a long-term plan or policy that a company formulates to maintain and improve its competitiveness.

[1389] "Customer satisfaction" is an indicator that shows the degree of satisfaction that customers feel with products and services.

[1390] "Competitiveness" refers to a company's ability to compete with other companies in the marketplace and is an element that gives it an advantage.

[1391] This invention is a system that utilizes generative artificial intelligence to analyze customer feedback and plan product development and business strategies based on the analysis results. Specific embodiments of this system are described below.

[1392] 1. Collecting and analyzing customer feedback

[1393] The device collects customer feedback data from the planning department, specifically survey results, social media comments, customer support logs, and more.

[1394] The server inputs the collected data into a generative AI model, which may use natural language processing technology (e.g., GPT-4).

[1395] The server uses a generative AI model to analyze customer needs, specifically by parsing text data and extracting important keywords and trends.

[1396] 2. Deciding on product design and functionality

[1397] The terminal receives the analysis results from the server and shares them with the design and development departments.

[1398] Users (design and development personnel) decide on the product's design and functionality based on shared needs. Specifically, they use CAD software and prototyping tools to create designs and define functional requirements.

[1399] The user uploads the final design and functionality to the server.

[1400] 3. Information sharing and consistent product development

[1401] The server provides a platform for sharing information across multiple channels and groups between departments, using communication tools such as Slack and Microsoft Teams.

[1402] Users (people from each department) share information in real time through the platform and carry out consistent product development. Specifically, prototype designs created by the design department are shared with the development department and feedback is received.

[1403] The device manages progress based on the shared information and assigns tasks and adjusts schedules as needed.

[1404] 4. Business strategy planning using emotion engines

[1405] The server analyzes the customer's emotions using an emotion engine, which uses, for example, emotion analysis technology (e.g., IBM Watson's Tone Analyzer).

[1406] The device provides the results of sentiment analysis to the marketing department for use in developing business strategies and initiatives.

[1407] The user (a marketing department employee) uses the results of the sentiment analysis to develop a marketing strategy for the new product, specifically, to plan an advertising campaign and promotional activities.

[1408] Examples of specific examples and prompts

[1409] As a concrete example, a company uses this system when developing a new smartphone. The planning department extracts "longer battery life" and "improved camera performance" as important needs from customer feedback. The design and development departments decide on the design and functions of the new smartphone based on these needs, and share information to carry out consistent product development. The marketing department then uses the analysis results of the emotion engine to implement an advertising campaign to heighten customer expectations.

[1410] Example prompt sentence:

[1411] Enter your customer feedback data. Example: "The new product has a simple design and is easy to use. I would be happy if the battery life was longer."

[1412]

[1413] Enter a prompt to analyze customer emotions using the emotion engine and reflect it in your business strategy. Example: "Analyze the expectations and concerns customers have about our new product and reflect that in your marketing strategy for the new product."

[1414] The flow of the identification process in the third embodiment will be described with reference to FIG.

[1415] Step 1:

[1416] The terminal collects customer feedback data from the planning department, specifically survey results, social media comments, customer support logs, etc. It receives customer feedback data as input and sends this data to the server as output.

[1417] Step 2:

[1418] The server inputs the collected data into a generative AI model. This generative AI model uses, for example, a model that uses natural language processing technology (e.g., GPT-4). It receives customer feedback data as input, processes the data by analyzing the text data, and extracts important keywords and trends. It generates analysis results as output.

[1419] Step 3:

[1420] The server receives the analysis results from the generative AI model as input and sends them to the device as output.

[1421] Step 4:

[1422] The terminal shares the analysis results received from the server with the design and development departments. It receives the analysis results from the server as input and sends them to the design and development departments as output.

[1423] Step 5:

[1424] Users (design and development staff) decide on the product's design and functionality based on shared needs. Specifically, they create designs using CAD software and prototyping tools and define functional requirements. They receive the analysis results as input, process the data, and decide on the design and functionality. As output, they upload the decided design and functionality to the server.

[1425] Step 6:

[1426] The server provides a platform for sharing information across multiple channels and groups between departments. This platform uses communication tools (e.g., Slack or Microsoft Teams). It receives design and functionality information as input and shares it with each department as output.

[1427] Step 7:

[1428] Users (people in each department) share information in real time through the platform and carry out consistent product development. Specifically, the design department shares prototype designs with the development department and receives feedback. As input, the shared information is received, and as data processing, feedback is provided and necessary corrections are made. As output, the corrected design and functions are uploaded to the server.

[1429] Step 8:

[1430] The device manages the progress based on the shared information, and allocates tasks and adjusts the schedule as necessary. It receives the shared information as input, processes the data, and manages the progress, allocates tasks, and adjusts the schedule. It outputs the updated progress and task information to the server.

[1431] Step 9:

[1432] The server analyzes customer emotions using an emotion engine. This emotion engine uses emotion analysis technology (e.g., IBM Watson's Tone Analyzer). It receives customer feedback data as input, processes the data, and analyzes emotions. It generates emotion analysis results as output.

[1433] Step 10:

[1434] The terminal provides the sentiment analysis results to the marketing department to utilize them in business strategy and policy planning. It receives the sentiment analysis results as input and sends them to the marketing department as output.

[1435] Step 11:

[1436] The user (a marketing department employee) plans a marketing strategy for a new product based on the results of the sentiment analysis. Specifically, they plan an advertising campaign and promotional activities. They receive the sentiment analysis results as input, process the data, and plan a marketing strategy. As output, they upload the planned marketing strategy to the server.

[1437] (Application example 3)

[1438] 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."

[1439] Conventional systems for analyzing Voice of Customer (VoC) have had the problem of being unable to fully grasp customer emotions, making it difficult to accurately grasp customers' true needs. Furthermore, there was a lack of means to reflect the results of customer emotion analysis in business strategies and policy planning, which meant that improvements in customer satisfaction and strengthened competitiveness were not fully achieved.

[1440] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes: means for analyzing voice of customer (VoC) using generative AI; means for providing insights to planning, design, and development personnel in business departments based on the analysis results; means for utilizing the provided insights across multiple channels and groups; means for analyzing customer emotions using an emotion engine; means for reflecting the results of the customer emotion analysis in business strategy and policy planning; and means for collecting customer reviews using an application installed on a smartphone and analyzing them using a generative AI model and an emotion engine. This enables accurate understanding of customer needs, improving customer satisfaction, and strengthening competitiveness.

[1441] "Generative AI" is a type of artificial intelligence, a technology that generates new information and content based on data.

[1442] "Voice of Customer (VoC)" refers to feedback such as opinions, impressions, and requests that customers have about products and services.

[1443] "Insights" refers to insights and understanding gained by analyzing data and information, and is particularly useful for formulating business strategies and policies.

[1444] "Multi-channel, group-wide" refers to sharing information across multiple different channels and departments and utilizing it in an integrated manner.

[1445] An "emotion engine" is a technology that analyzes human emotions from data such as text and voice, and estimates emotional states.

[1446] A "business strategy" refers to the plans and policies that a company formulates to achieve its long-term goals.

[1447] "Policy planning" refers to the process of formulating specific action plans and measures to achieve specific objectives.

[1448] A "smartphone" is a type of mobile phone that is a multi-function device that can connect to the Internet and use applications.

[1449] "Application" means a software program designed to provide a particular function or service.

[1450] "Customer reviews" refer to ratings and comments made by customers about products and services.

[1451] A "generative AI model" is a specific implementation of generative AI, and refers to algorithms and programs for generating new information and content based on data.

[1452] The following system configuration will be described as an embodiment of the present invention.

[1453] System Configuration

[1454] The system includes a server, smartphone, generative AI model, and emotion engine. The server uses generative AI to analyze the voice of the customer (VoC) and provides insights to business division planning, design, and development staff based on the analysis results. The provided insights are then utilized across multiple channels and groups. The emotion engine is also used to analyze customer emotions, and the results of the customer emotion analysis are reflected in business strategies and policy planning.

[1455] Program processing explanation

[1456] Hardware and software used

[1457] Hardware: Servers, smartphones

[1458] Software: Python, OpenAI API, TextBlob

[1459] Data processing and calculation

[1460] 1. Data collection: Customer reviews are collected using an application installed on a smartphone. Users enter reviews using their smartphones and send them to the server.

[1461] 2. Analysis using a generative AI model: The server uses the OpenAI API to extract customer needs from collected customer reviews. Specifically, it uses the following prompt sentence:

[1462] Analyze the following reviews to identify customer needs.

[1463] This product is very easy to use.

[1464] The design is great, but the functionality is a bit lacking.

[1465] I think the price is too high.

[1466] I can't wait for the new version.

[1467] 3. Sentiment Engine Analysis: The server analyzes the sentiment of customer reviews using TextBlob, and calculates the positivity and subjectivity of each review.

[1468] 4. Providing insights: Based on the analysis results of the generative AI model and emotion engine, the server provides insights to the planning, design, and development staff in the business division, allowing them to understand the true needs of customers and reflect them in business strategies and policy planning.

[1469] Specific examples

[1470] For example, consider the following customer reviews collected:

[1471] "This product is very easy to use." -> Positivity: 0.8, Subjectivity: 0.6

[1472] "The design is great, but the features are a bit lacking." -> Positivity: 0.2, Subjectivity: 0.7

[1473] The server analyzes these reviews using a generative AI model and an emotion engine to extract customer needs and emotions. For example, it can obtain insights such as, "Ease of use is important, but additional features are desired." Based on these insights, business divisions can develop new products and formulate marketing strategies.

[1474] In this way, a system is provided that can analyze customer opinions and emotions and use them in product development and marketing strategies.

[1475] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1476] Step 1:

[1477] A user launches the application using a smartphone and inputs a customer review. The input review is sent from the smartphone to a server. The input data is the customer review in text format. The output data is the customer review sent to the server.

[1478] Step 2:

[1479] The server inputs the received customer reviews into the generative AI model. The generative AI model uses OpenAI's API to extract customer needs from the customer reviews. The input data is the customer reviews in text format. The output data is text that indicates the customer needs. Specifically, the following prompt sentence is input into the generative AI model:

[1480] Analyze the following reviews to identify customer needs.

[1481] This product is very easy to use.

[1482] The design is great, but the functionality is a bit lacking.

[1483] I think the price is too high.

[1484] I can't wait for the new version.

[1485] Step 3:

[1486] The server stores the customer needs obtained from the generative AI model and then analyzes the sentiment of the customer reviews using a sentiment engine. The sentiment engine uses TextBlob to calculate the positivity and subjectivity of each review. The input data are customer reviews in text format. The output data are numbers indicating the positivity and subjectivity of each review. Specifically, sentiment analysis is performed using TextBlob as follows:

[1487] Review: "This product is very easy to use." -> Positivity: 0.8, Subjectivity: 0.6

[1488] Review: "Great design, but lacks functionality." -> Positivity: 0.2, Subjectivity: 0.7

[1489] Step 4:

[1490] The server integrates the analysis results obtained from the generative AI model and the emotion engine, and provides insights to the business division's planning, design, and development staff. The input data is text indicating customer needs and numerical values ​​indicating the positivity and subjectivity of each review. The output data is a report showing the integrated insights. Specifically, the server generates the following insights:

[1491] Insight: "Ease of use is important, but additional features are desired."

[1492] Step 5:

[1493] Based on the insights provided by the server, business department staff will formulate business strategies and measures. The input data is a report showing the insights provided by the server. The output data is a new product development plan and marketing strategy. Specifically, the staff will formulate measures such as the following:

[1494] Solution: "Add new product features, maintain ease of use, and set the price appropriately."

[1495] In this way, a system is provided that can analyze customer opinions and emotions and use them in product development and marketing strategies.

[1496] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1497] 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 performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1498] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1499] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1500] [Third embodiment]

[1501] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1502] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1503] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1504] 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.

[1505] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1506] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1507] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1508] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1509] 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.

[1510] 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.

[1511] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1512] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1513] "Example 1"

[1514] One embodiment of the present invention is a voice-of-customer (VoC) analysis system that utilizes generative AI. This system collects customer feedback and opinions and inputs them into the generative AI. The generative AI uses natural language processing (NLP) technology to extract and analyze customer needs and emotions from the collected data. The analysis results are provided to planning, design, and development staff in the business division to support their decision-making.

[1515] "Example 2"

[1516] Furthermore, the insights provided are utilized across multiple channels and groups. Specifically, they are shared by different departments within the company, such as the planning department, design department, and development department, and each department plans and implements measures based on customer needs. This makes it possible to improve customer satisfaction and strengthen competitiveness.

[1517] "Example 3"

[1518] As a concrete example, consider the case where a company uses this system when developing a new product. First, the planning department uses generative AI to analyze customer feedback and understand the customer needs necessary for developing a new product. Next, the design and development departments decide on the product's design and functionality based on those needs. Each department then shares information across multiple channels and the group to develop products consistently. This enables product development based on the true needs of customers, which is expected to improve customer satisfaction and strengthen competitiveness.

[1519] The processing flow of each embodiment will be described below.

[1520] "Example 1"

[1521] Step 1: Collect customer feedback and opinions. This data is referred to as Voice of Customer (VoC).

[1522] Step 2: Input the collected VoC data into a generative AI, which uses natural language processing (NLP) techniques to extract customer needs and emotions from the data.

[1523] Step 3: Provide the results of the generative AI analysis to the planning, design, and development staff in the business division to support their decision-making.

[1524] "Example 2"

[1525] Step 1: The analysis results from generative AI are shared across multiple channels and groups. Specifically, the analysis results are shared among different departments within the company, such as the planning department, design department, and development department.

[1526] Step 2: Each department will use the shared insights to plan and implement measures that address customer needs, thereby improving customer satisfaction and strengthening competitiveness.

[1527] "Example 3"

[1528] Step 1: The planning department uses generative AI to analyze customer feedback and understand customer needs necessary for developing new products.

[1529] Step 2: The design and development departments decide on the product design and functions based on the needs provided by the planning department.

[1530] Step 3: Each department shares information across multiple channels and the group to develop products consistently. This enables product development based on real customer needs, which is expected to improve customer satisfaction and strengthen competitiveness.

[1531] Example 1

[1532] 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 headset type terminal 314 will be referred to as a "terminal."

[1533] With conventional Voice of Customer (VoC) analysis systems, it was difficult to efficiently collect and accurately analyze customer feedback and opinions. Furthermore, there was a lack of appropriate means to provide the analysis results to business division planning, design, and development personnel to support their decision-making. Furthermore, it was difficult to utilize the insights provided across multiple channels and groups, which meant that improvements in customer satisfaction and strengthened competitiveness were not fully achieved.

[1534] 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.

[1535] In this invention, the server includes means for collecting customer feedback and opinions, means for preprocessing the collected data, means for inputting the preprocessed data into a generative AI model, means for extracting customer needs and emotions using the generative AI model, means for analyzing the extracted needs and emotions, means for providing the analysis results to planning, design, and development personnel in the business division, and means for utilizing the provided insights across multiple channels and groups. This enables efficient collection and accurate analysis of customer feedback and opinions. Furthermore, by providing the analysis results to business division personnel and supporting their decision-making, improved customer satisfaction and stronger competitiveness can be achieved.

[1536] "Means for collecting customer feedback and opinions" refers to methods and devices for collecting information such as opinions, impressions, and evaluations provided by customers.

[1537] "Means for pre-processing collected data" refers to methods or devices for converting collected raw data into a format that is easier to analyze, and specifically includes text cleaning, tokenization, removal of stop words, etc.

[1538] A "means for inputting preprocessed data into a generative AI model" is a method or apparatus for appropriately supplying preprocessed data to a generative AI model.

[1539] "Means for extracting customer needs and emotions using generative AI models" refers to methods or devices that utilize generative AI models to identify and extract needs and emotions from customer feedback.

[1540] "Means for analyzing extracted needs and emotions" refers to methods or devices for further analyzing the needs and emotions extracted by the generative AI model and obtaining specific insights.

[1541] "Means for providing analysis results to business division planning, design, and development personnel" refers to methods and devices for communicating the analysis results to business division personnel and supporting their decision-making.

[1542] "Means for utilizing the provided insights across multiple channels and groups" refers to methods and devices for sharing the obtained insights across multiple channels and groups and utilizing them in a unified manner.

[1543] This invention is a system for analyzing voice of customer (VoC) using generative AI. Specific embodiments of this system are described below.

[1544] System configuration

[1545] 1. Data Collection

[1546] Users collect customer feedback and opinions through online forms, emails, social media comments, etc.

[1547] The device sends the collected feedback to a server. Specifically, the application on the device sends data to the server through an API.

[1548] 2. Data Preprocessing

[1549] The server preprocesses the received data. Specifically, it performs the following processes:

[1550] Text cleaning: remove unnecessary characters and noise, for example, remove HTML tags and special characters.

[1551] Tokenization: Splitting text into words and phrases. For example, splitting "product delivery is slow" into "product", "of", "delivery", "is", and "slow".

[1552] Stop word removal: Remove meaningless words (e.g., "no" and "ga").

[1553] Software used: Python, NLTK (Natural Language Toolkit)

[1554] 3. Input to generative AI

[1555] The server inputs the preprocessed data into the generative AI model, specifically by sending the preprocessed text as a prompt to the generative AI.

[1556] Generative AI model used: A general generative AI model (e.g., GPT-4)

[1557] 4. Data Analysis

[1558] Generative AI uses natural language processing (NLP) technology to extract customer needs and emotions. For example, it can extract the need for "delayed delivery" and the emotion of "dissatisfaction."

[1559] The server receives the output from the generative AI and organizes the analysis results, specifically categorizing the extracted needs and emotions.

[1560] 5. Providing results

[1561] The server provides the analysis results to the planning, design, and development staff in the business division. Specifically, it generates the analysis results in report format and sends them to the staff by email.

[1562] The terminal displays the results to the person in charge, for example, by displaying the analysis results in graphs and charts on a dashboard.

[1563] Specific examples

[1564] For example, the following shows how an online shop processes feedback from a customer that "product delivery is slow."

[1565] 1. Data Collection

[1566] Users collect feedback about "slow delivery of product" through an online form.

[1567] The terminal sends this feedback to the server.

[1568] 2. Data Preprocessing

[1569] The server receives the text "Slow delivery of product" and cleans it, e.g. removes HTML tags and special characters.

[1570] The server tokenizes the text and splits it into "product," "delivery," and "slow."

[1571] The server removes the stop words "no" and "ga."

[1572] 3. Input to generative AI

[1573] The server sends the preprocessed text "Product delivery is slow" as a prompt to the generative AI model.

[1574] 4. Data Analysis

[1575] Generative AI extracts the need for "delayed delivery" and the emotion of "dissatisfaction."

[1576] The server organizes this information and generates an analysis result called "dissatisfaction with delivery delays."

[1577] 5. Providing results

[1578] The server generates the analysis results in the form of a report and sends it to the business department manager via email.

[1579] The terminal displays the results to the person in charge, and the analysis results, such as "dissatisfaction with delivery delays," are displayed in a graph on the dashboard.

[1580] Prompt Sentence Examples

[1581] An example of a prompt sentence to be input to the generative AI model is as follows:

[1582] Customer feedback: "Product delivery is slow"

[1583] Extract customer needs and feelings from this feedback.

[1584] In this way, the system efficiently analyzes the voice of the customer (VoC) and supports decision-making in business divisions.

[1585] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1586] Step 1:

[1587] Data collection

[1588] Users collect customer feedback and opinions through online forms, emails, social media comments, etc.

[1589] Input: Customer feedback and opinions (e.g., "Product delivery was slow")

[1590] The device sends the collected feedback to a server. Specifically, the application on the device sends data to the server through an API.

[1591] Output: Feedback data sent to the server

[1592] Step 2:

[1593] Data Preprocessing

[1594] The server preprocesses the received data. Specifically, it performs the following processes:

[1595] Text cleaning: remove unnecessary characters and noise, for example, remove HTML tags and special characters.

[1596] Tokenization: Splitting text into words and phrases. For example, splitting "product delivery is slow" into "product", "of", "delivery", "is", and "slow".

[1597] Stop word removal: Remove meaningless words (e.g., "no" and "ga").

[1598] Input: Feedback data sent to the server (e.g., "Product delivery was slow")

[1599] Output: Preprocessed text data (e.g., "Product delivery is slow")

[1600] Step 3:

[1601] Input to generative AI

[1602] The server inputs the preprocessed data into the generative AI model, specifically by sending the preprocessed text as a prompt to the generative AI.

[1603] Input: Preprocessed text data (e.g., "Product delivery is slow")

[1604] Output: The prompt input to the generative AI model

[1605] Step 4:

[1606] Data analysis

[1607] Generative AI uses natural language processing (NLP) technology to extract customer needs and emotions. For example, it can extract the need for "delayed delivery" and the emotion of "dissatisfaction."

[1608] Input: A prompt sentence entered into the generative AI model (e.g., "Product delivery is slow")

[1609] Output: Extracted needs and emotions (e.g., "Delayed delivery" or "Dissatisfied")

[1610] Step 5:

[1611] Providing results

[1612] The server receives the output from the generative AI and organizes the analysis results, specifically categorizing the extracted needs and emotions.

[1613] Input: Extracted needs and emotions (e.g., "Delayed delivery" or "Dissatisfied")

[1614] Output: Organized analysis results (e.g., "Dissatisfaction with delayed deliveries")

[1615] The server provides the analysis results to the planning, design, and development staff in the business division. Specifically, it generates the analysis results in report format and sends them to the staff by email.

[1616] The terminal displays the results to the person in charge, for example, by displaying the analysis results in graphs and charts on a dashboard.

[1617] Input: Organized analysis results (e.g., "Dissatisfaction with delayed deliveries")

[1618] Output: Reports and dashboards provided to the person in charge

[1619] In this way, the system efficiently analyzes the voice of the customer (VoC) and supports decision-making in business divisions.

[1620] (Application example 1)

[1621] 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."

[1622] Conventional Voice of Customer (VoC) analysis systems struggled to effectively collect and analyze customer feedback and reviews and provide them to business division planning, design, and development staff. Furthermore, there was a lack of a way to present the analysis results in a visually understandable manner, limiting the use of insights. This made it difficult to understand customers' true needs and reflect them in business strategies and policy planning. Furthermore, improvements in customer satisfaction and competitiveness were not fully achieved.

[1623] 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.

[1624] In this invention, the server includes: means for analyzing voice of customer (VoC) using generative AI; means for providing insights to business division planning, design, and development personnel based on the analysis results; means for utilizing the provided insights across multiple channels and groups; means for collecting customer reviews and feedback and analyzing them using generative AI; and means for visualizing the analysis results in graphs and charts and providing them to business division personnel. This enables customer feedback to be effectively collected and analyzed and presented in a visually easy-to-understand format. This allows the true needs of customers to be understood and reflected in business strategies and policy planning, thereby improving customer satisfaction and strengthening competitiveness.

[1625] "Generative AI" is a type of artificial intelligence that uses natural language processing technology to generate, analyze, and understand text data.

[1626] "Voice of Customer (VoC)" refers to text data such as customer feedback, reviews, and opinions.

[1627] The "analysis results" are data containing needs, emotions, and insights extracted by generative AI by analyzing customer feedback.

[1628] A "business division" refers to a department within a company that is responsible for planning, design, development, and other tasks.

[1629] "Insights" refers to the insights and knowledge gained by analyzing customer feedback, and is information that is useful for formulating business strategies and measures.

[1630] "Multi-channel, cross-group" refers to the sharing and utilization of information across multiple channels and departments.

[1631] A "review" refers to an evaluation or opinion that a customer gives about a product or service.

[1632] "Feedback" refers to information such as opinions, impressions, and areas for improvement provided by customers.

[1633] "Graphs and charts" refer to diagrams that visually represent data and are used to display analytical results in an easy-to-understand manner.

[1634] "Visualization" refers to displaying data or information visually to make it easier to understand.

[1635] A system for implementing the present invention is configured as follows.

[1636] Data collection

[1637] The server uses web scraping tools (e.g. BeautifulSoup, Scrapy) or APIs to collect customer reviews and feedback, which is then stored in a database as Voice of Customer (VoC).

[1638] Data Preprocessing

[1639] The server cleans the collected data and performs preprocessing such as tokenizing the text data, removing stop words, and stemming, using software such as Python, Pandas, and NLTK.

[1640] Generative AI analysis

[1641] The server inputs the preprocessed data into a generative AI model (e.g., GPT-4, BERT) to extract customer needs and emotions, enabling detailed analysis of customer feedback.

[1642] Visualizing the results

[1643] The server then visualizes the analysis results in graphs and charts and provides them to business unit personnel using software such as Dash and Plotly, allowing business personnel to gain insights in a visually easy-to-understand format.

[1644] Specific examples

[1645] For example, the following prompt sentence is input into the generative AI model:

[1646] Example prompt sentence:

[1647] Analyze the following customer reviews and extract key needs and emotions. Review: 'This product is very easy to use, but the battery life is poor. I like the design, but I wish it was a little lighter.'

[1648] An example output of a generative AI model is as follows:

[1649] Example output of a generative AI model:

[1650] "Primary needs: Improved battery life, lighter weight

[1651] Sentiments: Positive (easy to use, good design), Negative (poor battery life)

[1652] In this way, customer feedback can be collected and analyzed effectively and presented in a visually easy-to-understand format, allowing customers to understand their true needs and reflect them in business strategies and policy planning, thereby improving customer satisfaction and strengthening competitiveness.

[1653] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1654] Step 1:

[1655] The server collects customer reviews and feedback. Specifically, it uses web scraping tools (e.g., BeautifulSoup, Scrapy) and APIs to store the Voice of Customer (VoC) in a database. The input is customer reviews and feedback, and the output is text data stored in the database.

[1656] Step 2:

[1657] The server cleans the collected data. Specifically, it uses software such as Python, Pandas, and NLTK to perform preprocessing such as tokenizing the text data, removing stop words, and stemming. The input is the text data stored in the database, and the output is the preprocessed text data.

[1658] Step 3:

[1659] The server inputs the preprocessed data into a generative AI model (e.g., GPT-4, BERT) to extract customer needs and emotions. Specifically, it inputs a prompt into the generative AI model and analyzes the text output by the model. The input is the preprocessed text data and the prompt, and the output is the analysis results that include customer needs and emotions.

[1660] Step 4:

[1661] The server visualizes the analysis results in graphs and charts. Specifically, it uses software such as Dash or Plotly to visually display the analysis results. The input is the analysis results, including customer needs and sentiment, and the output is graphs and charts.

[1662] Step 5:

[1663] The terminal provides visualized analysis results to business department personnel. Specifically, the personnel can view the analysis results via a web browser or dedicated application. The input is graphs and charts, and the output is insights that the personnel can view.

[1664] Step 6:

[1665] Users can then use the provided insights to develop business strategies and measures. Specifically, they can use the visualized data as a reference to improve product development and marketing strategies. The input is the insights viewed by the person in charge, and the output is improved business strategies and measures.

[1666] Example 2

[1667] Next, a description will be given of 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."

[1668] In conventional systems for analyzing Voice of Customer (VoC), the process from collecting data to analyzing it and providing insights was inconsistent, making it difficult for each department to quickly plan and implement measures based on customer needs. Also, insights were not visualized or shared properly, and information sharing between departments was often not smooth. This led to issues such as insufficient improvement of customer satisfaction and strengthening of competitiveness.

[1669] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a data collection means, a data preprocessing means, a means for analyzing voice of customer (VoC) using a generative AI model, a means for providing insights to planning, design, and development personnel in the business department based on the analysis results, a means for utilizing the provided insights across multiple channels and groups, a means for visualizing the insights, and a means for sharing the insights with each department. This allows the processes from data collection to analysis, providing, visualizing, and sharing insights to be performed consistently, enabling each department to quickly plan and implement measures based on customer needs.

[1670] "Data collection methods" are methods used to collect customer feedback and market research data.

[1671] "Data preprocessing means" refers to means for preprocessing collected data, such as cleaning and normalizing the data, to prepare it in a format suitable for analysis.

[1672] A "generative AI model" is an artificial intelligence model that uses natural language processing to analyze data and generate insights.

[1673] "Means for analyzing voice of customer (VoC)" refers to means for analyzing customer feedback and opinions using generative AI models.

[1674] "Means of providing insights" refers to the means of providing useful information and suggestions to the planning, design, and development staff of the business division based on the results of the analysis.

[1675] "Means for multi-channel, group-wide utilization" refers to the means for sharing and utilizing the insights provided across different departments and channels within the company.

[1676] "Means for visualizing insights" refers to means for displaying analytical results in visual formats such as graphs and charts to make them easier to understand.

[1677] "Means for sharing insights with each department" refers to means for distributing the generated insights to each department within the company and sharing the information.

[1678] This invention is a system that utilizes a generative AI model to analyze voice of customer (VoC) and provide insights to each department within a company. Specific embodiments of this system are described below.

[1679] 1. Data Collection

[1680] The server collects customer feedback and market research data, such as website visit data, customer purchase history, and survey results, using tools like Google Analytics and Salesforce.

[1681] 2. Data Preprocessing

[1682] The server preprocesses the collected data by using the Python Pandas library to clean and normalize the data and prepare it for analysis, for example by imputing missing values, removing outliers, and encoding categorical variables.

[1683] 3. Data analysis

[1684] The server inputs the preprocessed data into a generative AI model to analyze customer needs and trends. For example, OpenAI's GPT-4 is used as the generative AI model. The server inputs the preprocessed data into the GPT-4 model and generates analysis results in natural language.

[1685] 4. Insight generation

[1686] The server generates insights to provide to each department based on the analysis results obtained from the generative AI model. The insights include specific action plans and areas for improvement. The server visualizes the generated analysis results using visualization tools such as Power BI and Tableau, providing the insights in a format that is easy for each department to understand.

[1687] 5. Insight sharing

[1688] The server shares the generated insights with each department within the company (planning, design, development, etc.). The server distributes the insights to each department via email or internal chat tools (such as Slack or Microsoft Teams). It also provides a dashboard for sharing insights so that each department can access them in real time.

[1689] Specific examples

[1690] When a user enters the prompt "Based on the latest customer feedback, what are the key trends for next product development?", the server does the following:

[1691] 1. Data collection: The server uses the Google Analytics API to obtain website visit data and the Salesforce API to collect customer purchase history and survey results.

[1692] 2. Data Preprocessing: The server uses the Pandas library to clean and normalize the data.

[1693] 3. Data analysis: The server inputs the preprocessed data into the GPT-4 model to analyze "which product features are most highly rated based on recent customer feedback."

[1694] 4. Insight generation: Based on the analysis results, the server visualizes them using Power BI and generates the insight that "improving user interfaces and revamping designs are important trends."

[1695] 5. Insight sharing: The server distributes the generated insights to each department via Slack and provides a dashboard that can be accessed in real time.

[1696] In this way, each department can plan and implement measures based on the insights provided, improving customer satisfaction and strengthening competitiveness.

[1697] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1698] Step 1: Data collection

[1699] The server collects customer feedback and market research data. Specifically, it uses tools such as Google Analytics and Salesforce to obtain website visit data, customer purchase history, survey results, etc. The input is raw data obtained through the API, and the output is a set of collected raw data.

[1700] Specific behavior:

[1701] The server periodically retrieves website visit data using the Google Analytics API, and also uses the Salesforce API to collect customer purchase history and survey results.

[1702] Step 2: Data Preprocessing

[1703] The server preprocesses the collected data by using the Python Pandas library to clean and normalize the data and prepare it for analysis. The input is the raw data collected, and the output is a preprocessed, clean dataset.

[1704] Specific behavior:

[1705] The server uses the Pandas library to impute missing values, remove outliers, normalize data, and encode categorical variables.

[1706] Step 3: Data analysis

[1707] The server inputs the preprocessed data into a generative AI model to analyze customer needs and trends. The generative AI model used is, for example, OpenAI's GPT-4. The input is a preprocessed, clean dataset, and the output is the analysis results from the generative AI model.

[1708] Specific behavior:

[1709] The server then inputs the preprocessed data into the GPT-4 model to generate natural language analysis results, such as "Which product features are most popular based on recent customer feedback?"

[1710] Step 4: Insight generation

[1711] The server generates insights to provide to each department based on the analysis results obtained from the generative AI model. The insights include specific measures and improvements. The input is the analysis results from the generative AI model, and the output is visualized insights.

[1712] Specific behavior:

[1713] The server visualizes the generated analytical results using visualization tools such as Power BI and Tableau, providing insights in a format that is easy for each department to understand.

[1714] Step 5: Share your insights

[1715] The server shares the generated insights with each department within the company (planning department, design department, development department, etc.). The input is the visualized insights, and the output is the insights distributed to each department.

[1716] Specific behavior:

[1717] The server distributes insights to each department via email or internal chat tools (such as Slack or Microsoft Teams), and also provides a dashboard for sharing insights so that each department can access them in real time.

[1718] (Application example 2)

[1719] 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."

[1720] With the previous system, it was difficult for different departments within a company to effectively share insights to plan and implement measures based on customer needs. Furthermore, there was a lack of means for each department within a factory to plan and implement measures based on market trends in real time. This meant that improvements in customer satisfaction and strengthening of competitiveness were not fully achieved.

[1721] 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.

[1722] In this invention, the server includes: means for analyzing voice of customer (VoC) using generative AI; means for providing insights to planning, design, and development personnel in business divisions based on the analysis results; means for utilizing the provided insights across multiple channels and groups; means for each division within the factory to plan and implement measures based on customer needs and market trends in real time; means for a robot to automatically collect data and generate insights using a generative AI model; and means for notifying each division of the insights via a smartphone, tablet, or AR smart glasses. This enables different divisions within a company to effectively share insights, enabling each division within the factory to plan and implement measures based on market trends in real time.

[1723] "Generative AI" is a system that uses artificial intelligence technology to generate data and perform analysis and predictions.

[1724] "Voice of the Customer (VoC)" refers to feedback, opinions, requests, etc. from customers.

[1725] "Insights" refers to insights and findings gained from data analysis.

[1726] "Multi-channel, cross-group" refers to the sharing and utilization of information across multiple channels and departments.

[1727] "Each department within the factory" refers to different departments within the factory, such as production control, quality control, and maintenance.

[1728] "Real-time" refers to data and information being processed immediately and available for immediate use.

[1729] "Market trends" refers to market changes, trends, customer needs, and other movements.

[1730] A "robot" refers to a mechanical device that performs work automatically.

[1731] A "generative AI model" refers to a model that uses generative AI to generate data and perform analysis and predictions.

[1732] A "smartphone" refers to a mobile phone that can connect to the Internet and use applications.

[1733] A "tablet" refers to a portable computer with a touchscreen.

[1734] "AR smart glasses" refers to a glasses-type device that displays information using augmented reality technology.

[1735] A system for carrying out this invention is configured as follows: The server includes means for analyzing voice of the customer (VoC) using generative AI, means for providing insights to planning, design, and development personnel in business divisions based on the analysis results, means for utilizing the provided insights across multiple channels and the group, means for each division within a factory to plan and implement measures based on customer needs and market trends in real time, means for a robot to automatically collect data and generate insights using a generative AI model, and means for notifying each division of the insights via a smartphone, tablet, or AR smart glasses.

[1736] Hardware and software used

[1737] Hardware: Sensors in factories, smartphones, tablets, AR smart glasses, robots

[1738] Software: Python, generative AI models (e.g., OpenAI's GPT-3)

[1739] Data processing and calculation

[1740] 1. Data collection: Robots collect data such as production rates, defect rates, scheduled maintenance dates, and customer feedback from sensors and APIs in the factory, enabling real-time data acquisition.

[1741] 2. Generating insights using a generative AI model: The collected data is input into a generative AI model to generate insights. The generative AI model can be, for example, OpenAI's GPT-3.

[1742] 3. Sharing insights: Generated insights are shared with each department via smartphone, tablet, or AR smart glasses, allowing each department to plan and implement measures based on market trends in real time.

[1743] Specific examples

[1744] For example, the production management department can adjust production schedules based on the generated insights, the quality control department can plan quality improvement measures based on defect rate data, and the maintenance department can implement preventive maintenance based on scheduled maintenance dates.

[1745] Prompt Sentence Examples

[1746] Production rate: 100%, Defect rate: 2%, Maintenance scheduled date: 2023-10-15, Customer feedback: Positive

[1747] By inputting this prompt into a generative AI model, the insights needed by each department can be generated.

[1748] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1749] Step 1:

[1750] The server collects data from sensors and APIs in the factory. Specifically, it acquires data such as production rates, defect rates, scheduled maintenance dates, and customer feedback. The input is data from sensors and APIs, and the output is the collected data. This makes it possible to acquire data in real time.

[1751] Step 2:

[1752] The server inputs the collected data into the generative AI model. Specifically, it converts the collected data into a prompt sentence format and sends it to the generative AI model (e.g., OpenAI's GPT-3). The input is the collected data, and the output is data in the prompt sentence format. This provides the basic data for the generative AI model to generate insights.

[1753] Step 3:

[1754] The server receives insights from the generative AI model. Specifically, it obtains the insights generated by the generative AI model based on prompt statements. The input is data in the form of prompt statements, and the output is the generated insights. This allows each department to obtain the insights they need.

[1755] Step 4:

[1756] The server notifies each department of the generated insights via smartphones, tablets, or AR smart glasses. Specifically, the insights are sent to each department's device and notified. The input is the generated insight, and the output is the insight displayed on each department's device. This enables each department to plan and implement measures based on market trends in real time.

[1757] Step 5:

[1758] Users (those in charge of each department) plan and implement measures based on the notified insights. Specifically, the production control department adjusts production schedules, the quality control department plans quality improvement measures, and the maintenance department implements preventive maintenance. The input is the notified insights, and the output is the measures implemented by each department. This leads to improved customer satisfaction and stronger competitiveness.

[1759] Example 3

[1760] 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."

[1761] With conventional systems for analyzing Voice of Customer (VoC), it was difficult to accurately grasp customer needs and develop products based on them. Furthermore, there was insufficient information sharing between departments, making consistent product development difficult. This meant that improvements in customer satisfaction and strengthened competitiveness were not being fully achieved.

[1762] 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.

[1763] In this invention, the server includes: means for analyzing Voice of Customer (VoC) using generative AI; means for providing insights to planning, design, and development personnel in business divisions based on the analysis results; means for utilizing the provided insights across multiple channels and groups; means for inputting prompts to the generative AI model; means for storing generated responses in a database; means for analyzing the stored responses; means for creating design proposals based on customer needs; means for determining technical specifications based on the design proposals; means for providing a platform for sharing information generated by each division; and means for updating the shared information in real time and notifying each division. This enables accurate understanding of customer needs and consistent product development based on those needs, thereby improving customer satisfaction and strengthening competitiveness.

[1764] "Generative AI" is a system that generates text and data using artificial intelligence technology.

[1765] "Voice of Customer (VoC)" is information such as feedback, opinions, and requests collected from customers.

[1766] "Insights" are insights and knowledge gained from data analysis, and are information that is useful for formulating business strategies and measures.

[1767] "Multi-channel, cross-group" refers to sharing information across multiple communication channels and departments.

[1768] A "prompt" is a question or instruction text that is input to a generative AI.

[1769] A "database" is a system for storing and managing data in an organized manner.

[1770] A "design proposal" is a specific blueprint or concept regarding the appearance and function of a product.

[1771] "Technical specifications" are detailed technical requirements and standards for the design and manufacture of a product.

[1772] A "platform" is a system that serves as the foundation for each department to share information and work together.

[1773] "Real-time updates" means that information is updated immediately and always reflects the latest status.

[1774] This invention is a system that uses generative AI to analyze the voice of the customer (VoC), provide insights to each business division, and utilize them across multiple channels and groups. A specific embodiment of this system is shown below.

[1775] First, the user (planning department) uses a device (PC or tablet) to input a prompt into the generative AI model. Examples of prompts include "What features do you want in a new smartphone?" or "What is the most important feature for your next smartphone purchase?"

[1776] The server then uses a generative AI model (e.g., a text generation system using artificial intelligence technology) to generate an answer to the input prompt. The generated answer may include specific needs, such as "long battery life," "good camera image quality," and "waterproof."

[1777] The server stores the generated answers in a database (e.g., a system for systematically storing and managing data), which is used for later analysis.

[1778] The user (the planning department) retrieves data from the database to analyze the saved responses, for example, by using a BI tool (business intelligence tool) to visualize and analyze customer needs.

[1779] Next, the user (design department) creates a design proposal based on the customer's needs. To create the design proposal, they use design software such as Adobe Creative Cloud. They create a specific design and upload the file to the server.

[1780] The user (development department) determines the technical specifications based on the design proposal. To determine the technical specifications, they use CAD software such as AutoCAD or SolidWorks. They design the technical details and save the files on the server.

[1781] The server provides a platform for sharing information generated by each department. Collaboration tools such as Microsoft Teams and Slack are used for the platform. This allows each department to share information in real time, enabling consistent product development.

[1782] Finally, the server updates the shared information in real time and notifies each department, allowing each department to proceed with their work based on the latest information.

[1783] In this way, it becomes possible to accurately grasp customer needs and develop consistent products based on them, thereby improving customer satisfaction and strengthening competitiveness. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1784] Step 1:

[1785] The user (planning department) uses a device (PC or tablet) to input a prompt to the generative AI model. Specifically, they log in to a dedicated interface and input a prompt such as, "What features do you want in a new smartphone?" The input prompt is sent to the server.

[1786] Step 2:

[1787] The server uses a generative AI model to generate an answer to the input prompt. The generative AI model analyzes the prompt and generates an answer based on related data. For example, it outputs specific needs such as "long battery life," "good camera image quality," and "waterproof." The generated answer is stored on the server.

[1788] Step 3:

[1789] The server stores the generated answers in a database. Specifically, it uses a database management system (e.g., MySQL or PostgreSQL) to store the generated answers in an appropriate format. The stored data is used for later analysis.

[1790] Step 4:

[1791] The user (planning department) retrieves data from the database to analyze the saved responses. Specifically, they use a BI tool (e.g., Tableau or Power BI) to access the database and visualize customer needs. Based on the visualized data, the user analyzes customer needs and gains insights.

[1792] Step 5:

[1793] The user (design department) creates a design proposal based on the customer's needs. Specifically, they use design software such as Adobe Creative Cloud to create a design that reflects the customer's needs. The created design proposal is uploaded to the server.

[1794] Step 6:

[1795] The user (development department) determines the technical specifications based on the design proposal. Specifically, they use CAD software such as AutoCAD or SolidWorks to design the technical details based on the design proposal. The designed technical specifications are saved on the server.

[1796] Step 7:

[1797] The server provides a platform for sharing information generated by each department. Specifically, it provides an interface for sharing information using collaboration tools such as Microsoft Teams and Slack. Each department uses this platform to share information.

[1798] Step 8:

[1799] The server updates the shared information in real time and notifies each department. Specifically, each time the information is updated, a notification is sent to each department. This allows each department to proceed with their work based on the latest information.

[1800] In this way, it becomes possible to accurately grasp customer needs and develop consistent products based on those needs, thereby improving customer satisfaction and strengthening competitiveness.

[1801] (Application example 3)

[1802] 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."

[1803] With conventional systems for analyzing Voice of the Customer (VoC), it was difficult to accurately grasp customer needs and quickly reflect them in product development. Furthermore, information sharing between departments was insufficient, resulting in inconsistent product development. This meant that improvements in customer satisfaction and strengthened competitiveness were not fully achieved.

[1804] The specific processing by the specific processing unit 290 of t...

Claims

1. a first means for collecting customer feedback and opinions; a second means for generating a prompt sentence for outputting the needs and feelings of the customer based on the feedback and opinions from the customer; A third means for inputting the generated prompt sentence into a generative AI model to generate the needs and emotions of the customer; A fourth means for transmitting the generated customer needs and emotions to a user terminal; A system including:

2. and a fifth means for presenting the received customer's emotion to a staff member of a physical store. The system of claim 1 .

3. A sixth means for determining a design and a function of a product based on the received customer needs.

3. A system according to claim 1 or 2.

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