System

Abstract

A system is provided.SOLUTION: This system is provided with a means for inputting the request of a salesperson, a natural language processing means for analyzing the request, a means for judging whether or not the request is suited to legal restrictions, a means for automatically generating a design suited to the restrictions, a means for allowing a section in charge to check the generated design, and a means for providing a finally approved design.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Modern sales activities demand a system that can quickly create legal and effective point-of-purchase (POP) displays. Currently, creating POP displays based on sales representatives' requests takes a lot of time and effort, and the process of checking legal restrictions is particularly cumbersome. This makes it difficult to quickly provide POP displays tailored to specific events or campaigns. There is also a risk that designs that do not comply with legal restrictions will be created. The present invention aims to provide a system that solves these current issues and streamlines sales activities. [Means for solving the problem]

[0005] The system of the present invention includes the following means: a means for inputting sales representative requests, a natural language processing means for analyzing the requests, a means for determining whether the requests comply with legal restrictions, a means for automatically generating designs that comply with the restrictions, a means for the responsible department to check the generated designs, and a means for finally providing the approved designs. This allows POPs based on sales representative requests to be provided quickly and in a legally compliant form. Furthermore, the use of natural language processing improves the accuracy of interpretation of requests, and legal restrictions are automatically confirmed by comparing them with an internal legal database. The system smoothly executes the entire process, from automatic design generation to final confirmation, allowing legal POPs to be created in a short period of time, supporting sales activities without missing the best sales opportunities.

[0006] "Means for inputting sales representative requests" refers to an interface and related input devices that allow sales representatives to input requests for creating POPs.

[0007] "Natural language processing means" refers to natural language processing technology and related software and hardware for analyzing requests entered by salespeople and understanding the meaning of the text.

[0008] "Means for determining compliance with legal constraints" refers to algorithms and databases for verifying whether the analyzed request complies with current laws and regulations.

[0009] "Means for automatically generating a design that complies with constraints" means design generation software and related algorithms for automatically generating a design in a legally compliant manner.

[0010] "Means for the responsible department to check" refers to the interface and tools that allow members of the responsible department to check the generated design and make corrections as necessary.

[0011] "Means for providing final approved design" refers to the system and method for distributing or providing the design approved by the responsible department to the sales force. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0013] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0016] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0017] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0018] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0025] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0032] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0033] This invention details a system that uses AI to analyze salesperson requests and automatically generate POPs (Points of Purchase) that comply with legal restrictions.

[0034] System Overview

[0035] This system consists of the following main elements:

[0036] 1. A means of inputting salesperson requests

[0037] 2. Natural language processing means for analyzing requests

[0038] 3. How to determine whether a request complies with legal restrictions

[0039] 4. A method for automatically generating designs that conform to constraints

[0040] 5. A means for the department in charge to check the generated design

[0041] 6. Means of providing the final approved design

[0042] The system allows salespeople to quickly and legally generate POPs for specific promotional campaigns and events.

[0043] Explanation of program processing

[0044] Step 1: Enter the sales representative's request

[0045] User (sales representative)

[0046] Salespeople access a dedicated input form using devices such as PCs, smartphones, and tablets. This form includes fields for entering the text content of the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). When the salesperson enters their request and clicks the "Submit" button, the request data is sent to the server.

[0047] Step 2: Analyze the request

[0048] server

[0049] The server stores the received request data and begins analyzing it using a natural language processing (NLP) engine. The NLP engine extracts important keywords and concepts from the input text. For example, keywords such as "limited-time discount," "new product," and "free sample" are extracted.

[0050] Step 3: Determine compliance with legal constraints

[0051] server

[0052] The server then checks the parsed request against an internal legal database to determine whether it complies with legal restrictions, such as whether the request constitutes false advertising, whether the product labeling contains misleading information, whether specific terminology is used, etc. If a violation is found, an appropriate error message is automatically generated.

[0053] Step 4: Automatically generate a design that meets the constraints

[0054] server

[0055] If the requirements are confirmed to comply with legal constraints, a design generation engine is put into action. This engine automatically generates layouts, fonts, color schemes, and images based on the specified requirements. For example, a design may be generated that highlights new product features and highlights discount information.

[0056] Step 5: The generated design is checked by the responsible department

[0057] Terminal

[0058] A member of the relevant department accesses a dedicated interface to preview the generated design, checks the content, and makes corrections or comments as necessary. Once corrections are complete, the design is given final approval.

[0059] Step 6: Deliver the final approved design

[0060] server

[0061] Once approved, the POP design is saved as a final version and moved to a folder accessible to sales representatives, who can then log in to the server using their own devices and download or print the approved POP.

[0062] Specific examples

[0063] For example, let's say Salesperson A wants to create a promotional POP for a new product "ABC." Salesperson A uses a terminal to access the input form, enters requests such as "emphasize the features of the new product and prominently display the limited-time discount," and clicks the "Submit" button.

[0064] The server receives the request, and the NLP engine begins analyzing it, extracting keywords such as "new product," "features," and "limited-time discount." The analysis results are compared with a legal database to check whether they comply with legal restrictions. If they are determined to comply with the restrictions, the design generation engine is activated, and a design incorporating images of the new product and discount information is automatically generated.

[0065] A member of the relevant department checks the generated design on the preview screen, makes minor adjustments such as making the discount rate more prominent, and then gives final approval. After approval, the design is saved as a final version and moved to a folder that Salesperson A can access. Salesperson A logs in to the server from his or her own device and downloads or prints the approved POP to use in sales promotion activities.

[0066] This system allows for the provision of prompt and appropriate POPs, improving the efficiency of sales activities.

[0067] The processing flow will be explained below.

[0068] Step 1:

[0069] User

[0070] The salesperson accesses a dedicated form on their terminal and enters their request for POP creation. Input items include the text content, desired theme and design, images to be used, deadline, etc. When the salesperson clicks the "Send" button, the request data is sent to the server.

[0071] Step 2:

[0072] server

[0073] The server stores the received request data in a database. At the same time, a natural language processing (NLP) engine begins analyzing the request data. Specifically, it extracts keywords and important phrases from the input text.

[0074] Step 3:

[0075] server

[0076] The NLP engine compares the extracted keywords and phrases with an internal legal database. For example, it checks for expressions that meet the criteria for "misleading advertising" or for words prohibited by specific laws. If a violation is detected, an error message is generated and the salesperson is asked to make corrections.

[0077] Step 4:

[0078] server

[0079] If the requirements are confirmed to comply with legal constraints, a design generation engine is put into action, which includes algorithms that automatically determine the layout, text style, color scheme, and image placement based on the specified requirements.

[0080] Step 5:

[0081] server

[0082] The generated POP design is stored in temporary storage, and the design data is sent to a dedicated interface that can be accessed by the relevant department.

[0083] Step 6:

[0084] Terminal

[0085] The person in charge of the relevant department accesses a dedicated interface to preview the generated POP design, check the content, and enter feedback and corrections as necessary.

[0086] Step 7:

[0087] server

[0088] When the person in charge clicks the "Final Approval" button, the final design data is saved as the final version. A notification of approval completion is also automatically sent to the sales representative.

[0089] Step 8:

[0090] User

[0091] Salespeople log in to the server using their own devices and download or print the approved POP design, allowing them to quickly obtain POPs that can be used in sales promotion activities.

[0092] Example 1

[0093] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0094] Point-of-purchase (POP) designs created by sales representatives for promotional activities must be fast and legal. However, the current process of sales representatives submitting design requests, checking whether they comply with legal restrictions, and generating appropriate designs is laborious and time-consuming. Furthermore, designs that include content that may violate legal restrictions are inappropriate and must be dealt with appropriately.

[0095] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0096] In this invention, the server includes means for inputting requests from sales representatives, natural language processing means for analyzing the requests, means for determining whether the requests comply with legal restrictions, means for automatically generating a design that complies with the restrictions, means for halting design generation and generating an error message if the requests violate legal restrictions, means for providing the final approved design, and means for moving the generated design to a folder accessible to the sales representative after final approval. This allows POP designs based on the sales representative's requests to be generated quickly and legally, making it possible to improve the efficiency of sales activities.

[0097] "Means for inputting salesperson requests" refers to the interface that salespersons use to input the text content and design of POPs, images and icons to be used, special instructions, etc. using devices such as PCs, smartphones, and tablets.

[0098] "Natural language processing means" refers to technical means, particularly a natural language processing engine, for analyzing received sales representative request data and extracting important keywords and concepts from the text.

[0099] "Means for determining compliance with legal constraints" refers to the means for comparing the analyzed request content with an internal database of laws and regulations to check whether it violates legal constraints.

[0100] "Means for automatically generating designs" refers to engines or software that automatically create legal and effective designs based on desired data.

[0101] The "means for generating an error message" refers to a technology that automatically generates an error message to notify a salesperson when content that violates legal restrictions is detected.

[0102] "Means for checking the generated design" refers to an interface that allows members of the responsible department to preview the generated design and confirm and modify its contents.

[0103] "Means for providing the final approved design" refers to the means by which the department in charge moves the design that has been finally approved to a folder that is accessible to the sales representative, so that the sales representative can download or print it.

[0104] "Means for moving the generated design to a folder accessible to sales personnel after final approval" refers to a means for saving the finalized design in a location easily accessible to sales personnel and making it available for use.

[0105] This invention details a system that uses AI to analyze salesperson requests and automatically generate POPs (Points of Purchase) that comply with legal restrictions.

[0106] System Overview

[0107] This system consists of the following main elements:

[0108] 1. A means of inputting salesperson requests

[0109] 2. Natural language processing means for analyzing requests

[0110] 3. How to determine whether a request complies with legal restrictions

[0111] 4. A method for automatically generating designs that conform to constraints

[0112] 5. A means for the department in charge to check the generated design

[0113] 6. Means of providing the final approved design

[0114] 7. A means to stop design generation and generate an error message if legal restrictions are violated.

[0115] 8. A way to move the generated designs to a folder accessible to sales personnel after final approval

[0116] Overview of the overall program processing

[0117] The program in this system receives the user's (salesperson's) request from the terminal and analyzes it using natural language processing means. The server compares the analyzed request with an internal database of laws and regulations to determine whether it complies with legal restrictions. If the request is determined to be compliant, the server activates a design generation engine to automatically generate a design based on the specified request. The generated design is then checked by the responsible department and given final approval. After approval, the design is moved to a folder accessible to the salesperson. Each step is described in detail below.

[0118] Hardware and software used

[0119] The following hardware and software are used in this system:

[0120] Terminal (PC, smartphone, tablet, etc.): A device used by sales staff to input, confirm, and download requests.

[0121] Server (high-performance workstation or cloud server): Receives requests, stores them, analyzes them, generates designs, and provides results.

[0122] Natural language processing engine (such as Google® NLP API): Analyzes salespeople's requests and extracts important keywords and concepts.

[0123] Legal database: Uses an internal legal database to determine compliance with legal constraints.

[0124] Design generation engine (e.g. Adobe Creative Cloud API): Automatically generate designs based on requests.

[0125] Specific examples

[0126] For example, let's say Salesperson A wants to create a promotional POP for a new product, "ABC." Salesperson A uses a terminal to access the input form, enters a request such as "emphasize the features of the new product and prominently display the limited-time discount," and clicks the "Submit" button. The following is a concrete example of a prompt sentence.

[0127] Example prompt sentence:

[0128] "I would like to create a promotional POP for our new product ABC. I would like the design to highlight the features of the new product and prominently display the limited-time discount."

[0129] The server receives the request, and the NLP engine begins analyzing it. Keywords such as "new product," "features," and "limited-time discount" are extracted and compared with a legal database. If it is determined to comply with legal restrictions, the design generation engine goes into action and automatically generates a design incorporating images of the new product and discount information. A member of the responsible department checks the generated design on a preview screen, makes minor adjustments such as making the discount rate more prominent, and then gives final approval. Once approved, the design is saved as a final version and moved to a folder that Salesperson A can access. Salesperson A logs in to the server from their own device and downloads or prints the approved POP to use in their sales promotion activities.

[0130] This system allows for the provision of prompt and legal POPs, improving the efficiency of sales activities.

[0131] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0132] Step 1: Enter your request

[0133] User

[0134] The user (salesperson) accesses a dedicated input form using a device such as a PC, smartphone, or tablet. This form has fields where users can enter the text content of the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). The user enters the required information in each field and clicks the "Submit" button. Input content includes new product features, discount information, event dates, etc. This input data is sent to the server.

[0135] Input: POP request data entered by the salesperson (text content, design requests, images, special instructions)

[0136] Output: Request data sent to the server

[0137] Step 2: Receiving and storing requests

[0138] server

[0139] The server receives the requested data sent by the user and stores it in a database for use in subsequent processing steps.

[0140] Input: Request data sent by sales representative

[0141] Output: Request data stored in a database

[0142] Step 3: Analyze the request

[0143] server

[0144] The server retrieves the stored request data from the database. It then uses a natural language processing (NLP) engine (e.g., Google NLP API) to analyze the request text and extract important keywords and concepts, such as "limited-time discount," "new product," and "free sample."

[0145] Input: Request data retrieved from the database

[0146] Output: List of extracted keywords

[0147] Step 4: Determine compliance with legal constraints

[0148] server

[0149] The server compares the extracted keyword list with an internal legal database. Here, it determines whether the analyzed request conforms to legal restrictions. For example, it checks whether there is any false advertising, whether there is any misleading information in the product labeling, and whether specific terminology is used. If any items that violate legal restrictions are found, it generates an error message to notify the user. If it is determined to conform, it proceeds to the next step.

[0150] Input: Extracted keyword list

[0151] Output: Legal compliance judgment result (compliant, non-compliant), error message (in case of non-compliance)

[0152] Step 5: Auto-generate the design

[0153] server

[0154] If the server determines that the request complies with legal restrictions, it launches a design generation engine (e.g., Adobe Creative Cloud API). The design generation engine automatically generates layouts, fonts, color schemes, and images based on the specified requests. For example, it generates a design that prominently displays the features of new products and highlights discount information.

[0155] Input: Request data, legal compliance judgment results

[0156] Output: Auto-generated design

[0157] Step 6: Check and approve the design

[0158] Terminal

[0159] Members of the relevant department access a dedicated interface to preview the generated design. They review the content and make corrections or comments as necessary. Once corrections are complete, the design is finalized with final approval.

[0160] Input: Auto-generated design

[0161] Output: Revised and final approved design

[0162] Step 7: Deliver the final design

[0163] server

[0164] The final approved POP design is saved on the server as a final version. It is then moved to a folder accessible to sales representatives. Sales representatives can then log in to the server using their own devices to download or print the approved POP.

[0165] Input: Final approved design

[0166] Output: Finalized design in a folder accessible to sales representatives

[0167] Through each processing step of this program, legal and effective POP designs based on the requests of sales representatives are quickly generated, thereby improving the efficiency of sales activities.

[0168] (Application example 1)

[0169] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0170] With conventional POP (Point of Purchase) generation systems, it was difficult to quickly provide legal designs based on sales representatives' requests. Furthermore, the time-consuming process of checking and correcting designs based on requests made it difficult to respond quickly on-site. Furthermore, the final approved design could not be sent to in-store display devices or printing devices in real time, making it necessary to respond quickly in sales promotion activities.

[0171] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0172] In this invention, the server includes means for inputting requests from sales representatives, natural language processing means for analyzing the requests, means for determining whether the requests comply with legal restrictions, means for automatically generating a design that complies with the restrictions, means for a responsible department to check the generated design, means for providing the final approved design, and means for transmitting the design to a display device or printer in the store in real time. This makes it possible to quickly and accurately analyze the requests from sales representatives, automatically generate a design that complies with legal restrictions, and simultaneously quickly carry out approval work, and provide the final design to the site in real time.

[0173] "Salesperson" means a professional or departmental employee who performs sales promotion activities.

[0174] The "means for inputting requests" refers to a device or interface that allows a salesperson to input requested information about a promotional POP by text or voice.

[0175] "Natural language processing means" refers to a technology or system that analyzes requests from salespeople, understands their meaning, and extracts important keywords.

[0176] "Legal Constraints" refers to laws and regulations regarding advertising and promotional materials, and are standards to ensure that violations do not occur.

[0177] "Means for automatically generating designs" refers to software or algorithms that automatically create designs according to the salesperson's requests.

[0178] "Means for the responsible department to check" refers to the process or interface by which members of the responsible department can check the generated design and make corrections or approvals as necessary.

[0179] The "means for providing the design" is a method or system for providing the final approved design for use by the sales force.

[0180] "Store display devices" refer to display devices such as displays and monitors installed in stores.

[0181] "Printing equipment" refers to equipment such as a printer that actually prints the POP design.

[0182] "Means for transmitting in real time" refers to communication means or technology for instantly transmitting the generated design to a display device or printing device in the store.

[0183] The present invention is a system that quickly and accurately analyzes the requests of salespeople, automatically generates designs that comply with legal restrictions, and provides the designs to store display devices and printers in real time. This system is composed of the following main elements:

[0184] System configuration

[0185] 1. A means of inputting salesperson requests

[0186] Salespeople can use devices such as smartphones or smart glasses to access a dedicated application and input the text content of the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). Voice input is also possible, allowing for faster operation.

[0187] 2. Natural language processing means for analyzing requests

[0188] The server uses an NLP engine (e.g., Google NLP API) to analyze the salesperson's input. This extracts important keywords and concepts from the input text. For example, keywords such as "new product," "features," and "limited-time discount" are extracted.

[0189] 3. How to determine compliance with legal restrictions

[0190] The server checks against a legal database to determine whether the extracted keywords comply with legal restrictions, checking for false advertising, misleading product information, etc. If a violation is found, an appropriate error message is generated.

[0191] 4. A method for automatically generating designs that conform to constraints

[0192] If the design is found to comply with legal restrictions, a design generation engine (e.g., Canva API) is put into action. Based on the specified requirements, the layout, fonts, color scheme, and images are automatically generated. For example, a design may be generated that highlights the features of new products and highlights discount information.

[0193] 5. A means for the department in charge to check the generated design

[0194] A member of the relevant department accesses a dedicated interface (for example, an app built with React Native) and previews the generated design. The person in charge checks the content and makes corrections or comments as necessary. Once the corrections are complete, the person in charge gives final approval.

[0195] 6. Means of providing the final approved design

[0196] The server saves the final approved design as a final version and moves it to a folder accessible to the sales representative, who can then log in to the server using their own device and download or print the approved POP.

[0197] 7. Means for transmitting data to a display or printing device in the store in real time

[0198] The final design is sent in real time to the store's display devices (displays and monitors) and printing equipment. Using real-time data synchronization technology such as Firebase, the design can be displayed and printed in-store instantly.

[0199] Specific examples

[0200] For example, consider the case where an employee at a brick-and-mortar store wants to create a promotional POP for a new product. Using a smartphone app, the employee voice-inputs, "I'd like you to create a promotional POP for new product 'XYZ' and display a 20% discount." The server receives the request and uses an NLP engine to extract the keywords "new product," "promotional POP," and "20% discount." After comparing this with a legal database, a legal design is automatically generated using the Canva API. The generated design is reviewed and revised by the relevant department, and after final approval, it is sent to the store's displays and printers in real time.

[0201] Prompt Sentence Examples

[0202] Salesperson's voice input: "I'd like to create a promotional POP for our new product 'XYZ', offering a 20% discount to new customers."

[0203] Keyword extraction using Google NLP API: "New product, promotional POP, new customer, 20% discount"

[0204] Legal constraint check: "New product, discount" → Legal constraint check

[0205] Create a design using the Canva API: "Add an image of a new product and the text '20% off'"

[0206] Real-time delivery by Firebase: "Send approved POP to in-store displays"

[0207] In this way, the detailed description of the invention can be utilized to quickly and accurately respond to the requests of salespeople.

[0208] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0209] Step 1:

[0210] Salespeople use smartphones or smart glasses to access a dedicated application and input their promotional POP requests. Input methods include text input and voice input, such as "Create a promotional POP for new product 'XYZ' and offer a 20% discount to new customers." This request data is sent to the server in real time. The input data includes the text content of the promotional POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.).

[0211] Step 2:

[0212] The server stores the received request data and analyzes it using a natural language processing (NLP) engine (e.g., Google NLP API). The NLP engine extracts important keywords from the input text, such as "new product," "promotional POP," "new customer," and "20% discount." The extracted keywords are used in the next step to check legal restrictions.

[0213] Step 3:

[0214] The server checks the extracted keywords against a legal database to determine whether they comply with legal restrictions. For example, it checks whether the keyword "20% discount" constitutes misleading advertising, or whether the display of "new products" contains misleading information. If a violation is found based on this check, an appropriate error message is automatically generated. This error message is sent to the user's device.

[0215] Step 4:

[0216] Based on the request, which is verified as complying with legal restrictions, the server triggers a design generation engine (e.g., the Canva API). The design generation engine automatically generates the layout, fonts, color scheme, and images based on the specified request. For example, a design may be created using images of new products to complement the text stating 20% ​​off. The generation process uses specific templates and elements according to the request.

[0217] Step 5:

[0218] The generated design is displayed on a dedicated interface for review by the relevant department. Members of the relevant department can preview the design using a PC or smart device, check the content, make corrections or comments as necessary, and once the corrections are complete, give final approval. Corrections are updated online in real time.

[0219] Step 6:

[0220] The final approved design is saved on the server as a final version. The saved design is then moved to a folder accessible to sales representatives. Sales representatives can then log in to the server using their own devices and download or print the approved POP.

[0221] Step 7:

[0222] The server sends the final design in real time to the store's display device (display or monitor) or printing device. This process uses real-time data synchronization technology such as Firebase, and the generated design is instantly displayed and printed in the store, enabling quick response to sales activities.

[0223] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0224] This invention details a system that uses AI to analyze salesperson requests and automatically generate POPs (points of purchase) that comply with legal restrictions, and further combines this with an emotion engine that recognizes the user's emotions, making it possible to provide designs that match the user's emotional state.

[0225] System Overview

[0226] This system consists of the following main elements:

[0227] 1. A means of inputting salesperson requests

[0228] 2. Natural language processing means for analyzing requests

[0229] 3. How to determine whether a request complies with legal restrictions

[0230] 4. A method for automatically generating designs that conform to constraints

[0231] 5. A means for the department in charge to check the generated design

[0232] 6. Means of providing the final approved design

[0233] 7. Emotion engine that recognizes user emotions

[0234] This system not only allows salespeople to quickly and legally generate POPs that correspond to specific promotional campaigns or events, but also provides designs that reflect the emotional state of the salespeople.

[0235] Explanation of program processing

[0236] Adding emotion engine processing

[0237] Step 1: Enter the sales representative's request

[0238] User (sales representative)

[0239] Salespeople access a dedicated input form using devices such as PCs, smartphones, and tablets. This form includes fields for entering the text content of the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). As the salesperson enters their request, the emotion engine analyzes the user's emotional state in real time based on the salesperson's input speed, facial expressions, voice, etc. When the salesperson clicks the "Send" button, the request data and emotional data are sent to the server.

[0240] Step 2: Analyze needs and emotions

[0241] server

[0242] The server stores the received request data and emotion data in a database. At the same time, a natural language processing (NLP) engine begins analyzing the request data. The NLP engine extracts keywords and important phrases from the input text. The emotion engine analyzes the emotion data and identifies the emotional state of the salesperson.

[0243] Step 3: Determine compliance with legal constraints

[0244] server

[0245] The NLP engine compares the extracted keywords, phrases, and sentiment data with an internal legal database. For example, it checks for expressions that meet the criteria for "misleading advertising" or for words prohibited by specific laws. If a violation is detected, an error message is generated, prompting the salesperson to make corrections.

[0246] Step 4: Automatically generate a design that meets the constraints

[0247] server

[0248] If the request is confirmed to comply with legal constraints, a design generation engine kicks in. The generation engine determines the layout, text style, color scheme, and image placement based on the specified request and reflects the emotional data. For example, if the salesperson's emotions indicate "excitement" or "happiness," it selects vibrant colors and font styles.

[0249] Step 5: The generated design is checked by the responsible department

[0250] Terminal

[0251] A member of the relevant department accesses a dedicated interface to preview the generated POP design, review it, and provide feedback and corrections as necessary.

[0252] Step 6: Deliver the final approved design

[0253] server

[0254] When the person in charge clicks the "Final Approval" button, the final design data is saved as the final version. A notification of approval completion is also automatically sent to the sales representative.

[0255] Step 7: Deliver your design

[0256] User

[0257] Salespeople log in to the server using their own devices and download or print the approved POP design, allowing them to quickly obtain POPs that can be used in sales promotion activities.

[0258] Specific examples

[0259] For example, let's say Salesperson A wants to create a promotional POP for a new product, "DEF." Salesperson A uses a terminal to access the input form, inputs requests such as "emphasize the features of the new product and prominently display the limited-time discount," and clicks the "Submit" button. The emotion engine recognizes that Salesperson A is in an "excited" state based on his facial expressions and tone of voice when he inputs his request.

[0260] The server receives the request, and the NLP engine begins analyzing it, extracting keywords such as "new product," "features," and "limited-time discount." The analysis results are compared with a legal database to check whether they comply with legal restrictions. If they are determined to comply with the restrictions, the design generation engine kicks in, generating a design that matches Salesperson A's emotions using vibrant colors and font styles.

[0261] A member of the relevant department checks the generated design on the preview screen, makes minor adjustments such as making the discount rate more prominent, and then gives final approval. After approval, the design is saved as a final version and moved to a folder that Salesperson A can access. Salesperson A can then log in to the server from their own device and download or print the approved POP to use in their sales promotion activities.

[0262] This system not only allows for the provision of quick and appropriate POP displays, but also generates designs that reflect the emotions of sales staff, further increasing the effectiveness of sales promotion activities.

[0263] The processing flow will be explained below.

[0264] Step 1:

[0265] User (sales representative)

[0266] Salespeople access a dedicated input form on their terminals and enter their requests for POP creation. Input items include the text content of the POP, the desired theme and design, the images and icons to be used, and special instructions (deadline date, event information, etc.). Furthermore, as the salesperson enters information, the emotion engine analyzes the salesperson's typing speed, keyboard swiftness, facial expressions (when using a camera), and voice tone (when using a microphone), collecting emotional data in real time. When the salesperson clicks the "Send" button, the request data and emotional data are sent to the server.

[0267] Step 2:

[0268] server

[0269] The emotion data is stored in a database along with the received request data. At the same time, a natural language processing (NLP) engine begins analyzing the request data. Specifically, it extracts keywords and important phrases from the input text and analyzes them to understand the context.

[0270] Step 3:

[0271] server

[0272] The analyzed keywords and phrases are compared with a legal database to determine whether they comply with legal restrictions. For example, it checks whether they contain expressions that constitute misleading advertising or words prohibited by law. If a violation is found, an appropriate error message is generated and the salesperson is asked to make the necessary corrections.

[0273] Step 4:

[0274] server

[0275] If the request is confirmed to comply with legal constraints, the design generation engine kicks in. Using emotion data, the engine adapts design elements to the salesperson's emotional state (e.g., excitement, joy, calm, etc.). For example, excitement and joy can be expressed with vibrant, bright colors and adjusted layout and font styles.

[0276] Step 5:

[0277] server

[0278] The generated POP design data is temporarily stored in storage, and the design is sent to a dedicated interface that can be accessed by the relevant department.

[0279] Step 6:

[0280] Terminal

[0281] A member of the relevant department accesses a dedicated interface to preview the generated POP design, check the design content, and enter correction instructions or feedback as necessary.

[0282] Step 7:

[0283] server

[0284] Once the person in charge has completed the final check and clicked the "Final Approval" button, the design is saved as the final version. A notification of approval completion is also automatically sent to the sales representative.

[0285] Step 8:

[0286] User (sales representative)

[0287] Salespeople log in to the server using their own devices and download or print the final approved POP design, enabling them to quickly obtain legal, emotionally appropriate POPs that can be used in sales promotion activities.

[0288] Example 2

[0289] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0290] Conventional sales promotion design generation systems were unable to reflect the emotional state of salespeople when they entered their requests, which meant that promotional materials often did not have a strong emotional impact on customers. Furthermore, checking legal restrictions one by one required a great deal of time and effort, making it difficult to generate designs quickly.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0292] In this invention, the server includes a means for inputting the sales representative's requests, a natural language processing means for analyzing the requests, a means for determining whether the requests comply with legal restrictions, a means for automatically generating a design that complies with the restrictions, a means for the responsible department to check the generated design, a means for providing the final approved design, and an emotion engine for recognizing the user's emotions. This makes it possible to automatically generate effective promotional designs that match the sales representative's emotional state while quickly reflecting the sales representative's requests. Furthermore, automating the checking of legal restrictions allows for both efficient design generation and compliance with laws and regulations.

[0293] "Requests" refers to information such as the text content of the POP, design preferences, images and icons to be used, and special instructions that salespeople need to promote sales.

[0294] "Natural language processing means" is a technology for analyzing input text data to extract and understand requirements.

[0295] "Legal Constraints" means laws and regulations that must be observed in advertising and promotional activities.

[0296] "Design generation means" is a technology that automatically creates designs for advertising and sales promotions based on input requests and analysis results.

[0297] The "department in charge" refers to a specialized department or team that reviews the content of the generated design and makes corrections or approvals as necessary.

[0298] An "emotion engine" is a technology that identifies emotions from the user's input speed, facial expressions, voice, etc., and analyzes their emotional state.

[0299] A "server" is a central computer system that processes and stores data for the entire system.

[0300] "Real-time" means that processing and analysis occurs immediately at the moment the input is made.

[0301] A "database" is a collection of data that efficiently manages related data and allows for quick search and manipulation.

[0302] A specific embodiment of the present invention is described in detail below. The present invention is a system that enables salespeople to quickly and legally create promotional POP (Point of Purchase) displays and provides designs that reflect the emotional state of the user.

[0303] Hardware and software used

[0304] The system uses the following main hardware and software:

[0305] Server: A central computer system that processes and stores data for the entire system. For example, we use servers from Amazon Web Services (AWS (registered trademark)) or Google Cloud Platform (GCP).

[0306] Terminal: A device such as a PC, smartphone, or tablet on which salespeople enter requests. Each terminal uses a web browser to access a dedicated input form.

[0307] Natural language processing means: Analyze request data using libraries such as Python's NLTK or SpaCy.

[0308] Design generation method: Designs are automatically generated using design tool APIs such as Adobe XD and Sketch.

[0309] Emotion engine: A technology for analyzing the speed, facial expressions, and voice of users when they input, using libraries such as OpenCV and TENSORFLOW (registered trademark). Specifically, this applies to AI models that perform facial recognition and voice emotion analysis.

[0310] Database: Desire data and emotion data are stored and managed using a database management system such as MySQL (registered trademark) or PostgreSQL.

[0311] System Operation

[0312] User input of requests

[0313] The user (salesperson) accesses a dedicated input form using a device such as a PC, smartphone, or tablet. This form contains fields for entering text content related to the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). When the salesperson enters their request, the emotion engine analyzes their input speed, facial expressions, and voice in real time to identify their emotional state. When the salesperson clicks the "Send" button, the request data and emotion data are sent to the server.

[0314] Analysis of needs and emotions

[0315] The server stores the received request data and emotion data in a database. At the same time, a natural language processing engine analyzes the request data and extracts important keywords and phrases. The emotion engine identifies the user's emotional state. Specifically, the request text is analyzed using the Python libraries NLTK and SpaCy, and emotion data is analyzed using OpenCV and TensorFlow.

[0316] Determining legal restrictions

[0317] The NLP engine compares the extracted keywords, phrases, and sentiment data with the server's internal legal database to check for expressions or terms that violate legal restrictions, and if a violation is detected, an error message is generated and a notification is sent to the salesperson urging them to make corrections.

[0318] Auto-generation of designs

[0319] Once legal restrictions are met, the server's design generation engine automatically generates a POP design based on the customer's needs and emotional data. Using Adobe XD and Sketch APIs, the server determines the layout, text style, color scheme, and image placement. For example, if the salesperson's emotional state is "excited," the server selects vibrant colors and eye-catching fonts.

[0320] Design review and approval

[0321] A member of the relevant department uses a dedicated interface to review the generated design, provide corrections and feedback as necessary, and finally approve it.

[0322] Design provision

[0323] Once the design data has been approved, it is stored in cloud storage and notified to the sales representative, who can then log in from their own device and download or print the approved design for use in their sales promotion activities.

[0324] Specific examples

[0325] For example, suppose a salesperson wants to create a promotional POP for a new product, "DEF." The salesperson uses a terminal to access the input form, inputs requests such as "emphasize the features of the new product and prominently display the limited-time discount," and clicks the "Submit" button. The emotion engine recognizes that the salesperson is in an "excited" state based on their facial expression and tone of voice when they input their request.

[0326] The server receives the request, and the NLP engine extracts keywords such as "new product," "features," and "limited-time discount." This is then compared with a legal database to check whether the request complies with legal restrictions. If the request is found to comply with the restrictions, the design generation engine generates a design using vibrant colors and font styles.

[0327] The relevant department reviews the POP, makes any necessary corrections, and then gives final approval. Sales representatives can download or print the approved POP from their own devices and use it in their sales promotion activities.

[0328] Examples of prompts for a generative AI model might include:

[0329] "Create a POP that highlights the features of your new product. Display the limited-time discount prominently and use a vibrant color background. Also, choose a font that will make an impact on customers."

[0330] In this way, the system reflects the desires and emotions of salespeople and provides fast and legal promotional designs.

[0331] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0332] Step 1:

[0333] The user (salesperson) accesses a dedicated input form using a PC, smartphone, or tablet device. The input form contains fields for entering the text content of the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). When the salesperson enters their request and clicks the "Send" button, the entered request data and emotional data (input speed, facial expression, and voice data) are sent to the server.

[0334] Input: POP text content, design requests, image icons, special instructions, etc.

[0335] Output: Desire data and emotion data

[0336] Step 2:

[0337] The server stores the received request data and emotion data in a database. A natural language processing (NLP) engine analyzes the request data and extracts important keywords and phrases. The emotion engine analyzes emotion data from the salesperson's typing speed, facial expressions, and voice to identify their emotional state. Specific analysis uses Python libraries NLTK and SpaCy, as well as OpenCV and TensorFlow.

[0338] Input: Desire data and emotion data

[0339] Output: extracted keywords and phrases, analyzed emotional states

[0340] Step 3:

[0341] The server checks the keywords, phrases, and sentiment data extracted by the NLP engine against an internal legal database to verify legal restrictions. For example, it checks whether the content contains expressions that violate legal restrictions, such as "exaggerated advertising" or "false representation." If a violation is detected, an error message is generated and a notification is automatically sent to the salesperson requesting correction.

[0342] Input: Extracted keywords, phrases, and sentiment data

[0343] Output: Legal constraint detection results, error messages (if necessary)

[0344] Step 4:

[0345] If the server verifies that the request complies with legal restrictions, a design generation engine automatically generates a POP design based on the request and emotion data. The engine uses APIs from Adobe XD and Sketch to determine the layout, text style, color scheme, and image placement. For example, if the salesperson's emotion is "excited," it uses bright colors and a bold font. The generative AI model selects these design elements.

[0346] Input: Legally compliant desire data and emotion data

[0347] Output: Generated POP design

[0348] Step 5:

[0349] The user (a member of the relevant department) accesses a dedicated interface and previews the generated POP design. The person in charge checks the design and makes corrections or provides feedback as necessary. The preview screen is built using front-end frameworks such as React and Vue.js.

[0350] Input: Generated POP design

[0351] Output: Reviewed and revised design, feedback

[0352] Step 6:

[0353] When the person in charge clicks the "Final Approval" button, the server saves the final design data to cloud storage (e.g., Amazon S3) and automatically sends a notification of approval completion to the sales representative.

[0354] Input: Final approved design

[0355] Output: Final design saved in cloud storage, approval notification

[0356] Step 7:

[0357] The user (salesperson) logs in to the server from their own terminal and downloads or prints the approved POP design. The salesperson can then start their sales promotion activities using this design.

[0358] Input: Server login credentials, request for approved POP design

[0359] Output: Downloaded or printed POP designs

[0360] (Application example 2)

[0361] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0362] Conventional design generation systems have limitations in automatically generating content that reflects the user's emotional state or in quickly generating content that meets legal constraints. Furthermore, they are unable to provide personalized content based on the user's emotional state, so there is a need to improve the user experience.

[0363] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting a request from a salesperson, natural language processing means for analyzing the request, means for determining whether the request complies with legal restrictions, means for automatically generating a design that complies with the restrictions, means for a responsible department to check the generated design, means for providing the final approved design, means for recognizing the user's emotions, and means for generating appropriate content based on the user's emotional state. This enables the automatic generation of personalized content that reflects the user's emotional state and the rapid provision of legally compliant content.

[0364] The "means for inputting the salesperson's requests" is a device or system that provides an interface for the salesperson to input their requests.

[0365] "Natural language processing means" refers to a technology or system that analyzes text data entered by salespeople and extracts important keywords and phrases.

[0366] "Means for determining compliance with legal constraints" refers to a system that checks whether the generated designs and content comply with legal regulations and standards.

[0367] "Means for automatically generating designs that comply with constraints" refers to a system or technology that automatically generates designs and layouts that comply with legal constraints.

[0368] "Means for the responsible department to check the generated design" refers to an interface or system that allows the designated responsible department to check and provide feedback on the generated design.

[0369] The "means for providing the final approved design" is a system or method for providing the final approved design to sales personnel and other interested parties.

[0370] "Means for recognizing user emotions" refers to technology or a system that analyzes and recognizes emotions from the user's facial expressions, tone of voice, input speed, etc.

[0371] The "means for generating appropriate content based on the emotional state of the user" refers to a system or technology that automatically generates optimal content for a user in accordance with the emotional state of the user.

[0372] To implement this invention, various hardware and software components must be integrated to build an entire system. This system supports a series of processes: a salesperson inputs their requirements, the requirements are analyzed, and a design that complies with legal restrictions is automatically generated. It also includes a component that recognizes the user's emotional state and generates personalized content based on that.

[0373] System configuration

[0374] Hardware elements

[0375] 1. Smartphones and smart glasses: Used to collect user input and emotional data. Equipped with a camera and microphone, they can collect facial expressions and voice data in real time.

[0376] 2. Server: Responsible for hosting the data processing and generation engine.

[0377] Software Elements

[0378] 1. Natural language processing engine (e.g., spaCy, BERT): Analyzes the salesperson's input and extracts keywords and important phrases.

[0379] 2. Emotion recognition engine (e.g., Affective Computing SDK, Emotion API): Identifies the user's emotional state from their facial expressions and voice.

[0380] 3. Content generation engine (e.g., OpenAI® GPT-4®, DALL-E): Generates appropriate content based on the user's emotional state.

[0381] 4. Internal legal database: Used to determine whether generated content complies with legal constraints.

[0382] Data processing and calculation

[0383] Emotion data collection and analysis

[0384] The device (smartphone or smart glasses) collects the user's facial expressions, voice tone, and input speed in real time, and sends this data to an emotion recognition engine to analyze the user's emotional state.

[0385] Input and analysis of requests

[0386] Salespeople access a specific input form and enter their requests using a PC or smart device. The text is sent to a server and analyzed by a natural language processing engine. The engine extracts keywords and important phrases and compares the content with a database to determine whether it complies with legal restrictions.

[0387] Generate the design

[0388] If the user's desires and emotional data are confirmed to comply with legal restrictions, the content generation engine will begin working and generate designs and content based on the user's emotions. For example, if the user is in an "excited" state, it will generate a flashy, dynamic design. Conversely, if the user is in a "relaxed" state, it will generate a calm design.

[0389] Check by the relevant department

[0390] The resulting design is checked by the relevant department, fine-tuned as necessary, and then, once finally approved, provided to the sales staff.

[0391] Specific examples

[0392] For example, let's say a salesperson wants a design to promote a new product, "ABC." The salesperson enters "emphasize the features of ABC and highlight the limited-time discount" into the form and submits it. The emotion recognition engine identifies the salesperson's emotion as "excitement," the server receives the request, and the natural language processing engine extracts keywords such as "ABC," "features," and "limited-time discount." After verifying that legal restrictions are met, the design generation engine generates a vivid and dynamic design.

[0393] This allows salespeople to quickly and accurately create promotional designs, enabling them to provide content experiences that are optimized to suit the user's emotions.

[0394] Example prompt sentence:

[0395] Analyze the user's facial and voice data to identify their emotional state and generate a music playlist based on that emotional state. If the user is relaxed, choose relaxing songs, and if the user is excited, choose upbeat songs. Also, make sure the generated playlist does not violate copyright laws.

[0396] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0397] Step 1:

[0398] Salespeople access a dedicated input form using devices such as PCs, smartphones, and tablets. This form includes fields for entering the text content of the POP, desired design, images and icons to be used, and special instructions (deadline date, event information, etc.). As the salesperson enters their request, the emotion engine analyzes the user's emotional state in real time based on the salesperson's input speed, facial expression, voice, etc. When the salesperson clicks the "Send" button, the request data and emotional data are sent to the server. Specifically, the input data consists of the text content, input speed, facial expression data, and voice tone. Once sent to the server, this data is stored in a database for analysis.

[0399] Step 2:

[0400] The server stores the request data and emotional data received in step 1 in a database. At the same time, the natural language processing engine begins analyzing the request data. It extracts keywords and important phrases from the input data (text content). The emotion recognition engine analyzes the input data (facial expression data and voice tone) and identifies the emotional state of the salesperson. For example, it determines that the salesperson is in an "excited" state. The server is then ready to pass the analysis results of the request data and the emotional state to the next step.

[0401] Step 3:

[0402] The server compares the keywords, phrases, and sentiment data extracted by the natural language processing engine with a legal database. This process checks whether the input data contains expressions or terms that violate laws and regulations. For example, it detects whether the data contains "exaggerated advertising" or expressions that "violate specific laws and regulations." Based on the results of this comparison, it determines whether the data conforms to legal restrictions, and if so, proceeds to the next step.

[0403] Step 4:

[0404] If the server verifies that the request complies with legal restrictions, the design generation engine begins operation. Based on the input data (analysis results, emotional state), an automatically generated design is determined. At this stage, for example, a design using vibrant colors and font styles is generated for a salesperson in an "excited" state. The generated design data is sent to the relevant department's interface for review.

[0405] Step 5:

[0406] A member of the department in charge logs into a dedicated interface and previews the generated design. They visually check the input data (the generated design) and provide feedback and corrections as necessary. A specific example would be to make the discount rate more prominent. This corrected data is then sent back to the server, where it continues to be checked until it is finalized.

[0407] Step 6:

[0408] When the department member finally clicks the "Final Approval" button, the server saves the final version of the design data as the final version. The server also automatically sends an approval completion notification to the sales representative, which then provides the sales representative with the final approved design.

[0409] Step 7:

[0410] Salespeople log in to the server using their own devices and download or print the approved POP design. This operation allows the generated design to be immediately used in the salesperson's sales promotion activities. Specific actions include downloading a file and printing it out to a printer.

[0411] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0412] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0413] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0414] [Second embodiment]

[0415] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0416] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0417] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0418] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0419] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0420] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0421] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0422] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0423] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0424] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0425] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0426] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0427] This invention details a system that uses AI to analyze salesperson requests and automatically generate POPs (Points of Purchase) that comply with legal restrictions.

[0428] System Overview

[0429] This system consists of the following main elements:

[0430] 1. A means of inputting salesperson requests

[0431] 2. Natural language processing means for analyzing requests

[0432] 3. How to determine whether a request complies with legal restrictions

[0433] 4. A method for automatically generating designs that conform to constraints

[0434] 5. A means for the department in charge to check the generated design

[0435] 6. Means of providing the final approved design

[0436] The system allows salespeople to quickly and legally generate POPs for specific promotional campaigns and events.

[0437] Explanation of program processing

[0438] Step 1: Enter the sales representative's request

[0439] User (sales representative)

[0440] Salespeople access a dedicated input form using devices such as PCs, smartphones, and tablets. This form includes fields for entering the text content of the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). When the salesperson enters their request and clicks the "Submit" button, the request data is sent to the server.

[0441] Step 2: Analyze the request

[0442] server

[0443] The server stores the received request data and begins analyzing it using a natural language processing (NLP) engine. The NLP engine extracts important keywords and concepts from the input text. For example, keywords such as "limited-time discount," "new product," and "free sample" are extracted.

[0444] Step 3: Determine compliance with legal constraints

[0445] server

[0446] The server then checks the parsed request against an internal legal database to determine whether it complies with legal restrictions, such as whether the request constitutes false advertising, whether the product labeling contains misleading information, whether specific terminology is used, etc. If a violation is found, an appropriate error message is automatically generated.

[0447] Step 4: Automatically generate a design that meets the constraints

[0448] server

[0449] If the requirements are confirmed to comply with legal constraints, a design generation engine is put into action. This engine automatically generates layouts, fonts, color schemes, and images based on the specified requirements. For example, a design may be generated that highlights new product features and highlights discount information.

[0450] Step 5: The generated design is checked by the responsible department

[0451] Terminal

[0452] A member of the relevant department accesses a dedicated interface to preview the generated design, checks the content, and makes corrections or comments as necessary. Once corrections are complete, the design is given final approval.

[0453] Step 6: Deliver the final approved design

[0454] server

[0455] Once approved, the POP design is saved as a final version and moved to a folder accessible to sales representatives, who can then log in to the server using their own devices and download or print the approved POP.

[0456] Specific examples

[0457] For example, let's say Salesperson A wants to create a promotional POP for a new product "ABC." Salesperson A uses a terminal to access the input form, enters requests such as "emphasize the features of the new product and prominently display the limited-time discount," and clicks the "Submit" button.

[0458] The server receives the request, and the NLP engine begins analyzing it, extracting keywords such as "new product," "features," and "limited-time discount." The analysis results are compared with a legal database to check whether they comply with legal restrictions. If they are determined to comply with the restrictions, the design generation engine is activated, and a design incorporating images of the new product and discount information is automatically generated.

[0459] A member of the relevant department checks the generated design on the preview screen, makes minor adjustments such as making the discount rate more prominent, and then gives final approval. After approval, the design is saved as a final version and moved to a folder that Salesperson A can access. Salesperson A logs in to the server from his or her own device and downloads or prints the approved POP to use in sales promotion activities.

[0460] This system allows for the provision of prompt and appropriate POPs, improving the efficiency of sales activities.

[0461] The processing flow will be explained below.

[0462] Step 1:

[0463] User

[0464] The salesperson accesses a dedicated form on their terminal and enters their request for POP creation. Input items include the text content, desired theme and design, images to be used, deadline, etc. When the salesperson clicks the "Send" button, the request data is sent to the server.

[0465] Step 2:

[0466] server

[0467] The server stores the received request data in a database. At the same time, a natural language processing (NLP) engine begins analyzing the request data. Specifically, it extracts keywords and important phrases from the input text.

[0468] Step 3:

[0469] server

[0470] The NLP engine compares the extracted keywords and phrases with an internal legal database. For example, it checks for expressions that meet the criteria for "misleading advertising" or for words prohibited by specific laws. If a violation is detected, an error message is generated and the salesperson is asked to make corrections.

[0471] Step 4:

[0472] server

[0473] If the requirements are confirmed to comply with legal constraints, a design generation engine is put into action, which includes algorithms that automatically determine the layout, text style, color scheme, and image placement based on the specified requirements.

[0474] Step 5:

[0475] server

[0476] The generated POP design is stored in temporary storage, and the design data is sent to a dedicated interface that can be accessed by the relevant department.

[0477] Step 6:

[0478] Terminal

[0479] The person in charge of the relevant department accesses a dedicated interface to preview the generated POP design, check the content, and enter feedback and corrections as necessary.

[0480] Step 7:

[0481] server

[0482] When the person in charge clicks the "Final Approval" button, the final design data is saved as the final version. A notification of approval completion is also automatically sent to the sales representative.

[0483] Step 8:

[0484] User

[0485] Salespeople log in to the server using their own devices and download or print the approved POP design, allowing them to quickly obtain POPs that can be used in sales promotion activities.

[0486] Example 1

[0487] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0488] Point-of-purchase (POP) designs created by sales representatives for promotional activities must be fast and legal. However, the current process of sales representatives submitting design requests, checking whether they comply with legal restrictions, and generating appropriate designs is laborious and time-consuming. Furthermore, designs that include content that may violate legal restrictions are inappropriate and must be dealt with appropriately.

[0489] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0490] In this invention, the server includes means for inputting requests from sales representatives, natural language processing means for analyzing the requests, means for determining whether the requests comply with legal restrictions, means for automatically generating a design that complies with the restrictions, means for halting design generation and generating an error message if the requests violate legal restrictions, means for providing the final approved design, and means for moving the generated design to a folder accessible to the sales representative after final approval. This allows POP designs based on the sales representative's requests to be generated quickly and legally, making it possible to improve the efficiency of sales activities.

[0491] "Means for inputting salesperson requests" refers to the interface that salespersons use to input the text content and design of POPs, images and icons to be used, special instructions, etc. using devices such as PCs, smartphones, and tablets.

[0492] "Natural language processing means" refers to technical means, particularly a natural language processing engine, for analyzing received sales representative request data and extracting important keywords and concepts from the text.

[0493] "Means for determining compliance with legal constraints" refers to the means for comparing the analyzed request content with an internal database of laws and regulations to check whether it violates legal constraints.

[0494] "Means for automatically generating designs" refers to engines or software that automatically create legal and effective designs based on desired data.

[0495] The "means for generating an error message" refers to a technology that automatically generates an error message to notify a salesperson when content that violates legal restrictions is detected.

[0496] "Means for checking the generated design" refers to an interface that allows members of the responsible department to preview the generated design and confirm and modify its contents.

[0497] "Means for providing the final approved design" refers to the means by which the department in charge moves the design that has been finally approved to a folder that is accessible to the sales representative, so that the sales representative can download or print it.

[0498] "Means for moving the generated design to a folder accessible to sales personnel after final approval" refers to a means for saving the finalized design in a location easily accessible to sales personnel and making it available for use.

[0499] This invention details a system that uses AI to analyze salesperson requests and automatically generate POPs (Points of Purchase) that comply with legal restrictions.

[0500] System Overview

[0501] This system consists of the following main elements:

[0502] 1. A means of inputting salesperson requests

[0503] 2. Natural language processing means for analyzing requests

[0504] 3. How to determine whether a request complies with legal restrictions

[0505] 4. A method for automatically generating designs that conform to constraints

[0506] 5. A means for the department in charge to check the generated design

[0507] 6. Means of providing the final approved design

[0508] 7. A means to stop design generation and generate an error message if legal restrictions are violated.

[0509] 8. A way to move the generated designs to a folder accessible to sales personnel after final approval

[0510] Overview of the overall program processing

[0511] The program in this system receives the user's (salesperson's) request from the terminal and analyzes it using natural language processing means. The server compares the analyzed request with an internal database of laws and regulations to determine whether it complies with legal restrictions. If the request is determined to be compliant, the server activates a design generation engine to automatically generate a design based on the specified request. The generated design is then checked by the responsible department and given final approval. After approval, the design is moved to a folder accessible to the salesperson. Each step is described in detail below.

[0512] Hardware and software used

[0513] The following hardware and software are used in this system:

[0514] Terminal (PC, smartphone, tablet, etc.): A device used by sales staff to input, confirm, and download requests.

[0515] Server (high-performance workstation or cloud server): Receives requests, stores them, analyzes them, generates designs, and provides results.

[0516] Natural language processing engine (such as Google NLP API): Analyzes sales representatives' requests and extracts important keywords and concepts.

[0517] Legal database: Uses an internal legal database to determine compliance with legal constraints.

[0518] Design generation engine (e.g. Adobe Creative Cloud API): Automatically generate designs based on requests.

[0519] Specific examples

[0520] For example, let's say Salesperson A wants to create a promotional POP for a new product, "ABC." Salesperson A uses a terminal to access the input form, enters a request such as "emphasize the features of the new product and prominently display the limited-time discount," and clicks the "Submit" button. The following is a concrete example of a prompt sentence.

[0521] Example prompt sentence:

[0522] "I would like to create a promotional POP for our new product ABC. I would like the design to highlight the features of the new product and prominently display the limited-time discount."

[0523] The server receives the request, and the NLP engine begins analyzing it. Keywords such as "new product," "features," and "limited-time discount" are extracted and compared with a legal database. If it is determined to comply with legal restrictions, the design generation engine goes into action and automatically generates a design incorporating images of the new product and discount information. A member of the responsible department checks the generated design on a preview screen, makes minor adjustments such as making the discount rate more prominent, and then gives final approval. Once approved, the design is saved as a final version and moved to a folder that Salesperson A can access. Salesperson A logs in to the server from their own device and downloads or prints the approved POP to use in their sales promotion activities.

[0524] This system allows for the provision of prompt and legal POPs, improving the efficiency of sales activities.

[0525] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0526] Step 1: Enter your request

[0527] User

[0528] The user (salesperson) accesses a dedicated input form using a device such as a PC, smartphone, or tablet. This form has fields where users can enter the text content of the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). The user enters the required information in each field and clicks the "Submit" button. Input content includes new product features, discount information, event dates, etc. This input data is sent to the server.

[0529] Input: POP request data entered by the salesperson (text content, design requests, images, special instructions)

[0530] Output: Request data sent to the server

[0531] Step 2: Receiving and storing requests

[0532] server

[0533] The server receives the requested data sent by the user and stores it in a database for use in subsequent processing steps.

[0534] Input: Request data sent by sales representative

[0535] Output: Request data stored in a database

[0536] Step 3: Analyze the request

[0537] server

[0538] The server retrieves the stored request data from the database. It then uses a natural language processing (NLP) engine (e.g., Google NLP API) to analyze the request text and extract important keywords and concepts, such as "limited-time discount," "new product," and "free sample."

[0539] Input: Request data retrieved from the database

[0540] Output: List of extracted keywords

[0541] Step 4: Determine compliance with legal constraints

[0542] server

[0543] The server compares the extracted keyword list with an internal legal database. Here, it determines whether the analyzed request conforms to legal restrictions. For example, it checks whether there is any false advertising, whether there is any misleading information in the product labeling, and whether specific terminology is used. If any items that violate legal restrictions are found, it generates an error message to notify the user. If it is determined to conform, it proceeds to the next step.

[0544] Input: Extracted keyword list

[0545] Output: Legal compliance judgment result (compliant, non-compliant), error message (in case of non-compliance)

[0546] Step 5: Auto-generate the design

[0547] server

[0548] If the server determines that the request complies with legal restrictions, it launches a design generation engine (e.g., Adobe Creative Cloud API). The design generation engine automatically generates layouts, fonts, color schemes, and images based on the specified requests. For example, it generates a design that prominently displays the features of new products and highlights discount information.

[0549] Input: Request data, legal compliance judgment results

[0550] Output: Auto-generated design

[0551] Step 6: Check and approve the design

[0552] Terminal

[0553] Members of the relevant department access a dedicated interface to preview the generated design. They review the content and make corrections or comments as necessary. Once corrections are complete, the design is finalized with final approval.

[0554] Input: Auto-generated design

[0555] Output: Revised and final approved design

[0556] Step 7: Deliver the final design

[0557] server

[0558] The final approved POP design is saved on the server as a final version. It is then moved to a folder accessible to sales representatives. Sales representatives can then log in to the server using their own devices to download or print the approved POP.

[0559] Input: Final approved design

[0560] Output: Finalized design in a folder accessible to sales representatives

[0561] Through each processing step of this program, legal and effective POP designs based on the requests of sales representatives are quickly generated, thereby improving the efficiency of sales activities.

[0562] (Application example 1)

[0563] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0564] With conventional POP (Point of Purchase) generation systems, it was difficult to quickly provide legal designs based on sales representatives' requests. Furthermore, the time-consuming process of checking and correcting designs based on requests made it difficult to respond quickly on-site. Furthermore, the final approved design could not be sent to in-store display devices or printing devices in real time, making it necessary to respond quickly in sales promotion activities.

[0565] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0566] In this invention, the server includes means for inputting requests from sales representatives, natural language processing means for analyzing the requests, means for determining whether the requests comply with legal restrictions, means for automatically generating a design that complies with the restrictions, means for a responsible department to check the generated design, means for providing the final approved design, and means for transmitting the design to a display device or printer in the store in real time. This makes it possible to quickly and accurately analyze the requests from sales representatives, automatically generate a design that complies with legal restrictions, and simultaneously quickly carry out approval work, and provide the final design to the site in real time.

[0567] "Salesperson" means a professional or departmental employee who performs sales promotion activities.

[0568] The "means for inputting requests" refers to a device or interface that allows a salesperson to input requested information about a promotional POP by text or voice.

[0569] "Natural language processing means" refers to a technology or system that analyzes requests from salespeople, understands their meaning, and extracts important keywords.

[0570] "Legal Constraints" refers to laws and regulations regarding advertising and promotional materials, and are standards to ensure that violations do not occur.

[0571] "Means for automatically generating designs" refers to software or algorithms that automatically create designs according to the salesperson's requests.

[0572] "Means for the responsible department to check" refers to the process or interface by which members of the responsible department can check the generated design and make corrections or approvals as necessary.

[0573] The "means for providing the design" is a method or system for providing the final approved design for use by the sales force.

[0574] "Store display devices" refer to display devices such as displays and monitors installed in stores.

[0575] "Printing equipment" refers to equipment such as a printer that actually prints the POP design.

[0576] "Means for transmitting in real time" refers to communication means or technology for instantly transmitting the generated design to a display device or printing device in the store.

[0577] The present invention is a system that quickly and accurately analyzes the requests of salespeople, automatically generates designs that comply with legal restrictions, and provides the designs to store display devices and printers in real time. This system is composed of the following main elements:

[0578] System configuration

[0579] 1. A means of inputting salesperson requests

[0580] Salespeople can use devices such as smartphones or smart glasses to access a dedicated application and input the text content of the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). Voice input is also possible, allowing for faster operation.

[0581] 2. Natural language processing means for analyzing requests

[0582] The server uses an NLP engine (e.g., Google NLP API) to analyze the salesperson's input. This extracts important keywords and concepts from the input text. For example, keywords such as "new product," "features," and "limited-time discount" are extracted.

[0583] 3. How to determine compliance with legal restrictions

[0584] The server checks against a legal database to determine whether the extracted keywords comply with legal restrictions, checking for false advertising, misleading product information, etc. If a violation is found, an appropriate error message is generated.

[0585] 4. A method for automatically generating designs that conform to constraints

[0586] If the design is found to comply with legal restrictions, a design generation engine (e.g., Canva API) is put into action. Based on the specified requirements, the layout, fonts, color scheme, and images are automatically generated. For example, a design may be generated that highlights the features of new products and highlights discount information.

[0587] 5. A means for the department in charge to check the generated design

[0588] A member of the relevant department accesses a dedicated interface (for example, an app built with React Native) and previews the generated design. The person in charge checks the content and makes corrections or comments as necessary. Once the corrections are complete, the person in charge gives final approval.

[0589] 6. Means of providing the final approved design

[0590] The server saves the final approved design as a final version and moves it to a folder accessible to the sales representative, who can then log in to the server using their own device and download or print the approved POP.

[0591] 7. Means for transmitting data to a display or printing device in the store in real time

[0592] The final design is sent in real time to the store's display devices (displays and monitors) and printing equipment. Using real-time data synchronization technology such as Firebase, the design can be displayed and printed in-store instantly.

[0593] Specific examples

[0594] For example, consider the case where an employee at a brick-and-mortar store wants to create a promotional POP for a new product. Using a smartphone app, the employee voice-inputs, "I'd like you to create a promotional POP for new product 'XYZ' and display a 20% discount." The server receives the request and uses an NLP engine to extract the keywords "new product," "promotional POP," and "20% discount." After comparing this with a legal database, a legal design is automatically generated using the Canva API. The generated design is reviewed and revised by the relevant department, and after final approval, it is sent to the store's displays and printers in real time.

[0595] Prompt Sentence Examples

[0596] Salesperson's voice input: "I'd like to create a promotional POP for our new product 'XYZ', offering a 20% discount to new customers."

[0597] Keyword extraction using Google NLP API: "New product, promotional POP, new customer, 20% discount"

[0598] Legal constraint check: "New product, discount" → Legal constraint check

[0599] Create a design using the Canva API: "Add an image of a new product and the text '20% off'"

[0600] Real-time delivery by Firebase: "Send approved POP to in-store displays"

[0601] In this way, the detailed description of the invention can be utilized to quickly and accurately respond to the requests of salespeople.

[0602] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0603] Step 1:

[0604] Salespeople use smartphones or smart glasses to access a dedicated application and input their promotional POP requests. Input methods include text input and voice input, such as "Create a promotional POP for new product 'XYZ' and offer a 20% discount to new customers." This request data is sent to the server in real time. The input data includes the text content of the promotional POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.).

[0605] Step 2:

[0606] The server stores the received request data and analyzes it using a natural language processing (NLP) engine (e.g., Google NLP API). The NLP engine extracts important keywords from the input text, such as "new product," "promotional POP," "new customer," and "20% discount." The extracted keywords are used in the next step to check legal restrictions.

[0607] Step 3:

[0608] The server checks the extracted keywords against a legal database to determine whether they comply with legal restrictions. For example, it checks whether the keyword "20% discount" constitutes misleading advertising, or whether the display of "new products" contains misleading information. If a violation is found based on this check, an appropriate error message is automatically generated. This error message is sent to the user's device.

[0609] Step 4:

[0610] Based on the request, which is verified as complying with legal restrictions, the server triggers a design generation engine (e.g., the Canva API). The design generation engine automatically generates the layout, fonts, color scheme, and images based on the specified request. For example, a design may be created using images of new products to complement the text stating 20% ​​off. The generation process uses specific templates and elements according to the request.

[0611] Step 5:

[0612] The generated design is displayed on a dedicated interface for review by the relevant department. Members of the relevant department can preview the design using a PC or smart device, check the content, make corrections or comments as necessary, and once the corrections are complete, give final approval. Corrections are updated online in real time.

[0613] Step 6:

[0614] The final approved design is saved on the server as a final version. The saved design is then moved to a folder accessible to sales representatives. Sales representatives can then log in to the server using their own devices and download or print the approved POP.

[0615] Step 7:

[0616] The server sends the final design in real time to the store's display device (display or monitor) or printing device. This process uses real-time data synchronization technology such as Firebase, and the generated design is instantly displayed and printed in the store, enabling quick response to sales activities.

[0617] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0618] This invention details a system that uses AI to analyze salesperson requests and automatically generate POPs (points of purchase) that comply with legal restrictions, and further combines this with an emotion engine that recognizes the user's emotions, making it possible to provide designs that match the user's emotional state.

[0619] System Overview

[0620] This system consists of the following main elements:

[0621] 1. A means of inputting salesperson requests

[0622] 2. Natural language processing means for analyzing requests

[0623] 3. How to determine whether a request complies with legal restrictions

[0624] 4. A method for automatically generating designs that conform to constraints

[0625] 5. A means for the department in charge to check the generated design

[0626] 6. Means of providing the final approved design

[0627] 7. Emotion engine that recognizes user emotions

[0628] This system not only allows salespeople to quickly and legally generate POPs that correspond to specific promotional campaigns or events, but also provides designs that reflect the emotional state of the salespeople.

[0629] Explanation of program processing

[0630] Adding emotion engine processing

[0631] Step 1: Enter the sales representative's request

[0632] User (sales representative)

[0633] Salespeople access a dedicated input form using devices such as PCs, smartphones, and tablets. This form includes fields for entering the text content of the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). As the salesperson enters their request, the emotion engine analyzes the user's emotional state in real time based on the salesperson's input speed, facial expressions, voice, etc. When the salesperson clicks the "Send" button, the request data and emotional data are sent to the server.

[0634] Step 2: Analyze needs and emotions

[0635] server

[0636] The server stores the received request data and emotion data in a database. At the same time, a natural language processing (NLP) engine begins analyzing the request data. The NLP engine extracts keywords and important phrases from the input text. The emotion engine analyzes the emotion data and identifies the emotional state of the salesperson.

[0637] Step 3: Determine compliance with legal constraints

[0638] server

[0639] The NLP engine compares the extracted keywords, phrases, and sentiment data with an internal legal database. For example, it checks for expressions that meet the criteria for "misleading advertising" or for words prohibited by specific laws. If a violation is detected, an error message is generated, prompting the salesperson to make corrections.

[0640] Step 4: Automatically generate a design that meets the constraints

[0641] server

[0642] If the request is confirmed to comply with legal constraints, a design generation engine kicks in. The generation engine determines the layout, text style, color scheme, and image placement based on the specified request and reflects the emotional data. For example, if the salesperson's emotions indicate "excitement" or "happiness," it selects vibrant colors and font styles.

[0643] Step 5: The generated design is checked by the responsible department

[0644] Terminal

[0645] A member of the relevant department accesses a dedicated interface to preview the generated POP design, review it, and provide feedback and corrections as necessary.

[0646] Step 6: Deliver the final approved design

[0647] server

[0648] When the person in charge clicks the "Final Approval" button, the final design data is saved as the final version. A notification of approval completion is also automatically sent to the sales representative.

[0649] Step 7: Deliver your design

[0650] User

[0651] Salespeople log in to the server using their own devices and download or print the approved POP design, allowing them to quickly obtain POPs that can be used in sales promotion activities.

[0652] Specific examples

[0653] For example, let's say Salesperson A wants to create a promotional POP for a new product, "DEF." Salesperson A uses a terminal to access the input form, inputs requests such as "emphasize the features of the new product and prominently display the limited-time discount," and clicks the "Submit" button. The emotion engine recognizes that Salesperson A is in an "excited" state based on his facial expressions and tone of voice when he inputs his request.

[0654] The server receives the request, and the NLP engine begins analyzing it, extracting keywords such as "new product," "features," and "limited-time discount." The analysis results are compared with a legal database to check whether they comply with legal restrictions. If they are determined to comply with the restrictions, the design generation engine kicks in, generating a design that matches Salesperson A's emotions using vibrant colors and font styles.

[0655] A member of the relevant department checks the generated design on the preview screen, makes minor adjustments such as making the discount rate more prominent, and then gives final approval. After approval, the design is saved as a final version and moved to a folder that Salesperson A can access. Salesperson A can then log in to the server from their own device and download or print the approved POP to use in their sales promotion activities.

[0656] This system not only allows for the provision of quick and appropriate POP displays, but also generates designs that reflect the emotions of sales staff, further increasing the effectiveness of sales promotion activities.

[0657] The processing flow will be explained below.

[0658] Step 1:

[0659] User (sales representative)

[0660] Salespeople access a dedicated input form on their terminals and enter their requests for POP creation. Input items include the text content of the POP, the desired theme and design, the images and icons to be used, and special instructions (deadline date, event information, etc.). Furthermore, as the salesperson enters information, the emotion engine analyzes the salesperson's typing speed, keyboard swiftness, facial expressions (when using a camera), and voice tone (when using a microphone), collecting emotional data in real time. When the salesperson clicks the "Send" button, the request data and emotional data are sent to the server.

[0661] Step 2:

[0662] server

[0663] The emotion data is stored in a database along with the received request data. At the same time, a natural language processing (NLP) engine begins analyzing the request data. Specifically, it extracts keywords and important phrases from the input text and analyzes them to understand the context.

[0664] Step 3:

[0665] server

[0666] The analyzed keywords and phrases are compared with a legal database to determine whether they comply with legal restrictions. For example, it checks whether they contain expressions that constitute misleading advertising or words prohibited by law. If a violation is found, an appropriate error message is generated and the salesperson is asked to make the necessary corrections.

[0667] Step 4:

[0668] server

[0669] If the request is confirmed to comply with legal constraints, the design generation engine kicks in. Using emotion data, the engine adapts design elements to the salesperson's emotional state (e.g., excitement, joy, calm, etc.). For example, excitement and joy can be expressed with vibrant, bright colors and adjusted layout and font styles.

[0670] Step 5:

[0671] server

[0672] The generated POP design data is temporarily stored in storage, and the design is sent to a dedicated interface that can be accessed by the relevant department.

[0673] Step 6:

[0674] Terminal

[0675] A member of the relevant department accesses a dedicated interface to preview the generated POP design, check the design content, and enter correction instructions or feedback as necessary.

[0676] Step 7:

[0677] server

[0678] Once the person in charge has completed the final check and clicked the "Final Approval" button, the design is saved as the final version. A notification of approval completion is also automatically sent to the sales representative.

[0679] Step 8:

[0680] User (sales representative)

[0681] Salespeople log in to the server using their own devices and download or print the final approved POP design, enabling them to quickly obtain legal, emotionally appropriate POPs that can be used in sales promotion activities.

[0682] Example 2

[0683] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0684] Conventional sales promotion design generation systems were unable to reflect the emotional state of salespeople when they entered their requests, which meant that promotional materials often did not have a strong emotional impact on customers. Furthermore, checking legal restrictions one by one required a great deal of time and effort, making it difficult to generate designs quickly.

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

[0686] In this invention, the server includes a means for inputting the sales representative's requests, a natural language processing means for analyzing the requests, a means for determining whether the requests comply with legal restrictions, a means for automatically generating a design that complies with the restrictions, a means for the responsible department to check the generated design, a means for providing the final approved design, and an emotion engine for recognizing the user's emotions. This makes it possible to automatically generate effective promotional designs that match the sales representative's emotional state while quickly reflecting the sales representative's requests. Furthermore, automating the checking of legal restrictions allows for both efficient design generation and compliance with laws and regulations.

[0687] "Requests" refers to information such as the text content of the POP, design preferences, images and icons to be used, and special instructions that salespeople need to promote sales.

[0688] "Natural language processing means" is a technology for analyzing input text data to extract and understand requirements.

[0689] "Legal Constraints" means laws and regulations that must be observed in advertising and promotional activities.

[0690] "Design generation means" is a technology that automatically creates designs for advertising and sales promotions based on input requests and analysis results.

[0691] The "department in charge" refers to a specialized department or team that reviews the content of the generated design and makes corrections or approvals as necessary.

[0692] An "emotion engine" is a technology that identifies emotions from the user's input speed, facial expressions, voice, etc., and analyzes their emotional state.

[0693] A "server" is a central computer system that processes and stores data for the entire system.

[0694] "Real-time" means that processing and analysis occurs immediately at the moment the input is made.

[0695] A "database" is a collection of data that efficiently manages related data and allows for quick search and manipulation.

[0696] A specific embodiment of the present invention is described in detail below. The present invention is a system that enables salespeople to quickly and legally create promotional POP (Point of Purchase) displays and provides designs that reflect the emotional state of the user.

[0697] Hardware and software used

[0698] The system uses the following main hardware and software:

[0699] Server: A central computing system that processes and stores data for the entire system. For example, we use servers from Amazon Web Services (AWS) or Google Cloud Platform (GCP).

[0700] Terminal: A device such as a PC, smartphone, or tablet on which salespeople enter requests. Each terminal uses a web browser to access a dedicated input form.

[0701] Natural language processing means: Analyze request data using libraries such as Python's NLTK or SpaCy.

[0702] Design generation method: Designs are automatically generated using design tool APIs such as Adobe XD and Sketch.

[0703] Emotion engine: A technology for analyzing the speed, facial expressions, and voice of users when they input, using libraries such as OpenCV and TensorFlow. Specifically, this applies to AI models that perform facial recognition and voice emotion analysis.

[0704] Database: We use a database management system such as MySQL or PostgreSQL to store and manage the request data and emotion data.

[0705] System Operation

[0706] User input of requests

[0707] The user (salesperson) accesses a dedicated input form using a device such as a PC, smartphone, or tablet. This form contains fields for entering text content related to the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). When the salesperson enters their request, the emotion engine analyzes their input speed, facial expressions, and voice in real time to identify their emotional state. When the salesperson clicks the "Send" button, the request data and emotion data are sent to the server.

[0708] Analysis of needs and emotions

[0709] The server stores the received request data and emotion data in a database. At the same time, a natural language processing engine analyzes the request data and extracts important keywords and phrases. The emotion engine identifies the user's emotional state. Specifically, the request text is analyzed using the Python libraries NLTK and SpaCy, and emotion data is analyzed using OpenCV and TensorFlow.

[0710] Determining legal restrictions

[0711] The NLP engine compares the extracted keywords, phrases, and sentiment data with the server's internal legal database to check for expressions or terms that violate legal restrictions, and if a violation is detected, an error message is generated and a notification is sent to the salesperson urging them to make corrections.

[0712] Auto-generation of designs

[0713] Once legal restrictions are met, the server's design generation engine automatically generates a POP design based on the customer's needs and emotional data. Using Adobe XD and Sketch APIs, the server determines the layout, text style, color scheme, and image placement. For example, if the salesperson's emotional state is "excited," the server selects vibrant colors and eye-catching fonts.

[0714] Design review and approval

[0715] A member of the relevant department uses a dedicated interface to review the generated design, provide corrections and feedback as necessary, and finally approve it.

[0716] Design provision

[0717] Once the design data has been approved, it is stored in cloud storage and notified to the sales representative, who can then log in from their own device and download or print the approved design for use in their sales promotion activities.

[0718] Specific examples

[0719] For example, suppose a salesperson wants to create a promotional POP for a new product, "DEF." The salesperson uses a terminal to access the input form, inputs requests such as "emphasize the features of the new product and prominently display the limited-time discount," and clicks the "Submit" button. The emotion engine recognizes that the salesperson is in an "excited" state based on their facial expression and tone of voice when they input their request.

[0720] The server receives the request, and the NLP engine extracts keywords such as "new product," "features," and "limited-time discount." This is then compared with a legal database to check whether the request complies with legal restrictions. If the request is found to comply with the restrictions, the design generation engine generates a design using vibrant colors and font styles.

[0721] The relevant department reviews the POP, makes any necessary corrections, and then gives final approval. Sales representatives can download or print the approved POP from their own devices and use it in their sales promotion activities.

[0722] Examples of prompts for a generative AI model might include:

[0723] "Create a POP that highlights the features of your new product. Display the limited-time discount prominently and use a vibrant color background. Also, choose a font that will make an impact on customers."

[0724] In this way, the system reflects the desires and emotions of salespeople and provides fast and legal promotional designs.

[0725] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0726] Step 1:

[0727] The user (salesperson) accesses a dedicated input form using a PC, smartphone, or tablet device. The input form contains fields for entering the text content of the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). When the salesperson enters their request and clicks the "Send" button, the entered request data and emotional data (input speed, facial expression, and voice data) are sent to the server.

[0728] Input: POP text content, design requests, image icons, special instructions, etc.

[0729] Output: Desire data and emotion data

[0730] Step 2:

[0731] The server stores the received request data and emotion data in a database. A natural language processing (NLP) engine analyzes the request data and extracts important keywords and phrases. The emotion engine analyzes emotion data from the salesperson's typing speed, facial expressions, and voice to identify their emotional state. Specific analysis uses Python libraries NLTK and SpaCy, as well as OpenCV and TensorFlow.

[0732] Input: Desire data and emotion data

[0733] Output: extracted keywords and phrases, analyzed emotional states

[0734] Step 3:

[0735] The server checks the keywords, phrases, and sentiment data extracted by the NLP engine against an internal legal database to verify legal restrictions. For example, it checks whether the content contains expressions that violate legal restrictions, such as "exaggerated advertising" or "false representation." If a violation is detected, an error message is generated and a notification is automatically sent to the salesperson requesting correction.

[0736] Input: Extracted keywords, phrases, and sentiment data

[0737] Output: Legal constraint detection results, error messages (if necessary)

[0738] Step 4:

[0739] If the server verifies that the request complies with legal restrictions, a design generation engine automatically generates a POP design based on the request and emotion data. The engine uses APIs from Adobe XD and Sketch to determine the layout, text style, color scheme, and image placement. For example, if the salesperson's emotion is "excited," it uses bright colors and a bold font. The generative AI model selects these design elements.

[0740] Input: Legally compliant desire data and emotion data

[0741] Output: Generated POP design

[0742] Step 5:

[0743] The user (a member of the relevant department) accesses a dedicated interface and previews the generated POP design. The person in charge checks the design and makes corrections or provides feedback as necessary. The preview screen is built using front-end frameworks such as React and Vue.js.

[0744] Input: Generated POP design

[0745] Output: Reviewed and revised design, feedback

[0746] Step 6:

[0747] When the person in charge clicks the "Final Approval" button, the server saves the final design data to cloud storage (e.g., Amazon S3) and automatically sends a notification of approval completion to the sales representative.

[0748] Input: Final approved design

[0749] Output: Final design saved in cloud storage, approval notification

[0750] Step 7:

[0751] The user (salesperson) logs in to the server from their own terminal and downloads or prints the approved POP design. The salesperson can then start their sales promotion activities using this design.

[0752] Input: Server login credentials, request for approved POP design

[0753] Output: Downloaded or printed POP designs

[0754] (Application example 2)

[0755] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0756] Conventional design generation systems have limitations in automatically generating content that reflects the user's emotional state or in quickly generating content that meets legal constraints. Furthermore, they are unable to provide personalized content based on the user's emotional state, so there is a need to improve the user experience.

[0757] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting a request from a salesperson, natural language processing means for analyzing the request, means for determining whether the request complies with legal restrictions, means for automatically generating a design that complies with the restrictions, means for a responsible department to check the generated design, means for providing the final approved design, means for recognizing the user's emotions, and means for generating appropriate content based on the user's emotional state. This enables the automatic generation of personalized content that reflects the user's emotional state and the rapid provision of legally compliant content.

[0758] The "means for inputting the salesperson's requests" is a device or system that provides an interface for the salesperson to input their requests.

[0759] "Natural language processing means" refers to a technology or system that analyzes text data entered by salespeople and extracts important keywords and phrases.

[0760] "Means for determining compliance with legal constraints" refers to a system that checks whether the generated designs and content comply with legal regulations and standards.

[0761] "Means for automatically generating designs that comply with constraints" refers to a system or technology that automatically generates designs and layouts that comply with legal constraints.

[0762] "Means for the responsible department to check the generated design" refers to an interface or system that allows the designated responsible department to check and provide feedback on the generated design.

[0763] The "means for providing the final approved design" is a system or method for providing the final approved design to sales personnel and other interested parties.

[0764] "Means for recognizing user emotions" refers to technology or a system that analyzes and recognizes emotions from the user's facial expressions, tone of voice, input speed, etc.

[0765] The "means for generating appropriate content based on the emotional state of the user" refers to a system or technology that automatically generates optimal content for a user in accordance with the emotional state of the user.

[0766] To implement this invention, various hardware and software components must be integrated to build an entire system. This system supports a series of processes: a salesperson inputs their requirements, the requirements are analyzed, and a design that complies with legal restrictions is automatically generated. It also includes a component that recognizes the user's emotional state and generates personalized content based on that.

[0767] System configuration

[0768] Hardware elements

[0769] 1. Smartphones and smart glasses: Used to collect user input and emotional data. Equipped with a camera and microphone, they can collect facial expressions and voice data in real time.

[0770] 2. Server: Responsible for hosting the data processing and generation engine.

[0771] Software Elements

[0772] 1. Natural language processing engine (e.g., spaCy, BERT): Analyzes the salesperson's input and extracts keywords and important phrases.

[0773] 2. Emotion recognition engine (e.g., Affective Computing SDK, Emotion API): Identifies the user's emotional state from their facial expressions and voice.

[0774] 3. Content generation engine (e.g., OpenAI GPT-4, DALL-E): Generates appropriate content based on the user's emotional state.

[0775] 4. Internal legal database: Used to determine whether generated content complies with legal constraints.

[0776] Data processing and calculation

[0777] Emotion data collection and analysis

[0778] The device (smartphone or smart glasses) collects the user's facial expressions, voice tone, and input speed in real time, and sends this data to an emotion recognition engine to analyze the user's emotional state.

[0779] Input and analysis of requests

[0780] Salespeople access a specific input form and enter their requests using a PC or smart device. The text is sent to a server and analyzed by a natural language processing engine. The engine extracts keywords and important phrases and compares the content with a database to determine whether it complies with legal restrictions.

[0781] Generate the design

[0782] If the user's desires and emotional data are confirmed to comply with legal restrictions, the content generation engine will begin working and generate designs and content based on the user's emotions. For example, if the user is in an "excited" state, it will generate a flashy, dynamic design. Conversely, if the user is in a "relaxed" state, it will generate a calm design.

[0783] Check by the relevant department

[0784] The resulting design is checked by the relevant department, fine-tuned as necessary, and then, once finally approved, provided to the sales staff.

[0785] Specific examples

[0786] For example, let's say a salesperson wants a design to promote a new product, "ABC." The salesperson enters "emphasize the features of ABC and highlight the limited-time discount" into the form and submits it. The emotion recognition engine identifies the salesperson's emotion as "excitement," the server receives the request, and the natural language processing engine extracts keywords such as "ABC," "features," and "limited-time discount." After verifying that legal restrictions are met, the design generation engine generates a vivid and dynamic design.

[0787] This allows salespeople to quickly and accurately create promotional designs, enabling them to provide content experiences that are optimized to suit the user's emotions.

[0788] Example prompt sentence:

[0789] Analyze the user's facial and voice data to identify their emotional state and generate a music playlist based on that emotional state. If the user is relaxed, choose relaxing songs, and if the user is excited, choose upbeat songs. Also, make sure the generated playlist does not violate copyright laws.

[0790] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0791] Step 1:

[0792] Salespeople access a dedicated input form using devices such as PCs, smartphones, and tablets. This form includes fields for entering the text content of the POP, desired design, images and icons to be used, and special instructions (deadline date, event information, etc.). As the salesperson enters their request, the emotion engine analyzes the user's emotional state in real time based on the salesperson's input speed, facial expression, voice, etc. When the salesperson clicks the "Send" button, the request data and emotional data are sent to the server. Specifically, the input data consists of the text content, input speed, facial expression data, and voice tone. Once sent to the server, this data is stored in a database for analysis.

[0793] Step 2:

[0794] The server stores the request data and emotional data received in step 1 in a database. At the same time, the natural language processing engine begins analyzing the request data. It extracts keywords and important phrases from the input data (text content). The emotion recognition engine analyzes the input data (facial expression data and voice tone) and identifies the emotional state of the salesperson. For example, it determines that the salesperson is in an "excited" state. The server is then ready to pass the analysis results of the request data and the emotional state to the next step.

[0795] Step 3:

[0796] The server compares the keywords, phrases, and sentiment data extracted by the natural language processing engine with a legal database. This process checks whether the input data contains expressions or terms that violate laws and regulations. For example, it detects whether the data contains "exaggerated advertising" or expressions that "violate specific laws and regulations." Based on the results of this comparison, it determines whether the data conforms to legal restrictions, and if so, proceeds to the next step.

[0797] Step 4:

[0798] If the server verifies that the request complies with legal restrictions, the design generation engine begins operation. Based on the input data (analysis results, emotional state), an automatically generated design is determined. At this stage, for example, a design using vibrant colors and font styles is generated for a salesperson in an "excited" state. The generated design data is sent to the relevant department's interface for review.

[0799] Step 5:

[0800] A member of the department in charge logs into a dedicated interface and previews the generated design. They visually check the input data (the generated design) and provide feedback and corrections as necessary. A specific example would be to make the discount rate more prominent. This corrected data is then sent back to the server, where it continues to be checked until it is finalized.

[0801] Step 6:

[0802] When the department member finally clicks the "Final Approval" button, the server saves the final version of the design data as the final version. The server also automatically sends an approval completion notification to the sales representative, which then provides the sales representative with the final approved design.

[0803] Step 7:

[0804] Salespeople log in to the server using their own devices and download or print the approved POP design. This operation allows the generated design to be immediately used in the salesperson's sales promotion activities. Specific actions include downloading a file and printing it out to a printer.

[0805] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0806] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0807] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0808] [Third embodiment]

[0809] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0810] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0811] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0812] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0813] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0814] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0815] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0816] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0817] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0818] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0819] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0820] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0821] This invention details a system that uses AI to analyze salesperson requests and automatically generate POPs (Points of Purchase) that comply with legal restrictions.

[0822] System Overview

[0823] This system consists of the following main elements:

[0824] 1. A means of inputting salesperson requests

[0825] 2. Natural language processing means for analyzing requests

[0826] 3. How to determine whether a request complies with legal restrictions

[0827] 4. A method for automatically generating designs that conform to constraints

[0828] 5. A means for the department in charge to check the generated design

[0829] 6. Means of providing the final approved design

[0830] The system allows salespeople to quickly and legally generate POPs for specific promotional campaigns and events.

[0831] Explanation of program processing

[0832] Step 1: Enter the sales representative's request

[0833] User (sales representative)

[0834] Salespeople access a dedicated input form using devices such as PCs, smartphones, and tablets. This form includes fields for entering the text content of the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). When the salesperson enters their request and clicks the "Submit" button, the request data is sent to the server.

[0835] Step 2: Analyze the request

[0836] server

[0837] The server stores the received request data and begins analyzing it using a natural language processing (NLP) engine. The NLP engine extracts important keywords and concepts from the input text. For example, keywords such as "limited-time discount," "new product," and "free sample" are extracted.

[0838] Step 3: Determine compliance with legal constraints

[0839] server

[0840] The server then checks the parsed request against an internal legal database to determine whether it complies with legal restrictions, such as whether the request constitutes false advertising, whether the product labeling contains misleading information, whether specific terminology is used, etc. If a violation is found, an appropriate error message is automatically generated.

[0841] Step 4: Automatically generate a design that meets the constraints

[0842] server

[0843] If the requirements are confirmed to comply with legal constraints, a design generation engine is put into action. This engine automatically generates layouts, fonts, color schemes, and images based on the specified requirements. For example, a design may be generated that highlights new product features and highlights discount information.

[0844] Step 5: The generated design is checked by the responsible department

[0845] Terminal

[0846] A member of the relevant department accesses a dedicated interface to preview the generated design, checks the content, and makes corrections or comments as necessary. Once corrections are complete, the design is given final approval.

[0847] Step 6: Deliver the final approved design

[0848] server

[0849] Once approved, the POP design is saved as a final version and moved to a folder accessible to sales representatives, who can then log in to the server using their own devices and download or print the approved POP.

[0850] Specific examples

[0851] For example, let's say Salesperson A wants to create a promotional POP for a new product "ABC." Salesperson A uses a terminal to access the input form, enters requests such as "emphasize the features of the new product and prominently display the limited-time discount," and clicks the "Submit" button.

[0852] The server receives the request, and the NLP engine begins analyzing it, extracting keywords such as "new product," "features," and "limited-time discount." The analysis results are compared with a legal database to check whether they comply with legal restrictions. If they are determined to comply with the restrictions, the design generation engine is activated, and a design incorporating images of the new product and discount information is automatically generated.

[0853] A member of the relevant department checks the generated design on the preview screen, makes minor adjustments such as making the discount rate more prominent, and then gives final approval. After approval, the design is saved as a final version and moved to a folder that Salesperson A can access. Salesperson A logs in to the server from his or her own device and downloads or prints the approved POP to use in sales promotion activities.

[0854] This system allows for the provision of prompt and appropriate POPs, improving the efficiency of sales activities.

[0855] The processing flow will be explained below.

[0856] Step 1:

[0857] User

[0858] The salesperson accesses a dedicated form on their terminal and enters their request for POP creation. Input items include the text content, desired theme and design, images to be used, deadline, etc. When the salesperson clicks the "Send" button, the request data is sent to the server.

[0859] Step 2:

[0860] server

[0861] The server stores the received request data in a database. At the same time, a natural language processing (NLP) engine begins analyzing the request data. Specifically, it extracts keywords and important phrases from the input text.

[0862] Step 3:

[0863] server

[0864] The NLP engine compares the extracted keywords and phrases with an internal legal database. For example, it checks for expressions that meet the criteria for "misleading advertising" or for words prohibited by specific laws. If a violation is detected, an error message is generated and the salesperson is asked to make corrections.

[0865] Step 4:

[0866] server

[0867] If the requirements are confirmed to comply with legal constraints, a design generation engine is put into action, which includes algorithms that automatically determine the layout, text style, color scheme, and image placement based on the specified requirements.

[0868] Step 5:

[0869] server

[0870] The generated POP design is stored in temporary storage, and the design data is sent to a dedicated interface that can be accessed by the relevant department.

[0871] Step 6:

[0872] Terminal

[0873] The person in charge of the relevant department accesses a dedicated interface to preview the generated POP design, check the content, and enter feedback and corrections as necessary.

[0874] Step 7:

[0875] server

[0876] When the person in charge clicks the "Final Approval" button, the final design data is saved as the final version. A notification of approval completion is also automatically sent to the sales representative.

[0877] Step 8:

[0878] User

[0879] Salespeople log in to the server using their own devices and download or print the approved POP design, allowing them to quickly obtain POPs that can be used in sales promotion activities.

[0880] Example 1

[0881] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0882] Point-of-purchase (POP) designs created by sales representatives for promotional activities must be fast and legal. However, the current process of sales representatives submitting design requests, checking whether they comply with legal restrictions, and generating appropriate designs is laborious and time-consuming. Furthermore, designs that include content that may violate legal restrictions are inappropriate and must be dealt with appropriately.

[0883] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0884] In this invention, the server includes means for inputting requests from sales representatives, natural language processing means for analyzing the requests, means for determining whether the requests comply with legal restrictions, means for automatically generating a design that complies with the restrictions, means for halting design generation and generating an error message if the requests violate legal restrictions, means for providing the final approved design, and means for moving the generated design to a folder accessible to the sales representative after final approval. This allows POP designs based on the sales representative's requests to be generated quickly and legally, making it possible to improve the efficiency of sales activities.

[0885] "Means for inputting salesperson requests" refers to the interface that salespersons use to input the text content and design of POPs, images and icons to be used, special instructions, etc. using devices such as PCs, smartphones, and tablets.

[0886] "Natural language processing means" refers to technical means, particularly a natural language processing engine, for analyzing received sales representative request data and extracting important keywords and concepts from the text.

[0887] "Means for determining compliance with legal constraints" refers to the means for comparing the analyzed request content with an internal database of laws and regulations to check whether it violates legal constraints.

[0888] "Means for automatically generating designs" refers to engines or software that automatically create legal and effective designs based on desired data.

[0889] The "means for generating an error message" refers to a technology that automatically generates an error message to notify a salesperson when content that violates legal restrictions is detected.

[0890] "Means for checking the generated design" refers to an interface that allows members of the responsible department to preview the generated design and confirm and modify its contents.

[0891] "Means for providing the final approved design" refers to the means by which the department in charge moves the design that has been finally approved to a folder that is accessible to the sales representative, so that the sales representative can download or print it.

[0892] "Means for moving the generated design to a folder accessible to sales personnel after final approval" refers to a means for saving the finalized design in a location easily accessible to sales personnel and making it available for use.

[0893] This invention details a system that uses AI to analyze salesperson requests and automatically generate POPs (Points of Purchase) that comply with legal restrictions.

[0894] System Overview

[0895] This system consists of the following main elements:

[0896] 1. A means of inputting salesperson requests

[0897] 2. Natural language processing means for analyzing requests

[0898] 3. How to determine whether a request complies with legal restrictions

[0899] 4. A method for automatically generating designs that conform to constraints

[0900] 5. A means for the department in charge to check the generated design

[0901] 6. Means of providing the final approved design

[0902] 7. A means to stop design generation and generate an error message if legal restrictions are violated.

[0903] 8. A way to move the generated designs to a folder accessible to sales personnel after final approval

[0904] Overview of the overall program processing

[0905] The program in this system receives the user's (salesperson's) request from the terminal and analyzes it using natural language processing means. The server compares the analyzed request with an internal database of laws and regulations to determine whether it complies with legal restrictions. If the request is determined to be compliant, the server activates a design generation engine to automatically generate a design based on the specified request. The generated design is then checked by the responsible department and given final approval. After approval, the design is moved to a folder accessible to the salesperson. Each step is described in detail below.

[0906] Hardware and software used

[0907] The following hardware and software are used in this system:

[0908] Terminal (PC, smartphone, tablet, etc.): A device used by sales staff to input, confirm, and download requests.

[0909] Server (high-performance workstation or cloud server): Receives requests, stores them, analyzes them, generates designs, and provides results.

[0910] Natural language processing engine (such as Google NLP API): Analyzes sales representatives' requests and extracts important keywords and concepts.

[0911] Legal database: Uses an internal legal database to determine compliance with legal constraints.

[0912] Design generation engine (e.g. Adobe Creative Cloud API): Automatically generate designs based on requests.

[0913] Specific examples

[0914] For example, let's say Salesperson A wants to create a promotional POP for a new product, "ABC." Salesperson A uses a terminal to access the input form, enters a request such as "emphasize the features of the new product and prominently display the limited-time discount," and clicks the "Submit" button. The following is a concrete example of a prompt sentence.

[0915] Example prompt sentence:

[0916] "I would like to create a promotional POP for our new product ABC. I would like the design to highlight the features of the new product and prominently display the limited-time discount."

[0917] The server receives the request, and the NLP engine begins analyzing it. Keywords such as "new product," "features," and "limited-time discount" are extracted and compared with a legal database. If it is determined to comply with legal restrictions, the design generation engine goes into action and automatically generates a design incorporating images of the new product and discount information. A member of the responsible department checks the generated design on a preview screen, makes minor adjustments such as making the discount rate more prominent, and then gives final approval. Once approved, the design is saved as a final version and moved to a folder that Salesperson A can access. Salesperson A logs in to the server from their own device and downloads or prints the approved POP to use in their sales promotion activities.

[0918] This system allows for the provision of prompt and legal POPs, improving the efficiency of sales activities.

[0919] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0920] Step 1: Enter your request

[0921] User

[0922] The user (salesperson) accesses a dedicated input form using a device such as a PC, smartphone, or tablet. This form has fields where users can enter the text content of the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). The user enters the required information in each field and clicks the "Submit" button. Input content includes new product features, discount information, event dates, etc. This input data is sent to the server.

[0923] Input: POP request data entered by the salesperson (text content, design requests, images, special instructions)

[0924] Output: Request data sent to the server

[0925] Step 2: Receiving and storing requests

[0926] server

[0927] The server receives the requested data sent by the user and stores it in a database for use in subsequent processing steps.

[0928] Input: Request data sent by sales representative

[0929] Output: Request data stored in a database

[0930] Step 3: Analyze the request

[0931] server

[0932] The server retrieves the stored request data from the database. It then uses a natural language processing (NLP) engine (e.g., Google NLP API) to analyze the request text and extract important keywords and concepts, such as "limited-time discount," "new product," and "free sample."

[0933] Input: Request data retrieved from the database

[0934] Output: List of extracted keywords

[0935] Step 4: Determine compliance with legal constraints

[0936] server

[0937] The server compares the extracted keyword list with an internal legal database. Here, it determines whether the analyzed request conforms to legal restrictions. For example, it checks whether there is any false advertising, whether there is any misleading information in the product labeling, and whether specific terminology is used. If any items that violate legal restrictions are found, it generates an error message to notify the user. If it is determined to conform, it proceeds to the next step.

[0938] Input: Extracted keyword list

[0939] Output: Legal compliance judgment result (compliant, non-compliant), error message (in case of non-compliance)

[0940] Step 5: Auto-generate the design

[0941] server

[0942] If the server determines that the request complies with legal restrictions, it launches a design generation engine (e.g., Adobe Creative Cloud API). The design generation engine automatically generates layouts, fonts, color schemes, and images based on the specified requests. For example, it generates a design that prominently displays the features of new products and highlights discount information.

[0943] Input: Request data, legal compliance judgment results

[0944] Output: Auto-generated design

[0945] Step 6: Check and approve the design

[0946] Terminal

[0947] Members of the relevant department access a dedicated interface to preview the generated design. They review the content and make corrections or comments as necessary. Once corrections are complete, the design is finalized with final approval.

[0948] Input: Auto-generated design

[0949] Output: Revised and final approved design

[0950] Step 7: Deliver the final design

[0951] server

[0952] The final approved POP design is saved on the server as a final version. It is then moved to a folder accessible to sales representatives. Sales representatives can then log in to the server using their own devices to download or print the approved POP.

[0953] Input: Final approved design

[0954] Output: Finalized design in a folder accessible to sales representatives

[0955] Through each processing step of this program, legal and effective POP designs based on the requests of sales representatives are quickly generated, thereby improving the efficiency of sales activities.

[0956] (Application example 1)

[0957] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0958] With conventional POP (Point of Purchase) generation systems, it was difficult to quickly provide legal designs based on sales representatives' requests. Furthermore, the time-consuming process of checking and correcting designs based on requests made it difficult to respond quickly on-site. Furthermore, the final approved design could not be sent to in-store display devices or printing devices in real time, making it necessary to respond quickly in sales promotion activities.

[0959] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0960] In this invention, the server includes means for inputting requests from sales representatives, natural language processing means for analyzing the requests, means for determining whether the requests comply with legal restrictions, means for automatically generating a design that complies with the restrictions, means for a responsible department to check the generated design, means for providing the final approved design, and means for transmitting the design to a display device or printer in the store in real time. This makes it possible to quickly and accurately analyze the requests from sales representatives, automatically generate a design that complies with legal restrictions, and simultaneously quickly carry out approval work, and provide the final design to the site in real time.

[0961] "Salesperson" means a professional or departmental employee who performs sales promotion activities.

[0962] The "means for inputting requests" refers to a device or interface that allows a salesperson to input requested information about a promotional POP by text or voice.

[0963] "Natural language processing means" refers to a technology or system that analyzes requests from salespeople, understands their meaning, and extracts important keywords.

[0964] "Legal Constraints" refers to laws and regulations regarding advertising and promotional materials, and are standards to ensure that violations do not occur.

[0965] "Means for automatically generating designs" refers to software or algorithms that automatically create designs according to the salesperson's requests.

[0966] "Means for the responsible department to check" refers to the process or interface by which members of the responsible department can check the generated design and make corrections or approvals as necessary.

[0967] The "means for providing the design" is a method or system for providing the final approved design for use by the sales force.

[0968] "Store display devices" refer to display devices such as displays and monitors installed in stores.

[0969] "Printing equipment" refers to equipment such as a printer that actually prints the POP design.

[0970] "Means for transmitting in real time" refers to communication means or technology for instantly transmitting the generated design to a display device or printing device in the store.

[0971] The present invention is a system that quickly and accurately analyzes the requests of salespeople, automatically generates designs that comply with legal restrictions, and provides the designs to store display devices and printers in real time. This system is composed of the following main elements:

[0972] System configuration

[0973] 1. A means of inputting salesperson requests

[0974] Salespeople can use devices such as smartphones or smart glasses to access a dedicated application and input the text content of the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). Voice input is also possible, allowing for faster operation.

[0975] 2. Natural language processing means for analyzing requests

[0976] The server uses an NLP engine (e.g., Google NLP API) to analyze the salesperson's input. This extracts important keywords and concepts from the input text. For example, keywords such as "new product," "features," and "limited-time discount" are extracted.

[0977] 3. How to determine compliance with legal restrictions

[0978] The server checks against a legal database to determine whether the extracted keywords comply with legal restrictions, checking for false advertising, misleading product information, etc. If a violation is found, an appropriate error message is generated.

[0979] 4. A method for automatically generating designs that conform to constraints

[0980] If the design is found to comply with legal restrictions, a design generation engine (e.g., Canva API) is put into action. Based on the specified requirements, the layout, fonts, color scheme, and images are automatically generated. For example, a design may be generated that highlights the features of new products and highlights discount information.

[0981] 5. A means for the department in charge to check the generated design

[0982] A member of the relevant department accesses a dedicated interface (for example, an app built with React Native) and previews the generated design. The person in charge checks the content and makes corrections or comments as necessary. Once the corrections are complete, the person in charge gives final approval.

[0983] 6. Means of providing the final approved design

[0984] The server saves the final approved design as a final version and moves it to a folder accessible to the sales representative, who can then log in to the server using their own device and download or print the approved POP.

[0985] 7. Means for transmitting data to a display or printing device in the store in real time

[0986] The final design is sent in real time to the store's display devices (displays and monitors) and printing equipment. Using real-time data synchronization technology such as Firebase, the design can be displayed and printed in-store instantly.

[0987] Specific examples

[0988] For example, consider the case where an employee at a brick-and-mortar store wants to create a promotional POP for a new product. Using a smartphone app, the employee voice-inputs, "I'd like you to create a promotional POP for new product 'XYZ' and display a 20% discount." The server receives the request and uses an NLP engine to extract the keywords "new product," "promotional POP," and "20% discount." After comparing this with a legal database, a legal design is automatically generated using the Canva API. The generated design is reviewed and revised by the relevant department, and after final approval, it is sent to the store's displays and printers in real time.

[0989] Prompt Sentence Examples

[0990] Salesperson's voice input: "I'd like to create a promotional POP for our new product 'XYZ', offering a 20% discount to new customers."

[0991] Keyword extraction using Google NLP API: "New product, promotional POP, new customer, 20% discount"

[0992] Legal constraint check: "New product, discount" → Legal constraint check

[0993] Create a design using the Canva API: "Add an image of a new product and the text '20% off'"

[0994] Real-time delivery by Firebase: "Send approved POP to in-store displays"

[0995] In this way, the detailed description of the invention can be utilized to quickly and accurately respond to the requests of salespeople.

[0996] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0997] Step 1:

[0998] Salespeople use smartphones or smart glasses to access a dedicated application and input their promotional POP requests. Input methods include text input and voice input, such as "Create a promotional POP for new product 'XYZ' and offer a 20% discount to new customers." This request data is sent to the server in real time. The input data includes the text content of the promotional POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.).

[0999] Step 2:

[1000] The server stores the received request data and analyzes it using a natural language processing (NLP) engine (e.g., Google NLP API). The NLP engine extracts important keywords from the input text, such as "new product," "promotional POP," "new customer," and "20% discount." The extracted keywords are used in the next step to check legal restrictions.

[1001] Step 3:

[1002] The server checks the extracted keywords against a legal database to determine whether they comply with legal restrictions. For example, it checks whether the keyword "20% discount" constitutes misleading advertising, or whether the display of "new products" contains misleading information. If a violation is found based on this check, an appropriate error message is automatically generated. This error message is sent to the user's device.

[1003] Step 4:

[1004] Based on the request, which is verified as complying with legal restrictions, the server triggers a design generation engine (e.g., the Canva API). The design generation engine automatically generates the layout, fonts, color scheme, and images based on the specified request. For example, a design may be created using images of new products to complement the text stating 20% ​​off. The generation process uses specific templates and elements according to the request.

[1005] Step 5:

[1006] The generated design is displayed on a dedicated interface for review by the relevant department. Members of the relevant department can preview the design using a PC or smart device, check the content, make corrections or comments as necessary, and once the corrections are complete, give final approval. Corrections are updated online in real time.

[1007] Step 6:

[1008] The final approved design is saved on the server as a final version. The saved design is then moved to a folder accessible to sales representatives. Sales representatives can then log in to the server using their own devices and download or print the approved POP.

[1009] Step 7:

[1010] The server sends the final design in real time to the store's display device (display or monitor) or printing device. This process uses real-time data synchronization technology such as Firebase, and the generated design is instantly displayed and printed in the store, enabling quick response to sales activities.

[1011] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1012] This invention details a system that uses AI to analyze salesperson requests and automatically generate POPs (points of purchase) that comply with legal restrictions, and further combines this with an emotion engine that recognizes the user's emotions, making it possible to provide designs that match the user's emotional state.

[1013] System Overview

[1014] This system consists of the following main elements:

[1015] 1. A means of inputting salesperson requests

[1016] 2. Natural language processing means for analyzing requests

[1017] 3. How to determine whether a request complies with legal restrictions

[1018] 4. A method for automatically generating designs that conform to constraints

[1019] 5. A means for the department in charge to check the generated design

[1020] 6. Means of providing the final approved design

[1021] 7. Emotion engine that recognizes user emotions

[1022] This system not only allows salespeople to quickly and legally generate POPs that correspond to specific promotional campaigns or events, but also provides designs that reflect the emotional state of the salespeople.

[1023] Explanation of program processing

[1024] Adding emotion engine processing

[1025] Step 1: Enter the sales representative's request

[1026] User (sales representative)

[1027] Salespeople access a dedicated input form using devices such as PCs, smartphones, and tablets. This form includes fields for entering the text content of the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). As the salesperson enters their request, the emotion engine analyzes the user's emotional state in real time based on the salesperson's input speed, facial expressions, voice, etc. When the salesperson clicks the "Send" button, the request data and emotional data are sent to the server.

[1028] Step 2: Analyze needs and emotions

[1029] server

[1030] The server stores the received request data and emotion data in a database. At the same time, a natural language processing (NLP) engine begins analyzing the request data. The NLP engine extracts keywords and important phrases from the input text. The emotion engine analyzes the emotion data and identifies the emotional state of the salesperson.

[1031] Step 3: Determine compliance with legal constraints

[1032] server

[1033] The NLP engine compares the extracted keywords, phrases, and sentiment data with an internal legal database. For example, it checks for expressions that meet the criteria for "misleading advertising" or for words prohibited by specific laws. If a violation is detected, an error message is generated, prompting the salesperson to make corrections.

[1034] Step 4: Automatically generate a design that meets the constraints

[1035] server

[1036] If the request is confirmed to comply with legal constraints, a design generation engine kicks in. The generation engine determines the layout, text style, color scheme, and image placement based on the specified request and reflects the emotional data. For example, if the salesperson's emotions indicate "excitement" or "happiness," it selects vibrant colors and font styles.

[1037] Step 5: The generated design is checked by the responsible department

[1038] Terminal

[1039] A member of the relevant department accesses a dedicated interface to preview the generated POP design, review it, and provide feedback and corrections as necessary.

[1040] Step 6: Deliver the final approved design

[1041] server

[1042] When the person in charge clicks the "Final Approval" button, the final design data is saved as the final version. A notification of approval completion is also automatically sent to the sales representative.

[1043] Step 7: Deliver your design

[1044] User

[1045] Salespeople log in to the server using their own devices and download or print the approved POP design, allowing them to quickly obtain POPs that can be used in sales promotion activities.

[1046] Specific examples

[1047] For example, let's say Salesperson A wants to create a promotional POP for a new product, "DEF." Salesperson A uses a terminal to access the input form, inputs requests such as "emphasize the features of the new product and prominently display the limited-time discount," and clicks the "Submit" button. The emotion engine recognizes that Salesperson A is in an "excited" state based on his facial expressions and tone of voice when he inputs his request.

[1048] The server receives the request, and the NLP engine begins analyzing it, extracting keywords such as "new product," "features," and "limited-time discount." The analysis results are compared with a legal database to check whether they comply with legal restrictions. If they are determined to comply with the restrictions, the design generation engine kicks in, generating a design that matches Salesperson A's emotions using vibrant colors and font styles.

[1049] A member of the relevant department checks the generated design on the preview screen, makes minor adjustments such as making the discount rate more prominent, and then gives final approval. After approval, the design is saved as a final version and moved to a folder that Salesperson A can access. Salesperson A can then log in to the server from their own device and download or print the approved POP to use in their sales promotion activities.

[1050] This system not only allows for the provision of quick and appropriate POP displays, but also generates designs that reflect the emotions of sales staff, further increasing the effectiveness of sales promotion activities.

[1051] The processing flow will be explained below.

[1052] Step 1:

[1053] User (sales representative)

[1054] Salespeople access a dedicated input form on their terminals and enter their requests for POP creation. Input items include the text content of the POP, the desired theme and design, the images and icons to be used, and special instructions (deadline date, event information, etc.). Furthermore, as the salesperson enters information, the emotion engine analyzes the salesperson's typing speed, keyboard swiftness, facial expressions (when using a camera), and voice tone (when using a microphone), collecting emotional data in real time. When the salesperson clicks the "Send" button, the request data and emotional data are sent to the server.

[1055] Step 2:

[1056] server

[1057] The emotion data is stored in a database along with the received request data. At the same time, a natural language processing (NLP) engine begins analyzing the request data. Specifically, it extracts keywords and important phrases from the input text and analyzes them to understand the context.

[1058] Step 3:

[1059] server

[1060] The analyzed keywords and phrases are compared with a legal database to determine whether they comply with legal restrictions. For example, it checks whether they contain expressions that constitute misleading advertising or words prohibited by law. If a violation is found, an appropriate error message is generated and the salesperson is asked to make the necessary corrections.

[1061] Step 4:

[1062] server

[1063] If the request is confirmed to comply with legal constraints, the design generation engine kicks in. Using emotion data, the engine adapts design elements to the salesperson's emotional state (e.g., excitement, joy, calm, etc.). For example, excitement and joy can be expressed with vibrant, bright colors and adjusted layout and font styles.

[1064] Step 5:

[1065] server

[1066] The generated POP design data is temporarily stored in storage, and the design is sent to a dedicated interface that can be accessed by the relevant department.

[1067] Step 6:

[1068] Terminal

[1069] A member of the relevant department accesses a dedicated interface to preview the generated POP design, check the design content, and enter correction instructions or feedback as necessary.

[1070] Step 7:

[1071] server

[1072] Once the person in charge has completed the final check and clicked the "Final Approval" button, the design is saved as the final version. A notification of approval completion is also automatically sent to the sales representative.

[1073] Step 8:

[1074] User (sales representative)

[1075] Salespeople log in to the server using their own devices and download or print the final approved POP design, enabling them to quickly obtain legal, emotionally appropriate POPs that can be used in sales promotion activities.

[1076] Example 2

[1077] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1078] Conventional sales promotion design generation systems were unable to reflect the emotional state of salespeople when they entered their requests, which meant that promotional materials often did not have a strong emotional impact on customers. Furthermore, checking legal restrictions one by one required a great deal of time and effort, making it difficult to generate designs quickly.

[1079] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1080] In this invention, the server includes a means for inputting the sales representative's requests, a natural language processing means for analyzing the requests, a means for determining whether the requests comply with legal restrictions, a means for automatically generating a design that complies with the restrictions, a means for the responsible department to check the generated design, a means for providing the final approved design, and an emotion engine for recognizing the user's emotions. This makes it possible to automatically generate effective promotional designs that match the sales representative's emotional state while quickly reflecting the sales representative's requests. Furthermore, automating the checking of legal restrictions allows for both efficient design generation and compliance with laws and regulations.

[1081] "Requests" refers to information such as the text content of the POP, design preferences, images and icons to be used, and special instructions that salespeople need to promote sales.

[1082] "Natural language processing means" is a technology for analyzing input text data to extract and understand requirements.

[1083] "Legal Constraints" means laws and regulations that must be observed in advertising and promotional activities.

[1084] "Design generation means" is a technology that automatically creates designs for advertising and sales promotions based on input requests and analysis results.

[1085] The "department in charge" refers to a specialized department or team that reviews the content of the generated design and makes corrections or approvals as necessary.

[1086] An "emotion engine" is a technology that identifies emotions from the user's input speed, facial expressions, voice, etc., and analyzes their emotional state.

[1087] A "server" is a central computer system that processes and stores data for the entire system.

[1088] "Real-time" means that processing and analysis occurs immediately at the moment the input is made.

[1089] A "database" is a collection of data that efficiently manages related data and allows for quick search and manipulation.

[1090] A specific embodiment of the present invention is described in detail below. The present invention is a system that enables salespeople to quickly and legally create promotional POP (Point of Purchase) displays and provides designs that reflect the emotional state of the user.

[1091] Hardware and software used

[1092] The system uses the following main hardware and software:

[1093] Server: A central computing system that processes and stores data for the entire system. For example, we use servers from Amazon Web Services (AWS) or Google Cloud Platform (GCP).

[1094] Terminal: A device such as a PC, smartphone, or tablet on which salespeople enter requests. Each terminal uses a web browser to access a dedicated input form.

[1095] Natural language processing means: Analyze request data using libraries such as Python's NLTK or SpaCy.

[1096] Design generation method: Designs are automatically generated using design tool APIs such as Adobe XD and Sketch.

[1097] Emotion engine: A technology for analyzing the speed, facial expressions, and voice of users when they input, using libraries such as OpenCV and TensorFlow. Specifically, this applies to AI models that perform facial recognition and voice emotion analysis.

[1098] Database: We use a database management system such as MySQL or PostgreSQL to store and manage the request data and emotion data.

[1099] System Operation

[1100] User input of requests

[1101] The user (salesperson) accesses a dedicated input form using a device such as a PC, smartphone, or tablet. This form contains fields for entering text content related to the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). When the salesperson enters their request, the emotion engine analyzes their input speed, facial expressions, and voice in real time to identify their emotional state. When the salesperson clicks the "Send" button, the request data and emotion data are sent to the server.

[1102] Analysis of needs and emotions

[1103] The server stores the received request data and emotion data in a database. At the same time, a natural language processing engine analyzes the request data and extracts important keywords and phrases. The emotion engine identifies the user's emotional state. Specifically, the request text is analyzed using the Python libraries NLTK and SpaCy, and emotion data is analyzed using OpenCV and TensorFlow.

[1104] Determining legal restrictions

[1105] The NLP engine compares the extracted keywords, phrases, and sentiment data with the server's internal legal database to check for expressions or terms that violate legal restrictions, and if a violation is detected, an error message is generated and a notification is sent to the salesperson urging them to make corrections.

[1106] Auto-generation of designs

[1107] Once legal restrictions are met, the server's design generation engine automatically generates a POP design based on the customer's needs and emotional data. Using Adobe XD and Sketch APIs, the server determines the layout, text style, color scheme, and image placement. For example, if the salesperson's emotional state is "excited," the server selects vibrant colors and eye-catching fonts.

[1108] Design review and approval

[1109] A member of the relevant department uses a dedicated interface to review the generated design, provide corrections and feedback as necessary, and finally approve it.

[1110] Design provision

[1111] Once the design data has been approved, it is stored in cloud storage and notified to the sales representative, who can then log in from their own device and download or print the approved design for use in their sales promotion activities.

[1112] Specific examples

[1113] For example, suppose a salesperson wants to create a promotional POP for a new product, "DEF." The salesperson uses a terminal to access the input form, inputs requests such as "emphasize the features of the new product and prominently display the limited-time discount," and clicks the "Submit" button. The emotion engine recognizes that the salesperson is in an "excited" state based on their facial expression and tone of voice when they input their request.

[1114] The server receives the request, and the NLP engine extracts keywords such as "new product," "features," and "limited-time discount." This is then compared with a legal database to check whether the request complies with legal restrictions. If the request is found to comply with the restrictions, the design generation engine generates a design using vibrant colors and font styles.

[1115] The relevant department reviews the POP, makes any necessary corrections, and then gives final approval. Sales representatives can download or print the approved POP from their own devices and use it in their sales promotion activities.

[1116] Examples of prompts for a generative AI model might include:

[1117] "Create a POP that highlights the features of your new product. Display the limited-time discount prominently and use a vibrant color background. Also, choose a font that will make an impact on customers."

[1118] In this way, the system reflects the desires and emotions of salespeople and provides fast and legal promotional designs.

[1119] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1120] Step 1:

[1121] The user (salesperson) accesses a dedicated input form using a PC, smartphone, or tablet device. The input form contains fields for entering the text content of the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). When the salesperson enters their request and clicks the "Send" button, the entered request data and emotional data (input speed, facial expression, and voice data) are sent to the server.

[1122] Input: POP text content, design requests, image icons, special instructions, etc.

[1123] Output: Desire data and emotion data

[1124] Step 2:

[1125] The server stores the received request data and emotion data in a database. A natural language processing (NLP) engine analyzes the request data and extracts important keywords and phrases. The emotion engine analyzes emotion data from the salesperson's typing speed, facial expressions, and voice to identify their emotional state. Specific analysis uses Python libraries NLTK and SpaCy, as well as OpenCV and TensorFlow.

[1126] Input: Desire data and emotion data

[1127] Output: extracted keywords and phrases, analyzed emotional states

[1128] Step 3:

[1129] The server checks the keywords, phrases, and sentiment data extracted by the NLP engine against an internal legal database to verify legal restrictions. For example, it checks whether the content contains expressions that violate legal restrictions, such as "exaggerated advertising" or "false representation." If a violation is detected, an error message is generated and a notification is automatically sent to the salesperson requesting correction.

[1130] Input: Extracted keywords, phrases, and sentiment data

[1131] Output: Legal constraint detection results, error messages (if necessary)

[1132] Step 4:

[1133] If the server verifies that the request complies with legal restrictions, a design generation engine automatically generates a POP design based on the request and emotion data. The engine uses APIs from Adobe XD and Sketch to determine the layout, text style, color scheme, and image placement. For example, if the salesperson's emotion is "excited," it uses bright colors and a bold font. The generative AI model selects these design elements.

[1134] Input: Legally compliant desire data and emotion data

[1135] Output: Generated POP design

[1136] Step 5:

[1137] The user (a member of the relevant department) accesses a dedicated interface and previews the generated POP design. The person in charge checks the design and makes corrections or provides feedback as necessary. The preview screen is built using front-end frameworks such as React and Vue.js.

[1138] Input: Generated POP design

[1139] Output: Reviewed and revised design, feedback

[1140] Step 6:

[1141] When the person in charge clicks the "Final Approval" button, the server saves the final design data to cloud storage (e.g., Amazon S3) and automatically sends a notification of approval completion to the sales representative.

[1142] Input: Final approved design

[1143] Output: Final design saved in cloud storage, approval notification

[1144] Step 7:

[1145] The user (salesperson) logs in to the server from their own terminal and downloads or prints the approved POP design. The salesperson can then start their sales promotion activities using this design.

[1146] Input: Server login credentials, request for approved POP design

[1147] Output: Downloaded or printed POP designs

[1148] (Application example 2)

[1149] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1150] Conventional design generation systems have limitations in automatically generating content that reflects the user's emotional state or in quickly generating content that meets legal constraints. Furthermore, they are unable to provide personalized content based on the user's emotional state, so there is a need to improve the user experience.

[1151] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting a request from a salesperson, natural language processing means for analyzing the request, means for determining whether the request complies with legal restrictions, means for automatically generating a design that complies with the restrictions, means for a responsible department to check the generated design, means for providing the final approved design, means for recognizing the user's emotions, and means for generating appropriate content based on the user's emotional state. This enables the automatic generation of personalized content that reflects the user's emotional state and the rapid provision of legally compliant content.

[1152] The "means for inputting the salesperson's requests" is a device or system that provides an interface for the salesperson to input their requests.

[1153] "Natural language processing means" refers to a technology or system that analyzes text data entered by salespeople and extracts important keywords and phrases.

[1154] "Means for determining compliance with legal constraints" refers to a system that checks whether the generated designs and content comply with legal regulations and standards.

[1155] "Means for automatically generating designs that comply with constraints" refers to a system or technology that automatically generates designs and layouts that comply with legal constraints.

[1156] "Means for the responsible department to check the generated design" refers to an interface or system that allows the designated responsible department to check and provide feedback on the generated design.

[1157] The "means for providing the final approved design" is a system or method for providing the final approved design to sales personnel and other interested parties.

[1158] "Means for recognizing user emotions" refers to technology or a system that analyzes and recognizes emotions from the user's facial expressions, tone of voice, input speed, etc.

[1159] The "means for generating appropriate content based on the emotional state of the user" refers to a system or technology that automatically generates optimal content for a user in accordance with the emotional state of the user.

[1160] To implement this invention, various hardware and software components must be integrated to build an entire system. This system supports a series of processes: a salesperson inputs their requirements, the requirements are analyzed, and a design that complies with legal restrictions is automatically generated. It also includes a component that recognizes the user's emotional state and generates personalized content based on that.

[1161] System configuration

[1162] Hardware elements

[1163] 1. Smartphones and smart glasses: Used to collect user input and emotional data. Equipped with a camera and microphone, they can collect facial expressions and voice data in real time.

[1164] 2. Server: Responsible for hosting the data processing and generation engine.

[1165] Software Elements

[1166] 1. Natural language processing engine (e.g., spaCy, BERT): Analyzes the salesperson's input and extracts keywords and important phrases.

[1167] 2. Emotion recognition engine (e.g., Affective Computing SDK, Emotion API): Identifies the user's emotional state from their facial expressions and voice.

[1168] 3. Content generation engine (e.g., OpenAI GPT-4, DALL-E): Generates appropriate content based on the user's emotional state.

[1169] 4. Internal legal database: Used to determine whether generated content complies with legal constraints.

[1170] Data processing and calculation

[1171] Emotion data collection and analysis

[1172] The device (smartphone or smart glasses) collects the user's facial expressions, voice tone, and input speed in real time, and sends this data to an emotion recognition engine to analyze the user's emotional state.

[1173] Input and analysis of requests

[1174] Salespeople access a specific input form and enter their requests using a PC or smart device. The text is sent to a server and analyzed by a natural language processing engine. The engine extracts keywords and important phrases and compares the content with a database to determine whether it complies with legal restrictions.

[1175] Generate the design

[1176] If the user's desires and emotional data are confirmed to comply with legal restrictions, the content generation engine will begin working and generate designs and content based on the user's emotions. For example, if the user is in an "excited" state, it will generate a flashy, dynamic design. Conversely, if the user is in a "relaxed" state, it will generate a calm design.

[1177] Check by the relevant department

[1178] The resulting design is checked by the relevant department, fine-tuned as necessary, and then, once finally approved, provided to the sales staff.

[1179] Specific examples

[1180] For example, let's say a salesperson wants a design to promote a new product, "ABC." The salesperson enters "emphasize the features of ABC and highlight the limited-time discount" into the form and submits it. The emotion recognition engine identifies the salesperson's emotion as "excitement," the server receives the request, and the natural language processing engine extracts keywords such as "ABC," "features," and "limited-time discount." After verifying that legal restrictions are met, the design generation engine generates a vivid and dynamic design.

[1181] This allows salespeople to quickly and accurately create promotional designs, enabling them to provide content experiences that are optimized to suit the user's emotions.

[1182] Example prompt sentence:

[1183] Analyze the user's facial and voice data to identify their emotional state and generate a music playlist based on that emotional state. If the user is relaxed, choose relaxing songs, and if the user is excited, choose upbeat songs. Also, make sure the generated playlist does not violate copyright laws.

[1184] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1185] Step 1:

[1186] Salespeople access a dedicated input form using devices such as PCs, smartphones, and tablets. This form includes fields for entering the text content of the POP, desired design, images and icons to be used, and special instructions (deadline date, event information, etc.). As the salesperson enters their request, the emotion engine analyzes the user's emotional state in real time based on the salesperson's input speed, facial expression, voice, etc. When the salesperson clicks the "Send" button, the request data and emotional data are sent to the server. Specifically, the input data consists of the text content, input speed, facial expression data, and voice tone. Once sent to the server, this data is stored in a database for analysis.

[1187] Step 2:

[1188] The server stores the request data and emotional data received in step 1 in a database. At the same time, the natural language processing engine begins analyzing the request data. It extracts keywords and important phrases from the input data (text content). The emotion recognition engine analyzes the input data (facial expression data and voice tone) and identifies the emotional state of the salesperson. For example, it determines that the salesperson is in an "excited" state. The server is then ready to pass the analysis results of the request data and the emotional state to the next step.

[1189] Step 3:

[1190] The server compares the keywords, phrases, and sentiment data extracted by the natural language processing engine with a legal database. This process checks whether the input data contains expressions or terms that violate laws and regulations. For example, it detects whether the data contains "exaggerated advertising" or expressions that "violate specific laws and regulations." Based on the results of this comparison, it determines whether the data conforms to legal restrictions, and if so, proceeds to the next step.

[1191] Step 4:

[1192] If the server verifies that the request complies with legal restrictions, the design generation engine begins operation. Based on the input data (analysis results, emotional state), an automatically generated design is determined. At this stage, for example, a design using vibrant colors and font styles is generated for a salesperson in an "excited" state. The generated design data is sent to the relevant department's interface for review.

[1193] Step 5:

[1194] A member of the department in charge logs into a dedicated interface and previews the generated design. They visually check the input data (the generated design) and provide feedback and corrections as necessary. A specific example would be to make the discount rate more prominent. This corrected data is then sent back to the server, where it continues to be checked until it is finalized.

[1195] Step 6:

[1196] When the department member finally clicks the "Final Approval" button, the server saves the final version of the design data as the final version. The server also automatically sends an approval completion notification to the sales representative, which then provides the sales representative with the final approved design.

[1197] Step 7:

[1198] Salespeople log in to the server using their own devices and download or print the approved POP design. This operation allows the generated design to be immediately used in the salesperson's sales promotion activities. Specific actions include downloading a file and printing it out to a printer.

[1199] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1200] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1201] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1202] [Fourth embodiment]

[1203] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1204] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1205] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1206] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1207] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1208] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1209] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1210] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1211] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1212] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1213] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1214] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1215] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1216] This invention details a system that uses AI to analyze salesperson requests and automatically generate POPs (Points of Purchase) that comply with legal restrictions.

[1217] System Overview

[1218] This system consists of the following main elements:

[1219] 1. A means of inputting salesperson requests

[1220] 2. Natural language processing means for analyzing requests

[1221] 3. How to determine whether a request complies with legal restrictions

[1222] 4. A method for automatically generating designs that conform to constraints

[1223] 5. A means for the department in charge to check the generated design

[1224] 6. Means of providing the final approved design

[1225] The system allows salespeople to quickly and legally generate POPs for specific promotional campaigns and events.

[1226] Explanation of program processing

[1227] Step 1: Enter the sales representative's request

[1228] User (sales representative)

[1229] Salespeople access a dedicated input form using devices such as PCs, smartphones, and tablets. This form includes fields for entering the text content of the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). When the salesperson enters their request and clicks the "Submit" button, the request data is sent to the server.

[1230] Step 2: Analyze the request

[1231] server

[1232] The server stores the received request data and begins analyzing it using a natural language processing (NLP) engine. The NLP engine extracts important keywords and concepts from the input text. For example, keywords such as "limited-time discount," "new product," and "free sample" are extracted.

[1233] Step 3: Determine compliance with legal constraints

[1234] server

[1235] The server then checks the parsed request against an internal legal database to determine whether it complies with legal restrictions, such as whether the request constitutes false advertising, whether the product labeling contains misleading information, whether specific terminology is used, etc. If a violation is found, an appropriate error message is automatically generated.

[1236] Step 4: Automatically generate a design that meets the constraints

[1237] server

[1238] If the requirements are confirmed to comply with legal constraints, a design generation engine is put into action. This engine automatically generates layouts, fonts, color schemes, and images based on the specified requirements. For example, a design may be generated that highlights new product features and highlights discount information.

[1239] Step 5: The generated design is checked by the responsible department

[1240] Terminal

[1241] A member of the relevant department accesses a dedicated interface to preview the generated design, checks the content, and makes corrections or comments as necessary. Once corrections are complete, the design is given final approval.

[1242] Step 6: Deliver the final approved design

[1243] server

[1244] Once approved, the POP design is saved as a final version and moved to a folder accessible to sales representatives, who can then log in to the server using their own devices and download or print the approved POP.

[1245] Specific examples

[1246] For example, let's say Salesperson A wants to create a promotional POP for a new product "ABC." Salesperson A uses a terminal to access the input form, enters requests such as "emphasize the features of the new product and prominently display the limited-time discount," and clicks the "Submit" button.

[1247] The server receives the request, and the NLP engine begins analyzing it, extracting keywords such as "new product," "features," and "limited-time discount." The analysis results are compared with a legal database to check whether they comply with legal restrictions. If they are determined to comply with the restrictions, the design generation engine is activated, and a design incorporating images of the new product and discount information is automatically generated.

[1248] A member of the relevant department checks the generated design on the preview screen, makes minor adjustments such as making the discount rate more prominent, and then gives final approval. After approval, the design is saved as a final version and moved to a folder that Salesperson A can access. Salesperson A logs in to the server from his or her own device and downloads or prints the approved POP to use in sales promotion activities.

[1249] This system allows for the provision of prompt and appropriate POPs, improving the efficiency of sales activities.

[1250] The processing flow will be explained below.

[1251] Step 1:

[1252] User

[1253] The salesperson accesses a dedicated form on their terminal and enters their request for POP creation. Input items include the text content, desired theme and design, images to be used, deadline, etc. When the salesperson clicks the "Send" button, the request data is sent to the server.

[1254] Step 2:

[1255] server

[1256] The server stores the received request data in a database. At the same time, a natural language processing (NLP) engine begins analyzing the request data. Specifically, it extracts keywords and important phrases from the input text.

[1257] Step 3:

[1258] server

[1259] The NLP engine compares the extracted keywords and phrases with an internal legal database. For example, it checks for expressions that meet the criteria for "misleading advertising" or for words prohibited by specific laws. If a violation is detected, an error message is generated and the salesperson is asked to make corrections.

[1260] Step 4:

[1261] server

[1262] If the requirements are confirmed to comply with legal constraints, a design generation engine is put into action, which includes algorithms that automatically determine the layout, text style, color scheme, and image placement based on the specified requirements.

[1263] Step 5:

[1264] server

[1265] The generated POP design is stored in temporary storage, and the design data is sent to a dedicated interface that can be accessed by the relevant department.

[1266] Step 6:

[1267] Terminal

[1268] The person in charge of the relevant department accesses a dedicated interface to preview the generated POP design, check the content, and enter feedback and corrections as necessary.

[1269] Step 7:

[1270] server

[1271] When the person in charge clicks the "Final Approval" button, the final design data is saved as the final version. A notification of approval completion is also automatically sent to the sales representative.

[1272] Step 8:

[1273] User

[1274] Salespeople log in to the server using their own devices and download or print the approved POP design, allowing them to quickly obtain POPs that can be used in sales promotion activities.

[1275] Example 1

[1276] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1277] Point-of-purchase (POP) designs created by sales representatives for promotional activities must be fast and legal. However, the current process of sales representatives submitting design requests, checking whether they comply with legal restrictions, and generating appropriate designs is laborious and time-consuming. Furthermore, designs that include content that may violate legal restrictions are inappropriate and must be dealt with appropriately.

[1278] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1279] In this invention, the server includes means for inputting requests from sales representatives, natural language processing means for analyzing the requests, means for determining whether the requests comply with legal restrictions, means for automatically generating a design that complies with the restrictions, means for halting design generation and generating an error message if the requests violate legal restrictions, means for providing the final approved design, and means for moving the generated design to a folder accessible to the sales representative after final approval. This allows POP designs based on the sales representative's requests to be generated quickly and legally, making it possible to improve the efficiency of sales activities.

[1280] "Means for inputting salesperson requests" refers to the interface that salespersons use to input the text content and design of POPs, images and icons to be used, special instructions, etc. using devices such as PCs, smartphones, and tablets.

[1281] "Natural language processing means" refers to technical means, particularly a natural language processing engine, for analyzing received sales representative request data and extracting important keywords and concepts from the text.

[1282] "Means for determining compliance with legal constraints" refers to the means for comparing the analyzed request content with an internal database of laws and regulations to check whether it violates legal constraints.

[1283] "Means for automatically generating designs" refers to engines or software that automatically create legal and effective designs based on desired data.

[1284] The "means for generating an error message" refers to a technology that automatically generates an error message to notify a salesperson when content that violates legal restrictions is detected.

[1285] "Means for checking the generated design" refers to an interface that allows members of the responsible department to preview the generated design and confirm and modify its contents.

[1286] "Means for providing the final approved design" refers to the means by which the department in charge moves the design that has been finally approved to a folder that is accessible to the sales representative, so that the sales representative can download or print it.

[1287] "Means for moving the generated design to a folder accessible to sales personnel after final approval" refers to a means for saving the finalized design in a location easily accessible to sales personnel and making it available for use.

[1288] This invention details a system that uses AI to analyze salesperson requests and automatically generate POPs (Points of Purchase) that comply with legal restrictions.

[1289] System Overview

[1290] This system consists of the following main elements:

[1291] 1. A means of inputting salesperson requests

[1292] 2. Natural language processing means for analyzing requests

[1293] 3. How to determine whether a request complies with legal restrictions

[1294] 4. A method for automatically generating designs that conform to constraints

[1295] 5. A means for the department in charge to check the generated design

[1296] 6. Means of providing the final approved design

[1297] 7. A means to stop design generation and generate an error message if legal restrictions are violated.

[1298] 8. A way to move the generated designs to a folder accessible to sales personnel after final approval

[1299] Overview of the overall program processing

[1300] The program in this system receives the user's (salesperson's) request from the terminal and analyzes it using natural language processing means. The server compares the analyzed request with an internal database of laws and regulations to determine whether it complies with legal restrictions. If the request is determined to be compliant, the server activates a design generation engine to automatically generate a design based on the specified request. The generated design is then checked by the responsible department and given final approval. After approval, the design is moved to a folder accessible to the salesperson. Each step is described in detail below.

[1301] Hardware and software used

[1302] The following hardware and software are used in this system:

[1303] Terminal (PC, smartphone, tablet, etc.): A device used by sales staff to input, confirm, and download requests.

[1304] Server (high-performance workstation or cloud server): Receives requests, stores them, analyzes them, generates designs, and provides results.

[1305] Natural language processing engine (such as Google NLP API): Analyzes sales representatives' requests and extracts important keywords and concepts.

[1306] Legal database: Uses an internal legal database to determine compliance with legal constraints.

[1307] Design generation engine (e.g. Adobe Creative Cloud API): Automatically generate designs based on requests.

[1308] Specific examples

[1309] For example, let's say Salesperson A wants to create a promotional POP for a new product, "ABC." Salesperson A uses a terminal to access the input form, enters a request such as "emphasize the features of the new product and prominently display the limited-time discount," and clicks the "Submit" button. The following is a concrete example of a prompt sentence.

[1310] Example prompt sentence:

[1311] "I would like to create a promotional POP for our new product ABC. I would like the design to highlight the features of the new product and prominently display the limited-time discount."

[1312] The server receives the request, and the NLP engine begins analyzing it. Keywords such as "new product," "features," and "limited-time discount" are extracted and compared with a legal database. If it is determined to comply with legal restrictions, the design generation engine goes into action and automatically generates a design incorporating images of the new product and discount information. A member of the responsible department checks the generated design on a preview screen, makes minor adjustments such as making the discount rate more prominent, and then gives final approval. Once approved, the design is saved as a final version and moved to a folder that Salesperson A can access. Salesperson A logs in to the server from their own device and downloads or prints the approved POP to use in their sales promotion activities.

[1313] This system allows for the provision of prompt and legal POPs, improving the efficiency of sales activities.

[1314] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1315] Step 1: Enter your request

[1316] User

[1317] The user (salesperson) accesses a dedicated input form using a device such as a PC, smartphone, or tablet. This form has fields where users can enter the text content of the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). The user enters the required information in each field and clicks the "Submit" button. Input content includes new product features, discount information, event dates, etc. This input data is sent to the server.

[1318] Input: POP request data entered by the salesperson (text content, design requests, images, special instructions)

[1319] Output: Request data sent to the server

[1320] Step 2: Receiving and storing requests

[1321] server

[1322] The server receives the requested data sent by the user and stores it in a database for use in subsequent processing steps.

[1323] Input: Request data sent by sales representative

[1324] Output: Request data stored in a database

[1325] Step 3: Analyze the request

[1326] server

[1327] The server retrieves the stored request data from the database. It then uses a natural language processing (NLP) engine (e.g., Google NLP API) to analyze the request text and extract important keywords and concepts, such as "limited-time discount," "new product," and "free sample."

[1328] Input: Request data retrieved from the database

[1329] Output: List of extracted keywords

[1330] Step 4: Determine compliance with legal constraints

[1331] server

[1332] The server compares the extracted keyword list with an internal legal database. Here, it determines whether the analyzed request conforms to legal restrictions. For example, it checks whether there is any false advertising, whether there is any misleading information in the product labeling, and whether specific terminology is used. If any items that violate legal restrictions are found, it generates an error message to notify the user. If it is determined to conform, it proceeds to the next step.

[1333] Input: Extracted keyword list

[1334] Output: Legal compliance judgment result (compliant, non-compliant), error message (in case of non-compliance)

[1335] Step 5: Auto-generate the design

[1336] server

[1337] If the server determines that the request complies with legal restrictions, it launches a design generation engine (e.g., Adobe Creative Cloud API). The design generation engine automatically generates layouts, fonts, color schemes, and images based on the specified requests. For example, it generates a design that prominently displays the features of new products and highlights discount information.

[1338] Input: Request data, legal compliance judgment results

[1339] Output: Auto-generated design

[1340] Step 6: Check and approve the design

[1341] Terminal

[1342] Members of the relevant department access a dedicated interface to preview the generated design. They review the content and make corrections or comments as necessary. Once corrections are complete, the design is finalized with final approval.

[1343] Input: Auto-generated design

[1344] Output: Revised and final approved design

[1345] Step 7: Deliver the final design

[1346] server

[1347] The final approved POP design is saved on the server as a final version. It is then moved to a folder accessible to sales representatives. Sales representatives can then log in to the server using their own devices to download or print the approved POP.

[1348] Input: Final approved design

[1349] Output: Finalized design in a folder accessible to sales representatives

[1350] Through each processing step of this program, legal and effective POP designs based on the requests of sales representatives are quickly generated, thereby improving the efficiency of sales activities.

[1351] (Application example 1)

[1352] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1353] With conventional POP (Point of Purchase) generation systems, it was difficult to quickly provide legal designs based on sales representatives' requests. Furthermore, the time-consuming process of checking and correcting designs based on requests made it difficult to respond quickly on-site. Furthermore, the final approved design could not be sent to in-store display devices or printing devices in real time, making it necessary to respond quickly in sales promotion activities.

[1354] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1355] In this invention, the server includes means for inputting requests from sales representatives, natural language processing means for analyzing the requests, means for determining whether the requests comply with legal restrictions, means for automatically generating a design that complies with the restrictions, means for a responsible department to check the generated design, means for providing the final approved design, and means for transmitting the design to a display device or printer in the store in real time. This makes it possible to quickly and accurately analyze the requests from sales representatives, automatically generate a design that complies with legal restrictions, and simultaneously quickly carry out approval work, and provide the final design to the site in real time.

[1356] "Salesperson" means a professional or departmental employee who performs sales promotion activities.

[1357] The "means for inputting requests" refers to a device or interface that allows a salesperson to input requested information about a promotional POP by text or voice.

[1358] "Natural language processing means" refers to a technology or system that analyzes requests from salespeople, understands their meaning, and extracts important keywords.

[1359] "Legal Constraints" refers to laws and regulations regarding advertising and promotional materials, and are standards to ensure that violations do not occur.

[1360] "Means for automatically generating designs" refers to software or algorithms that automatically create designs according to the salesperson's requests.

[1361] "Means for the responsible department to check" refers to the process or interface by which members of the responsible department can check the generated design and make corrections or approvals as necessary.

[1362] The "means for providing the design" is a method or system for providing the final approved design for use by the sales force.

[1363] "Store display devices" refer to display devices such as displays and monitors installed in stores.

[1364] "Printing equipment" refers to equipment such as a printer that actually prints the POP design.

[1365] "Means for transmitting in real time" refers to communication means or technology for instantly transmitting the generated design to a display device or printing device in the store.

[1366] The present invention is a system that quickly and accurately analyzes the requests of salespeople, automatically generates designs that comply with legal restrictions, and provides the designs to store display devices and printers in real time. This system is composed of the following main elements:

[1367] System configuration

[1368] 1. A means of inputting salesperson requests

[1369] Salespeople can use devices such as smartphones or smart glasses to access a dedicated application and input the text content of the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). Voice input is also possible, allowing for faster operation.

[1370] 2. Natural language processing means for analyzing requests

[1371] The server uses an NLP engine (e.g., Google NLP API) to analyze the salesperson's input. This extracts important keywords and concepts from the input text. For example, keywords such as "new product," "features," and "limited-time discount" are extracted.

[1372] 3. How to determine compliance with legal restrictions

[1373] The server checks against a legal database to determine whether the extracted keywords comply with legal restrictions, checking for false advertising, misleading product information, etc. If a violation is found, an appropriate error message is generated.

[1374] 4. A method for automatically generating designs that conform to constraints

[1375] If the design is found to comply with legal restrictions, a design generation engine (e.g., Canva API) is put into action. Based on the specified requirements, the layout, fonts, color scheme, and images are automatically generated. For example, a design may be generated that highlights the features of new products and highlights discount information.

[1376] 5. A means for the department in charge to check the generated design

[1377] A member of the relevant department accesses a dedicated interface (for example, an app built with React Native) and previews the generated design. The person in charge checks the content and makes corrections or comments as necessary. Once the corrections are complete, the person in charge gives final approval.

[1378] 6. Means of providing the final approved design

[1379] The server saves the final approved design as a final version and moves it to a folder accessible to the sales representative, who can then log in to the server using their own device and download or print the approved POP.

[1380] 7. Means for transmitting data to a display or printing device in the store in real time

[1381] The final design is sent in real time to the store's display devices (displays and monitors) and printing equipment. Using real-time data synchronization technology such as Firebase, the design can be displayed and printed in-store instantly.

[1382] Specific examples

[1383] For example, consider the case where an employee at a brick-and-mortar store wants to create a promotional POP for a new product. Using a smartphone app, the employee voice-inputs, "I'd like you to create a promotional POP for new product 'XYZ' and display a 20% discount." The server receives the request and uses an NLP engine to extract the keywords "new product," "promotional POP," and "20% discount." After comparing this with a legal database, a legal design is automatically generated using the Canva API. The generated design is reviewed and revised by the relevant department, and after final approval, it is sent to the store's displays and printers in real time.

[1384] Prompt Sentence Examples

[1385] Salesperson's voice input: "I'd like to create a promotional POP for our new product 'XYZ', offering a 20% discount to new customers."

[1386] Keyword extraction using Google NLP API: "New product, promotional POP, new customer, 20% discount"

[1387] Legal constraint check: "New product, discount" → Legal constraint check

[1388] Create a design using the Canva API: "Add an image of a new product and the text '20% off'"

[1389] Real-time delivery by Firebase: "Send approved POP to in-store displays"

[1390] In this way, the detailed description of the invention can be utilized to quickly and accurately respond to the requests of salespeople.

[1391] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1392] Step 1:

[1393] Salespeople use smartphones or smart glasses to access a dedicated application and input their promotional POP requests. Input methods include text input and voice input, such as "Create a promotional POP for new product 'XYZ' and offer a 20% discount to new customers." This request data is sent to the server in real time. The input data includes the text content of the promotional POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.).

[1394] Step 2:

[1395] The server stores the received request data and analyzes it using a natural language processing (NLP) engine (e.g., Google NLP API). The NLP engine extracts important keywords from the input text, such as "new product," "promotional POP," "new customer," and "20% discount." The extracted keywords are used in the next step to check legal restrictions.

[1396] Step 3:

[1397] The server checks the extracted keywords against a legal database to determine whether they comply with legal restrictions. For example, it checks whether the keyword "20% discount" constitutes misleading advertising, or whether the display of "new products" contains misleading information. If a violation is found based on this check, an appropriate error message is automatically generated. This error message is sent to the user's device.

[1398] Step 4:

[1399] Based on the request, which is verified as complying with legal restrictions, the server triggers a design generation engine (e.g., the Canva API). The design generation engine automatically generates the layout, fonts, color scheme, and images based on the specified request. For example, a design may be created using images of new products to complement the text stating 20% ​​off. The generation process uses specific templates and elements according to the request.

[1400] Step 5:

[1401] The generated design is displayed on a dedicated interface for review by the relevant department. Members of the relevant department can preview the design using a PC or smart device, check the content, make corrections or comments as necessary, and once the corrections are complete, give final approval. Corrections are updated online in real time.

[1402] Step 6:

[1403] The final approved design is saved on the server as a final version. The saved design is then moved to a folder accessible to sales representatives. Sales representatives can then log in to the server using their own devices and download or print the approved POP.

[1404] Step 7:

[1405] The server sends the final design in real time to the store's display device (display or monitor) or printing device. This process uses real-time data synchronization technology such as Firebase, and the generated design is instantly displayed and printed in the store, enabling quick response to sales activities.

[1406] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1407] This invention details a system that uses AI to analyze salesperson requests and automatically generate POPs (points of purchase) that comply with legal restrictions, and further combines this with an emotion engine that recognizes the user's emotions, making it possible to provide designs that match the user's emotional state.

[1408] System Overview

[1409] This system consists of the following main elements:

[1410] 1. A means of inputting salesperson requests

[1411] 2. Natural language processing means for analyzing requests

[1412] 3. How to determine whether a request complies with legal restrictions

[1413] 4. A method for automatically generating designs that conform to constraints

[1414] 5. A means for the department in charge to check the generated design

[1415] 6. Means of providing the final approved design

[1416] 7. Emotion engine that recognizes user emotions

[1417] This system not only allows salespeople to quickly and legally generate POPs that correspond to specific promotional campaigns or events, but also provides designs that reflect the emotional state of the salespeople.

[1418] Explanation of program processing

[1419] Adding emotion engine processing

[1420] Step 1: Enter the sales representative's request

[1421] User (sales representative)

[1422] Salespeople access a dedicated input form using devices such as PCs, smartphones, and tablets. This form includes fields for entering the text content of the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). As the salesperson enters their request, the emotion engine analyzes the user's emotional state in real time based on the salesperson's input speed, facial expressions, voice, etc. When the salesperson clicks the "Send" button, the request data and emotional data are sent to the server.

[1423] Step 2: Analyze needs and emotions

[1424] server

[1425] The server stores the received request data and emotion data in a database. At the same time, a natural language processing (NLP) engine begins analyzing the request data. The NLP engine extracts keywords and important phrases from the input text. The emotion engine analyzes the emotion data and identifies the emotional state of the salesperson.

[1426] Step 3: Determine compliance with legal constraints

[1427] server

[1428] The NLP engine compares the extracted keywords, phrases, and sentiment data with an internal legal database. For example, it checks for expressions that meet the criteria for "misleading advertising" or for words prohibited by specific laws. If a violation is detected, an error message is generated, prompting the salesperson to make corrections.

[1429] Step 4: Automatically generate a design that meets the constraints

[1430] server

[1431] If the request is confirmed to comply with legal constraints, a design generation engine kicks in. The generation engine determines the layout, text style, color scheme, and image placement based on the specified request and reflects the emotional data. For example, if the salesperson's emotions indicate "excitement" or "happiness," it selects vibrant colors and font styles.

[1432] Step 5: The generated design is checked by the responsible department

[1433] Terminal

[1434] A member of the relevant department accesses a dedicated interface to preview the generated POP design, review it, and provide feedback and corrections as necessary.

[1435] Step 6: Deliver the final approved design

[1436] server

[1437] When the person in charge clicks the "Final Approval" button, the final design data is saved as the final version. A notification of approval completion is also automatically sent to the sales representative.

[1438] Step 7: Deliver your design

[1439] User

[1440] Salespeople log in to the server using their own devices and download or print the approved POP design, allowing them to quickly obtain POPs that can be used in sales promotion activities.

[1441] Specific examples

[1442] For example, let's say Salesperson A wants to create a promotional POP for a new product, "DEF." Salesperson A uses a terminal to access the input form, inputs requests such as "emphasize the features of the new product and prominently display the limited-time discount," and clicks the "Submit" button. The emotion engine recognizes that Salesperson A is in an "excited" state based on his facial expressions and tone of voice when he inputs his request.

[1443] The server receives the request, and the NLP engine begins analyzing it, extracting keywords such as "new product," "features," and "limited-time discount." The analysis results are compared with a legal database to check whether they comply with legal restrictions. If they are determined to comply with the restrictions, the design generation engine kicks in, generating a design that matches Salesperson A's emotions using vibrant colors and font styles.

[1444] A member of the relevant department checks the generated design on the preview screen, makes minor adjustments such as making the discount rate more prominent, and then gives final approval. After approval, the design is saved as a final version and moved to a folder that Salesperson A can access. Salesperson A can then log in to the server from their own device and download or print the approved POP to use in their sales promotion activities.

[1445] This system not only allows for the provision of quick and appropriate POP displays, but also generates designs that reflect the emotions of sales staff, further increasing the effectiveness of sales promotion activities.

[1446] The processing flow will be explained below.

[1447] Step 1:

[1448] User (sales representative)

[1449] Salespeople access a dedicated input form on their terminals and enter their requests for POP creation. Input items include the text content of the POP, the desired theme and design, the images and icons to be used, and special instructions (deadline date, event information, etc.). Furthermore, as the salesperson enters information, the emotion engine analyzes the salesperson's typing speed, keyboard swiftness, facial expressions (when using a camera), and voice tone (when using a microphone), collecting emotional data in real time. When the salesperson clicks the "Send" button, the request data and emotional data are sent to the server.

[1450] Step 2:

[1451] server

[1452] The emotion data is stored in a database along with the received request data. At the same time, a natural language processing (NLP) engine begins analyzing the request data. Specifically, it extracts keywords and important phrases from the input text and analyzes them to understand the context.

[1453] Step 3:

[1454] server

[1455] The analyzed keywords and phrases are compared with a legal database to determine whether they comply with legal restrictions. For example, it checks whether they contain expressions that constitute misleading advertising or words prohibited by law. If a violation is found, an appropriate error message is generated and the salesperson is asked to make the necessary corrections.

[1456] Step 4:

[1457] server

[1458] If the request is confirmed to comply with legal constraints, the design generation engine kicks in. Using emotion data, the engine adapts design elements to the salesperson's emotional state (e.g., excitement, joy, calm, etc.). For example, excitement and joy can be expressed with vibrant, bright colors and adjusted layout and font styles.

[1459] Step 5:

[1460] server

[1461] The generated POP design data is temporarily stored in storage, and the design is sent to a dedicated interface that can be accessed by the relevant department.

[1462] Step 6:

[1463] Terminal

[1464] A member of the relevant department accesses a dedicated interface to preview the generated POP design, check the design content, and enter correction instructions or feedback as necessary.

[1465] Step 7:

[1466] server

[1467] Once the person in charge has completed the final check and clicked the "Final Approval" button, the design is saved as the final version. A notification of approval completion is also automatically sent to the sales representative.

[1468] Step 8:

[1469] User (sales representative)

[1470] Salespeople log in to the server using their own devices and download or print the final approved POP design, enabling them to quickly obtain legal, emotionally appropriate POPs that can be used in sales promotion activities.

[1471] Example 2

[1472] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1473] Conventional sales promotion design generation systems were unable to reflect the emotional state of salespeople when they entered their requests, which meant that promotional materials often did not have a strong emotional impact on customers. Furthermore, checking legal restrictions one by one required a great deal of time and effort, making it difficult to generate designs quickly.

[1474] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1475] In this invention, the server includes a means for inputting the sales representative's requests, a natural language processing means for analyzing the requests, a means for determining whether the requests comply with legal restrictions, a means for automatically generating a design that complies with the restrictions, a means for the responsible department to check the generated design, a means for providing the final approved design, and an emotion engine for recognizing the user's emotions. This makes it possible to automatically generate effective promotional designs that match the sales representative's emotional state while quickly reflecting the sales representative's requests. Furthermore, automating the checking of legal restrictions allows for both efficient design generation and compliance with laws and regulations.

[1476] "Requests" refers to information such as the text content of the POP, design preferences, images and icons to be used, and special instructions that salespeople need to promote sales.

[1477] "Natural language processing means" is a technology for analyzing input text data to extract and understand requirements.

[1478] "Legal Constraints" means laws and regulations that must be observed in advertising and promotional activities.

[1479] "Design generation means" is a technology that automatically creates designs for advertising and sales promotions based on input requests and analysis results.

[1480] The "department in charge" refers to a specialized department or team that reviews the content of the generated design and makes corrections or approvals as necessary.

[1481] An "emotion engine" is a technology that identifies emotions from the user's input speed, facial expressions, voice, etc., and analyzes their emotional state.

[1482] A "server" is a central computer system that processes and stores data for the entire system.

[1483] "Real-time" means that processing and analysis occurs immediately at the moment the input is made.

[1484] A "database" is a collection of data that efficiently manages related data and allows for quick search and manipulation.

[1485] A specific embodiment of the present invention is described in detail below. The present invention is a system that enables salespeople to quickly and legally create promotional POP (Point of Purchase) displays and provides designs that reflect the emotional state of the user.

[1486] Hardware and software used

[1487] The system uses the following main hardware and software:

[1488] Server: A central computing system that processes and stores data for the entire system. For example, we use servers from Amazon Web Services (AWS) or Google Cloud Platform (GCP).

[1489] Terminal: A device such as a PC, smartphone, or tablet on which salespeople enter requests. Each terminal uses a web browser to access a dedicated input form.

[1490] Natural language processing means: Analyze request data using libraries such as Python's NLTK or SpaCy.

[1491] Design generation method: Designs are automatically generated using design tool APIs such as Adobe XD and Sketch.

[1492] Emotion engine: A technology for analyzing the speed, facial expressions, and voice of users when they input, using libraries such as OpenCV and TensorFlow. Specifically, this applies to AI models that perform facial recognition and voice emotion analysis.

[1493] Database: We use a database management system such as MySQL or PostgreSQL to store and manage the request data and emotion data.

[1494] System Operation

[1495] User input of requests

[1496] The user (salesperson) accesses a dedicated input form using a device such as a PC, smartphone, or tablet. This form contains fields for entering text content related to the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). When the salesperson enters their request, the emotion engine analyzes their input speed, facial expressions, and voice in real time to identify their emotional state. When the salesperson clicks the "Send" button, the request data and emotion data are sent to the server.

[1497] Analysis of needs and emotions

[1498] The server stores the received request data and emotion data in a database. At the same time, a natural language processing engine analyzes the request data and extracts important keywords and phrases. The emotion engine identifies the user's emotional state. Specifically, the request text is analyzed using the Python libraries NLTK and SpaCy, and emotion data is analyzed using OpenCV and TensorFlow.

[1499] Determining legal restrictions

[1500] The NLP engine compares the extracted keywords, phrases, and sentiment data with the server's internal legal database to check for expressions or terms that violate legal restrictions, and if a violation is detected, an error message is generated and a notification is sent to the salesperson urging them to make corrections.

[1501] Auto-generation of designs

[1502] Once legal restrictions are met, the server's design generation engine automatically generates a POP design based on the customer's needs and emotional data. Using Adobe XD and Sketch APIs, the server determines the layout, text style, color scheme, and image placement. For example, if the salesperson's emotional state is "excited," the server selects vibrant colors and eye-catching fonts.

[1503] Design review and approval

[1504] A member of the relevant department uses a dedicated interface to review the generated design, provide corrections and feedback as necessary, and finally approve it.

[1505] Design provision

[1506] Once the design data has been approved, it is stored in cloud storage and notified to the sales representative, who can then log in from their own device and download or print the approved design for use in their sales promotion activities.

[1507] Specific examples

[1508] For example, suppose a salesperson wants to create a promotional POP for a new product, "DEF." The salesperson uses a terminal to access the input form, inputs requests such as "emphasize the features of the new product and prominently display the limited-time discount," and clicks the "Submit" button. The emotion engine recognizes that the salesperson is in an "excited" state based on their facial expression and tone of voice when they input their request.

[1509] The server receives the request, and the NLP engine extracts keywords such as "new product," "features," and "limited-time discount." This is then compared with a legal database to check whether the request complies with legal restrictions. If the request is found to comply with the restrictions, the design generation engine generates a design using vibrant colors and font styles.

[1510] The relevant department reviews the POP, makes any necessary corrections, and then gives final approval. Sales representatives can download or print the approved POP from their own devices and use it in their sales promotion activities.

[1511] Examples of prompts for a generative AI model might include:

[1512] "Create a POP that highlights the features of your new product. Display the limited-time discount prominently and use a vibrant color background. Also, choose a font that will make an impact on customers."

[1513] In this way, the system reflects the desires and emotions of salespeople and provides fast and legal promotional designs.

[1514] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1515] Step 1:

[1516] The user (salesperson) accesses a dedicated input form using a PC, smartphone, or tablet device. The input form contains fields for entering the text content of the POP, desired design, images and icons to be used, and special instructions (deadline, event information, etc.). When the salesperson enters their request and clicks the "Send" button, the entered request data and emotional data (input speed, facial expression, and voice data) are sent to the server.

[1517] Input: POP text content, design requests, image icons, special instructions, etc.

[1518] Output: Desire data and emotion data

[1519] Step 2:

[1520] The server stores the received request data and emotion data in a database. A natural language processing (NLP) engine analyzes the request data and extracts important keywords and phrases. The emotion engine analyzes emotion data from the salesperson's typing speed, facial expressions, and voice to identify their emotional state. Specific analysis uses Python libraries NLTK and SpaCy, as well as OpenCV and TensorFlow.

[1521] Input: Desire data and emotion data

[1522] Output: extracted keywords and phrases, analyzed emotional states

[1523] Step 3:

[1524] The server checks the keywords, phrases, and sentiment data extracted by the NLP engine against an internal legal database to verify legal restrictions. For example, it checks whether the content contains expressions that violate legal restrictions, such as "exaggerated advertising" or "false representation." If a violation is detected, an error message is generated and a notification is automatically sent to the salesperson requesting correction.

[1525] Input: Extracted keywords, phrases, and sentiment data

[1526] Output: Legal constraint detection results, error messages (if necessary)

[1527] Step 4:

[1528] If the server verifies that the request complies with legal restrictions, a design generation engine automatically generates a POP design based on the request and emotion data. The engine uses APIs from Adobe XD and Sketch to determine the layout, text style, color scheme, and image placement. For example, if the salesperson's emotion is "excited," it uses bright colors and a bold font. The generative AI model selects these design elements.

[1529] Input: Legally compliant desire data and emotion data

[1530] Output: Generated POP design

[1531] Step 5:

[1532] The user (a member of the relevant department) accesses a dedicated interface and previews the generated POP design. The person in charge checks the design and makes corrections or provides feedback as necessary. The preview screen is built using front-end frameworks such as React and Vue.js.

[1533] Input: Generated POP design

[1534] Output: Reviewed and revised design, feedback

[1535] Step 6:

[1536] When the person in charge clicks the "Final Approval" button, the server saves the final design data to cloud storage (e.g., Amazon S3) and automatically sends a notification of approval completion to the sales representative.

[1537] Input: Final approved design

[1538] Output: Final design saved in cloud storage, approval notification

[1539] Step 7:

[1540] The user (salesperson) logs in to the server from their own terminal and downloads or prints the approved POP design. The salesperson can then start their sales promotion activities using this design.

[1541] Input: Server login credentials, request for approved POP design

[1542] Output: Downloaded or printed POP designs

[1543] (Application example 2)

[1544] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1545] Conventional design generation systems have limitations in automatically generating content that reflects the user's emotional state or in quickly generating content that meets legal constraints. Furthermore, they are unable to provide personalized content based on the user's emotional state, so there is a need to improve the user experience.

[1546] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting a request from a salesperson, natural language processing means for analyzing the request, means for determining whether the request complies with legal restrictions, means for automatically generating a design that complies with the restrictions, means for a responsible department to check the generated design, means for providing the final approved design, means for recognizing the user's emotions, and means for generating appropriate content based on the user's emotional state. This enables the automatic generation of personalized content that reflects the user's emotional state and the rapid provision of legally compliant content.

[1547] The "means for inputting the salesperson's requests" is a device or system that provides an interface for the salesperson to input their requests.

[1548] "Natural language processing means" refers to a technology or system that analyzes text data entered by salespeople and extracts important keywords and phrases.

[1549] "Means for determining compliance with legal constraints" refers to a system that checks whether the generated designs and content comply with legal regulations and standards.

[1550] "Means for automatically generating designs that comply with constraints" refers to a system or technology that automatically generates designs and layouts that comply with legal constraints.

[1551] "Means for the responsible department to check the generated design" refers to an interface or system that allows the designated responsible department to check and provide feedback on the generated design.

[1552] The "means for providing the final approved design" is a system or method for providing the final approved design to sales personnel and other interested parties.

[1553] "Means for recognizing user emotions" refers to technology or a system that analyzes and recognizes emotions from the user's facial expressions, tone of voice, input speed, etc.

[1554] The "means for generating appropriate content based on the emotional state of the user" refers to a system or technology that automatically generates optimal content for a user in accordance with the emotional state of the user.

[1555] To implement this invention, various hardware and software components must be integrated to build an entire system. This system supports a series of processes: a salesperson inputs their requirements, the requirements are analyzed, and a design that complies with legal restrictions is automatically generated. It also includes a component that recognizes the user's emotional state and generates personalized content based on that.

[1556] System configuration

[1557] Hardware elements

[1558] 1. Smartphones and smart glasses: Used to collect user input and emotional data. Equipped with a camera and microphone, they can collect facial expressions and voice data in real time.

[1559] 2. Server: Responsible for hosting the data processing and generation engine.

[1560] Software Elements

[1561] 1. Natural language processing engine (e.g., spaCy, BERT): Analyzes the salesperson's input and extracts keywords and important phrases.

[1562] 2. Emotion recognition engine (e.g., Affective Computing SDK, Emotion API): Identifies the user's emotional state from their facial expressions and voice.

[1563] 3. Content generation engine (e.g., OpenAI GPT-4, DALL-E): Generates appropriate content based on the user's emotional state.

[1564] 4. Internal legal database: Used to determine whether generated content complies with legal constraints.

[1565] Data processing and calculation

[1566] Emotion data collection and analysis

[1567] The device (smartphone or smart glasses) collects the user's facial expressions, voice tone, and input speed in real time, and sends this data to an emotion recognition engine to analyze the user's emotional state.

[1568] Input and analysis of requests

[1569] Salespeople access a specific input form and enter their requests using a PC or smart device. The text is sent to a server and analyzed by a natural language processing engine. The engine extracts keywords and important phrases and compares the content with a database to determine whether it complies with legal restrictions.

[1570] Generate the design

[1571] If the user's desires and emotional data are confirmed to comply with legal restrictions, the content generation engine will begin working and generate designs and content based on the user's emotions. For example, if the user is in an "excited" state, it will generate a flashy, dynamic design. Conversely, if the user is in a "relaxed" state, it will generate a calm design.

[1572] Check by the relevant department

[1573] The resulting design is checked by the relevant department, fine-tuned as necessary, and then, once finally approved, provided to the sales staff.

[1574] Specific examples

[1575] For example, let's say a salesperson wants a design to promote a new product, "ABC." The salesperson enters "emphasize the features of ABC and highlight the limited-time discount" into the form and submits it. The emotion recognition engine identifies the salesperson's emotion as "excitement," the server receives the request, and the natural language processing engine extracts keywords such as "ABC," "features," and "limited-time discount." After verifying that legal restrictions are met, the design generation engine generates a vivid and dynamic design.

[1576] This allows salespeople to quickly and accurately create promotional designs, enabling them to provide content experiences that are optimized to suit the user's emotions.

[1577] Example prompt sentence:

[1578] Analyze the user's facial and voice data to identify their emotional state and generate a music playlist based on that emotional state. If the user is relaxed, choose relaxing songs, and if the user is excited, choose upbeat songs. Also, make sure the generated playlist does not violate copyright laws.

[1579] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1580] Step 1:

[1581] Salespeople access a dedicated input form using devices such as PCs, smartphones, and tablets. This form includes fields for entering the text content of the POP, desired design, images and icons to be used, and special instructions (deadline date, event information, etc.). As the salesperson enters their request, the emotion engine analyzes the user's emotional state in real time based on the salesperson's input speed, facial expression, voice, etc. When the salesperson clicks the "Send" button, the request data and emotional data are sent to the server. Specifically, the input data consists of the text content, input speed, facial expression data, and voice tone. Once sent to the server, this data is stored in a database for analysis.

[1582] Step 2:

[1583] The server stores the request data and emotional data received in step 1 in a database. At the same time, the natural language processing engine begins analyzing the request data. It extracts keywords and important phrases from the input data (text content). The emotion recognition engine analyzes the input data (facial expression data and voice tone) and identifies the emotional state of the salesperson. For example, it determines that the salesperson is in an "excited" state. The server is then ready to pass the analysis results of the request data and the emotional state to the next step.

[1584] Step 3:

[1585] The server compares the keywords, phrases, and sentiment data extracted by the natural language processing engine with a legal database. This process checks whether the input data contains expressions or terms that violate laws and regulations. For example, it detects whether the data contains "exaggerated advertising" or expressions that "violate specific laws and regulations." Based on the results of this comparison, it determines whether the data conforms to legal restrictions, and if so, proceeds to the next step.

[1586] Step 4:

[1587] If the server verifies that the request complies with legal restrictions, the design generation engine begins operation. Based on the input data (analysis results, emotional state), an automatically generated design is determined. At this stage, for example, a design using vibrant colors and font styles is generated for a salesperson in an "excited" state. The generated design data is sent to the relevant department's interface for review.

[1588] Step 5:

[1589] A member of the department in charge logs into a dedicated interface and previews the generated design. They visually check the input data (the generated design) and provide feedback and corrections as necessary. A specific example would be to make the discount rate more prominent. This corrected data is then sent back to the server, where it continues to be checked until it is finalized.

[1590] Step 6:

[1591] When the department member finally clicks the "Final Approval" button, the server saves the final version of the design data as the final version. The server also automatically sends an approval completion notification to the sales representative, which then provides the sales representative with the final approved design.

[1592] Step 7:

[1593] Salespeople log in to the server using their own devices and download or print the approved POP design. This operation allows the generated design to be immediately used in the salesperson's sales promotion activities. Specific actions include downloading a file and printing it out to a printer.

[1594] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1595] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1596] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1597] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1598] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1599] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1600] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1601] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1602] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1603] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1604] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1605] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1606] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1607] 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.

[1608] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1609] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1610] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1611] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1612] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1613] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1614] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1615] The following is further disclosed regarding the above embodiment.

[1616] (Claim 1)

[1617] a means for inputting requests from salespeople;

[1618] natural language processing means for analyzing the request;

[1619] means for determining whether the request complies with legal restrictions;

[1620] A means for automatically generating a design that conforms to the constraints;

[1621] A means for the department in charge to check the generated design,

[1622] A means to provide the final approved design

[1623] A system including:

[1624] (Claim 2)

[1625] 2. The system according to claim 1, wherein the natural language processing means extracts desired keywords and compares the extracted keywords with an internal database.

[1626] (Claim 3)

[1627] 2. The system according to claim 1, wherein the means for determining compliance with the legal constraints uses an internal database of laws and regulations.

[1628] "Example 1"

[1629] (Claim 1)

[1630] a means for inputting requests from salespeople;

[1631] natural language processing means for analyzing the request;

[1632] means for determining whether the request complies with legal restrictions;

[1633] A means for automatically generating a design that conforms to the constraints;

[1634] A means for the department in charge to check the generated design,

[1635] A means of providing a final approved design;

[1636] a means to stop design generation and generate an error message if legal restrictions are violated;

[1637] Means for moving the generated design to a folder accessible to sales personnel after final approval

[1638] A system including:

[1639] (Claim 2)

[1640] 2. The system according to claim 1, wherein the natural language processing means extracts desired keywords and compares the extracted keywords with an internal database.

[1641] (Claim 3)

[1642] 2. The system according to claim 1, wherein the means for determining compliance with the legal constraints uses an internal database of laws and regulations.

[1643] "Application Example 1"

[1644] (Claim 1)

[1645] a means for inputting requests from salespeople;

[1646] natural language processing means for analyzing the request;

[1647] means for determining whether the request complies with legal restrictions;

[1648] A means for automatically generating a design that conforms to the constraints;

[1649] A means for the department in charge to check the generated design,

[1650] A means of providing a final approved design;

[1651] means for transmitting said design in real time to a display or printing device in the store;

[1652] A system including:

[1653] (Claim 2)

[1654] 2. The system according to claim 1, wherein the natural language processing means extracts desired keywords and compares the extracted keywords with an internal database.

[1655] (Claim 3)

[1656] 2. The system according to claim 1, wherein the means for determining compliance with the legal constraints uses an internal database of laws and regulations.

[1657] "Example 2: Combining Emotion Engines"

[1658] (Claim 1)

[1659] a means for inputting requests from salespeople;

[1660] natural language processing means for analyzing the request;

[1661] means for determining whether the request complies with legal restrictions;

[1662] A means for automatically generating a design that conforms to the constraints;

[1663] A means for the department in charge to check the generated design,

[1664] A means of providing a final approved design;

[1665] A system including an emotion engine that recognizes user emotions.

[1666] (Claim 2)

[1667] 2. The system according to claim 1, wherein the natural language processing means extracts desired keywords and compares the extracted keywords with an internal database.

[1668] (Claim 3)

[1669] 2. The system according to claim 1, wherein the means for determining compliance with the legal constraints uses an internal database of laws and regulations.

[1670] (Claim 4)

[1671] 2. The system according to claim 1, wherein the emotion engine analyzes emotion data from the user's input speed, facial expression, and voice in real time and reflects the data when generating the design.

[1672] "Application example 2 when combining emotion engines"

[1673] (Claim 1)

[1674] a means for inputting requests from salespeople;

[1675] natural language processing means for analyzing the request;

[1676] means for determining whether the request complies with legal restrictions;

[1677] A means for automatically generating a design that conforms to the constraints;

[1678] A means for the department in charge to check the generated design,

[1679] A means of providing a final approved design;

[1680] means for recognizing a user's emotion;

[1681] means for generating appropriate content based on the emotional state of the user;

[1682] A system including:

[1683] (Claim 2)

[1684] 2. The system according to claim 1, wherein the natural language processing means extracts desired keywords and compares the extracted keywords with an internal database.

[1685] (Claim 3)

[1686] 2. The system according to claim 1, wherein the means for determining compliance with the legal constraints uses an internal database of laws and regulations. [Explanation of symbols]

[1687] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for inputting requests from salespeople; natural language processing means for analyzing the request; means for determining whether the request complies with legal restrictions; A means for automatically generating a design that conforms to the constraints; A means for the department in charge to check the generated design, A means to deliver the final approved design A system including:

2. 2. The system according to claim 1, wherein said natural language processing means extracts desired keywords and compares the extracted keywords with an internal database.

3. 2. The system of claim 1, wherein the means for determining compliance with legal restrictions uses an internal database of laws and regulations.

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