system

Abstract

We provide the system. [Solution] A means of receiving improvement requests from users, Means for sending the aforementioned improvement request to the server, The server provides a means for passing data to the generative AI based on the aforementioned improvement request, A means by which a generative AI analyzes the aforementioned data and generates improvement advice, The server receives the generated advice and formats it into a format that the user can understand, The terminal provides the means for providing the formatted advice to the user. A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional sales promotion activities, salespersons have to devise effective tools and strategies to attract customers' attention by themselves. Since their choices often rely on intuition, it has been difficult to obtain consistent effects. In addition, accurately incorporating elements that attract customers' psychology and interests requires advanced technology and experience, so there is a problem of relying on specific salespersons. Therefore, there is a demand for the efficiency and standardization of sales promotion activities.

Means for Solving the Problems

[0005] In the present invention, a system is provided in which a user inputs an improvement request and transmits this information to a server, and a generative AI analyzes the request content and generates effective advice. This system includes the following means:

[0006] 1. Means for receiving improvement requests from users,

[0007] 2. Means for sending improvement requests to the server,

[0008] 3. A means by which the server passes data to the generative AI based on improvement requests.

[0009] 4. A means by which a generative AI analyzes data and generates improvement advice.

[0010] 5. A means for the server to receive the generated advice and format it into a format that the user can understand.

[0011] 6. A means by which the device provides formatted advice to the user.

[0012] This enables users to utilize generative AI advice to create and improve effective promotional tools, thereby streamlining and standardizing promotional activities. Furthermore, by incorporating elements of mentalism into the advice generated by the generative AI, effective promotional activities based on customer psychology can also be achieved.

[0013] "User" refers to a person who operates the system or an end-user, and in particular, to the person responsible for creating and improving promotional tools.

[0014] An "improvement request" refers to information that users input into the system, such as the tools they are currently using, specific areas they would like to improve, and customer feedback.

[0015] A "server" refers to a computing device or service that processes data received from users and passes it on to a generative AI.

[0016] "Generative AI" refers to an artificial intelligence system that analyzes user requests and automatically generates appropriate improvement advice.

[0017] "The means of passing data" refers to the process by which the server provides the request data received from the user to the generative AI.

[0018] "The means of analyzing data" refers to the process of analyzing the data received by the generative AI to generate improvement advice.

[0019] "Improvement advice" refers to specific proposals or instructions that can be used by the user, generated by the generative AI as a result of data analysis.

[0020] "The means of formatting" refers to the process by which the server converts the advice received from the generative AI into a form that is easy for the user to understand.

[0021] "The means of providing" refers to the process by which the terminal displays or presents the formatted advice to the user.

[0022] "Mentality" refers to a method for realizing more effective promotional activities by utilizing the psychology and behavior patterns of customers.

Brief Description of Drawings

[0023] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7]This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0024] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0025] First, let's explain the terminology used in the following explanation.

[0026] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0027] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0028] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0029] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0030] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0031] [First Embodiment]

[0032] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0033] As shown in Figure 1, the 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.

[0034] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0035] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0036] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0037] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0038] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0039] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0041] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0042] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0043] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0044] This invention is a system for effectively supporting sales promotion activities, and includes a process in which a user inputs a request into the system, and a generative AI provides advice based on that request. The specific form for implementing this system is described below.

[0045] System Configuration

[0046] This system consists of a user-operated terminal, a server for processing data, and a generative AI. The terminal receives improvement requests from the user through a user interface. The server passes the received request data to the generative AI, which then formats the generated advice and sends it back to the terminal. The generative AI is responsible for analysis and advice generation.

[0047] Program processing

[0048] Receiving user input

[0049] Users access the system through their terminal and input requests regarding the tools they are currently using, areas for improvement, and customer feedback. The terminal then formats this request data appropriately and sends it to the server.

[0050] Data processing and transmission

[0051] The server verifies and formats the request data received from the terminal and passes it to the generative AI. At this time, it checks for consistency in the data format and whether it contains the necessary information.

[0052] Analysis and advice generation using generative AI.

[0053] Generative AI analyzes request data and generates optimal improvement advice. Based on past data and training data, the generative AI considers the request content and generates specific advice that takes into account color selection, wording, and elements of mentalism.

[0054] Formatting and sending advice

[0055] The server verifies and formats the advice received from the generative AI, converting it into a user-friendly format. The formatted advice is then sent back to the terminal and presented to the user. Here, the advice is tagged to allow users to quickly understand the necessary information.

[0056] Explanation of specific examples

[0057] For example, if a user (crew member) enters, "I want to improve the advertising pop-up for the new washing machine. I'd especially like advice on the colors and wording," then:

[0058] 1. The user enters a request into the terminal stating, "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording."

[0059] 2. The device sends this request to the server.

[0060] 3. The server receives this request and passes the data to the generative AI.

[0061] 4. The generative AI analyzes the request and generates advice such as, "Use a vibrant blue for the background color," and "The tagline should be 'Amazing cleaning power, on sale for a limited time only.'"

[0062] 5. The server receives the generated advice, formats it, and sends it to the terminal.

[0063] 6. The device displays formatted advice to the user, who then uses it to improve their promotional tools.

[0064] This system allows salespeople to receive support from generative AI and achieve consistent results without relying on intuition. Furthermore, by receiving advice that incorporates elements of mentalism, more effective promotional activities based on customer psychology become possible.

[0065] The following describes the processing flow.

[0066] Step 1:

[0067] Users access the system through their devices and enter specific improvement requests. These requests include the tools currently being used, the points they want to improve, and customer feedback. For example, a user might enter, "I want to improve the advertising pop-up for our new washing machine. I'd especially like advice on the colors and wording."

[0068] Step 2:

[0069] The device sends the request received from the user to the server. Before sending, it verifies that the data is in the correct format and formats it as needed. The collected data may take the following format, for example: {'request_type': 'advice', 'product': 'washing machine', 'focus_areas': ['color', 'wording']}.

[0070] Step 3:

[0071] The server receives the request data from the terminal. The server performs a simple check on the request data to verify that the data format and required fields are correct.

[0072] Step 4:

[0073] The server prepares to pass data to the generative AI. It converts the data into a format suitable for the generative AI and formats it to include all the necessary information.

[0074] Step 5:

[0075] The generative AI analyzes data received from the server and generates improvement advice based on the user's request. The generative AI considers color selection, wording, and elements of mentalism to provide specific advice. For example, it might use a vibrant blue for the background color and generate advice such as "Amazing cleaning power, limited-time sale" for the tagline.

[0076] Step 6:

[0077] The server re-examines the advice received from the generative AI and formats it into a user-friendly format. Specifically, it tags the advice so that the necessary information can be understood at a glance.

[0078] Step 7:

[0079] The server sends formatted advice to the terminal. The data sent is appropriately formatted as a response to the user's request.

[0080] Step 8:

[0081] The terminal displays advice sent from the server to the user through the user interface. This allows the user to improve their promotional tools based on the advice provided.

[0082] This series of steps allows users to create and improve effective promotional tools using advice from generative AI, thereby streamlining and standardizing their promotional activities.

[0083] (Example 1)

[0084] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0085] In traditional sales promotion activities, obtaining advice necessary for improving promotional tools relied on human intuition and experience, requiring a great deal of trial and error to succeed. Furthermore, selecting effective designs and wording required specialized knowledge and experience, making it difficult for everyone to easily obtain optimal advice. This resulted in inconsistent sales promotion effectiveness and difficulties in adopting customer psychology-based approaches.

[0086] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0087] In this invention, the server includes means for receiving improvement requests from users, means for formatting the improvement requests into a data format, means for transmitting the formatted data to a generative AI, means for the generative AI to analyze the data and generate improvement advice, means for the server to tag and format the generated advice in a format that the user can understand, and means for providing the formatted advice to a terminal. This makes it possible for anyone to easily and effectively obtain improvement advice for promotional tools, enabling the realization of promotional activities with consistent results.

[0088] A "user" refers to an entity that uses the system to input improvement requests and receive advice.

[0089] A "request for improvement" refers to information that users enter regarding the system, including requests and suggestions for improvements.

[0090] A "server" refers to a computer device that receives requests from users, sends data to a generative AI, and formats the generated advice.

[0091] "Generative AI" refers to artificial intelligence models that analyze received data and generate specific improvement advice based on a particular algorithm.

[0092] "Analysis" refers to the process by which a generative AI analyzes information based on the data it receives and derives optimal advice.

[0093] "Improvement advice" refers to instructions and suggestions regarding improvements to promotional tools generated by a generative AI based on its analysis results.

[0094] "Formatting" refers to the process of converting data into a specific format to facilitate communication and processing.

[0095] "Formatting" refers to the process of converting generated advice into a format that is easy for the user to understand.

[0096] "Tagging" refers to the process of adding specific labels or metadata to generated advice to organize and classify information.

[0097] A "terminal" refers to a device that a user uses to access a system, enter requests, and receive advice.

[0098] "Mentalism" refers to elements based on customer psychology and behavior that generative AI considers during analysis.

[0099] "Data format" refers to a specific data structure or format used when exchanging data between systems.

[0100] This invention provides a system for effectively supporting sales promotion activities, in which the user provides requests through a terminal, and the server generates and provides appropriate improvement advice using generative AI. The following describes how to implement this system in detail. This system consists of a terminal operated by the user, a server that processes data, and generative AI that performs analysis and advice generation.

[0101] Users access the system using a terminal. The terminal is built using web technologies such as HTML, CSS, and JavaScript (registered trademark), and users input information such as the tools they are currently using, areas for improvement, and customer feedback through the interface. For example, a user might input, "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording."

[0102] The terminal formats this request into the appropriate JSON format and sends it to the server via an HTTP POST request. The server parses the received request data and verifies that there are no omissions or inconsistencies. The server uses programming languages ​​such as Python or Node.js to reshape the data and convert it into a format that can be parsed by generative AI.

[0103] The server sends the formatted data to a generative AI. The generative AI uses models such as OpenAI's GPT-3®, which analyzes the request based on past data and training data and generates improvement advice. The generative AI also incorporates elements of mentalism, considering customer psychology and behavior during the analysis, to generate advice.

[0104] The advice generated by the generative AI includes specific details such as "Use a vibrant blue for the background color" and "The tagline should be 'Amazing cleaning power, limited-time sale!'" The server receives this generated advice, tags it, and formats it to make it easier for users to understand. Finally, it sends the formatted advice to the device and provides it to the user.

[0105] This allows salespeople to improve promotional tools with the support of generative AI, enabling them to create consistently effective tools without relying on intuition. An example of a prompt is as follows:

[0106] "I'd like to improve the promotional pop-up for our new washing machine. I'd especially appreciate specific advice on the background color and catchphrase."

[0107] This system allows users to receive effective advice quickly and accurately, making it easy to improve their promotional activities.

[0108] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0109] Step 1:

[0110] Users submit improvement requests through their devices.

[0111] The user enters a specific request into the terminal interface, such as, "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording." The terminal formats this input data into JSON format. The input is natural language text, and the output is JSON data like the following:

[0112] json

[0113] {

[0114] "request_type": "promotion_improvement",

[0115] "details": {

[0116] "product": "washing_machine",

[0117] "focus_areas": ["color", "wording"]

[0118] }

[0119] }

[0120] The terminal validates the input and checks for formatting accuracy.

[0121] Step 2:

[0122] The terminal sends the formatted data to the server.

[0123] The terminal uses an HTTP POST request to send the above JSON data to the server. The input is the formatted JSON data, and the output is a success message sent to the server. Specifically, the terminal checks the network status before sending to ensure the data is transmitted correctly.

[0124] Step 3:

[0125] The server receives the data and formats it for analysis.

[0126] The server receives JSON data and formats it into a parseable format. The input is JSON data received from the terminal, and the output is further formatted in a way that is easier for generative AI to process. At this time, the server checks for data integrity and verifies that all necessary fields are present. For example, it formats the data as shown below.

[0127] json

[0128] {

[0129] "prompt": "Provide advice for improving the promotion of a new washing machine, focusing on color and wording."

[0130] }

[0131] The server logs the formatted data and manages the progress of the analysis.

[0132] Step 4:

[0133] The server sends the formatted data to the generative AI.

[0134] The server sends formatted data to a generative AI via an HTTP request. The generative AI uses, for example, OpenAI's GPT-3 model. The input is formatted data, and the output is advice including analysis results. The server adjusts the transmission process to minimize response delays.

[0135] Step 5:

[0136] The generative AI analyzes the request and generates improvement advice.

[0137] The generative AI analyzes the received request data and generates advice for improving promotional tools. For example, based on the request data, it generates specific advice such as "Use a vibrant blue for the background color" and "The catchphrase should be 'Amazing cleaning power, limited-time sale!'" The input is a prompt for analysis, and the output is text containing specific advice. The generative AI logs the process and records the analysis.

[0138] Step 6:

[0139] The server receives the generated advice and formats it.

[0140] The server verifies the advice received from the generative AI and formats it into a user-friendly format. For example, the generated advice is tagged as follows:

[0141] json

[0142] {

[0143] "advice": {

[0144] "color": "Vivid blue",

[0145] "Wording": "Amazing cleaning power, on sale for a limited time only"

[0146] }

[0147] }

[0148] The input is advice text from a generative AI, and the output is formatted advice data. The server logs the results of the formatting process.

[0149] Step 7:

[0150] The server sends formatted advice to the terminal.

[0151] The server sends the formatted advice to the terminal as an HTTP response in JSON format. The input is the formatted advice data, and the output is a message from the terminal confirming receipt. The server logs the message to confirm successful transmission.

[0152] Step 8:

[0153] The device provides advice to the user.

[0154] The terminal analyzes the advice data received from the server and displays it through a user-friendly interface. Specifically, the terminal uses appropriate UI components to set the background color and display catchphrases. The input is formatted advice data received from the server, and the output is specific advice displayed to the user. The user then uses this to improve their promotional tools.

[0155] Through the above processing steps, users can quickly obtain effective advice and consistently improve their promotional activities.

[0156] (Application Example 1)

[0157] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0158] In traditional promotional activities, users (store staff) had to rely on their own intuition to identify areas for improvement and provide advice, and the effectiveness of these methods was not always consistent. Furthermore, obtaining appropriate advice required significant time and resources, making rapid improvement difficult. Additionally, there was often a lack of concrete, immediately actionable advice, hindering the implementation of effective promotional activities based on customer psychology.

[0159] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0160] In this invention, the server includes means for receiving improvement requests from users, means for transmitting the improvement requests to an internet server, means for the internet server to pass data to a generative AI based on the improvement requests, means for the generative AI to analyze the data and generate improvement advice, means for the internet server to receive the generated advice and format it into a format that the user can understand, and means for a smart device to provide the formatted advice to the user. As a result, users can obtain specific advice that can be immediately put into practice in real time, enabling effective sales promotion activities.

[0161] A "user" refers to an individual or organization that operates the system and submits improvement requests.

[0162] An "Internet server" is a computer system that processes, transmits, and receives data over a network.

[0163] "Generative AI" is an artificial intelligence technology that analyzes request data and automatically generates optimal improvement advice.

[0164] A "smart device" is an advanced electronic device that is connected to the internet and can provide users with information in real time.

[0165] An "improvement request" is information that users enter into the system, indicating specific points that need improvement or their desired improvements.

[0166] "Means of data transfer" refers to methods or devices for transferring data between different components within a system.

[0167] "Analysis" is a computational process that involves understanding input data and extracting necessary information.

[0168] "Improvement advice" refers to specific suggestions and suggestions provided in response to submitted improvement requests.

[0169] "Shaping methods" refer to methods or devices for converting generated advice into a format that is easy for the user to understand.

[0170] "Means of providing" refers to methods or devices for informing and displaying the generated advice to the user.

[0171] This invention relates to a system for supporting sales promotion activities, and includes a process in which a user inputs a request into the system, and a generative AI provides advice based on that request.

[0172] System Configuration

[0173] This system consists of a user-operated smart device (such as smart glasses or a smartphone), an internet server that processes data, and a generative AI. The smart device receives improvement requests from the user through a user interface. The internet server passes the received request data to the generative AI, which then formats the generated advice and sends it back to the smart device. The generative AI is responsible for analysis and advice generation.

[0174] Program processing

[0175] Receiving user input

[0176] Users access the system via smart devices and input requests regarding the tools they are currently using, areas for improvement, and customer feedback. The smart device then formats this request data appropriately and sends it to the internet server.

[0177] Data processing and transmission

[0178] The internet server verifies and formats the request data received from the smart device and passes it to the generative AI. At this time, it checks for consistency in the data format and whether it contains the necessary information.

[0179] Analysis and advice generation using generative AI.

[0180] Generative AI analyzes request data and generates optimal improvement advice. Based on past data and training data, generative AI considers the request content and generates specific advice.

[0181] Formatting and sending advice

[0182] An internet server verifies and formats the advice received from the generative AI, converting it into a user-friendly format. The formatted advice is then sent back to the smart device and presented to the user. Here, the advice is tagged to allow users to quickly understand the necessary information.

[0183] Explanation of specific examples

[0184] For example, if a user (store staff) inputs via voice, "I'd like some advice on how to arrange the display of new products":

[0185] 1. The user enters a request into their smart device via voice, stating, "I would like advice on how to arrange the display for the new product."

[0186] 2. The smart device sends this request to an internet server.

[0187] 3. The internet server receives this request and passes the data to the generative AI.

[0188] 4. The generative AI analyzes the request and generates advice such as, "For displaying new products, it's best to place them at eye level and in a position where customers can easily pick them up. Also, consider placing POP displays around them to attract customers' attention."

[0189] 5. The internet server receives the generated advice, formats it, and sends it to the smart device.

[0190] 6. Smart devices display formatted advice to the user, who then uses it to improve their promotional tools.

[0191] This system allows store staff to implement effective promotional activities immediately with the support of generative AI. An example of a prompt message would be: "A salesperson has entered a request for advice on how to display new products. Please provide the best advice."

[0192] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0193] Step 1:

[0194] The user provides voice input to a smart device. For example, the user might voice a request such as, "I'd like advice on how to arrange the display for the new product." The smart device converts this voice data into text data and formats it. Converting voice data (input) to text data (output) enables subsequent processing.

[0195] Step 2:

[0196] The terminal sends the converted text data to the internet server. The terminal's sending function is used to upload the request data to the internet server. Text data is given as input, and the output is the data sent to the internet server.

[0197] Step 3:

[0198] The internet server verifies and formats the received request data. The server checks the integrity of the request data and verifies that there are no errors. Next, it formats it into a format that is easy for the generative AI to process. The request data is given as input, and the formatted data is passed to the generative AI as output.

[0199] Step 4:

[0200] The internet server passes formatted data to the generative AI. The server's internal data transfer function is used to send the formatted data to the generative AI. Formatted data is received as input, and data passed to the generative AI is obtained as output.

[0201] Step 5:

[0202] The generative AI analyzes the request data and generates improvement advice. The generative AI uses historical and training data to analyze the request and generate specific improvement advice. Formatted request data is given as input, and the generated advice is returned as output.

[0203] Step 6:

[0204] An internet server verifies and formats the advice received from a generative AI. The server checks whether the advice is appropriate and formats it into a user-friendly format. The generated advice is given as input, and the formatted advice is obtained as output.

[0205] Step 7:

[0206] An internet server sends formatted advice to a smart device. The server uses its transmission function to send the formatted advice to the smart device. Formatted advice is given as input, and data to be sent to the smart device is obtained as output.

[0207] Step 8:

[0208] A smart device displays formatted advice to the user. The smart device's display capabilities are used to visually present the advice to the user. Formatted advice is provided as input, and the output is the advice the user receives visually.

[0209] Each processing step in this system allows users to receive specific improvement advice based on their requests in real time.

[0210] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0211] This invention is a system for effectively supporting sales promotion activities, comprising a process in which a user inputs a request into the system, and a generative AI provides advice based on that request. The system further incorporates an emotion engine that recognizes the user's emotions and includes a method for optimizing requests and advice based on the user's emotions.

[0212] System Configuration

[0213] This system consists of a user-operated terminal, a server for processing data, a generative AI, and an emotion engine. The terminal receives improvement requests from the user through a user interface and collects the user's emotional data. The server passes the received request data and emotional data to the generative AI, which then formats the generated advice and sends it back to the terminal. The generative AI is responsible for analysis and advice generation. The emotion engine analyzes the user's emotional state and adjusts the request content and advice based on that analysis.

[0214] Program processing

[0215] Receiving user input and sentiment data

[0216] Users access the system through a terminal and enter specific improvement requests. These requests include the tools currently being used, points to be improved, and customer feedback. Along with these requests, the terminal extracts emotional data from the user's voice and facial expressions and sends it to the server.

[0217] Data processing and transmission

[0218] The server verifies and formats the request data and emotion data received from the terminal and passes them to the generative AI. At this time, it checks for consistency in the data format and whether the necessary information is included. The emotion engine analyzes the emotion data to identify the user's current emotional state and reflects this in the generative AI's analysis.

[0219] Analysis and advice generation using generative AI.

[0220] The generative AI analyzes request data and the user's emotional state to generate optimal improvement advice. Based on the request content and emotional data, the generative AI considers color selection, wording, and mentalism elements to provide specific advice. Information from the emotional engine is used to adjust the generated advice to best suit the user's emotional state.

[0221] Formatting and sending advice

[0222] The server re-examines the advice received from the generative AI and formats it into a user-friendly format. Specifically, it tags the advice so that the necessary information can be understood at a glance. Emotional data provided by the emotion engine is also taken into consideration.

[0223] Presentation to the user

[0224] The terminal displays advice sent from the server to the user through the user interface. Because this advice is optimized for the user's emotional state, the user can use the provided advice to improve their promotional tools and conduct optimal promotional activities.

[0225] Explanation of specific examples

[0226] For example, if a user (crew member) enters, "I want to improve the advertising pop-up for the new washing machine. I'd especially like advice on the colors and wording," then:

[0227] 1. The user enters a request into their device: "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording."

[0228] 2. Along with this request, the device collects emotional data from the user's voice and facial expressions.

[0229] 3. The server passes the received request data and emotion data to the generative AI.

[0230] 4. The generative AI analyzes the request and emotional data to generate advice such as, "Use blue for the background color based on the user's preferences and emotional state," and "Use 'Amazing cleaning power, limited-time sale' as the tagline."

[0231] 5. The server receives the generated advice, formats it, and sends it to the terminal in a way that takes the user's emotional state into consideration.

[0232] 6. The device displays formatted advice to the user through the user interface.

[0233] This system allows users to create and improve effective promotional tools optimized for emotional states, with the support of generative AI and an emotion engine. This enables more efficient and standardized promotional activities, as well as more effective promotional activities based on customer psychology.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] Users access the system through their terminals and input requests detailing the tools they are currently using, areas for improvement, and specific requests. For example, a user might input, "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording."

[0237] Step 2:

[0238] Before the device sends a request received from the user to the server, it collects the user's voice and facial expression data and extracts emotional data with the help of an emotion engine. The extracted emotional data represents emotional states such as "excitement," "anxiety," and "joy."

[0239] Step 3:

[0240] The device sends request data and emotion data to the server. The data is formatted in an appropriate format, for example, {'request_type': 'advice', 'product': 'washing machine', 'focus_areas': ['color', 'wording'], 'emotion': 'excitement'}.

[0241] Step 4:

[0242] The server checks and verifies the request data and sentiment data received from the terminal. It verifies that the data format is correct, that required fields are included, and formats the data as needed.

[0243] Step 5:

[0244] The server prepares to pass data to the generative AI. Here, the request data and sentiment data are converted into the appropriate format so that the generative AI can use it for analysis.

[0245] Step 6:

[0246] The generative AI analyzes request data and sentiment data received from the server to generate optimal improvement advice. Based on past data and training data, the AI ​​considers the request content and provides advice that is most appropriate to the user's emotional state. For example, it might generate advice such as, "Use blue for the background color, based on the user's preferences and emotional state," or "Use the tagline, 'Amazing cleaning power, limited-time sale!'"

[0247] Step 7:

[0248] The server re-examines the advice received from the generative AI and formats it into a format that is easy for the user to understand. During this process, information from the emotion engine is also taken into consideration, and advice is provided in a way that matches the user's emotional state.

[0249] Step 8:

[0250] The server sends formatted advice to the terminal. The transmitted data is appropriately formatted as the best response to the user's request. For example, it might be formatted as follows: "Background color: Blue" "Catchphrase: Amazing cleaning power, limited time sale."

[0251] Step 9:

[0252] The terminal displays formatted advice received from the server to the user through the user interface. This allows the user to improve promotional tools based on advice optimized for their emotional state, thereby achieving more effective promotional activities.

[0253] Through this series of steps, users can create and improve effective promotional tools optimized for emotional states, with the support of generative AI and emotion engines, thereby achieving greater efficiency and standardization in their promotional activities.

[0254] (Example 2)

[0255] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0256] In modern marketing activities, providing accurate advice that takes user emotions into account is crucial, but the lack of an effective system for this means that advice may not always be tailored to the user's emotional state. Traditional systems have struggled to utilize user emotional data, resulting in sometimes inappropriate advice. Furthermore, the complex data shaping and analysis processes required to effectively use generative AI models have sometimes reduced the overall efficiency of the system.

[0257] The specific processing performed 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 means for receiving improvement requests and user emotion data from the user, means for verifying the improvement requests and emotion data and passing the data to a generative AI, means for the generative AI to analyze the data and generate improvement advice, and means for the server to verify and format the generated advice. This makes it possible to provide appropriate advice optimized for the user's emotional state.

[0258] A "user" is a person who operates the system, inputs improvement requests, and receives advice from the system.

[0259] An "improvement request" is a request or wish that a user enters into the system regarding improvements to promotional activities or various tools.

[0260] "Emotional data" refers to data extracted from the user's voice, facial expressions, etc., that indicates the user's current emotional state.

[0261] A "terminal" is a device equipped with a user interface that allows a user to access a system. Examples include computers and smartphones.

[0262] A "server" is a central processing unit that receives, verifies, and formats request data and sentiment data, and works in conjunction with generative AI to generate advice.

[0263] "Generative AI" refers to artificial intelligence that analyzes received data and generates advice based on user requests.

[0264] "Improvement advice" refers to specific suggestions for improvements and enhancements in response to user requests, generated by a generative AI based on its analysis results.

[0265] An "emotion engine" is a function or software that analyzes user emotional data and incorporates the results into the analysis of generative AI.

[0266] "Verifying and formatting data" is the process of checking and adjusting the format and content of received data so that it can be properly analyzed by generative AI.

[0267] A "user interface" is an interface that allows a user to interact with a system, and includes devices such as screens and input devices.

[0268] Modes for carrying out the invention

[0269] This invention is a system that analyzes improvement requests regarding promotional activities entered by users and provides optimal advice based on the user's emotional state. This system is primarily implemented using the following hardware and software:

[0270] The device operated by the user

[0271] The system receives improvement requests from users through the user interface and collects user sentiment data.

[0272] Facial recognition camera, microphone, and speech analysis software (e.g., OpenCV, Google® Cloud Speech-to-Text API)

[0273] server

[0274] The received request data and sentiment data are verified and formatted, then passed on to the generative AI.

[0275] JSON schema validation tool for verifying data format integrity

[0276] Emotion Engine (e.g., Microsoft (registered trademark) Azure (registered trademark) Emotion API)

[0277] Generative AI

[0278] Responsible for analysis and advice generation

[0279] AI model (e.g., OpenAI GPT-4 (registered trademark))

[0280] Emotion Engine

[0281] Analyze the user's emotional state and adjust the content of the request and advice based on it

[0282] Specific operations of the system

[0283] The user accesses the system through the terminal and enters a specific improvement request such as "I want to improve the promotion pop-up of the new washing machine. Especially, I want advice on colors and wording." The terminal collects emotional data from the user's voice and expression along with this request. For example, capture the user's facial expression with a camera and record the voice with a microphone. Then, extract the emotional data using voice analysis algorithms and facial recognition software.

[0284] The server verifies and formats the request data and emotional data received from the terminal. Check the consistency of the data format and whether the necessary information is included. The verified data is converted into JSON format and sent as a prompt to the generative AI. At this time, the emotion engine analyzes the emotional data and reflects the result in the prompt.

[0285] The generative AI generates optimal improvement advice based on the user's request data and emotional data. For example, generate specific advice such as "Use blue for the background color" and "Use 'Surprising cleaning power, limited-time sale now' for the catchphrase." This advice is adjusted based on the emotional data.

[0286] The server re-verifies the advice received from the generative AI and formats it into a form that is easy for the user to understand. The formatted advice is sent to the terminal, and the terminal displays it to the user through the user interface. Based on the provided advice, the user can improve the promotional tool and conduct optimal promotional activities.

[0287] Specific Example

[0288] For example, when the user (store staff) inputs "I want to improve the promotion pop-up for the new washing machine. I especially want advice on the color and the wording":

[0289] 1. The user inputs a request into the terminal.

[0290] 2. The terminal collects emotional data from the user's voice and facial expressions along with the request.

[0291] 3. The server passes the received request data and emotional data to the generative AI.

[0292] 4. The generative AI analyzes the request and emotional data and generates advice such as "Use blue based on the user's preferences and emotional state for the background color" and "For the catchphrase, use 'Surprising cleaning power, limited-time sale'".

[0293] 5. The server receives the generated advice, formats it, and sends it to the terminal in a form that takes into account the user's emotional state.

[0294] 6. The terminal displays the formatted advice to the user through the user interface.

[0295] With this system, the user can create and improve an effective promotional tool optimized for the emotional state with the support of the generative AI and the emotion engine. As a result, it is possible to improve the efficiency and standardization of promotional activities and to realize more effective promotional activities based on customer psychology.

[0296] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0297] Step 1: User request input and collection of sentiment data

[0298] The user operates a terminal to access the system and enter a specific improvement request. For example, they might enter, "I want to improve the advertising pop-up for the new washing machine. I'd especially like advice on the colors and wording." The terminal receives this request as text data. The terminal also uses its camera and microphone to collect the user's voice and facial expression data. Facial recognition software and voice analysis algorithms are used to extract emotional data from the voice and facial expression data.

[0299] Input: User request (text), audio data, facial expression data

[0300] Output: Request data (text), sentiment data

[0301] Step 2: Server-based data validation and sentiment analysis

[0302] Request data and sentiment data sent from the device reach the server. The server first verifies the integrity of the data format and confirms that all necessary information is included. Next, it passes the sentiment data to an emotion engine (e.g., Microsoft Azure Emotion API) for analysis to identify the user's current sentiment state. Finally, it generates a dataset that integrates the request data and analysis results.

[0303] Input: Request data (text), sentiment data

[0304] Output: Integrated dataset (request data, sentiment analysis results)

[0305] Step 3: Analysis and advice generation using generative AI

[0306] The server sends the integrated dataset to a generative AI (e.g., OpenAI GPT-4) as a prompt for advice generation. The generative AI analyzes the prompt and generates optimal improvement advice based on the request content and sentiment data. For example, it generates specific advice such as "Use blue for the background color" and "Use 'Amazing cleaning power, limited-time sale now' for the catchphrase". This advice is returned to the server in JSON format.

[0307] Input: Integrated dataset (request data, sentiment analysis result)

[0308] Output: Generated advice (JSON format)

[0309] Step 4: Advice formatting and re-verification by the server

[0310] The server re-verifies the advice received from the generative AI to check if there are any deficiencies in the content. If there are no deficiencies, the server formats the advice into a form that is easy for the user to understand. For example, it performs tagging, paragraphing, bullet-point listing, etc. Also, based on the sentiment analysis result, it adjusts the tone and emphasis points of the advice. The formatted advice is finally sent to the terminal.

[0311] Input: Generated advice (JSON format)

[0312] Output: Formatted advice (HTML or other format)

[0313] Step 5: Presentation of advice to the user by the terminal

[0314] The terminal receives the formatted advice sent from the server. It displays the advice to the user through the user interface. When displaying, it adjusts the color, font, and layout of the advice based on the sentiment analysis result. It displays in the notification panel "It is recommended to use blue for the background color. Please use 'Amazing cleaning power, limited-time sale now' for the catchphrase".

[0315] Input: Formatted advice (HTML or other format)

[0316] Output: Advice presented to the user (visual display)

[0317] (Application Example 2)

[0318] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0319] To maximize the effectiveness of promotional tools in physical stores, it is necessary to accurately reflect customers' emotions and potential needs. However, it is difficult for sales staff and store managers to instantly analyze the emotions of individual customers and determine the optimal promotional strategy based on that analysis. Traditional promotional systems have struggled to provide personalized advice that takes into account the emotional state of individual customers, resulting in limited effectiveness of promotional activities.

[0320] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0321] In this invention, the server includes means for receiving improvement requests from users, means for passing data to a generative AI, and means including a generative AI that generates improvement advice based on the user's emotional state. This makes it possible to analyze the emotional state of individual customers in real time and provide optimal sales promotion advice based on that analysis.

[0322] "User" refers to an individual or organization that uses this system.

[0323] An "improvement request" refers to specific requests or instructions from users for improvements to promotional tools.

[0324] A "server" refers to a device that receives improvement requests from users, passes the data to a generative AI, and then reshapes the generated advice before providing it to the user.

[0325] "Generative AI" refers to an artificial intelligence system that analyzes data received from users and generates optimal improvement advice.

[0326] An "emotion engine" refers to a system that analyzes a user's emotional state from their facial expressions and voice, and provides that information to a generative AI.

[0327] A "terminal" is a device used by a user to operate something, and refers to a device used for inputting requests and receiving and displaying advice.

[0328] "Mentalism" refers to a method of understanding human psychological states and behaviors and providing advice that takes them into consideration.

[0329] This invention is a system for effectively supporting promotional activities in physical stores. Specific embodiments are described below.

[0330] System Configuration

[0331] This system consists of a user-operated terminal, a server for processing data, a generative AI, and an emotion engine. Users use the terminal to input specific improvement requests and receive advice based on those requests.

[0332] Receiving user input and sentiment data

[0333] Users access the system via devices such as smartphones and submit requests for improvements to promotional tools. These requests include information about the promotional tools currently in use, areas for improvement, and customer feedback. Furthermore, the device collects emotional data such as the user's facial expressions and tone of voice, and sends it to the server.

[0334] Data processing and transmission

[0335] The server verifies and formats the request data and emotion data received from the terminal and passes them to the generative AI. At this time, the consistency of the data format and the inclusion of necessary information are checked. The emotion engine analyzes the emotion data to identify the user's current emotional state and reflects this in the generative AI's analysis.

[0336] Analysis and advice generation using generative AI.

[0337] The generative AI analyzes request data and the user's emotional state to generate optimal improvement advice. Based on the request content and emotional data, the generative AI considers color selection, wording, and mentalism elements to provide specific advice. Information from the emotional engine is used to adjust the generated advice to best suit the user's emotional state.

[0338] Formatting and sending advice

[0339] The server re-examines the advice received from the generative AI and formats it into a user-friendly format. Specifically, it tags the advice so that the necessary information can be understood at a glance. Emotional data provided by the emotion engine is also taken into consideration.

[0340] Presentation to the user

[0341] The terminal displays advice sent from the server to the user through the user interface. Because this advice is optimized for the user's emotional state, the user can use the provided advice to improve their promotional tools and conduct optimal promotional activities.

[0342] Hardware and software

[0343] In addition to smartphones, tablet PCs and desktop PCs can also be used as terminals. The emotion engine includes a camera used for facial recognition and a microphone used for speech recognition. For generative AI, a natural language processing model using, for example, the Transformers library is used.

[0344] Explanation of specific examples

[0345] For example, if a user (sales staff) enters "I want to improve the promotional pop-up for the new washing machine. I'd especially like advice on the colors and wording":

[0346] 1. The user enters a request into their device: "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording."

[0347] 2. Along with this request, the device collects emotional data from the user's voice and facial expressions.

[0348] 3. The server passes the received request data and emotion data to the generative AI.

[0349] 4. The generative AI analyzes the request and emotional data to generate advice such as, "Use blue for the background color based on the user's preferences and emotional state," and "Use 'Amazing cleaning power, limited-time sale' as the tagline."

[0350] 5. The server receives the generated advice, formats it, and sends it to the terminal in a way that takes the user's emotional state into consideration.

[0351] 6. The device displays formatted advice to the user through the user interface.

[0352] As an example of a prompt statement,

[0353] Request: I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording. User sentiment: Happy. Please generate the best advice.

[0354] These are some examples.

[0355] This system allows users to create and improve effective promotional tools optimized for emotional states, with the support of generative AI and an emotion engine. This enables more efficient and standardized promotional activities, as well as more effective promotional activities based on customer psychology.

[0356] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0357] Step 1:

[0358] Users use a device to input improvement requests for promotional tools. These requests include information about the tool to be improved, customer feedback, and desired improvements. The device also collects emotional data, such as user voice and facial expressions.

[0359] Input: User improvement requests, sentiment data (voice, facial expressions)

[0360] Output: Improvement request data, sentiment data

[0361] Step 2:

[0362] The device sends the collected improvement request data and sentiment data to the server. The data format consistency and necessary information are verified.

[0363] Input: Improvement request data, sentiment data

[0364] Output: Request data sent to the server, sentiment data

[0365] Step 3:

[0366] The server verifies and formats the received request data and sentiment data, and then passes them to the generative AI. During data formatting, the sentiment data is converted into a format that can be processed by the generative AI.

[0367] Input: Request data, sentiment data (received by the server)

[0368] Output: Formatted request data and emotion data to be passed to the generative AI.

[0369] Step 4:

[0370] The generative AI analyzes formatted request data and sentiment data to generate optimal improvement advice. Specifically, it generates advice based on the request content and sentiment data, taking into account color selection, wording, and elements of mentalism.

[0371] Input: Formatted request data, sentiment data

[0372] Output: Generated improvement advice

[0373] Step 5:

[0374] The server re-examines the advice received from the generative AI and formats it into a format that the user can understand. Specifically, it tags the advice and formats it so that it can be understood at a glance.

[0375] Input: Advice from a generative AI

[0376] Output: User-friendly formatted advice

[0377] Step 6:

[0378] The terminal displays formatted advice sent from the server to the user through the user interface. Because the advice is optimized for the user's emotional state, the user can effectively improve their promotional tools.

[0379] Input: Formatted advice

[0380] Output: Advice displayed in the user interface

[0381] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0382] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0383] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0384] [Second Embodiment]

[0385] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0386] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0387] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0388] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0389] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0390] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0391] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0392] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0393] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0394] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0395] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0396] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0397] This invention is a system for effectively supporting sales promotion activities, and includes a process in which a user inputs a request into the system, and a generative AI provides advice based on that request. The specific form for implementing this system is described below.

[0398] System Configuration

[0399] This system consists of a user-operated terminal, a server for processing data, and a generative AI. The terminal receives improvement requests from the user through a user interface. The server passes the received request data to the generative AI, which then formats the generated advice and sends it back to the terminal. The generative AI is responsible for analysis and advice generation.

[0400] Program processing

[0401] Receiving user input

[0402] Users access the system through their terminal and input requests regarding the tools they are currently using, areas for improvement, and customer feedback. The terminal then formats this request data appropriately and sends it to the server.

[0403] Data processing and transmission

[0404] The server verifies and formats the request data received from the terminal and passes it to the generative AI. At this time, it checks for consistency in the data format and whether it contains the necessary information.

[0405] Analysis and advice generation using generative AI.

[0406] Generative AI analyzes request data and generates optimal improvement advice. Based on past data and training data, the generative AI considers the request content and generates specific advice that takes into account color selection, wording, and elements of mentalism.

[0407] Formatting and sending advice

[0408] The server verifies and formats the advice received from the generative AI, converting it into a user-friendly format. The formatted advice is then sent back to the terminal and presented to the user. Here, the advice is tagged to allow users to quickly understand the necessary information.

[0409] Explanation of specific examples

[0410] For example, if a user (crew member) enters, "I want to improve the advertising pop-up for the new washing machine. I'd especially like advice on the colors and wording," then:

[0411] 1. The user enters a request into the terminal stating, "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording."

[0412] 2. The device sends this request to the server.

[0413] 3. The server receives this request and passes the data to the generative AI.

[0414] 4. The generative AI analyzes the request and generates advice such as, "Use a vibrant blue for the background color," and "The tagline should be 'Amazing cleaning power, on sale for a limited time only.'"

[0415] 5. The server receives the generated advice, formats it, and sends it to the terminal.

[0416] 6. The device displays formatted advice to the user, who then uses it to improve their promotional tools.

[0417] This system allows salespeople to receive support from generative AI and achieve consistent results without relying on intuition. Furthermore, by receiving advice that incorporates elements of mentalism, more effective promotional activities based on customer psychology become possible.

[0418] The following describes the processing flow.

[0419] Step 1:

[0420] Users access the system through their devices and enter specific improvement requests. These requests include the tools currently being used, the points they want to improve, and customer feedback. For example, a user might enter, "I want to improve the advertising pop-up for our new washing machine. I'd especially like advice on the colors and wording."

[0421] Step 2:

[0422] The device sends the request received from the user to the server. Before sending, it verifies that the data is in the correct format and formats it as needed. The collected data may take the following format, for example: {'request_type': 'advice', 'product': 'washing machine', 'focus_areas': ['color', 'wording']}.

[0423] Step 3:

[0424] The server receives the request data from the terminal. The server performs a simple check on the request data to verify that the data format and required fields are correct.

[0425] Step 4:

[0426] The server prepares to pass data to the generative AI. It converts the data into a format suitable for the generative AI and formats it to include all the necessary information.

[0427] Step 5:

[0428] The generative AI analyzes data received from the server and generates improvement advice based on the user's request. The generative AI considers color selection, wording, and elements of mentalism to provide specific advice. For example, it might use a vibrant blue for the background color and generate advice such as "Amazing cleaning power, limited-time sale" for the tagline.

[0429] Step 6:

[0430] The server re-examines the advice received from the generative AI and formats it into a user-friendly format. Specifically, it tags the advice so that the necessary information can be understood at a glance.

[0431] Step 7:

[0432] The server sends formatted advice to the terminal. The data sent is appropriately formatted as a response to the user's request.

[0433] Step 8:

[0434] The terminal displays advice sent from the server to the user through the user interface. This allows the user to improve their promotional tools based on the advice provided.

[0435] This series of steps allows users to create and improve effective promotional tools using advice from generative AI, thereby streamlining and standardizing their promotional activities.

[0436] (Example 1)

[0437] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0438] In traditional sales promotion activities, obtaining advice necessary for improving promotional tools relied on human intuition and experience, requiring a great deal of trial and error to succeed. Furthermore, selecting effective designs and wording required specialized knowledge and experience, making it difficult for everyone to easily obtain optimal advice. This resulted in inconsistent sales promotion effectiveness and difficulties in adopting customer psychology-based approaches.

[0439] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0440] In this invention, the server includes means for receiving improvement requests from users, means for formatting the improvement requests into a data format, means for transmitting the formatted data to a generative AI, means for the generative AI to analyze the data and generate improvement advice, means for the server to tag and format the generated advice in a format that the user can understand, and means for providing the formatted advice to a terminal. This makes it possible for anyone to easily and effectively obtain improvement advice for promotional tools, enabling the realization of promotional activities with consistent results.

[0441] A "user" refers to an entity that uses the system to input improvement requests and receive advice.

[0442] A "request for improvement" refers to information that users enter regarding the system, including requests and suggestions for improvements.

[0443] A "server" refers to a computer device that receives requests from users, sends data to a generative AI, and formats the generated advice.

[0444] "Generative AI" refers to artificial intelligence models that analyze received data and generate specific improvement advice based on a particular algorithm.

[0445] "Analysis" refers to the process by which a generative AI analyzes information based on the data it receives and derives optimal advice.

[0446] "Improvement advice" refers to instructions and suggestions regarding improvements to promotional tools generated by a generative AI based on its analysis results.

[0447] "Formatting" refers to the process of converting data into a specific format to facilitate communication and processing.

[0448] "Formatting" refers to the process of converting generated advice into a format that is easy for the user to understand.

[0449] "Tagging" refers to the process of adding specific labels or metadata to generated advice to organize and classify information.

[0450] A "terminal" refers to a device that a user uses to access a system, enter requests, and receive advice.

[0451] "Mentalism" refers to elements based on customer psychology and behavior that generative AI considers during analysis.

[0452] "Data format" refers to a specific data structure or format used when exchanging data between systems.

[0453] This invention provides a system for effectively supporting sales promotion activities, in which the user provides requests through a terminal, and the server generates and provides appropriate improvement advice using generative AI. The following describes how to implement this system in detail. This system consists of a terminal operated by the user, a server that processes data, and generative AI that performs analysis and advice generation.

[0454] Users access the system using a terminal. The terminal is built using web technologies such as HTML, CSS, and JavaScript, and users input information such as the tools they are currently using, areas for improvement, and customer feedback through the interface. For example, a user might input, "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording."

[0455] The terminal formats this request into the appropriate JSON format and sends it to the server via an HTTP POST request. The server parses the received request data and verifies that there are no omissions or inconsistencies. The server uses programming languages ​​such as Python or Node.js to reshape the data and convert it into a format that can be parsed by generative AI.

[0456] The server sends the formatted data to a generative AI. The generative AI, such as OpenAI's GPT-3 model, analyzes the request based on past data and training data, and generates improvement advice. The generative AI also incorporates mentalism elements, considering customer psychology and behavior during the analysis process, to generate the advice.

[0457] The advice generated by the generative AI includes specific details such as "Use a vibrant blue for the background color" and "The tagline should be 'Amazing cleaning power, limited-time sale!'" The server receives this generated advice, tags it, and formats it to make it easier for users to understand. Finally, it sends the formatted advice to the device and provides it to the user.

[0458] This allows salespeople to improve promotional tools with the support of generative AI, enabling them to create consistently effective tools without relying on intuition. An example of a prompt is as follows:

[0459] "I'd like to improve the promotional pop-up for our new washing machine. I'd especially appreciate specific advice on the background color and catchphrase."

[0460] This system allows users to receive effective advice quickly and accurately, making it easy to improve their promotional activities.

[0461] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0462] Step 1:

[0463] Users submit improvement requests through their devices.

[0464] The user enters a specific request into the terminal interface, such as, "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording." The terminal formats this input data into JSON format. The input is natural language text, and the output is JSON data like the following:

[0465] json

[0466] {

[0467] "request_type": "promotion_improvement",

[0468] "details": {

[0469] "product": "washing_machine",

[0470] "focus_areas": ["color", "wording"]

[0471] }

[0472] }

[0473] The terminal validates the input and checks for formatting accuracy.

[0474] Step 2:

[0475] The terminal sends the formatted data to the server.

[0476] The terminal uses an HTTP POST request to send the above JSON data to the server. The input is the formatted JSON data, and the output is a success message sent to the server. Specifically, the terminal checks the network status before sending to ensure the data is transmitted correctly.

[0477] Step 3:

[0478] The server receives the data and formats it for analysis.

[0479] The server receives JSON data and formats it into a parseable format. The input is JSON data received from the terminal, and the output is further formatted in a way that is easier for generative AI to process. At this time, the server checks for data integrity and verifies that all necessary fields are present. For example, it formats the data as shown below.

[0480] json

[0481] {

[0482] "prompt": "Provide advice for improving the promotion of a new washing machine, focusing on color and wording."

[0483] }

[0484] The server logs the formatted data and manages the progress of the analysis.

[0485] Step 4:

[0486] The server sends the formatted data to the generative AI.

[0487] The server sends formatted data to a generative AI via an HTTP request. The generative AI uses, for example, OpenAI's GPT-3 model. The input is formatted data, and the output is advice including analysis results. The server adjusts the transmission process to minimize response delays.

[0488] Step 5:

[0489] The generative AI analyzes the request and generates improvement advice.

[0490] The generative AI analyzes the received request data and generates advice for improving promotional tools. For example, based on the request data, it generates specific advice such as "Use a vibrant blue for the background color" and "The catchphrase should be 'Amazing cleaning power, limited-time sale!'" The input is a prompt for analysis, and the output is text containing specific advice. The generative AI logs the process and records the analysis.

[0491] Step 6:

[0492] The server receives the generated advice and formats it.

[0493] The server verifies the advice received from the generative AI and formats it into a user-friendly format. For example, the generated advice is tagged as follows:

[0494] json

[0495] {

[0496] "advice": {

[0497] "color": "Vivid blue",

[0498] "Wording": "Amazing cleaning power, on sale for a limited time only"

[0499] }

[0500] }

[0501] The input is advice text from a generative AI, and the output is formatted advice data. The server logs the results of the formatting process.

[0502] Step 7:

[0503] The server sends formatted advice to the terminal.

[0504] The server sends the formatted advice to the terminal as an HTTP response in JSON format. The input is the formatted advice data, and the output is a message from the terminal confirming receipt. The server logs the message to confirm successful transmission.

[0505] Step 8:

[0506] The device provides advice to the user.

[0507] The terminal analyzes the advice data received from the server and displays it through a user-friendly interface. Specifically, the terminal uses appropriate UI components to set the background color and display catchphrases. The input is formatted advice data received from the server, and the output is specific advice displayed to the user. The user then uses this to improve their promotional tools.

[0508] Through the above processing steps, users can quickly obtain effective advice and consistently improve their promotional activities.

[0509] (Application Example 1)

[0510] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0511] In traditional promotional activities, users (store staff) had to rely on their own intuition to identify areas for improvement and provide advice, and the effectiveness of these methods was not always consistent. Furthermore, obtaining appropriate advice required significant time and resources, making rapid improvement difficult. Additionally, there was often a lack of concrete, immediately actionable advice, hindering the implementation of effective promotional activities based on customer psychology.

[0512] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0513] In this invention, the server includes means for receiving improvement requests from users, means for transmitting the improvement requests to an internet server, means for the internet server to pass data to a generative AI based on the improvement requests, means for the generative AI to analyze the data and generate improvement advice, means for the internet server to receive the generated advice and format it into a format that the user can understand, and means for a smart device to provide the formatted advice to the user. As a result, users can obtain specific advice that can be immediately put into practice in real time, enabling effective sales promotion activities.

[0514] A "user" refers to an individual or organization that operates the system and submits improvement requests.

[0515] An "Internet server" is a computer system that processes, transmits, and receives data over a network.

[0516] "Generative AI" is an artificial intelligence technology that analyzes request data and automatically generates optimal improvement advice.

[0517] A "smart device" is an advanced electronic device that is connected to the internet and can provide users with information in real time.

[0518] An "improvement request" is information that users enter into the system, indicating specific points that need improvement or their desired improvements.

[0519] "Means of data transfer" refers to methods or devices for transferring data between different components within a system.

[0520] "Analysis" is a computational process that involves understanding input data and extracting necessary information.

[0521] "Improvement advice" refers to specific suggestions and suggestions provided in response to submitted improvement requests.

[0522] "Shaping methods" refer to methods or devices for converting generated advice into a format that is easy for the user to understand.

[0523] "Means of providing" refers to methods or devices for informing and displaying the generated advice to the user.

[0524] This invention relates to a system for supporting sales promotion activities, and includes a process in which a user inputs a request into the system, and a generative AI provides advice based on that request.

[0525] System Configuration

[0526] This system consists of a user-operated smart device (such as smart glasses or a smartphone), an internet server that processes data, and a generative AI. The smart device receives improvement requests from the user through a user interface. The internet server passes the received request data to the generative AI, which then formats the generated advice and sends it back to the smart device. The generative AI is responsible for analysis and advice generation.

[0527] Program processing

[0528] Receiving user input

[0529] Users access the system via smart devices and input requests regarding the tools they are currently using, areas for improvement, and customer feedback. The smart device then formats this request data appropriately and sends it to the internet server.

[0530] Data processing and transmission

[0531] The internet server verifies and formats the request data received from the smart device and passes it to the generative AI. At this time, it checks for consistency in the data format and whether it contains the necessary information.

[0532] Analysis and advice generation using generative AI.

[0533] Generative AI analyzes request data and generates optimal improvement advice. Based on past data and training data, generative AI considers the request content and generates specific advice.

[0534] Formatting and sending advice

[0535] An internet server verifies and formats the advice received from the generative AI, converting it into a user-friendly format. The formatted advice is then sent back to the smart device and presented to the user. Here, the advice is tagged to allow users to quickly understand the necessary information.

[0536] Explanation of specific examples

[0537] For example, if a user (store staff) inputs via voice, "I'd like some advice on how to arrange the display of new products":

[0538] 1. The user enters a request into their smart device via voice, stating, "I would like advice on how to arrange the display for the new product."

[0539] 2. The smart device sends this request to an internet server.

[0540] 3. The internet server receives this request and passes the data to the generative AI.

[0541] 4. The generative AI analyzes the request and generates advice such as, "For displaying new products, it's best to place them at eye level and in a position where customers can easily pick them up. Also, consider placing POP displays around them to attract customers' attention."

[0542] 5. The internet server receives the generated advice, formats it, and sends it to the smart device.

[0543] 6. Smart devices display formatted advice to the user, who then uses it to improve their promotional tools.

[0544] This system allows store staff to implement effective promotional activities immediately with the support of generative AI. An example of a prompt message would be: "A salesperson has entered a request for advice on how to display new products. Please provide the best advice."

[0545] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0546] Step 1:

[0547] The user provides voice input to a smart device. For example, the user might voice a request such as, "I'd like advice on how to arrange the display for the new product." The smart device converts this voice data into text data and formats it. Converting voice data (input) to text data (output) enables subsequent processing.

[0548] Step 2:

[0549] The terminal sends the converted text data to the internet server. The terminal's sending function is used to upload the request data to the internet server. Text data is given as input, and the output is the data sent to the internet server.

[0550] Step 3:

[0551] The internet server verifies and formats the received request data. The server checks the integrity of the request data and verifies that there are no errors. Next, it formats it into a format that is easy for the generative AI to process. The request data is given as input, and the formatted data is passed to the generative AI as output.

[0552] Step 4:

[0553] The internet server passes formatted data to the generative AI. The server's internal data transfer function is used to send the formatted data to the generative AI. Formatted data is received as input, and data passed to the generative AI is obtained as output.

[0554] Step 5:

[0555] The generative AI analyzes the request data and generates improvement advice. The generative AI uses historical and training data to analyze the request and generate specific improvement advice. Formatted request data is given as input, and the generated advice is returned as output.

[0556] Step 6:

[0557] An internet server verifies and formats the advice received from a generative AI. The server checks whether the advice is appropriate and formats it into a user-friendly format. The generated advice is given as input, and the formatted advice is obtained as output.

[0558] Step 7:

[0559] An internet server sends formatted advice to a smart device. The server uses its transmission function to send the formatted advice to the smart device. Formatted advice is given as input, and data to be sent to the smart device is obtained as output.

[0560] Step 8:

[0561] A smart device displays formatted advice to the user. The smart device's display capabilities are used to visually present the advice to the user. Formatted advice is provided as input, and the output is the advice the user receives visually.

[0562] Each processing step in this system allows users to receive specific improvement advice based on their requests in real time.

[0563] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0564] This invention is a system for effectively supporting sales promotion activities, comprising a process in which a user inputs a request into the system, and a generative AI provides advice based on that request. The system further incorporates an emotion engine that recognizes the user's emotions and includes a method for optimizing requests and advice based on the user's emotions.

[0565] System Configuration

[0566] This system consists of a user-operated terminal, a server for processing data, a generative AI, and an emotion engine. The terminal receives improvement requests from the user through a user interface and collects the user's emotional data. The server passes the received request data and emotional data to the generative AI, which then formats the generated advice and sends it back to the terminal. The generative AI is responsible for analysis and advice generation. The emotion engine analyzes the user's emotional state and adjusts the request content and advice based on that analysis.

[0567] Program processing

[0568] Receiving user input and sentiment data

[0569] Users access the system through a terminal and enter specific improvement requests. These requests include the tools currently being used, points to be improved, and customer feedback. Along with these requests, the terminal extracts emotional data from the user's voice and facial expressions and sends it to the server.

[0570] Data processing and transmission

[0571] The server verifies and formats the request data and emotion data received from the terminal and passes them to the generative AI. At this time, it checks for consistency in the data format and whether the necessary information is included. The emotion engine analyzes the emotion data to identify the user's current emotional state and reflects this in the generative AI's analysis.

[0572] Analysis and advice generation using generative AI.

[0573] The generative AI analyzes request data and the user's emotional state to generate optimal improvement advice. Based on the request content and emotional data, the generative AI considers color selection, wording, and mentalism elements to provide specific advice. Information from the emotional engine is used to adjust the generated advice to best suit the user's emotional state.

[0574] Formatting and sending advice

[0575] The server re-examines the advice received from the generative AI and formats it into a user-friendly format. Specifically, it tags the advice so that the necessary information can be understood at a glance. Emotional data provided by the emotion engine is also taken into consideration.

[0576] Presentation to the user

[0577] The terminal displays advice sent from the server to the user through the user interface. Because this advice is optimized for the user's emotional state, the user can use the provided advice to improve their promotional tools and conduct optimal promotional activities.

[0578] Explanation of specific examples

[0579] For example, if a user (crew member) enters, "I want to improve the advertising pop-up for the new washing machine. I'd especially like advice on the colors and wording," then:

[0580] 1. The user enters a request into their device: "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording."

[0581] 2. Along with this request, the device collects emotional data from the user's voice and facial expressions.

[0582] 3. The server passes the received request data and emotion data to the generative AI.

[0583] 4. The generative AI analyzes the request and emotional data to generate advice such as, "Use blue for the background color based on the user's preferences and emotional state," and "Use 'Amazing cleaning power, limited-time sale' as the tagline."

[0584] 5. The server receives the generated advice, formats it, and sends it to the terminal in a way that takes the user's emotional state into consideration.

[0585] 6. The device displays formatted advice to the user through the user interface.

[0586] This system allows users to create and improve effective promotional tools optimized for emotional states, with the support of generative AI and an emotion engine. This enables more efficient and standardized promotional activities, as well as more effective promotional activities based on customer psychology.

[0587] The following describes the processing flow.

[0588] Step 1:

[0589] Users access the system through their terminals and input requests detailing the tools they are currently using, areas for improvement, and specific requests. For example, a user might input, "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording."

[0590] Step 2:

[0591] Before the device sends a request received from the user to the server, it collects the user's voice and facial expression data and extracts emotional data with the help of an emotion engine. The extracted emotional data represents emotional states such as "excitement," "anxiety," and "joy."

[0592] Step 3:

[0593] The device sends request data and emotion data to the server. The data is formatted in an appropriate format, for example, {'request_type': 'advice', 'product': 'washing machine', 'focus_areas': ['color', 'wording'], 'emotion': 'excitement'}.

[0594] Step 4:

[0595] The server checks and verifies the request data and sentiment data received from the terminal. It verifies that the data format is correct, that required fields are included, and formats the data as needed.

[0596] Step 5:

[0597] The server prepares to pass data to the generative AI. Here, the request data and sentiment data are converted into the appropriate format so that the generative AI can use it for analysis.

[0598] Step 6:

[0599] The generative AI analyzes request data and sentiment data received from the server to generate optimal improvement advice. Based on past data and training data, the AI ​​considers the request content and provides advice that is most appropriate to the user's emotional state. For example, it might generate advice such as, "Use blue for the background color, based on the user's preferences and emotional state," or "Use the tagline, 'Amazing cleaning power, limited-time sale!'"

[0600] Step 7:

[0601] The server re-examines the advice received from the generative AI and formats it into a format that is easy for the user to understand. During this process, information from the emotion engine is also taken into consideration, and advice is provided in a way that matches the user's emotional state.

[0602] Step 8:

[0603] The server sends formatted advice to the terminal. The transmitted data is appropriately formatted as the best response to the user's request. For example, it might be formatted as follows: "Background color: Blue" "Catchphrase: Amazing cleaning power, limited time sale."

[0604] Step 9:

[0605] The terminal displays formatted advice received from the server to the user through the user interface. This allows the user to improve promotional tools based on advice optimized for their emotional state, thereby achieving more effective promotional activities.

[0606] Through this series of steps, users can create and improve effective promotional tools optimized for emotional states, with the support of generative AI and emotion engines, thereby achieving greater efficiency and standardization in their promotional activities.

[0607] (Example 2)

[0608] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0609] In modern marketing activities, providing accurate advice that takes user emotions into account is crucial, but the lack of an effective system for this means that advice may not always be tailored to the user's emotional state. Traditional systems have struggled to utilize user emotional data, resulting in sometimes inappropriate advice. Furthermore, the complex data shaping and analysis processes required to effectively use generative AI models have sometimes reduced the overall efficiency of the system.

[0610] The specific processing performed 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 means for receiving improvement requests and user emotion data from the user, means for verifying the improvement requests and emotion data and passing the data to a generative AI, means for the generative AI to analyze the data and generate improvement advice, and means for the server to verify and format the generated advice. This makes it possible to provide appropriate advice optimized for the user's emotional state.

[0611] A "user" is a person who operates the system, inputs improvement requests, and receives advice from the system.

[0612] An "improvement request" is a request or wish that a user enters into the system regarding improvements to promotional activities or various tools.

[0613] "Emotional data" refers to data extracted from the user's voice, facial expressions, etc., that indicates the user's current emotional state.

[0614] A "terminal" is a device equipped with a user interface that allows a user to access a system. Examples include computers and smartphones.

[0615] A "server" is a central processing unit that receives, verifies, and formats request data and sentiment data, and works in conjunction with generative AI to generate advice.

[0616] "Generative AI" refers to artificial intelligence that analyzes received data and generates advice based on user requests.

[0617] "Improvement advice" refers to specific suggestions for improvements and enhancements in response to user requests, generated by a generative AI based on its analysis results.

[0618] An "emotion engine" is a function or software that analyzes user emotional data and incorporates the results into the analysis of generative AI.

[0619] "Verifying and formatting data" is the process of checking and adjusting the format and content of received data so that it can be properly analyzed by generative AI.

[0620] A "user interface" is an interface that allows a user to interact with a system, and includes devices such as screens and input devices.

[0621] Modes for carrying out the invention

[0622] This invention is a system that analyzes improvement requests regarding promotional activities entered by users and provides optimal advice based on the user's emotional state. This system is primarily implemented using the following hardware and software:

[0623] The device operated by the user

[0624] The system receives improvement requests from users through the user interface and collects user sentiment data.

[0625] Facial recognition camera, microphone, and speech analysis software (e.g., OpenCV, Google Cloud Speech-to-Text API)

[0626] server

[0627] The received request data and sentiment data are verified and formatted, then passed on to the generative AI.

[0628] JSON schema validation tool for verifying data format integrity

[0629] Emotion engine (e.g., Microsoft Azure Emotion API)

[0630] Generative AI

[0631] Responsible for analysis and generating advice.

[0632] AI models (e.g., OpenAI GPT-4)

[0633] Emotional Engine

[0634] The system analyzes the user's emotional state and adjusts the content of requests and advice accordingly.

[0635] Specific operation of the system

[0636] Users access the system through a terminal and input specific improvement requests, such as, "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording." Along with these requests, the terminal collects emotional data from the user's voice and facial expressions. For example, it captures the user's facial expressions with a camera and records their voice with a microphone. Then, it extracts emotional data using voice analysis algorithms and facial recognition software.

[0637] The server verifies and formats the request data and sentiment data received from the terminal. It checks for consistency in the data format and verifies that the necessary information is included. The verified data is converted to JSON format and sent as a prompt to the generative AI. At this point, the sentiment engine analyzes the sentiment data and reflects the results in the prompt.

[0638] Generative AI generates optimal improvement advice based on user request data and sentiment data. For example, it might generate specific advice such as "Use blue for the background color" or "Use the tagline 'Amazing cleaning power, limited-time sale'." This advice is then adjusted based on sentiment data.

[0639] The server re-examines the advice received from the generative AI and formats it into a user-friendly format. The formatted advice is sent to the terminal, which displays it to the user through the user interface. Based on the provided advice, the user can improve their promotional tools and conduct optimal promotional activities.

[0640] Specific example

[0641] For example, if a user (store staff) enters "I want to improve the promotional pop-up for the new washing machine. I'd especially like advice on the colors and wording," then:

[0642] 1. The user enters a request into the device.

[0643] 2. The device collects emotional data from the user's voice and facial expressions along with the request.

[0644] 3. The server passes the received request data and emotion data to the generative AI.

[0645] 4. The generative AI analyzes the request and emotional data to generate advice such as, "Use blue for the background color based on the user's preferences and emotional state," and "Use 'Amazing cleaning power, limited-time sale' as the tagline."

[0646] 5. The server receives the generated advice, formats it, and sends it to the terminal in a way that takes the user's emotional state into consideration.

[0647] 6. The device displays formatted advice to the user through the user interface.

[0648] This system allows users to create and improve effective promotional tools optimized for emotional states, with the support of generative AI and an emotion engine. This enables more efficient and standardized promotional activities, as well as more effective promotional efforts based on customer psychology.

[0649] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0650] Step 1: User request input and collection of sentiment data

[0651] The user operates a terminal to access the system and enter a specific improvement request. For example, they might enter, "I want to improve the advertising pop-up for the new washing machine. I'd especially like advice on the colors and wording." The terminal receives this request as text data. The terminal also uses its camera and microphone to collect the user's voice and facial expression data. Facial recognition software and voice analysis algorithms are used to extract emotional data from the voice and facial expression data.

[0652] Input: User request (text), audio data, facial expression data

[0653] Output: Request data (text), sentiment data

[0654] Step 2: Server-based data validation and sentiment analysis

[0655] Request data and sentiment data sent from the device reach the server. The server first verifies the integrity of the data format and confirms that all necessary information is included. Next, it passes the sentiment data to an emotion engine (e.g., Microsoft Azure Emotion API) for analysis to identify the user's current sentiment state. Finally, it generates a dataset that integrates the request data and analysis results.

[0656] Input: Request data (text), sentiment data

[0657] Output: Integrated dataset (request data, sentiment analysis results)

[0658] Step 3: Analysis and advice generation using generative AI

[0659] The server sends the integrated dataset to a generative AI (e.g., OpenAI GPT-4) as a prompt for advice generation. The generative AI analyzes the prompt and generates optimal improvement advice based on the request content and sentiment data. For example, it might generate specific advice such as "Use blue for the background color" and "Use the tagline 'Amazing cleaning power, limited-time sale'." This advice is returned to the server in JSON format.

[0660] Input: Integrated dataset (request data, sentiment analysis results)

[0661] Output: Generated advice (JSON format)

[0662] Step 4: Server-side formatting and re-verification of advice

[0663] The server re-verifies the advice received from the generative AI to check for any flaws. If there are no flaws, it formats the advice into a user-friendly format, for example, by adding tags, paragraphs, and bullet points. It also adjusts the tone and emphasis of the advice based on sentiment analysis results. The formatted advice is finally sent to the device.

[0664] Input: Generated advice (JSON format)

[0665] Output: Formatted advice (HTML or other format)

[0666] Step 5: Providing advice to the user via the device.

[0667] The device receives formatted advice sent from the server. The advice is displayed to the user through the user interface. When displayed, the color, font, and layout of the advice are adjusted based on the sentiment analysis results. The notification panel displays, "Blue is a good background color. Use 'Amazing cleaning power, limited time sale' as the tagline."

[0668] Input: Formatted advice (HTML or other format)

[0669] Output: Advice presented to the user (visual display)

[0670] (Application Example 2)

[0671] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0672] To maximize the effectiveness of promotional tools in physical stores, it is necessary to accurately reflect customers' emotions and potential needs. However, it is difficult for sales staff and store managers to instantly analyze the emotions of individual customers and determine the optimal promotional strategy based on that analysis. Traditional promotional systems have struggled to provide personalized advice that takes into account the emotional state of individual customers, resulting in limited effectiveness of promotional activities.

[0673] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0674] In this invention, the server includes means for receiving improvement requests from users, means for passing data to a generative AI, and means including a generative AI that generates improvement advice based on the user's emotional state. This makes it possible to analyze the emotional state of individual customers in real time and provide optimal sales promotion advice based on that analysis.

[0675] "User" refers to an individual or organization that uses this system.

[0676] An "improvement request" refers to specific requests or instructions from users for improvements to promotional tools.

[0677] A "server" refers to a device that receives improvement requests from users, passes the data to a generative AI, and then reshapes the generated advice before providing it to the user.

[0678] "Generative AI" refers to an artificial intelligence system that analyzes data received from users and generates optimal improvement advice.

[0679] An "emotion engine" refers to a system that analyzes a user's emotional state from their facial expressions and voice, and provides that information to a generative AI.

[0680] A "terminal" is a device used by a user to operate something, and refers to a device used for inputting requests and receiving and displaying advice.

[0681] "Mentalism" refers to a method of understanding human psychological states and behaviors and providing advice that takes them into consideration.

[0682] This invention is a system for effectively supporting promotional activities in physical stores. Specific embodiments are described below.

[0683] System Configuration

[0684] This system consists of a user-operated terminal, a server for processing data, a generative AI, and an emotion engine. Users use the terminal to input specific improvement requests and receive advice based on those requests.

[0685] Receiving user input and sentiment data

[0686] Users access the system via devices such as smartphones and submit requests for improvements to promotional tools. These requests include information about the promotional tools currently in use, areas for improvement, and customer feedback. Furthermore, the device collects emotional data such as the user's facial expressions and tone of voice, and sends it to the server.

[0687] Data processing and transmission

[0688] The server verifies and formats the request data and emotion data received from the terminal and passes them to the generative AI. At this time, the consistency of the data format and the inclusion of necessary information are checked. The emotion engine analyzes the emotion data to identify the user's current emotional state and reflects this in the generative AI's analysis.

[0689] Analysis and advice generation using generative AI.

[0690] The generative AI analyzes request data and the user's emotional state to generate optimal improvement advice. Based on the request content and emotional data, the generative AI considers color selection, wording, and mentalism elements to provide specific advice. Information from the emotional engine is used to adjust the generated advice to best suit the user's emotional state.

[0691] Formatting and sending advice

[0692] The server re-examines the advice received from the generative AI and formats it into a user-friendly format. Specifically, it tags the advice so that the necessary information can be understood at a glance. Emotional data provided by the emotion engine is also taken into consideration.

[0693] Presentation to the user

[0694] The terminal displays advice sent from the server to the user through the user interface. Because this advice is optimized for the user's emotional state, the user can use the provided advice to improve their promotional tools and conduct optimal promotional activities.

[0695] Hardware and software

[0696] In addition to smartphones, tablet PCs and desktop PCs can also be used as terminals. The emotion engine includes a camera used for facial recognition and a microphone used for speech recognition. For generative AI, a natural language processing model using, for example, the Transformers library is used.

[0697] Explanation of specific examples

[0698] For example, if a user (sales staff) enters "I want to improve the promotional pop-up for the new washing machine. I'd especially like advice on the colors and wording":

[0699] 1. The user enters a request into their device: "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording."

[0700] 2. Along with this request, the device collects emotional data from the user's voice and facial expressions.

[0701] 3. The server passes the received request data and emotion data to the generative AI.

[0702] 4. The generative AI analyzes the request and emotional data to generate advice such as, "Use blue for the background color based on the user's preferences and emotional state," and "Use 'Amazing cleaning power, limited-time sale' as the tagline."

[0703] 5. The server receives the generated advice, formats it, and sends it to the terminal in a way that takes the user's emotional state into consideration.

[0704] 6. The device displays formatted advice to the user through the user interface.

[0705] As an example of a prompt statement,

[0706] Request: I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording. User sentiment: Happy. Please generate the best advice.

[0707] These are some examples.

[0708] This system allows users to create and improve effective promotional tools optimized for emotional states, with the support of generative AI and an emotion engine. This enables more efficient and standardized promotional activities, as well as more effective promotional activities based on customer psychology.

[0709] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0710] Step 1:

[0711] Users use a device to input improvement requests for promotional tools. These requests include information about the tool to be improved, customer feedback, and desired improvements. The device also collects emotional data, such as user voice and facial expressions.

[0712] Input: User improvement requests, sentiment data (voice, facial expressions)

[0713] Output: Improvement request data, sentiment data

[0714] Step 2:

[0715] The device sends the collected improvement request data and sentiment data to the server. The data format consistency and necessary information are verified.

[0716] Input: Improvement request data, sentiment data

[0717] Output: Request data sent to the server, sentiment data

[0718] Step 3:

[0719] The server verifies and formats the received request data and sentiment data, and then passes them to the generative AI. During data formatting, the sentiment data is converted into a format that can be processed by the generative AI.

[0720] Input: Request data, sentiment data (received by the server)

[0721] Output: Formatted request data and emotion data to be passed to the generative AI.

[0722] Step 4:

[0723] The generative AI analyzes formatted request data and sentiment data to generate optimal improvement advice. Specifically, it generates advice based on the request content and sentiment data, taking into account color selection, wording, and elements of mentalism.

[0724] Input: Formatted request data, sentiment data

[0725] Output: Generated improvement advice

[0726] Step 5:

[0727] The server re-examines the advice received from the generative AI and formats it into a format that the user can understand. Specifically, it tags the advice and formats it so that it can be understood at a glance.

[0728] Input: Advice from a generative AI

[0729] Output: User-friendly formatted advice

[0730] Step 6:

[0731] The terminal displays formatted advice sent from the server to the user through the user interface. Because the advice is optimized for the user's emotional state, the user can effectively improve their promotional tools.

[0732] Input: Formatted advice

[0733] Output: Advice displayed in the user interface

[0734] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0735] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0736] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0737] [Third Embodiment]

[0738] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0739] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0740] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0741] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0742] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0743] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0744] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0745] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0746] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0747] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0748] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0749] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0750] This invention is a system for effectively supporting sales promotion activities, and includes a process in which a user inputs a request into the system, and a generative AI provides advice based on that request. The specific form for implementing this system is described below.

[0751] System Configuration

[0752] This system consists of a user-operated terminal, a server for processing data, and a generative AI. The terminal receives improvement requests from the user through a user interface. The server passes the received request data to the generative AI, which then formats the generated advice and sends it back to the terminal. The generative AI is responsible for analysis and advice generation.

[0753] Program processing

[0754] Receiving user input

[0755] Users access the system through their terminal and input requests regarding the tools they are currently using, areas for improvement, and customer feedback. The terminal then formats this request data appropriately and sends it to the server.

[0756] Data processing and transmission

[0757] The server verifies and formats the request data received from the terminal and passes it to the generative AI. At this time, it checks for consistency in the data format and whether it contains the necessary information.

[0758] Analysis and advice generation using generative AI.

[0759] Generative AI analyzes request data and generates optimal improvement advice. Based on past data and training data, the generative AI considers the request content and generates specific advice that takes into account color selection, wording, and elements of mentalism.

[0760] Formatting and sending advice

[0761] The server verifies and formats the advice received from the generative AI, converting it into a user-friendly format. The formatted advice is then sent back to the terminal and presented to the user. Here, the advice is tagged to allow users to quickly understand the necessary information.

[0762] Explanation of specific examples

[0763] For example, if a user (crew member) enters, "I want to improve the advertising pop-up for the new washing machine. I'd especially like advice on the colors and wording," then:

[0764] 1. The user enters a request into the terminal stating, "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording."

[0765] 2. The device sends this request to the server.

[0766] 3. The server receives this request and passes the data to the generative AI.

[0767] 4. The generative AI analyzes the request and generates advice such as, "Use a vibrant blue for the background color," and "The tagline should be 'Amazing cleaning power, on sale for a limited time only.'"

[0768] 5. The server receives the generated advice, formats it, and sends it to the terminal.

[0769] 6. The device displays formatted advice to the user, who then uses it to improve their promotional tools.

[0770] This system allows salespeople to receive support from generative AI and achieve consistent results without relying on intuition. Furthermore, by receiving advice that incorporates elements of mentalism, more effective promotional activities based on customer psychology become possible.

[0771] The following describes the processing flow.

[0772] Step 1:

[0773] Users access the system through their devices and enter specific improvement requests. These requests include the tools currently being used, the points they want to improve, and customer feedback. For example, a user might enter, "I want to improve the advertising pop-up for our new washing machine. I'd especially like advice on the colors and wording."

[0774] Step 2:

[0775] The device sends the request received from the user to the server. Before sending, it verifies that the data is in the correct format and formats it as needed. The collected data may take the following format, for example: {'request_type': 'advice', 'product': 'washing machine', 'focus_areas': ['color', 'wording']}.

[0776] Step 3:

[0777] The server receives the request data from the terminal. The server performs a simple check on the request data to verify that the data format and required fields are correct.

[0778] Step 4:

[0779] The server prepares to pass data to the generative AI. It converts the data into a format suitable for the generative AI and formats it to include all the necessary information.

[0780] Step 5:

[0781] The generative AI analyzes data received from the server and generates improvement advice based on the user's request. The generative AI considers color selection, wording, and elements of mentalism to provide specific advice. For example, it might use a vibrant blue for the background color and generate advice such as "Amazing cleaning power, limited-time sale" for the tagline.

[0782] Step 6:

[0783] The server re-examines the advice received from the generative AI and formats it into a user-friendly format. Specifically, it tags the advice so that the necessary information can be understood at a glance.

[0784] Step 7:

[0785] The server sends formatted advice to the terminal. The data sent is appropriately formatted as a response to the user's request.

[0786] Step 8:

[0787] The terminal displays advice sent from the server to the user through the user interface. This allows the user to improve their promotional tools based on the advice provided.

[0788] This series of steps allows users to create and improve effective promotional tools using advice from generative AI, thereby streamlining and standardizing their promotional activities.

[0789] (Example 1)

[0790] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0791] In traditional sales promotion activities, obtaining advice necessary for improving promotional tools relied on human intuition and experience, requiring a great deal of trial and error to succeed. Furthermore, selecting effective designs and wording required specialized knowledge and experience, making it difficult for everyone to easily obtain optimal advice. This resulted in inconsistent sales promotion effectiveness and difficulties in adopting customer psychology-based approaches.

[0792] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0793] In this invention, the server includes means for receiving improvement requests from users, means for formatting the improvement requests into a data format, means for transmitting the formatted data to a generative AI, means for the generative AI to analyze the data and generate improvement advice, means for the server to tag and format the generated advice in a format that the user can understand, and means for providing the formatted advice to a terminal. This makes it possible for anyone to easily and effectively obtain improvement advice for promotional tools, enabling the realization of promotional activities with consistent results.

[0794] A "user" refers to an entity that uses the system to input improvement requests and receive advice.

[0795] A "request for improvement" refers to information that users enter regarding the system, including requests and suggestions for improvements.

[0796] A "server" refers to a computer device that receives requests from users, sends data to a generative AI, and formats the generated advice.

[0797] "Generative AI" refers to artificial intelligence models that analyze received data and generate specific improvement advice based on a particular algorithm.

[0798] "Analysis" refers to the process by which a generative AI analyzes information based on the data it receives and derives optimal advice.

[0799] "Improvement advice" refers to instructions and suggestions regarding improvements to promotional tools generated by a generative AI based on its analysis results.

[0800] "Formatting" refers to the process of converting data into a specific format to facilitate communication and processing.

[0801] "Formatting" refers to the process of converting generated advice into a format that is easy for the user to understand.

[0802] "Tagging" refers to the process of adding specific labels or metadata to generated advice to organize and classify information.

[0803] A "terminal" refers to a device that a user uses to access a system, enter requests, and receive advice.

[0804] "Mentalism" refers to elements based on customer psychology and behavior that generative AI considers during analysis.

[0805] "Data format" refers to a specific data structure or format used when exchanging data between systems.

[0806] This invention provides a system for effectively supporting sales promotion activities, in which the user provides requests through a terminal, and the server generates and provides appropriate improvement advice using generative AI. The following describes how to implement this system in detail. This system consists of a terminal operated by the user, a server that processes data, and generative AI that performs analysis and advice generation.

[0807] Users access the system using a terminal. The terminal is built using web technologies such as HTML, CSS, and JavaScript, and users input information such as the tools they are currently using, areas for improvement, and customer feedback through the interface. For example, a user might input, "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording."

[0808] The terminal formats this request into the appropriate JSON format and sends it to the server via an HTTP POST request. The server parses the received request data and verifies that there are no omissions or inconsistencies. The server uses programming languages ​​such as Python or Node.js to reshape the data and convert it into a format that can be parsed by generative AI.

[0809] The server sends the formatted data to a generative AI. The generative AI, such as OpenAI's GPT-3 model, analyzes the request based on past data and training data, and generates improvement advice. The generative AI also incorporates mentalism elements, considering customer psychology and behavior during the analysis process, to generate the advice.

[0810] The advice generated by the generative AI includes specific details such as "Use a vibrant blue for the background color" and "The tagline should be 'Amazing cleaning power, limited-time sale!'" The server receives this generated advice, tags it, and formats it to make it easier for users to understand. Finally, it sends the formatted advice to the device and provides it to the user.

[0811] This allows salespeople to improve promotional tools with the support of generative AI, enabling them to create consistently effective tools without relying on intuition. An example of a prompt is as follows:

[0812] "I'd like to improve the promotional pop-up for our new washing machine. I'd especially appreciate specific advice on the background color and catchphrase."

[0813] This system allows users to receive effective advice quickly and accurately, making it easy to improve their promotional activities.

[0814] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0815] Step 1:

[0816] Users submit improvement requests through their devices.

[0817] The user enters a specific request into the terminal interface, such as, "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording." The terminal formats this input data into JSON format. The input is natural language text, and the output is JSON data like the following:

[0818] json

[0819] {

[0820] "request_type": "promotion_improvement",

[0821] "details": {

[0822] "product": "washing_machine",

[0823] "focus_areas": ["color", "wording"]

[0824] }

[0825] }

[0826] The terminal validates the input and checks for formatting accuracy.

[0827] Step 2:

[0828] The terminal sends the formatted data to the server.

[0829] The terminal uses an HTTP POST request to send the above JSON data to the server. The input is the formatted JSON data, and the output is a success message sent to the server. Specifically, the terminal checks the network status before sending to ensure the data is transmitted correctly.

[0830] Step 3:

[0831] The server receives the data and formats it for analysis.

[0832] The server receives JSON data and formats it into a parseable format. The input is JSON data received from the terminal, and the output is further formatted in a way that is easier for generative AI to process. At this time, the server checks for data integrity and verifies that all necessary fields are present. For example, it formats the data as shown below.

[0833] json

[0834] {

[0835] "prompt": "Provide advice for improving the promotion of a new washing machine, focusing on color and wording."

[0836] }

[0837] The server logs the formatted data and manages the progress of the analysis.

[0838] Step 4:

[0839] The server sends the formatted data to the generative AI.

[0840] The server sends formatted data to a generative AI via an HTTP request. The generative AI uses, for example, OpenAI's GPT-3 model. The input is formatted data, and the output is advice including analysis results. The server adjusts the transmission process to minimize response delays.

[0841] Step 5:

[0842] The generative AI analyzes the request and generates improvement advice.

[0843] The generative AI analyzes the received request data and generates advice for improving promotional tools. For example, based on the request data, it generates specific advice such as "Use a vibrant blue for the background color" and "The catchphrase should be 'Amazing cleaning power, limited-time sale!'" The input is a prompt for analysis, and the output is text containing specific advice. The generative AI logs the process and records the analysis.

[0844] Step 6:

[0845] The server receives the generated advice and formats it.

[0846] The server verifies the advice received from the generative AI and formats it into a user-friendly format. For example, the generated advice is tagged as follows:

[0847] json

[0848] {

[0849] "advice": {

[0850] "color": "Vivid blue",

[0851] "Wording": "Amazing cleaning power, on sale for a limited time only"

[0852] }

[0853] }

[0854] The input is advice text from a generative AI, and the output is formatted advice data. The server logs the results of the formatting process.

[0855] Step 7:

[0856] The server sends formatted advice to the terminal.

[0857] The server sends the formatted advice to the terminal as an HTTP response in JSON format. The input is the formatted advice data, and the output is a message from the terminal confirming receipt. The server logs the message to confirm successful transmission.

[0858] Step 8:

[0859] The device provides advice to the user.

[0860] The terminal analyzes the advice data received from the server and displays it through a user-friendly interface. Specifically, the terminal uses appropriate UI components to set the background color and display catchphrases. The input is formatted advice data received from the server, and the output is specific advice displayed to the user. The user then uses this to improve their promotional tools.

[0861] Through the above processing steps, users can quickly obtain effective advice and consistently improve their promotional activities.

[0862] (Application Example 1)

[0863] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0864] In traditional promotional activities, users (store staff) had to rely on their own intuition to identify areas for improvement and provide advice, and the effectiveness of these methods was not always consistent. Furthermore, obtaining appropriate advice required significant time and resources, making rapid improvement difficult. Additionally, there was often a lack of concrete, immediately actionable advice, hindering the implementation of effective promotional activities based on customer psychology.

[0865] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0866] In this invention, the server includes means for receiving improvement requests from users, means for transmitting the improvement requests to an internet server, means for the internet server to pass data to a generative AI based on the improvement requests, means for the generative AI to analyze the data and generate improvement advice, means for the internet server to receive the generated advice and format it into a format that the user can understand, and means for a smart device to provide the formatted advice to the user. As a result, users can obtain specific advice that can be immediately put into practice in real time, enabling effective sales promotion activities.

[0867] A "user" refers to an individual or organization that operates the system and submits improvement requests.

[0868] An "Internet server" is a computer system that processes, transmits, and receives data over a network.

[0869] "Generative AI" is an artificial intelligence technology that analyzes request data and automatically generates optimal improvement advice.

[0870] A "smart device" is an advanced electronic device that is connected to the internet and can provide users with information in real time.

[0871] An "improvement request" is information that users enter into the system, indicating specific points that need improvement or their desired improvements.

[0872] "Means of data transfer" refers to methods or devices for transferring data between different components within a system.

[0873] "Analysis" is a computational process that involves understanding input data and extracting necessary information.

[0874] "Improvement advice" refers to specific suggestions and suggestions provided in response to submitted improvement requests.

[0875] "Shaping methods" refer to methods or devices for converting generated advice into a format that is easy for the user to understand.

[0876] "Means of providing" refers to methods or devices for informing and displaying the generated advice to the user.

[0877] This invention relates to a system for supporting sales promotion activities, and includes a process in which a user inputs a request into the system, and a generative AI provides advice based on that request.

[0878] System Configuration

[0879] This system consists of a user-operated smart device (such as smart glasses or a smartphone), an internet server that processes data, and a generative AI. The smart device receives improvement requests from the user through a user interface. The internet server passes the received request data to the generative AI, which then formats the generated advice and sends it back to the smart device. The generative AI is responsible for analysis and advice generation.

[0880] Program processing

[0881] Receiving user input

[0882] Users access the system via smart devices and input requests regarding the tools they are currently using, areas for improvement, and customer feedback. The smart device then formats this request data appropriately and sends it to the internet server.

[0883] Data processing and transmission

[0884] The internet server verifies and formats the request data received from the smart device and passes it to the generative AI. At this time, it checks for consistency in the data format and whether it contains the necessary information.

[0885] Analysis and advice generation using generative AI.

[0886] Generative AI analyzes request data and generates optimal improvement advice. Based on past data and training data, generative AI considers the request content and generates specific advice.

[0887] Formatting and sending advice

[0888] An internet server verifies and formats the advice received from the generative AI, converting it into a user-friendly format. The formatted advice is then sent back to the smart device and presented to the user. Here, the advice is tagged to allow users to quickly understand the necessary information.

[0889] Explanation of specific examples

[0890] For example, if a user (store staff) inputs via voice, "I'd like some advice on how to arrange the display of new products":

[0891] 1. The user enters a request into their smart device via voice, stating, "I would like advice on how to arrange the display for the new product."

[0892] 2. The smart device sends this request to an internet server.

[0893] 3. The internet server receives this request and passes the data to the generative AI.

[0894] 4. The generative AI analyzes the request and generates advice such as, "For displaying new products, it's best to place them at eye level and in a position where customers can easily pick them up. Also, consider placing POP displays around them to attract customers' attention."

[0895] 5. The internet server receives the generated advice, formats it, and sends it to the smart device.

[0896] 6. Smart devices display formatted advice to the user, who then uses it to improve their promotional tools.

[0897] This system allows store staff to implement effective promotional activities immediately with the support of generative AI. An example of a prompt message would be: "A salesperson has entered a request for advice on how to display new products. Please provide the best advice."

[0898] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0899] Step 1:

[0900] The user provides voice input to a smart device. For example, the user might voice a request such as, "I'd like advice on how to arrange the display for the new product." The smart device converts this voice data into text data and formats it. Converting voice data (input) to text data (output) enables subsequent processing.

[0901] Step 2:

[0902] The terminal sends the converted text data to the internet server. The terminal's sending function is used to upload the request data to the internet server. Text data is given as input, and the output is the data sent to the internet server.

[0903] Step 3:

[0904] The internet server verifies and formats the received request data. The server checks the integrity of the request data and verifies that there are no errors. Next, it formats it into a format that is easy for the generative AI to process. The request data is given as input, and the formatted data is passed to the generative AI as output.

[0905] Step 4:

[0906] The internet server passes formatted data to the generative AI. The server's internal data transfer function is used to send the formatted data to the generative AI. Formatted data is received as input, and data passed to the generative AI is obtained as output.

[0907] Step 5:

[0908] The generative AI analyzes the request data and generates improvement advice. The generative AI uses historical and training data to analyze the request and generate specific improvement advice. Formatted request data is given as input, and the generated advice is returned as output.

[0909] Step 6:

[0910] An internet server verifies and formats the advice received from a generative AI. The server checks whether the advice is appropriate and formats it into a user-friendly format. The generated advice is given as input, and the formatted advice is obtained as output.

[0911] Step 7:

[0912] An internet server sends formatted advice to a smart device. The server uses its transmission function to send the formatted advice to the smart device. Formatted advice is given as input, and data to be sent to the smart device is obtained as output.

[0913] Step 8:

[0914] A smart device displays formatted advice to the user. The smart device's display capabilities are used to visually present the advice to the user. Formatted advice is provided as input, and the output is the advice the user receives visually.

[0915] Each processing step in this system allows users to receive specific improvement advice based on their requests in real time.

[0916] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0917] This invention is a system for effectively supporting sales promotion activities, comprising a process in which a user inputs a request into the system, and a generative AI provides advice based on that request. The system further incorporates an emotion engine that recognizes the user's emotions and includes a method for optimizing requests and advice based on the user's emotions.

[0918] System Configuration

[0919] This system consists of a user-operated terminal, a server for processing data, a generative AI, and an emotion engine. The terminal receives improvement requests from the user through a user interface and collects the user's emotional data. The server passes the received request data and emotional data to the generative AI, which then formats the generated advice and sends it back to the terminal. The generative AI is responsible for analysis and advice generation. The emotion engine analyzes the user's emotional state and adjusts the request content and advice based on that analysis.

[0920] Program processing

[0921] Receiving user input and sentiment data

[0922] Users access the system through a terminal and enter specific improvement requests. These requests include the tools currently being used, points to be improved, and customer feedback. Along with these requests, the terminal extracts emotional data from the user's voice and facial expressions and sends it to the server.

[0923] Data processing and transmission

[0924] The server verifies and formats the request data and emotion data received from the terminal and passes them to the generative AI. At this time, it checks for consistency in the data format and whether the necessary information is included. The emotion engine analyzes the emotion data to identify the user's current emotional state and reflects this in the generative AI's analysis.

[0925] Analysis and advice generation using generative AI.

[0926] The generative AI analyzes request data and the user's emotional state to generate optimal improvement advice. Based on the request content and emotional data, the generative AI considers color selection, wording, and mentalism elements to provide specific advice. Information from the emotional engine is used to adjust the generated advice to best suit the user's emotional state.

[0927] Formatting and sending advice

[0928] The server re-examines the advice received from the generative AI and formats it into a user-friendly format. Specifically, it tags the advice so that the necessary information can be understood at a glance. Emotional data provided by the emotion engine is also taken into consideration.

[0929] Presentation to the user

[0930] The terminal displays advice sent from the server to the user through the user interface. Because this advice is optimized for the user's emotional state, the user can use the provided advice to improve their promotional tools and conduct optimal promotional activities.

[0931] Explanation of specific examples

[0932] For example, if a user (crew member) enters, "I want to improve the advertising pop-up for the new washing machine. I'd especially like advice on the colors and wording," then:

[0933] 1. The user enters a request into their device: "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording."

[0934] 2. Along with this request, the device collects emotional data from the user's voice and facial expressions.

[0935] 3. The server passes the received request data and emotion data to the generative AI.

[0936] 4. The generative AI analyzes the request and emotional data to generate advice such as, "Use blue for the background color based on the user's preferences and emotional state," and "Use 'Amazing cleaning power, limited-time sale' as the tagline."

[0937] 5. The server receives the generated advice, formats it, and sends it to the terminal in a way that takes the user's emotional state into consideration.

[0938] 6. The device displays formatted advice to the user through the user interface.

[0939] This system allows users to create and improve effective promotional tools optimized for emotional states, with the support of generative AI and an emotion engine. This enables more efficient and standardized promotional activities, as well as more effective promotional activities based on customer psychology.

[0940] The following describes the processing flow.

[0941] Step 1:

[0942] Users access the system through their terminals and input requests detailing the tools they are currently using, areas for improvement, and specific requests. For example, a user might input, "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording."

[0943] Step 2:

[0944] Before the device sends a request received from the user to the server, it collects the user's voice and facial expression data and extracts emotional data with the help of an emotion engine. The extracted emotional data represents emotional states such as "excitement," "anxiety," and "joy."

[0945] Step 3:

[0946] The device sends request data and emotion data to the server. The data is formatted in an appropriate format, for example, {'request_type': 'advice', 'product': 'washing machine', 'focus_areas': ['color', 'wording'], 'emotion': 'excitement'}.

[0947] Step 4:

[0948] The server checks and verifies the request data and sentiment data received from the terminal. It verifies that the data format is correct, that required fields are included, and formats the data as needed.

[0949] Step 5:

[0950] The server prepares to pass data to the generative AI. Here, the request data and sentiment data are converted into the appropriate format so that the generative AI can use it for analysis.

[0951] Step 6:

[0952] The generative AI analyzes request data and sentiment data received from the server to generate optimal improvement advice. Based on past data and training data, the AI ​​considers the request content and provides advice that is most appropriate to the user's emotional state. For example, it might generate advice such as, "Use blue for the background color, based on the user's preferences and emotional state," or "Use the tagline, 'Amazing cleaning power, limited-time sale!'"

[0953] Step 7:

[0954] The server re-examines the advice received from the generative AI and formats it into a format that is easy for the user to understand. During this process, information from the emotion engine is also taken into consideration, and advice is provided in a way that matches the user's emotional state.

[0955] Step 8:

[0956] The server sends formatted advice to the terminal. The transmitted data is appropriately formatted as the best response to the user's request. For example, it might be formatted as follows: "Background color: Blue" "Catchphrase: Amazing cleaning power, limited time sale."

[0957] Step 9:

[0958] The terminal displays formatted advice received from the server to the user through the user interface. This allows the user to improve promotional tools based on advice optimized for their emotional state, thereby achieving more effective promotional activities.

[0959] Through this series of steps, users can create and improve effective promotional tools optimized for emotional states, with the support of generative AI and emotion engines, thereby achieving greater efficiency and standardization in their promotional activities.

[0960] (Example 2)

[0961] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0962] In modern marketing activities, providing accurate advice that takes user emotions into account is crucial, but the lack of an effective system for this means that advice may not always be tailored to the user's emotional state. Traditional systems have struggled to utilize user emotional data, resulting in sometimes inappropriate advice. Furthermore, the complex data shaping and analysis processes required to effectively use generative AI models have sometimes reduced the overall efficiency of the system.

[0963] The specific processing performed 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 means for receiving improvement requests and user emotion data from the user, means for verifying the improvement requests and emotion data and passing the data to a generative AI, means for the generative AI to analyze the data and generate improvement advice, and means for the server to verify and format the generated advice. This makes it possible to provide appropriate advice optimized for the user's emotional state.

[0964] A "user" is a person who operates the system, inputs improvement requests, and receives advice from the system.

[0965] An "improvement request" is a request or wish that a user enters into the system regarding improvements to promotional activities or various tools.

[0966] "Emotional data" refers to data extracted from the user's voice, facial expressions, etc., that indicates the user's current emotional state.

[0967] A "terminal" is a device equipped with a user interface that allows a user to access a system. Examples include computers and smartphones.

[0968] A "server" is a central processing unit that receives, verifies, and formats request data and sentiment data, and works in conjunction with generative AI to generate advice.

[0969] "Generative AI" refers to artificial intelligence that analyzes received data and generates advice based on user requests.

[0970] "Improvement advice" refers to specific suggestions for improvements and enhancements in response to user requests, generated by a generative AI based on its analysis results.

[0971] An "emotion engine" is a function or software that analyzes user emotional data and incorporates the results into the analysis of generative AI.

[0972] "Verifying and formatting data" is the process of checking and adjusting the format and content of received data so that it can be properly analyzed by generative AI.

[0973] A "user interface" is an interface that allows a user to interact with a system, and includes devices such as screens and input devices.

[0974] Modes for carrying out the invention

[0975] This invention is a system that analyzes improvement requests regarding promotional activities entered by users and provides optimal advice based on the user's emotional state. This system is primarily implemented using the following hardware and software:

[0976] The device operated by the user

[0977] The system receives improvement requests from users through the user interface and collects user sentiment data.

[0978] Facial recognition camera, microphone, and speech analysis software (e.g., OpenCV, Google Cloud Speech-to-Text API)

[0979] server

[0980] The received request data and sentiment data are verified and formatted, then passed on to the generative AI.

[0981] JSON schema validation tool for verifying data format integrity

[0982] Emotion engine (e.g., Microsoft Azure Emotion API)

[0983] Generative AI

[0984] Responsible for analysis and generating advice.

[0985] AI models (e.g., OpenAI GPT-4)

[0986] Emotional Engine

[0987] The system analyzes the user's emotional state and adjusts the content of requests and advice accordingly.

[0988] Specific operation of the system

[0989] Users access the system through a terminal and input specific improvement requests, such as, "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording." Along with these requests, the terminal collects emotional data from the user's voice and facial expressions. For example, it captures the user's facial expressions with a camera and records their voice with a microphone. Then, it extracts emotional data using voice analysis algorithms and facial recognition software.

[0990] The server verifies and formats the request data and sentiment data received from the terminal. It checks for consistency in the data format and verifies that the necessary information is included. The verified data is converted to JSON format and sent as a prompt to the generative AI. At this point, the sentiment engine analyzes the sentiment data and reflects the results in the prompt.

[0991] Generative AI generates optimal improvement advice based on user request data and sentiment data. For example, it might generate specific advice such as "Use blue for the background color" or "Use the tagline 'Amazing cleaning power, limited-time sale'." This advice is then adjusted based on sentiment data.

[0992] The server re-examines the advice received from the generative AI and formats it into a user-friendly format. The formatted advice is sent to the terminal, which displays it to the user through the user interface. Based on the provided advice, the user can improve their promotional tools and conduct optimal promotional activities.

[0993] Specific example

[0994] For example, if a user (store staff) enters "I want to improve the promotional pop-up for the new washing machine. I'd especially like advice on the colors and wording," then:

[0995] 1. The user enters a request into the device.

[0996] 2. The device collects emotional data from the user's voice and facial expressions along with the request.

[0997] 3. The server passes the received request data and emotion data to the generative AI.

[0998] 4. The generative AI analyzes the request and emotional data to generate advice such as, "Use blue for the background color based on the user's preferences and emotional state," and "Use 'Amazing cleaning power, limited-time sale' as the tagline."

[0999] 5. The server receives the generated advice, formats it, and sends it to the terminal in a way that takes the user's emotional state into consideration.

[1000] 6. The device displays formatted advice to the user through the user interface.

[1001] This system allows users to create and improve effective promotional tools optimized for emotional states, with the support of generative AI and an emotion engine. This enables more efficient and standardized promotional activities, as well as more effective promotional efforts based on customer psychology.

[1002] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1003] Step 1: User request input and collection of sentiment data

[1004] The user operates a terminal to access the system and enter a specific improvement request. For example, they might enter, "I want to improve the advertising pop-up for the new washing machine. I'd especially like advice on the colors and wording." The terminal receives this request as text data. The terminal also uses its camera and microphone to collect the user's voice and facial expression data. Facial recognition software and voice analysis algorithms are used to extract emotional data from the voice and facial expression data.

[1005] Input: User request (text), audio data, facial expression data

[1006] Output: Request data (text), sentiment data

[1007] Step 2: Server-based data validation and sentiment analysis

[1008] Request data and sentiment data sent from the device reach the server. The server first verifies the integrity of the data format and confirms that all necessary information is included. Next, it passes the sentiment data to an emotion engine (e.g., Microsoft Azure Emotion API) for analysis to identify the user's current sentiment state. Finally, it generates a dataset that integrates the request data and analysis results.

[1009] Input: Request data (text), sentiment data

[1010] Output: Integrated dataset (request data, sentiment analysis results)

[1011] Step 3: Analysis and advice generation using generative AI

[1012] The server sends the integrated dataset to a generative AI (e.g., OpenAI GPT-4) as a prompt for advice generation. The generative AI analyzes the prompt and generates optimal improvement advice based on the request content and sentiment data. For example, it might generate specific advice such as "Use blue for the background color" and "Use the tagline 'Amazing cleaning power, limited-time sale'." This advice is returned to the server in JSON format.

[1013] Input: Integrated dataset (request data, sentiment analysis results)

[1014] Output: Generated advice (JSON format)

[1015] Step 4: Server-side formatting and re-verification of advice

[1016] The server re-verifies the advice received from the generative AI to check for any flaws. If there are no flaws, it formats the advice into a user-friendly format, for example, by adding tags, paragraphs, and bullet points. It also adjusts the tone and emphasis of the advice based on sentiment analysis results. The formatted advice is finally sent to the device.

[1017] Input: Generated advice (JSON format)

[1018] Output: Formatted advice (HTML or other format)

[1019] Step 5: Providing advice to the user via the device.

[1020] The device receives formatted advice sent from the server. The advice is displayed to the user through the user interface. When displayed, the color, font, and layout of the advice are adjusted based on the sentiment analysis results. The notification panel displays, "Blue is a good background color. Use 'Amazing cleaning power, limited time sale' as the tagline."

[1021] Input: Formatted advice (HTML or other format)

[1022] Output: Advice presented to the user (visual display)

[1023] (Application Example 2)

[1024] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1025] To maximize the effectiveness of promotional tools in physical stores, it is necessary to accurately reflect customers' emotions and potential needs. However, it is difficult for sales staff and store managers to instantly analyze the emotions of individual customers and determine the optimal promotional strategy based on that analysis. Traditional promotional systems have struggled to provide personalized advice that takes into account the emotional state of individual customers, resulting in limited effectiveness of promotional activities.

[1026] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1027] In this invention, the server includes means for receiving improvement requests from users, means for passing data to a generative AI, and means including a generative AI that generates improvement advice based on the user's emotional state. This makes it possible to analyze the emotional state of individual customers in real time and provide optimal sales promotion advice based on that analysis.

[1028] "User" refers to an individual or organization that uses this system.

[1029] An "improvement request" refers to specific requests or instructions from users for improvements to promotional tools.

[1030] A "server" refers to a device that receives improvement requests from users, passes the data to a generative AI, and then reshapes the generated advice before providing it to the user.

[1031] "Generative AI" refers to an artificial intelligence system that analyzes data received from users and generates optimal improvement advice.

[1032] An "emotion engine" refers to a system that analyzes a user's emotional state from their facial expressions and voice, and provides that information to a generative AI.

[1033] A "terminal" is a device used by a user to operate something, and refers to a device used for inputting requests and receiving and displaying advice.

[1034] "Mentalism" refers to a method of understanding human psychological states and behaviors and providing advice that takes them into consideration.

[1035] This invention is a system for effectively supporting promotional activities in physical stores. Specific embodiments are described below.

[1036] System Configuration

[1037] This system consists of a user-operated terminal, a server for processing data, a generative AI, and an emotion engine. Users use the terminal to input specific improvement requests and receive advice based on those requests.

[1038] Receiving user input and sentiment data

[1039] Users access the system via devices such as smartphones and submit requests for improvements to promotional tools. These requests include information about the promotional tools currently in use, areas for improvement, and customer feedback. Furthermore, the device collects emotional data such as the user's facial expressions and tone of voice, and sends it to the server.

[1040] Data processing and transmission

[1041] The server verifies and formats the request data and emotion data received from the terminal and passes them to the generative AI. At this time, the consistency of the data format and the inclusion of necessary information are checked. The emotion engine analyzes the emotion data to identify the user's current emotional state and reflects this in the generative AI's analysis.

[1042] Analysis and advice generation using generative AI.

[1043] The generative AI analyzes request data and the user's emotional state to generate optimal improvement advice. Based on the request content and emotional data, the generative AI considers color selection, wording, and mentalism elements to provide specific advice. Information from the emotional engine is used to adjust the generated advice to best suit the user's emotional state.

[1044] Formatting and sending advice

[1045] The server re-examines the advice received from the generative AI and formats it into a user-friendly format. Specifically, it tags the advice so that the necessary information can be understood at a glance. Emotional data provided by the emotion engine is also taken into consideration.

[1046] Presentation to the user

[1047] The terminal displays advice sent from the server to the user through the user interface. Because this advice is optimized for the user's emotional state, the user can use the provided advice to improve their promotional tools and conduct optimal promotional activities.

[1048] Hardware and software

[1049] In addition to smartphones, tablet PCs and desktop PCs can also be used as terminals. The emotion engine includes a camera used for facial recognition and a microphone used for speech recognition. For generative AI, a natural language processing model using, for example, the Transformers library is used.

[1050] Explanation of specific examples

[1051] For example, if a user (sales staff) enters "I want to improve the promotional pop-up for the new washing machine. I'd especially like advice on the colors and wording":

[1052] 1. The user enters a request into their device: "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording."

[1053] 2. Along with this request, the device collects emotional data from the user's voice and facial expressions.

[1054] 3. The server passes the received request data and emotion data to the generative AI.

[1055] 4. The generative AI analyzes the request and emotional data to generate advice such as, "Use blue for the background color based on the user's preferences and emotional state," and "Use 'Amazing cleaning power, limited-time sale' as the tagline."

[1056] 5. The server receives the generated advice, formats it, and sends it to the terminal in a way that takes the user's emotional state into consideration.

[1057] 6. The device displays formatted advice to the user through the user interface.

[1058] As an example of a prompt statement,

[1059] Request: I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording. User sentiment: Happy. Please generate the best advice.

[1060] These are some examples.

[1061] This system allows users to create and improve effective promotional tools optimized for emotional states, with the support of generative AI and an emotion engine. This enables more efficient and standardized promotional activities, as well as more effective promotional activities based on customer psychology.

[1062] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1063] Step 1:

[1064] Users use a device to input improvement requests for promotional tools. These requests include information about the tool to be improved, customer feedback, and desired improvements. The device also collects emotional data, such as user voice and facial expressions.

[1065] Input: User improvement requests, sentiment data (voice, facial expressions)

[1066] Output: Improvement request data, sentiment data

[1067] Step 2:

[1068] The device sends the collected improvement request data and sentiment data to the server. The data format consistency and necessary information are verified.

[1069] Input: Improvement request data, sentiment data

[1070] Output: Request data sent to the server, sentiment data

[1071] Step 3:

[1072] The server verifies and formats the received request data and sentiment data, and then passes them to the generative AI. During data formatting, the sentiment data is converted into a format that can be processed by the generative AI.

[1073] Input: Request data, sentiment data (received by the server)

[1074] Output: Formatted request data and emotion data to be passed to the generative AI.

[1075] Step 4:

[1076] The generative AI analyzes formatted request data and sentiment data to generate optimal improvement advice. Specifically, it generates advice based on the request content and sentiment data, taking into account color selection, wording, and elements of mentalism.

[1077] Input: Formatted request data, sentiment data

[1078] Output: Generated improvement advice

[1079] Step 5:

[1080] The server re-examines the advice received from the generative AI and formats it into a format that the user can understand. Specifically, it tags the advice and formats it so that it can be understood at a glance.

[1081] Input: Advice from a generative AI

[1082] Output: User-friendly formatted advice

[1083] Step 6:

[1084] The terminal displays formatted advice sent from the server to the user through the user interface. Because the advice is optimized for the user's emotional state, the user can effectively improve their promotional tools.

[1085] Input: Formatted advice

[1086] Output: Advice displayed in the user interface

[1087] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1088] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1089] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1090] [Fourth Embodiment]

[1091] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1092] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1093] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1094] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1095] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1096] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1097] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1098] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1099] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1100] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1101] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1102] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1103] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1104] This invention is a system for effectively supporting sales promotion activities, and includes a process in which a user inputs a request into the system, and a generative AI provides advice based on that request. The specific form for implementing this system is described below.

[1105] System Configuration

[1106] This system consists of a user-operated terminal, a server for processing data, and a generative AI. The terminal receives improvement requests from the user through a user interface. The server passes the received request data to the generative AI, which then formats the generated advice and sends it back to the terminal. The generative AI is responsible for analysis and advice generation.

[1107] Program processing

[1108] Receiving user input

[1109] Users access the system through their terminal and input requests regarding the tools they are currently using, areas for improvement, and customer feedback. The terminal then formats this request data appropriately and sends it to the server.

[1110] Data processing and transmission

[1111] The server verifies and formats the request data received from the terminal and passes it to the generative AI. At this time, it checks for consistency in the data format and whether it contains the necessary information.

[1112] Analysis and advice generation using generative AI.

[1113] Generative AI analyzes request data and generates optimal improvement advice. Based on past data and training data, the generative AI considers the request content and generates specific advice that takes into account color selection, wording, and elements of mentalism.

[1114] Formatting and sending advice

[1115] The server verifies and formats the advice received from the generative AI, converting it into a user-friendly format. The formatted advice is then sent back to the terminal and presented to the user. Here, the advice is tagged to allow users to quickly understand the necessary information.

[1116] Explanation of specific examples

[1117] For example, if a user (crew member) enters, "I want to improve the advertising pop-up for the new washing machine. I'd especially like advice on the colors and wording," then:

[1118] 1. The user enters a request into the terminal stating, "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording."

[1119] 2. The device sends this request to the server.

[1120] 3. The server receives this request and passes the data to the generative AI.

[1121] 4. The generative AI analyzes the request and generates advice such as, "Use a vibrant blue for the background color," and "The tagline should be 'Amazing cleaning power, on sale for a limited time only.'"

[1122] 5. The server receives the generated advice, formats it, and sends it to the terminal.

[1123] 6. The device displays formatted advice to the user, who then uses it to improve their promotional tools.

[1124] This system allows salespeople to receive support from generative AI and achieve consistent results without relying on intuition. Furthermore, by receiving advice that incorporates elements of mentalism, more effective promotional activities based on customer psychology become possible.

[1125] The following describes the processing flow.

[1126] Step 1:

[1127] Users access the system through their devices and enter specific improvement requests. These requests include the tools currently being used, the points they want to improve, and customer feedback. For example, a user might enter, "I want to improve the advertising pop-up for our new washing machine. I'd especially like advice on the colors and wording."

[1128] Step 2:

[1129] The device sends the request received from the user to the server. Before sending, it verifies that the data is in the correct format and formats it as needed. The collected data may take the following format, for example: {'request_type': 'advice', 'product': 'washing machine', 'focus_areas': ['color', 'wording']}.

[1130] Step 3:

[1131] The server receives the request data from the terminal. The server performs a simple check on the request data to verify that the data format and required fields are correct.

[1132] Step 4:

[1133] The server prepares to pass data to the generative AI. It converts the data into a format suitable for the generative AI and formats it to include all the necessary information.

[1134] Step 5:

[1135] The generative AI analyzes data received from the server and generates improvement advice based on the user's request. The generative AI considers color selection, wording, and elements of mentalism to provide specific advice. For example, it might use a vibrant blue for the background color and generate advice such as "Amazing cleaning power, limited-time sale" for the tagline.

[1136] Step 6:

[1137] The server re-examines the advice received from the generative AI and formats it into a user-friendly format. Specifically, it tags the advice so that the necessary information can be understood at a glance.

[1138] Step 7:

[1139] The server sends formatted advice to the terminal. The data sent is appropriately formatted as a response to the user's request.

[1140] Step 8:

[1141] The terminal displays advice sent from the server to the user through the user interface. This allows the user to improve their promotional tools based on the advice provided.

[1142] This series of steps allows users to create and improve effective promotional tools using advice from generative AI, thereby streamlining and standardizing their promotional activities.

[1143] (Example 1)

[1144] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1145] In traditional sales promotion activities, obtaining advice necessary for improving promotional tools relied on human intuition and experience, requiring a great deal of trial and error to succeed. Furthermore, selecting effective designs and wording required specialized knowledge and experience, making it difficult for everyone to easily obtain optimal advice. This resulted in inconsistent sales promotion effectiveness and difficulties in adopting customer psychology-based approaches.

[1146] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1147] In this invention, the server includes means for receiving improvement requests from users, means for formatting the improvement requests into a data format, means for transmitting the formatted data to a generative AI, means for the generative AI to analyze the data and generate improvement advice, means for the server to tag and format the generated advice in a format that the user can understand, and means for providing the formatted advice to a terminal. This makes it possible for anyone to easily and effectively obtain improvement advice for promotional tools, enabling the realization of promotional activities with consistent results.

[1148] A "user" refers to an entity that uses the system to input improvement requests and receive advice.

[1149] A "request for improvement" refers to information that users enter regarding the system, including requests and suggestions for improvements.

[1150] A "server" refers to a computer device that receives requests from users, sends data to a generative AI, and formats the generated advice.

[1151] "Generative AI" refers to artificial intelligence models that analyze received data and generate specific improvement advice based on a particular algorithm.

[1152] "Analysis" refers to the process by which a generative AI analyzes information based on the data it receives and derives optimal advice.

[1153] "Improvement advice" refers to instructions and suggestions regarding improvements to promotional tools generated by a generative AI based on its analysis results.

[1154] "Formatting" refers to the process of converting data into a specific format to facilitate communication and processing.

[1155] "Formatting" refers to the process of converting generated advice into a format that is easy for the user to understand.

[1156] "Tagging" refers to the process of adding specific labels or metadata to generated advice to organize and classify information.

[1157] A "terminal" refers to a device that a user uses to access a system, enter requests, and receive advice.

[1158] "Mentalism" refers to elements based on customer psychology and behavior that generative AI considers during analysis.

[1159] "Data format" refers to a specific data structure or format used when exchanging data between systems.

[1160] This invention provides a system for effectively supporting sales promotion activities, in which the user provides requests through a terminal, and the server generates and provides appropriate improvement advice using generative AI. The following describes how to implement this system in detail. This system consists of a terminal operated by the user, a server that processes data, and generative AI that performs analysis and advice generation.

[1161] Users access the system using a terminal. The terminal is built using web technologies such as HTML, CSS, and JavaScript, and users input information such as the tools they are currently using, areas for improvement, and customer feedback through the interface. For example, a user might input, "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording."

[1162] The terminal formats this request into the appropriate JSON format and sends it to the server via an HTTP POST request. The server parses the received request data and verifies that there are no omissions or inconsistencies. The server uses programming languages ​​such as Python or Node.js to reshape the data and convert it into a format that can be parsed by generative AI.

[1163] The server sends the formatted data to a generative AI. The generative AI, such as OpenAI's GPT-3 model, analyzes the request based on past data and training data, and generates improvement advice. The generative AI also incorporates mentalism elements, considering customer psychology and behavior during the analysis process, to generate the advice.

[1164] The advice generated by the generative AI includes specific details such as "Use a vibrant blue for the background color" and "The tagline should be 'Amazing cleaning power, limited-time sale!'" The server receives this generated advice, tags it, and formats it to make it easier for users to understand. Finally, it sends the formatted advice to the device and provides it to the user.

[1165] This allows salespeople to improve promotional tools with the support of generative AI, enabling them to create consistently effective tools without relying on intuition. An example of a prompt is as follows:

[1166] "I'd like to improve the promotional pop-up for our new washing machine. I'd especially appreciate specific advice on the background color and catchphrase."

[1167] This system allows users to receive effective advice quickly and accurately, making it easy to improve their promotional activities.

[1168] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1169] Step 1:

[1170] Users submit improvement requests through their devices.

[1171] The user enters a specific request into the terminal interface, such as, "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording." The terminal formats this input data into JSON format. The input is natural language text, and the output is JSON data like the following:

[1172] json

[1173] {

[1174] "request_type": "promotion_improvement",

[1175] "details": {

[1176] "product": "washing_machine",

[1177] "focus_areas": ["color", "wording"]

[1178] }

[1179] }

[1180] The terminal validates the input and checks for formatting accuracy.

[1181] Step 2:

[1182] The terminal sends the formatted data to the server.

[1183] The terminal uses an HTTP POST request to send the above JSON data to the server. The input is the formatted JSON data, and the output is a success message sent to the server. Specifically, the terminal checks the network status before sending to ensure the data is transmitted correctly.

[1184] Step 3:

[1185] The server receives the data and formats it for analysis.

[1186] The server receives JSON data and formats it into a parseable format. The input is JSON data received from the terminal, and the output is further formatted in a way that is easier for generative AI to process. At this time, the server checks for data integrity and verifies that all necessary fields are present. For example, it formats the data as shown below.

[1187] json

[1188] {

[1189] "prompt": "Provide advice for improving the promotion of a new washing machine, focusing on color and wording."

[1190] }

[1191] The server logs the formatted data and manages the progress of the analysis.

[1192] Step 4:

[1193] The server sends the formatted data to the generative AI.

[1194] The server sends formatted data to a generative AI via an HTTP request. The generative AI uses, for example, OpenAI's GPT-3 model. The input is formatted data, and the output is advice including analysis results. The server adjusts the transmission process to minimize response delays.

[1195] Step 5:

[1196] The generative AI analyzes the request and generates improvement advice.

[1197] The generative AI analyzes the received request data and generates advice for improving promotional tools. For example, based on the request data, it generates specific advice such as "Use a vibrant blue for the background color" and "The catchphrase should be 'Amazing cleaning power, limited-time sale!'" The input is a prompt for analysis, and the output is text containing specific advice. The generative AI logs the process and records the analysis.

[1198] Step 6:

[1199] The server receives the generated advice and formats it.

[1200] The server verifies the advice received from the generative AI and formats it into a user-friendly format. For example, the generated advice is tagged as follows:

[1201] json

[1202] {

[1203] "advice": {

[1204] "color": "Vivid blue",

[1205] "Wording": "Amazing cleaning power, on sale for a limited time only"

[1206] }

[1207] }

[1208] The input is advice text from a generative AI, and the output is formatted advice data. The server logs the results of the formatting process.

[1209] Step 7:

[1210] The server sends formatted advice to the terminal.

[1211] The server sends the formatted advice to the terminal as an HTTP response in JSON format. The input is the formatted advice data, and the output is a message from the terminal confirming receipt. The server logs the message to confirm successful transmission.

[1212] Step 8:

[1213] The device provides advice to the user.

[1214] The terminal analyzes the advice data received from the server and displays it through a user-friendly interface. Specifically, the terminal uses appropriate UI components to set the background color and display catchphrases. The input is formatted advice data received from the server, and the output is specific advice displayed to the user. The user then uses this to improve their promotional tools.

[1215] Through the above processing steps, users can quickly obtain effective advice and consistently improve their promotional activities.

[1216] (Application Example 1)

[1217] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1218] In traditional promotional activities, users (store staff) had to rely on their own intuition to identify areas for improvement and provide advice, and the effectiveness of these methods was not always consistent. Furthermore, obtaining appropriate advice required significant time and resources, making rapid improvement difficult. Additionally, there was often a lack of concrete, immediately actionable advice, hindering the implementation of effective promotional activities based on customer psychology.

[1219] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1220] In this invention, the server includes means for receiving improvement requests from users, means for transmitting the improvement requests to an internet server, means for the internet server to pass data to a generative AI based on the improvement requests, means for the generative AI to analyze the data and generate improvement advice, means for the internet server to receive the generated advice and format it into a format that the user can understand, and means for a smart device to provide the formatted advice to the user. As a result, users can obtain specific advice that can be immediately put into practice in real time, enabling effective sales promotion activities.

[1221] A "user" refers to an individual or organization that operates the system and submits improvement requests.

[1222] An "Internet server" is a computer system that processes, transmits, and receives data over a network.

[1223] "Generative AI" is an artificial intelligence technology that analyzes request data and automatically generates optimal improvement advice.

[1224] A "smart device" is an advanced electronic device that is connected to the internet and can provide users with information in real time.

[1225] An "improvement request" is information that users enter into the system, indicating specific points that need improvement or their desired improvements.

[1226] "Means of data transfer" refers to methods or devices for transferring data between different components within a system.

[1227] "Analysis" is a computational process that involves understanding input data and extracting necessary information.

[1228] "Improvement advice" refers to specific suggestions and suggestions provided in response to submitted improvement requests.

[1229] "Shaping methods" refer to methods or devices for converting generated advice into a format that is easy for the user to understand.

[1230] "Means of providing" refers to methods or devices for informing and displaying the generated advice to the user.

[1231] This invention relates to a system for supporting sales promotion activities, and includes a process in which a user inputs a request into the system, and a generative AI provides advice based on that request.

[1232] System Configuration

[1233] This system consists of a user-operated smart device (such as smart glasses or a smartphone), an internet server that processes data, and a generative AI. The smart device receives improvement requests from the user through a user interface. The internet server passes the received request data to the generative AI, which then formats the generated advice and sends it back to the smart device. The generative AI is responsible for analysis and advice generation.

[1234] Program processing

[1235] Receiving user input

[1236] Users access the system via smart devices and input requests regarding the tools they are currently using, areas for improvement, and customer feedback. The smart device then formats this request data appropriately and sends it to the internet server.

[1237] Data processing and transmission

[1238] The internet server verifies and formats the request data received from the smart device and passes it to the generative AI. At this time, it checks for consistency in the data format and whether it contains the necessary information.

[1239] Analysis and advice generation using generative AI.

[1240] Generative AI analyzes request data and generates optimal improvement advice. Based on past data and training data, generative AI considers the request content and generates specific advice.

[1241] Formatting and sending advice

[1242] An internet server verifies and formats the advice received from the generative AI, converting it into a user-friendly format. The formatted advice is then sent back to the smart device and presented to the user. Here, the advice is tagged to allow users to quickly understand the necessary information.

[1243] Explanation of specific examples

[1244] For example, if a user (store staff) inputs via voice, "I'd like some advice on how to arrange the display of new products":

[1245] 1. The user enters a request into their smart device via voice, stating, "I would like advice on how to arrange the display for the new product."

[1246] 2. The smart device sends this request to an internet server.

[1247] 3. The internet server receives this request and passes the data to the generative AI.

[1248] 4. The generative AI analyzes the request and generates advice such as, "For displaying new products, it's best to place them at eye level and in a position where customers can easily pick them up. Also, consider placing POP displays around them to attract customers' attention."

[1249] 5. The internet server receives the generated advice, formats it, and sends it to the smart device.

[1250] 6. Smart devices display formatted advice to the user, who then uses it to improve their promotional tools.

[1251] This system allows store staff to implement effective promotional activities immediately with the support of generative AI. An example of a prompt message would be: "A salesperson has entered a request for advice on how to display new products. Please provide the best advice."

[1252] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1253] Step 1:

[1254] The user provides voice input to a smart device. For example, the user might voice a request such as, "I'd like advice on how to arrange the display for the new product." The smart device converts this voice data into text data and formats it. Converting voice data (input) to text data (output) enables subsequent processing.

[1255] Step 2:

[1256] The terminal sends the converted text data to the internet server. The terminal's sending function is used to upload the request data to the internet server. Text data is given as input, and the output is the data sent to the internet server.

[1257] Step 3:

[1258] The internet server verifies and formats the received request data. The server checks the integrity of the request data and verifies that there are no errors. Next, it formats it into a format that is easy for the generative AI to process. The request data is given as input, and the formatted data is passed to the generative AI as output.

[1259] Step 4:

[1260] The internet server passes formatted data to the generative AI. The server's internal data transfer function is used to send the formatted data to the generative AI. Formatted data is received as input, and data passed to the generative AI is obtained as output.

[1261] Step 5:

[1262] The generative AI analyzes the request data and generates improvement advice. The generative AI uses historical and training data to analyze the request and generate specific improvement advice. Formatted request data is given as input, and the generated advice is returned as output.

[1263] Step 6:

[1264] An internet server verifies and formats the advice received from a generative AI. The server checks whether the advice is appropriate and formats it into a user-friendly format. The generated advice is given as input, and the formatted advice is obtained as output.

[1265] Step 7:

[1266] An internet server sends formatted advice to a smart device. The server uses its transmission function to send the formatted advice to the smart device. Formatted advice is given as input, and data to be sent to the smart device is obtained as output.

[1267] Step 8:

[1268] A smart device displays formatted advice to the user. The smart device's display capabilities are used to visually present the advice to the user. Formatted advice is provided as input, and the output is the advice the user receives visually.

[1269] Each processing step in this system allows users to receive specific improvement advice based on their requests in real time.

[1270] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1271] This invention is a system for effectively supporting sales promotion activities, comprising a process in which a user inputs a request into the system, and a generative AI provides advice based on that request. The system further incorporates an emotion engine that recognizes the user's emotions and includes a method for optimizing requests and advice based on the user's emotions.

[1272] System Configuration

[1273] This system consists of a user-operated terminal, a server for processing data, a generative AI, and an emotion engine. The terminal receives improvement requests from the user through a user interface and collects the user's emotional data. The server passes the received request data and emotional data to the generative AI, which then formats the generated advice and sends it back to the terminal. The generative AI is responsible for analysis and advice generation. The emotion engine analyzes the user's emotional state and adjusts the request content and advice based on that analysis.

[1274] Program processing

[1275] Receiving user input and sentiment data

[1276] Users access the system through a terminal and enter specific improvement requests. These requests include the tools currently being used, points to be improved, and customer feedback. Along with these requests, the terminal extracts emotional data from the user's voice and facial expressions and sends it to the server.

[1277] Data processing and transmission

[1278] The server verifies and formats the request data and emotion data received from the terminal and passes them to the generative AI. At this time, it checks for consistency in the data format and whether the necessary information is included. The emotion engine analyzes the emotion data to identify the user's current emotional state and reflects this in the generative AI's analysis.

[1279] Analysis and advice generation using generative AI.

[1280] The generative AI analyzes request data and the user's emotional state to generate optimal improvement advice. Based on the request content and emotional data, the generative AI considers color selection, wording, and mentalism elements to provide specific advice. Information from the emotional engine is used to adjust the generated advice to best suit the user's emotional state.

[1281] Formatting and sending advice

[1282] The server re-examines the advice received from the generative AI and formats it into a user-friendly format. Specifically, it tags the advice so that the necessary information can be understood at a glance. Emotional data provided by the emotion engine is also taken into consideration.

[1283] Presentation to the user

[1284] The terminal displays advice sent from the server to the user through the user interface. Because this advice is optimized for the user's emotional state, the user can use the provided advice to improve their promotional tools and conduct optimal promotional activities.

[1285] Explanation of specific examples

[1286] For example, if a user (crew member) enters, "I want to improve the advertising pop-up for the new washing machine. I'd especially like advice on the colors and wording," then:

[1287] 1. The user enters a request into their device: "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording."

[1288] 2. Along with this request, the device collects emotional data from the user's voice and facial expressions.

[1289] 3. The server passes the received request data and emotion data to the generative AI.

[1290] 4. The generative AI analyzes the request and emotional data to generate advice such as, "Use blue for the background color based on the user's preferences and emotional state," and "Use 'Amazing cleaning power, limited-time sale' as the tagline."

[1291] 5. The server receives the generated advice, formats it, and sends it to the terminal in a way that takes the user's emotional state into consideration.

[1292] 6. The device displays formatted advice to the user through the user interface.

[1293] This system allows users to create and improve effective promotional tools optimized for emotional states, with the support of generative AI and an emotion engine. This enables more efficient and standardized promotional activities, as well as more effective promotional activities based on customer psychology.

[1294] The following describes the processing flow.

[1295] Step 1:

[1296] Users access the system through their terminals and input requests detailing the tools they are currently using, areas for improvement, and specific requests. For example, a user might input, "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording."

[1297] Step 2:

[1298] Before the device sends a request received from the user to the server, it collects the user's voice and facial expression data and extracts emotional data with the help of an emotion engine. The extracted emotional data represents emotional states such as "excitement," "anxiety," and "joy."

[1299] Step 3:

[1300] The device sends request data and emotion data to the server. The data is formatted in an appropriate format, for example, {'request_type': 'advice', 'product': 'washing machine', 'focus_areas': ['color', 'wording'], 'emotion': 'excitement'}.

[1301] Step 4:

[1302] The server checks and verifies the request data and sentiment data received from the terminal. It verifies that the data format is correct, that required fields are included, and formats the data as needed.

[1303] Step 5:

[1304] The server prepares to pass data to the generative AI. Here, the request data and sentiment data are converted into the appropriate format so that the generative AI can use it for analysis.

[1305] Step 6:

[1306] The generative AI analyzes request data and sentiment data received from the server to generate optimal improvement advice. Based on past data and training data, the AI ​​considers the request content and provides advice that is most appropriate to the user's emotional state. For example, it might generate advice such as, "Use blue for the background color, based on the user's preferences and emotional state," or "Use the tagline, 'Amazing cleaning power, limited-time sale!'"

[1307] Step 7:

[1308] The server re-examines the advice received from the generative AI and formats it into a format that is easy for the user to understand. During this process, information from the emotion engine is also taken into consideration, and advice is provided in a way that matches the user's emotional state.

[1309] Step 8:

[1310] The server sends formatted advice to the terminal. The transmitted data is appropriately formatted as the best response to the user's request. For example, it might be formatted as follows: "Background color: Blue" "Catchphrase: Amazing cleaning power, limited time sale."

[1311] Step 9:

[1312] The terminal displays formatted advice received from the server to the user through the user interface. This allows the user to improve promotional tools based on advice optimized for their emotional state, thereby achieving more effective promotional activities.

[1313] Through this series of steps, users can create and improve effective promotional tools optimized for emotional states, with the support of generative AI and emotion engines, thereby achieving greater efficiency and standardization in their promotional activities.

[1314] (Example 2)

[1315] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1316] In modern marketing activities, providing accurate advice that takes user emotions into account is crucial, but the lack of an effective system for this means that advice may not always be tailored to the user's emotional state. Traditional systems have struggled to utilize user emotional data, resulting in sometimes inappropriate advice. Furthermore, the complex data shaping and analysis processes required to effectively use generative AI models have sometimes reduced the overall efficiency of the system.

[1317] The specific processing performed 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 means for receiving improvement requests and user emotion data from the user, means for verifying the improvement requests and emotion data and passing the data to a generative AI, means for the generative AI to analyze the data and generate improvement advice, and means for the server to verify and format the generated advice. This makes it possible to provide appropriate advice optimized for the user's emotional state.

[1318] A "user" is a person who operates the system, inputs improvement requests, and receives advice from the system.

[1319] An "improvement request" is a request or wish that a user enters into the system regarding improvements to promotional activities or various tools.

[1320] "Emotional data" refers to data extracted from the user's voice, facial expressions, etc., that indicates the user's current emotional state.

[1321] A "terminal" is a device equipped with a user interface that allows a user to access a system. Examples include computers and smartphones.

[1322] A "server" is a central processing unit that receives, verifies, and formats request data and sentiment data, and works in conjunction with generative AI to generate advice.

[1323] "Generative AI" refers to artificial intelligence that analyzes received data and generates advice based on user requests.

[1324] "Improvement advice" refers to specific suggestions for improvements and enhancements in response to user requests, generated by a generative AI based on its analysis results.

[1325] An "emotion engine" is a function or software that analyzes user emotional data and incorporates the results into the analysis of generative AI.

[1326] "Verifying and formatting data" is the process of checking and adjusting the format and content of received data so that it can be properly analyzed by generative AI.

[1327] A "user interface" is an interface that allows a user to interact with a system, and includes devices such as screens and input devices.

[1328] Modes for carrying out the invention

[1329] This invention is a system that analyzes improvement requests regarding promotional activities entered by users and provides optimal advice based on the user's emotional state. This system is primarily implemented using the following hardware and software:

[1330] The device operated by the user

[1331] The system receives improvement requests from users through the user interface and collects user sentiment data.

[1332] Facial recognition camera, microphone, and speech analysis software (e.g., OpenCV, Google Cloud Speech-to-Text API)

[1333] server

[1334] The received request data and sentiment data are verified and formatted, then passed on to the generative AI.

[1335] JSON schema validation tool for verifying data format integrity

[1336] Emotion engine (e.g., Microsoft Azure Emotion API)

[1337] Generative AI

[1338] Responsible for analysis and generating advice.

[1339] AI models (e.g., OpenAI GPT-4)

[1340] Emotional Engine

[1341] The system analyzes the user's emotional state and adjusts the content of requests and advice accordingly.

[1342] Specific operation of the system

[1343] Users access the system through a terminal and input specific improvement requests, such as, "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording." Along with these requests, the terminal collects emotional data from the user's voice and facial expressions. For example, it captures the user's facial expressions with a camera and records their voice with a microphone. Then, it extracts emotional data using voice analysis algorithms and facial recognition software.

[1344] The server verifies and formats the request data and sentiment data received from the terminal. It checks for consistency in the data format and verifies that the necessary information is included. The verified data is converted to JSON format and sent as a prompt to the generative AI. At this point, the sentiment engine analyzes the sentiment data and reflects the results in the prompt.

[1345] Generative AI generates optimal improvement advice based on user request data and sentiment data. For example, it might generate specific advice such as "Use blue for the background color" or "Use the tagline 'Amazing cleaning power, limited-time sale'." This advice is then adjusted based on sentiment data.

[1346] The server re-examines the advice received from the generative AI and formats it into a user-friendly format. The formatted advice is sent to the terminal, which displays it to the user through the user interface. Based on the provided advice, the user can improve their promotional tools and conduct optimal promotional activities.

[1347] Specific example

[1348] For example, if a user (store staff) enters "I want to improve the promotional pop-up for the new washing machine. I'd especially like advice on the colors and wording," then:

[1349] 1. The user enters a request into the device.

[1350] 2. The device collects emotional data from the user's voice and facial expressions along with the request.

[1351] 3. The server passes the received request data and emotion data to the generative AI.

[1352] 4. The generative AI analyzes the request and emotional data to generate advice such as, "Use blue for the background color based on the user's preferences and emotional state," and "Use 'Amazing cleaning power, limited-time sale' as the tagline."

[1353] 5. The server receives the generated advice, formats it, and sends it to the terminal in a way that takes the user's emotional state into consideration.

[1354] 6. The device displays formatted advice to the user through the user interface.

[1355] This system allows users to create and improve effective promotional tools optimized for emotional states, with the support of generative AI and an emotion engine. This enables more efficient and standardized promotional activities, as well as more effective promotional efforts based on customer psychology.

[1356] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1357] Step 1: User request input and collection of sentiment data

[1358] The user operates a terminal to access the system and enter a specific improvement request. For example, they might enter, "I want to improve the advertising pop-up for the new washing machine. I'd especially like advice on the colors and wording." The terminal receives this request as text data. The terminal also uses its camera and microphone to collect the user's voice and facial expression data. Facial recognition software and voice analysis algorithms are used to extract emotional data from the voice and facial expression data.

[1359] Input: User request (text), audio data, facial expression data

[1360] Output: Request data (text), sentiment data

[1361] Step 2: Server-based data validation and sentiment analysis

[1362] Request data and sentiment data sent from the device reach the server. The server first verifies the integrity of the data format and confirms that all necessary information is included. Next, it passes the sentiment data to an emotion engine (e.g., Microsoft Azure Emotion API) for analysis to identify the user's current sentiment state. Finally, it generates a dataset that integrates the request data and analysis results.

[1363] Input: Request data (text), sentiment data

[1364] Output: Integrated dataset (request data, sentiment analysis results)

[1365] Step 3: Analysis and advice generation using generative AI

[1366] The server sends the integrated dataset to a generative AI (e.g., OpenAI GPT-4) as a prompt for advice generation. The generative AI analyzes the prompt and generates optimal improvement advice based on the request content and sentiment data. For example, it might generate specific advice such as "Use blue for the background color" and "Use the tagline 'Amazing cleaning power, limited-time sale'." This advice is returned to the server in JSON format.

[1367] Input: Integrated dataset (request data, sentiment analysis results)

[1368] Output: Generated advice (JSON format)

[1369] Step 4: Server-side formatting and re-verification of advice

[1370] The server re-verifies the advice received from the generative AI to check for any flaws. If there are no flaws, it formats the advice into a user-friendly format, for example, by adding tags, paragraphs, and bullet points. It also adjusts the tone and emphasis of the advice based on sentiment analysis results. The formatted advice is finally sent to the device.

[1371] Input: Generated advice (JSON format)

[1372] Output: Formatted advice (HTML or other format)

[1373] Step 5: Providing advice to the user via the device.

[1374] The device receives formatted advice sent from the server. The advice is displayed to the user through the user interface. When displayed, the color, font, and layout of the advice are adjusted based on the sentiment analysis results. The notification panel displays, "Blue is a good background color. Use 'Amazing cleaning power, limited time sale' as the tagline."

[1375] Input: Formatted advice (HTML or other format)

[1376] Output: Advice presented to the user (visual display)

[1377] (Application Example 2)

[1378] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1379] To maximize the effectiveness of promotional tools in physical stores, it is necessary to accurately reflect customers' emotions and potential needs. However, it is difficult for sales staff and store managers to instantly analyze the emotions of individual customers and determine the optimal promotional strategy based on that analysis. Traditional promotional systems have struggled to provide personalized advice that takes into account the emotional state of individual customers, resulting in limited effectiveness of promotional activities.

[1380] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1381] In this invention, the server includes means for receiving improvement requests from users, means for passing data to a generative AI, and means including a generative AI that generates improvement advice based on the user's emotional state. This makes it possible to analyze the emotional state of individual customers in real time and provide optimal sales promotion advice based on that analysis.

[1382] "User" refers to an individual or organization that uses this system.

[1383] An "improvement request" refers to specific requests or instructions from users for improvements to promotional tools.

[1384] A "server" refers to a device that receives improvement requests from users, passes the data to a generative AI, and then reshapes the generated advice before providing it to the user.

[1385] "Generative AI" refers to an artificial intelligence system that analyzes data received from users and generates optimal improvement advice.

[1386] An "emotion engine" refers to a system that analyzes a user's emotional state from their facial expressions and voice, and provides that information to a generative AI.

[1387] A "terminal" is a device used by a user to operate something, and refers to a device used for inputting requests and receiving and displaying advice.

[1388] "Mentalism" refers to a method of understanding human psychological states and behaviors and providing advice that takes them into consideration.

[1389] This invention is a system for effectively supporting promotional activities in physical stores. Specific embodiments are described below.

[1390] System Configuration

[1391] This system consists of a user-operated terminal, a server for processing data, a generative AI, and an emotion engine. Users use the terminal to input specific improvement requests and receive advice based on those requests.

[1392] Receiving user input and sentiment data

[1393] Users access the system via devices such as smartphones and submit requests for improvements to promotional tools. These requests include information about the promotional tools currently in use, areas for improvement, and customer feedback. Furthermore, the device collects emotional data such as the user's facial expressions and tone of voice, and sends it to the server.

[1394] Data processing and transmission

[1395] The server verifies and formats the request data and emotion data received from the terminal and passes them to the generative AI. At this time, the consistency of the data format and the inclusion of necessary information are checked. The emotion engine analyzes the emotion data to identify the user's current emotional state and reflects this in the generative AI's analysis.

[1396] Analysis and advice generation using generative AI.

[1397] The generative AI analyzes request data and the user's emotional state to generate optimal improvement advice. Based on the request content and emotional data, the generative AI considers color selection, wording, and mentalism elements to provide specific advice. Information from the emotional engine is used to adjust the generated advice to best suit the user's emotional state.

[1398] Formatting and sending advice

[1399] The server re-examines the advice received from the generative AI and formats it into a user-friendly format. Specifically, it tags the advice so that the necessary information can be understood at a glance. Emotional data provided by the emotion engine is also taken into consideration.

[1400] Presentation to the user

[1401] The terminal displays advice sent from the server to the user through the user interface. Because this advice is optimized for the user's emotional state, the user can use the provided advice to improve their promotional tools and conduct optimal promotional activities.

[1402] Hardware and software

[1403] In addition to smartphones, tablet PCs and desktop PCs can also be used as terminals. The emotion engine includes a camera used for facial recognition and a microphone used for speech recognition. For generative AI, a natural language processing model using, for example, the Transformers library is used.

[1404] Explanation of specific examples

[1405] For example, if a user (sales staff) enters "I want to improve the promotional pop-up for the new washing machine. I'd especially like advice on the colors and wording":

[1406] 1. The user enters a request into their device: "I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording."

[1407] 2. Along with this request, the device collects emotional data from the user's voice and facial expressions.

[1408] 3. The server passes the received request data and emotion data to the generative AI.

[1409] 4. The generative AI analyzes the request and emotional data to generate advice such as, "Use blue for the background color based on the user's preferences and emotional state," and "Use 'Amazing cleaning power, limited-time sale' as the tagline."

[1410] 5. The server receives the generated advice, formats it, and sends it to the terminal in a way that takes the user's emotional state into consideration.

[1411] 6. The device displays formatted advice to the user through the user interface.

[1412] As an example of a prompt statement,

[1413] Request: I want to improve the advertising pop-up for my new washing machine. I'd especially like advice on the colors and wording. User sentiment: Happy. Please generate the best advice.

[1414] These are some examples.

[1415] This system allows users to create and improve effective promotional tools optimized for emotional states, with the support of generative AI and an emotion engine. This enables more efficient and standardized promotional activities, as well as more effective promotional activities based on customer psychology.

[1416] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1417] Step 1:

[1418] Users use a device to input improvement requests for promotional tools. These requests include information about the tool to be improved, customer feedback, and desired improvements. The device also collects emotional data, such as user voice and facial expressions.

[1419] Input: User improvement requests, sentiment data (voice, facial expressions)

[1420] Output: Improvement request data, sentiment data

[1421] Step 2:

[1422] The device sends the collected improvement request data and sentiment data to the server. The data format consistency and necessary information are verified.

[1423] Input: Improvement request data, sentiment data

[1424] Output: Request data sent to the server, sentiment data

[1425] Step 3:

[1426] The server verifies and formats the received request data and sentiment data, and then passes them to the generative AI. During data formatting, the sentiment data is converted into a format that can be processed by the generative AI.

[1427] Input: Request data, sentiment data (received by the server)

[1428] Output: Formatted request data and emotion data to be passed to the generative AI.

[1429] Step 4:

[1430] The generative AI analyzes formatted request data and sentiment data to generate optimal improvement advice. Specifically, it generates advice based on the request content and sentiment data, taking into account color selection, wording, and elements of mentalism.

[1431] Input: Formatted request data, sentiment data

[1432] Output: Generated improvement advice

[1433] Step 5:

[1434] The server re-examines the advice received from the generative AI and formats it into a format that the user can understand. Specifically, it tags the advice and formats it so that it can be understood at a glance.

[1435] Input: Advice from a generative AI

[1436] Output: User-friendly formatted advice

[1437] Step 6:

[1438] The terminal displays formatted advice sent from the server to the user through the user interface. Because the advice is optimized for the user's emotional state, the user can effectively improve their promotional tools.

[1439] Input: Formatted advice

[1440] Output: Advice displayed in the user interface

[1441] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1442] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1443] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1444] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1445] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1446] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1447] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1448] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1449] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1450] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1451] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1452] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1453] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1454] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1455] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1456] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1457] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1458] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1459] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1460] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1461] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1462] The following is further disclosed regarding the embodiments described above.

[1463] (Claim 1)

[1464] A means of receiving improvement requests from users,

[1465] Means for sending the aforementioned improvement request to the server,

[1466] The server provides a means for passing data to the generative AI based on the aforementioned improvement request,

[1467] A means by which a generative AI analyzes the aforementioned data and generates improvement advice,

[1468] The server receives the generated advice and formats it into a format that the user can understand,

[1469] The terminal provides the means for providing the formatted advice to the user.

[1470] A system that includes this.

[1471] (Claim 2)

[1472] The system according to claim 1, comprising means for a generative AI to generate the advice while taking into account elements of mentalism during analysis.

[1473] (Claim 3)

[1474] The system according to claim 1, wherein the improvement request includes means that the improvement request includes information about the tool to be improved, customer feedback, and points for improvement.

[1475] "Example 1"

[1476] (Claim 1)

[1477] A means of receiving improvement requests from users,

[1478] A means for formatting the aforementioned improvement request into a data format,

[1479] Means for sending the formatted data to the server,

[1480] A server provides means for formatting the data into an analyzable format based on the improvement request,

[1481] A means for the server to send formatted data to a generative AI,

[1482] A means by which a generative AI analyzes the aforementioned data and generates improvement advice,

[1483] A server receives the generated advice and formats it by tagging it in a way that the user can understand,

[1484] The terminal provides the means for providing the formatted advice to the user.

[1485] A system that includes this.

[1486] (Claim 2)

[1487] The system according to claim 1, wherein the generative AI includes means for generating the advice by taking into account elements of mentalism in the analysis of the generated advice.

[1488] (Claim 3)

[1489] The system according to claim 1, wherein the improvement request includes means of including information on the product to be improved, customer feedback, and points for improvement.

[1490] "Application Example 1"

[1491] (Claim 1)

[1492] A means of receiving improvement requests from users,

[1493] Means for sending the aforementioned improvement request to an Internet server,

[1494] A means by which an internet server passes data to a generative AI based on the aforementioned improvement request,

[1495] A means by which a generative AI analyzes the aforementioned data and generates improvement advice,

[1496] An internet server receives the generated advice and formats it into a format that the user can understand,

[1497] A smart device provides the user with the formatted advice.

[1498] A system that includes this.

[1499] (Claim 2)

[1500] The system according to claim 1, comprising means for a generative AI to generate the advice while taking psychological elements into consideration during analysis.

[1501] (Claim 3)

[1502] The system according to claim 1, wherein the improvement request includes means for which the improvement request includes product information to be improved, customer feedback, and points for improvement.

[1503] "Example 2 of combining an emotion engine"

[1504] (Claim 1)

[1505] A means of receiving improvement requests from users,

[1506] A means for sending the aforementioned improvement request and user sentiment data to a server,

[1507] The server verifies the aforementioned improvement request and sentiment data, and provides the data to the generative AI.

[1508] A means by which a generative AI analyzes the aforementioned data and generates improvement advice,

[1509] The server provides means for verifying and formatting the generated advice,

[1510] The terminal provides means for providing the formatted advice to the user,

[1511] A system that includes this.

[1512] (Claim 2)

[1513] The system according to claim 1, comprising means for the emotion engine to analyze the user's emotional state and reflect the results in the analysis.

[1514] (Claim 3)

[1515] The system according to claim 1, comprising means for a generative AI to generate the advice while taking into account elements of mentalism during analysis.

[1516] "Application example 2 when combining with an emotional engine"

[1517] (Claim 1)

[1518] A means of receiving improvement requests from users,

[1519] Means for sending the aforementioned improvement request to the server,

[1520] The server provides a means for passing data to the generative AI based on the aforementioned improvement request,

[1521] A means by which a generative AI analyzes the data and generates improvement advice based on the user's emotional state,

[1522] The server receives the generated advice and formats it into a format that the user can understand,

[1523] The terminal provides means for providing the formatted advice to the user,

[1524] A system that includes an emotion engine to recognize user emotions.

[1525] (Claim 2)

[1526] The system according to claim 1, comprising means for a generative AI to generate the advice while taking into account elements of mentalism during analysis.

[1527] (Claim 3)

[1528] The system according to claim 1, wherein the improvement request includes means that the improvement request includes information about the tool to be improved, customer feedback, and points for improvement. [Explanation of symbols]

[1529] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving improvement requests from users, Means for sending the aforementioned improvement request to the server, The server provides a means for passing data to the generative AI based on the aforementioned improvement request, A means by which a generative AI analyzes the aforementioned data and generates improvement advice, The server receives the generated advice and formats it into a format that the user can understand, The terminal provides the means for providing the formatted advice to the user. A system that includes this.

2. The system according to claim 1, which includes means for a generative AI to generate the advice while taking into account elements of mentalism during analysis.

3. The system according to claim 1, wherein the improvement request includes means such as information on the tool to be improved, customer feedback, and points for improvement.

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