system

Abstract

Provide a system. The system includes: a means for receiving product information and sales promotion conditions input by a user; means for analyzing the product information and sales promotion conditions and generating analysis data; A means for applying a learning model of past sales promotion cases and target attributes based on the analysis data to generate an optimal sales promotion plan; means for presenting the generated sales promotion plan to a user; A system including:

Description

[Technical Field]

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

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

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

[0004] In recent years, a wide variety of products have appeared on the market, and manufacturers and retailers must cater to different target demographics and promotional requirements. This makes it difficult to quickly create effective promotional plans, and they often rely on limited knowledge and experience. This leads to issues such as wasted advertising budgets and a lack of effective approaches to target demographics. Another problem is that return on investment is not maximized due to insufficient use of past promotional data and target attributes. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides a system comprising: means for receiving product information and promotional conditions entered by a user; means for analyzing the product information and promotional conditions and generating analytical data; means for applying a learning model of past promotional cases and target attributes based on the analytical data to generate an optimal promotional plan; and means for presenting the generated promotional plan to the user.

[0006] Specifically, the user first inputs the product name, target demographic, promotion budget, promotion period, and specific conditions. This information is then analyzed by the server, and the generated data is passed to the generation AI. The generation AI applies a model learned from past promotional cases and target attributes to generate an optimal promotion plan. This plan includes the promotional media to be used, messaging and advertising copy, budget allocation, and a detailed campaign schedule. Finally, the generated promotion plan is presented to the user, with the aim of maximizing return on investment.

[0007] "Product information" refers to information such as the name and characteristics of the product being promoted.

[0008] "Sales promotion conditions" refers to information that indicates various conditions for effectively selling a product, such as the target demographic, budget, period, and specific conditions.

[0009] "Analysis data" refers to data generated by the server after analyzing the product information and sales promotion conditions entered by the user.

[0010] "Past sales promotion cases" refers to recorded information about the results and methods of various sales promotion activities that have been carried out in the past.

[0011] "Target attributes" refers to information about the characteristics and behavioral patterns of consumers who are the target of sales promotion.

[0012] A "learning model" is a predictive model constructed by generative AI based on past sales promotion cases and target attributes.

[0013] A "promotion plan" is a plan for promotional activities that includes the promotional media to be used, messaging and advertising copy, budget allocation, detailed campaign schedule, etc., based on the conditions entered by the user.

[0014] "Generative AI" is an algorithm or system that uses artificial intelligence technology to generate promotional plans.

[0015] The "server" is a computer system that receives, analyzes, and stores data from users, and works with the generation AI to generate promotional plans.

[0016] A "terminal" is a device that a user uses to input product information and promotional terms.

[0017] A "user" is a person in charge of a manufacturer or retail store who intends to use this system to create a sales promotion plan. [Brief explanation of the drawings]

[0018] [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 illustrating 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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] This invention is a system in which a generation AI creates an optimal sales promotion plan based on product information and sales promotion conditions entered by a user, and presents that plan to the user. This system consists of a terminal that receives user input, a server that processes the received data, and a generation AI that learns from past sales promotion cases and target attributes.

[0040] System Overview

[0041] User Input

[0042] A user accesses a dedicated form using a terminal and enters product information (product name, characteristics, etc.) and promotional conditions (target demographic, budget, period, specific conditions, etc.). For example, to sell a new smartwatch, the target demographic might be "men aged 25-45 living in urban areas," the promotional budget might be "5 million yen," the promotion period might be "three months," and the specific conditions might be "mainly through online channels."

[0043] Sending input to the server

[0044] The terminal sends the entered information to the server in JSON format.

[0045] Analysis on the server

[0046] The server parses the received JSON data, extracts each field, and structures it, so that product information and promotional terms are neatly organized and ready to be passed to the generation AI.

[0047] Applying generative AI

[0048] The server sends the analysis data to the generation AI, which uses a database of past promotional cases and a model trained based on target attributes to generate an optimal promotional plan. This plan generation includes a process of predicting the effectiveness of each medium, such as social media advertising, email marketing, and influencer marketing, and optimizing budget allocation and messaging.

[0049] Proposal plan generation and presentation

[0050] The sales promotion plan created by the generation AI is sent back to the server, which then presents it to the user. For example, it may include budget allocation, such as allocating 3 million yen to social media advertising, 1 million yen to email marketing, and 1 million yen to influencer marketing, as well as the start date and frequency of advertising.

[0051] Specific example explanation

[0052] For example, if a user is selling a "new smartwatch," the process would proceed as follows: Using a device, the user enters the product name "new smartwatch," the target demographic "men aged 25-45 living in urban areas," the promotional budget "5 million yen," the promotion period "3 months," and the specific conditions "mainly online channels." This information is sent from the device to the server, which analyzes the data and passes it on to the generation AI. The generation AI generates an optimal promotional plan based on past cases and target attributes, proposing a plan consisting of 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing. Finally, this plan is presented to the user, with specific advertising messages and schedules detailed.

[0053] In this way, the present invention provides a system that automatically generates an optimal sales promotion plan based on information entered by a user through advanced analysis and learning, thereby maximizing return on investment.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] The user accesses a dedicated form on their device and enters product information and promotion conditions. Product information includes the product name and characteristics, while promotion conditions include the target demographic, promotion budget, promotion period, and other specific conditions. For example, to create a promotion plan for a new smartwatch, the user enters the target demographic as "men aged 25-45 living in urban areas," a promotion budget of "5 million yen," a promotion period of "3 months," and specific conditions as "mainly through online channels."

[0057] Step 2:

[0058] Once the user has completed the input, they press the "Submit" button, which causes the device to convert the information into JSON format and send it to the server as an HTTP POST request.

[0059] Step 3:

[0060] The server receives the HTTP request and parses the JSON data. Using a JSON parser, the server extracts each field according to the data structure and saves the product information and promotion terms as structured data.

[0061] Step 4:

[0062] The server passes the analyzed data to the generation AI, which then makes an API request to the generation AI and sends the analyzed product information and promotional terms.

[0063] Step 5:

[0064] The generation AI applies a learning model based on a database of past promotional cases and target attributes. The generation AI receives requests, simulates the effectiveness of each promotional method (social media advertising, email marketing, influencer marketing, etc.), and generates the optimal promotional plan.

[0065] Step 6:

[0066] The AI ​​then generates an optimal sales promotion plan and sends it back to the server. The plan includes the promotional media to be used, advertising copy and messaging, budget allocation, and a detailed campaign schedule. For example, a specific plan could be generated that allocates 3 million yen to social media advertising, 1 million yen to email marketing, and 1 million yen to influencer marketing.

[0067] Step 7:

[0068] The server analyzes the promotional plan received from the generation AI and formats it as necessary.

[0069] Step 8:

[0070] The server sends the formatted promotion plan to the terminal and displays it to the user. The user can view the proposed promotion plan through the terminal, and can check details of the budget allocation, advertising message, and campaign period, and can also submit a request for modification if necessary.

[0071] In this way, the server and generation AI work together to automatically generate the optimal sales promotion plan based on the product information and sales promotion conditions entered by the user, making it possible to quickly implement effective sales promotion activities.

[0072] Example 1

[0073] 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."

[0074] Creating a sales promotion plan has traditionally required a great deal of specialized knowledge and time, placing a significant burden on small and medium-sized enterprises and sole proprietors. Furthermore, there was a lack of methods for automatically generating effective plans that utilized past examples and target attributes. As a result, it was difficult to find the optimal marketing method, making it difficult to implement efficient sales promotion activities.

[0075] 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.

[0076] In this invention, the server includes means for receiving product information and promotional conditions entered by a user, means for analyzing the product information and promotional conditions and generating analysis data, means for transmitting the analysis data to the server in JSON format, means for applying a generative AI model that applies a learning model of past promotional cases and target attributes based on the analysis data to generate an optimal promotional plan, and means for presenting the generated promotional plan to the user. This allows users to easily automatically generate optimal promotional plans and enable efficient marketing activities.

[0077] "Product information" is information related to the product for sale provided by the user, and specifically includes the product name, characteristics, etc.

[0078] "Sales promotion conditions" are conditions for sales promotion activities set by the user, and specifically include the target demographic, sales promotion budget, sales promotion period, and specific conditions.

[0079] "Analysis data" refers to data that is analyzed and structured based on the product information and sales promotion conditions received by the server.

[0080] A "generative AI model" is an artificial intelligence model that learns from past promotional cases and target attributes, and generates the optimal promotional plan based on the input conditions.

[0081] "JSON format" is an abbreviation for JavaScript (registered trademark) Object Notation, and is a standard format for expressing data in text format.

[0082] A "server" is a computing device that receives data entered by a user, analyzes it, and transmits it to a generative AI model.

[0083] A "terminal" is a device that allows a user to input data, and includes electronic devices such as computers and smartphones.

[0084] A "sales promotion plan" is a specific execution plan for sales promotion activities that is created by the generative AI model based on the analysis data.

[0085] The "HTTPS protocol" stands for Hypertext Transfer Protocol Secure, an internet communication protocol that encrypts data communications.

[0086] A "prompt" is a textual instruction entered into a generative AI model to instruct it to output appropriate data.

[0087] This invention is a system in which a generative AI model creates an optimal sales promotion plan based on product information and sales promotion conditions entered by a user, and presents that plan to the user. This system consists of a terminal that receives user input, a server that processes the received data, and a generative AI model that learns past sales promotion examples and target attributes.

[0088] System Overview

[0089] User Input

[0090] Users use their devices to access a dedicated form and enter product information and promotional conditions. Product information includes, for example, the name and characteristics of a new product, while promotional conditions include the target demographic, budget, period, and specific conditions. For example, to sell a new type of smartwatch, users might enter the target demographic as "men aged 25-45 living in urban areas," the promotional budget as "5 million yen," the promotion period as "3 months," and the specific conditions as "mainly through online channels."

[0091] Sending input to the server

[0092] The terminal converts the entered information into JSON format and sends it to the server using the HTTPS protocol, a procedure that ensures the security of the data.

[0093] Analysis on the server

[0094] The server parses the received JSON data, extracts each field, and structurizes it. This analysis is performed using Python libraries such as Pandas and NumPy. It also checks the integrity of the data and performs error handling if there is missing data.

[0095] Applying generative AI

[0096] The server passes the analysis data to the generative AI model. The generative AI model uses an artificial intelligence model trained based on a database of past promotional cases and target attributes. In particular, OpenAI's (registered trademark) GPT-4 (registered trademark) model is applied. The generative AI automatically generates the optimal promotional plan based on the input conditions.

[0097] Examples of prompts:

[0098] "Please create a sales promotion plan for a new smartwatch. The target demographic is men aged 25-45 living in urban areas. The sales promotion budget is 5 million yen, the sales promotion period is 3 months, and the specific conditions are that it will be mainly conducted through online channels."

[0099] Proposal plan generation and presentation

[0100] The server receives the promotional plan created by the generative AI model and presents it to the user. This plan includes budget allocation, such as allocating 3 million yen to social media advertising, 1 million yen to email marketing, and 1 million yen to influencer marketing, as well as the start date and frequency of advertising. The user can check this information on their device and view the detailed promotional plan.

[0101] Specific examples

[0102] For example, a user enters the following information to sell a "new smartwatch":

[0103] Product name: New smartwatch

[0104] Target demographic: Men aged 25-45, living in urban areas

[0105] Promotional budget: 5 million yen

[0106] Promotion period: 3 months

[0107] Specific conditions: Focus on online channels

[0108] This information is sent from the device to a server, where the data is analyzed and passed to a generative AI model. The generative AI model generates an optimal sales promotion plan based on past cases and target attributes. For example, a plan may be created that allocates 3 million yen to social media advertising, 1 million yen to email marketing, and 1 million yen to influencer marketing. This plan is then presented to the user, with specific advertising messages and schedules shown in detail.

[0109] In this way, the present invention provides a system that automatically generates an optimal sales promotion plan through advanced analysis and learning based on information entered by the user, thereby maximizing return on investment.

[0110] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0111] Step 1:

[0112] Users use their devices to access a dedicated form and enter product information and promotion conditions. This input includes the product name, target demographic, promotion budget, promotion period, and specific conditions. For example, for a new smartwatch, users would enter "new smartwatch," "male, aged 25-45, living in an urban area," "5 million yen," "3 months," and "mainly through online channels."

[0113] Input: Product information and promotion terms entered by the user into the form

[0114] Output: User input is stored on the terminal

[0115] Step 2:

[0116] The terminal converts the information entered by the user into JSON format. Specifically, it converts the entered data into a JSON object and prepares it for transmission.

[0117] Input: Product information and promotion terms entered by the user into the form

[0118] Output: Data converted to JSON format

[0119] Step 3:

[0120] The device then sends the converted JSON data to the server using the HTTPS protocol, with SSL / TLS encryption applied to the communication to ensure secure data transmission.

[0121] Input: Data converted to JSON format

[0122] Output: Data received by the server

[0123] Step 4:

[0124] The server parses the received JSON data, extracts each field, and structurizes it. This analysis is performed using Python libraries such as Pandas and NumPy. The server also checks the integrity of the data and performs error handling if there is missing data.

[0125] Input: JSON data received by the server

[0126] Output: Structured data (analysis data)

[0127] Step 5:

[0128] The server passes the structured data to a generative AI model. The generative AI model used is an artificial intelligence model trained based on past promotional cases and target attributes. In particular, OpenAI's GPT-4 model is applied. The generative AI model automatically generates the optimal promotional plan based on the prompt text.

[0129] Examples of prompts:

[0130] "Please create a sales promotion plan for a new smartwatch. The target demographic is men aged 25-45 living in urban areas. The sales promotion budget is 5 million yen, the sales promotion period is 3 months, and the specific conditions are that it will be mainly conducted through online channels."

[0131] Input: Structured data (analysis data), prompt statement

[0132] Output: Generated trade plan

[0133] Step 6:

[0134] The server receives the sales promotion plan created by the generative AI model and organizes its contents, specifically detailing budget allocation and advertising schedules for social media advertising, email marketing, and influencer marketing.

[0135] Input: Promotional plan received from the generative AI model

[0136] Output: Organized promotional plan

[0137] Step 7:

[0138] The server presents the organized sales promotion plan to the user, who can then check this information through his / her terminal and view the detailed sales promotion plan.

[0139] Input: Organized promotional plan

[0140] Output: Promotion offered to the user

[0141] These steps allow users to easily and automatically generate optimal sales promotion plans, enabling efficient marketing activities.

[0142] (Application example 1)

[0143] 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."

[0144] Conventional sales promotion plan generation systems require users to create detailed strategies themselves, requiring specialized knowledge and time. Furthermore, the data analysis and predictions required to generate optimal plans are often performed manually, limiting their efficiency and accuracy. Furthermore, it is difficult to provide the generated plans in a readily usable format, requiring a great deal of effort and cost. It is necessary to provide a system that can solve these issues and quickly and accurately generate and present optimal sales promotion plans.

[0145] 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.

[0146] In this invention, the server includes means for receiving product information and promotion conditions entered by a user, means for analyzing the product information and promotion conditions and generating analysis data, means for applying a learning model of past promotional cases and target attributes based on the analysis data to generate an optimal promotion plan, means for presenting the generated promotion plan to a user, and means for using a smartphone application to display the generated promotion plan, thereby enabling a user to quickly and accurately generate and present an optimal promotion plan without requiring specialized knowledge or time.

[0147] "Product information" is information indicating the name and characteristics of the product that the user is trying to sell.

[0148] "Sales promotion conditions" are requirements including the target group, budget, period, and specific conditions when carrying out sales promotion activities.

[0149] "Analysis data" refers to data obtained by analyzing the input product information and sales promotion conditions.

[0150] A "learning model" is a predictive model that the generation AI learns from past sales promotion cases and target attributes.

[0151] The "optimal sales promotion plan" is the most effective sales promotion strategy proposed by the generative AI based on analytical data and learning models.

[0152] "Smartphone application" means software that runs on a smartphone and allows users to display and operate promotional plans.

[0153] This invention is a system in which a generation AI creates an optimal sales promotion plan based on product information and sales promotion conditions entered by a user, and presents that plan to the user. This system consists of a terminal that receives user input, a server that processes the received data, and a generation AI that learns from past sales promotion cases and target attributes.

[0154] System Components

[0155] 1. User input receiving terminal

[0156] Users use their smartphones to access a dedicated form and enter product information (product name, characteristics, etc.) and promotional conditions (target demographic, budget, period, specific conditions, etc.).

[0157] 2. Data processing on the server

[0158] The smartphone sends the user's input in JSON format to the server, which then parses the received JSON data, extracts each field, and structures it. This neatly organizes product information and promotional terms, and prepares them for passing to the generation AI.

[0159] 3. Applying generative AI

[0160] The server sends the analysis data to the generation AI, which uses a database of past promotional cases and a model trained based on target attributes to generate the optimal promotional plan. For example, it predicts the effectiveness of each medium, such as social media advertising, email marketing, and influencer marketing, and optimizes budget allocation and messaging.

[0161] 4. Presentation of sales promotion plan

[0162] The generated AI sends the sales promotion plan back to the server, which then presents it to the user. This process includes displaying the plan via a smartphone application. Specific budget allocations are shown, such as 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing.

[0163] Examples and prompts

[0164] Specific examples

[0165] To sell a new type of smartwatch, a user inputs the target demographic as "men aged 25-45 living in urban areas," a promotional budget of "5 million yen," a promotional period of "3 months," and specific conditions of "mainly online channels." This information is sent from the smartphone to a server, which analyzes the data and passes it on to a generation AI. The generation AI generates an optimal promotional plan based on past cases and target attributes, proposing a plan consisting of 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing. Finally, this plan is presented to the user, with specific advertising messages and schedules shown in detail.

[0166] Prompt Sentence Examples

[0167] Product name: New smartwatch

[0168] Characteristics: High-end functionality, health management

[0169] Target demographic: Men aged 25-45, living in urban areas

[0170] Budget: 5 million yen

[0171] Duration: 3 months

[0172] Specific conditions: Focus on online channels

[0173] In this way, the present invention provides a system that automatically generates an optimal sales promotion plan through a smartphone application based on information entered by the user, thereby maximizing return on investment.

[0174] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0175] Step 1:

[0176] The user accesses a dedicated form using a device and enters product information and promotional conditions. Specifically, the user opens a smartphone application and enters information such as the product name, target demographic, budget, period, and specific conditions. An example of input data could be: new smartwatch, male aged 25-45, 5 million yen, 3 months, mainly online channels.

[0177] Step 2:

[0178] The device sends the entered information to the server in JSON format. The smartphone application compiles the product information and promotional conditions entered by the user and sends it to the server as an HTTP POST request. Examples of data to send include {"product_name": "New smartwatch", "target_audience": "Males aged 25-45", "budget": 5000000, "duration": "3 months", "conditions": "Mainly online channels"}.

[0179] Step 3:

[0180] The server analyzes the received JSON data, extracting and structuring each field. The server parses the received data and stores information such as the product name, target demographic, budget, period, and conditions in individual variables. This analysis neatly organizes the product information and sales promotion conditions. Specifically, the analyzed data will be formatted as "Product name: New smartwatch," "Target demographic: Men aged 25-45," and "Budget: 5 million yen."

[0181] Step 4:

[0182] The server sends the analysis data to the generation AI. The server passes the structured data to the generation AI model. This data becomes an input prompt, and the generation AI generates a sales promotion plan based on this. The generation AI predicts the optimal plan using a database of past sales promotion cases and target attributes.

[0183] Step 5:

[0184] The generation AI generates the optimal sales promotion plan. Specifically, the generation AI predicts the effectiveness of each medium, such as social media advertising, email marketing, and influencer marketing, and creates a detailed plan including budget allocation, messaging, and schedule. Examples of output data include social media advertising: 3 million yen, email marketing: 1 million yen, and influencer marketing: 1 million yen.

[0185] Step 6:

[0186] The server receives the sales promotion plan created by the generation AI. The server receives the data from the generation AI and formats it for presentation to the user. This format is JSON format and layout that is easy to read in smartphone applications.

[0187] Step 7:

[0188] The server sends the prepared sales promotion plan back to the device. The smartphone app receives the response from the server and displays it in a format that the user can view. Specifically, the budget allocation for each sales promotion medium, advertising messages, and campaign schedule are presented in a visually easy-to-understand format. The user is then ready to carry out specific sales promotion activities based on this information.

[0189] Through the above steps, a user can quickly obtain an efficient and effective sales promotion plan without having specialized knowledge.

[0190] 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.

[0191] The present invention is a system in which a generation AI creates an optimal sales promotion plan based on product information and sales promotion conditions entered by the user, and presents that plan to the user.By further combining it with an emotion engine, it is possible to take into account the user's emotional information.

[0192] System Overview

[0193] User Input

[0194] The user uses a terminal to access a dedicated form and enters product information and promotion conditions. Product information includes the product name and characteristics, while promotion conditions include the target demographic, promotion budget, promotion period, and other specific conditions. For example, to create a promotion plan for a new smartwatch, the user enters the target demographic as "men aged 25-45 living in urban areas," a promotion budget of "5 million yen," a promotion period of "3 months," and specific conditions as "mainly through online channels."

[0195] Incorporating an emotion engine

[0196] As the user types and interacts, the emotion engine kicks in. The emotion engine analyzes the user's input speed, interface manipulation patterns, and other biometric information to identify the user's emotional state.

[0197] Sending input to the server

[0198] The device sends the input information and the emotion information identified by the emotion engine to the server in JSON format.

[0199] Analysis on the server

[0200] The server parses the received JSON data, extracts each field according to the data structure using a JSON parser, and saves the product information, promotional terms, and sentiment information as structured data.

[0201] Applying generative AI

[0202] The server passes the analyzed data to the generation AI, which applies a learning model based on a database of past promotional cases and target attributes, and generates an optimal promotional plan taking into account the user's emotional information.

[0203] Proposal plan generation and presentation

[0204] The generated AI sends the promotional plan it has created back to the server. The plan includes the promotional media to be used, the advertising copy and message to be included, budget allocation, and a detailed campaign schedule. For example, if the emotional information indicates "excitement" or "interest," it will select more aggressive advertising copy and allocate a larger budget to social media advertising and influencers.

[0205] final offer

[0206] The server sends the formatted sales promotion plan to the terminal and displays it to the user. The user can view the proposed sales promotion plan through the terminal and check details of budget allocation, advertising messages, and campaign period linked to emotional information. The user can also send a request for revisions as needed.

[0207] Specific example explanation

[0208] For example, if a user is selling a "new smartwatch," the process would proceed as follows: The user uses a device to enter the product name "New Smartwatch," the target demographic "Men aged 25-45, living in urban areas," the promotion budget "5 million yen," the promotion period "3 months," and the specific conditions "mainly online channels" into a form. This information, along with the user's emotional information (for example, if "excitement" is recognized during input), is sent from the device to the server. The server analyzes the data and passes it to the generation AI. The generation AI generates an optimal promotional plan based on past cases and target attributes, proposing, for example, 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing. The emotional information is reflected in the plan, and the proposal is made taking into account more impactful advertising copy and budget allocation. Finally, this plan is presented to the user, with specific advertising messages and schedules detailed.

[0209] In this way, the present invention provides a system that automatically generates an optimal sales promotion plan through advanced analysis and learning based on information and emotional information input by the user, thereby maximizing return on investment.

[0210] The processing flow will be explained below.

[0211] Step 1:

[0212] The user accesses a dedicated form on their device and enters product information and promotion conditions. Product information includes the product name and characteristics, while promotion conditions include the target demographic, promotion budget, promotion period, and other specific conditions. For example, to create a promotion plan for a new smartwatch, the user enters the target demographic as "men aged 25-45 living in urban areas," a promotion budget of "5 million yen," a promotion period of "3 months," and specific conditions as "mainly through online channels."

[0213] Step 2:

[0214] Once the user has completed the input, they press the "Submit" button, which causes the device to convert the information into JSON format and send it to the server as an HTTP POST request.

[0215] Step 3:

[0216] The emotion engine analyzes user input and interactions in real time and identifies the user's emotional state (e.g., excitement, interest, stress, etc.) based on the user's typing speed, interface operation patterns, and additional data such as voice and facial expressions.

[0217] Step 4:

[0218] The device converts the emotional information recognized by the emotion engine into JSON format and sends it to the server, along with product information and sales promotion conditions.

[0219] Step 5:

[0220] The server receives the HTTP request and parses the JSON data. Using a JSON parser, the server extracts each field according to the data structure and saves the product information, promotion terms, and sentiment information as structured data.

[0221] Step 6:

[0222] The server passes the analyzed data to the generation AI. The server makes an API request to the generation AI and sends the analyzed product information, promotional conditions, and emotion information.

[0223] Step 7:

[0224] The generation AI applies a learning model based on a database of past promotional cases and target attributes, and also takes into account user emotional information. The generation AI simulates the effectiveness of each promotional method (social media advertising, email marketing, influencer marketing, etc.) and generates an optimal promotional plan. For example, if the user's emotion indicates "excitement," it selects more aggressive advertising copy and allocates a larger budget to social media advertising and influencers.

[0225] Step 8:

[0226] The AI ​​then generates an optimal promotional plan and sends it back to the server, which includes the promotional media to be used, the advertising copy and message to include, budget allocation, and a detailed campaign schedule.

[0227] Step 9:

[0228] The server analyzes the promotional plan received from the generation AI and formats it as necessary.

[0229] Step 10:

[0230] The server sends the formatted sales promotion plan to the terminal and displays it to the user. The user can view the proposed sales promotion plan through the terminal and check details of budget allocation, advertising messages, and campaign period linked to emotional information. The user can also send a request for revisions as needed.

[0231] In this way, the server and generation AI work together to automatically generate an optimal sales promotion plan based on the product information and sales promotion conditions entered by the user, as well as an emotion engine that recognizes emotions in real time, and present this to the user, making it possible to quickly implement effective sales promotion activities.

[0232] Example 2

[0233] 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."

[0234] Conventional sales promotion plan creation systems generate plans based only on product information and sales promotion conditions entered by the user. As a result, it is difficult to create optimal sales promotion plans because they are unable to take into account the user's emotions and interaction situations. There is also a need to provide more detailed and effective sales promotion plans by utilizing emotional information.

[0235] 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.

[0236] In this invention, the server includes means for receiving product information and promotion conditions entered by a user, means for analyzing the product information and promotion conditions and generating analysis data, means for analyzing the user's input speed and operation patterns and identifying the user's emotional state, means for applying a learning model of past promotional cases and target attributes based on the analysis data and emotional information to generate an optimal promotion plan, and means for presenting the generated promotion plan to the user. This enables the generation of a more sophisticated and effective promotion plan that reflects the user's emotional information.

[0237] "Product information" is data such as the name, characteristics, and specifications of the product sold by the user.

[0238] "Promotional conditions" are requirements related to promotional activities, including target audience, promotional budget, promotional period, and specific conditions.

[0239] "Analysis data" refers to data generated as a result of analyzing product information and sales promotion conditions.

[0240] "User's emotional state" is information that indicates the psychological state of the user when operating the system.

[0241] A "learning model" is an artificial intelligence model used to generate optimal sales promotion plans based on past data.

[0242] A "sales promotion plan" refers to a plan or strategy formulated to effectively sell a product.

[0243] "Emotion information" is information about the user's psychological state that is analyzed based on the user's input speed and operation patterns.

[0244] The system of the present invention uses a generation AI to create an optimal sales promotion plan based on product information and sales promotion conditions entered by the user, and presents the plan to the user. Furthermore, by combining it with an emotion engine, it is possible to take into account the user's emotional information. Specific embodiments of this system are described below.

[0245] System Overview

[0246] User Input

[0247] The user uses a terminal to access a dedicated form and enters product information and promotion conditions. Product information includes the product name and characteristics, while promotion conditions include the target demographic, promotion budget, promotion period, and other specific conditions. For example, to create a promotion plan for a new smartwatch, the user enters the target demographic as "men aged 25-45 living in urban areas," a promotion budget of "5 million yen," a promotion period of "3 months," and specific conditions as "mainly through online channels."

[0248] Incorporating an emotion engine

[0249] As the user types and interacts, the emotion engine kicks in. The emotion engine analyzes the user's input speed, interface manipulation patterns, and other biometric information to identify the user's emotional state.

[0250] Sending input to the server

[0251] The device sends the input information and the emotion information identified by the emotion engine to the server in JSON format.

[0252] Analysis on the server

[0253] The server parses the received JSON data, extracts each field according to the data structure using a JSON parser (e.g., Jackson or Gson), and saves the product information, promotional terms, and sentiment information as structured data.

[0254] Applying generative AI

[0255] The server passes the analyzed data to the generation AI, which applies a learning model (using TENSORFLOW®, for example) based on a database of past promotional cases (e.g., Google® Cloud Bigtable) and target attributes, and generates an optimal promotional plan taking into account the user's emotional information.

[0256] Proposal plan generation and presentation

[0257] The generated AI sends the promotional plan it has created back to the server. The plan includes the promotional media to be used, the advertising copy and message to be included, budget allocation, and a detailed campaign schedule. For example, if the emotional information indicates "excitement" or "interest," it will select more aggressive advertising copy and allocate a larger budget to social media advertising and influencers.

[0258] final offer

[0259] The server sends the formatted sales promotion plan to the terminal and displays it to the user. The user can view the proposed sales promotion plan through the terminal and check details of budget allocation, advertising messages, and campaign period linked to emotional information. The user can also send a request for revisions as needed.

[0260] Specific example explanation

[0261] For example, if a user is selling a "new smartwatch," the process would proceed as follows: The user uses a device to enter the product name "New Smartwatch," the target demographic "Men aged 25-45, living in urban areas," the promotion budget "5 million yen," the promotion period "3 months," and the specific conditions "mainly online channels" into a form. This information, along with the user's emotional information (for example, if "excitement" is recognized during input), is sent from the device to the server. The server analyzes the data and passes it to the generation AI. The generation AI generates an optimal promotional plan based on past cases and target attributes, proposing, for example, 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing. The emotional information is reflected in the plan, and the proposal is made taking into account more impactful advertising copy and budget allocation. Finally, this plan is presented to the user, with specific advertising messages and schedules detailed.

[0262] Prompt Sentence Examples

[0263] An example prompt is:

[0264] Product name: New smartwatch

[0265] Target demographic: Men aged 25-45, living in urban areas

[0266] Promotional budget: 5 million yen

[0267] Promotion period: 3 months

[0268] Specific conditions: Focus on online channels

[0269] Emotion information: excitement

[0270] In this way, the present invention provides a system that automatically generates an optimal sales promotion plan through advanced analysis and learning based on information and emotional information input by the user, thereby maximizing return on investment.

[0271] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0272] Step 1:

[0273] The user uses a terminal to access a dedicated form and enters product information and promotion conditions. The information entered includes the product name, target demographic, promotion budget, promotion period, and specific conditions. This information is temporarily saved in the terminal.

[0274] Input: Product name "New smartwatch", target demographic "Men aged 25-45, living in urban areas", sales promotion budget "5 million yen", sales promotion period "3 months", specific conditions "mainly online channels"

[0275] Output: The product information and promotional terms entered by the user are saved in the terminal.

[0276] Step 2:

[0277] The device instantly verifies the product information and promotional conditions collected, and then activates the emotion engine, which collects and analyzes data such as the user's operation patterns and input speed in real time to identify the user's emotional state.

[0278] Input: User operation patterns, input speed, and other biometric information

[0279] Output: Emotion information identified by the emotion engine (e.g., excited, relaxed)

[0280] Step 3:

[0281] The terminal packages the product information and promotional conditions entered, as well as the emotional information identified by the emotion engine, in JSON format and sends it to the server.

[0282] Input: Product information, promotional conditions, emotional information

[0283] Output: JSON format data (e.g., product name "New Smartwatch", target demographic "Men aged 25-45", sales promotion budget "5 million yen", emotional information "Excitement")

[0284] Step 4:

[0285] The server parses the received JSON data and uses a JSON parser (e.g., Jackson or Gson) to extract product information, promotion terms, and sentiment information, and stores them as structured data in the database.

[0286] Input: JSON format data

[0287] Output: The extracted product information, promotional terms, and emotion information are stored in a database.

[0288] Step 5:

[0289] The server passes the analyzed data to a generative AI model, which applies a learning model (using TensorFlow, for example) based on a database of past promotional cases and target attribute data, and generates an optimal promotional plan taking into account the user's emotional information.

[0290] Input: Product information, promotional conditions, emotional information

[0291] Output: Generated optimal sales promotion plan (e.g., SNS advertising 3 million yen, email marketing 1 million yen, influencer marketing 1 million yen)

[0292] Step 6:

[0293] The server formats the sales promotion plan received from the generation AI and presents it to the user. It uses an HTML template engine (e.g., Thymeleaf) to convert it into a format that is easy for the user to understand.

[0294] Input: Generated promotion plan

[0295] Output: Formatted promotional plan

[0296] Step 7:

[0297] The server sends the formatted sales promotion plan to the terminal and displays it to the user. The user can view the proposed sales promotion plan through the terminal and check details of budget allocation linked to emotional information, advertising messages, and campaign period. In addition, the user can send a request for corrections as necessary.

[0298] Input: Formatted promotional plan

[0299] Output: Promotion plans that users can view, and the ability to submit amendment requests

[0300] (Application example 2)

[0301] 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."

[0302] Conventional sales promotion plan generation systems are unable to take into account the user's emotional information, and the generated sales promotion plan may not match the user's actual emotional state. As a result, the optimal sales promotion plan for the user is not provided, making it difficult to maximize the effectiveness of sales promotion activities. Furthermore, because they only use learning models based on past sales promotion cases and target attributes, they are unable to reflect dynamic user responses in real time. These problems need to be solved.

[0303] The specification processing by the specification 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 receiving product information and promotion conditions entered by the user, means for analyzing the product information and promotion conditions and generating analysis data, means for identifying the user's emotional information by combining an emotion engine and adding the identified emotional information to the analysis data, means for applying a learning model of past promotional cases and target attributes based on the analysis data to generate an optimal promotion plan, and means for presenting the generated promotion plan to the user. This makes it possible to provide a promotion plan that incorporates the user's emotional information.

[0304] "Product information" is information about the name and characteristics of a product that is input by the user when creating a sales promotion plan.

[0305] "Sales promotion conditions" are specific conditions for product sales promotion desired by the user, such as target demographic, sales promotion budget, sales promotion period, and specific conditions.

[0306] "Analysis data" refers to data generated as a result of analysis by the system based on the product information and sales promotion conditions entered by the user.

[0307] A "learning model" is a machine learning model used to create optimal sales promotion plans based on past sales promotion case data and target attribute data.

[0308] The "emotion engine" is a software module for identifying a user's emotional state based on the user's input speed, operation patterns, etc.

[0309] "Emotional information" is data about the user's emotional state as identified by the emotion engine.

[0310] A "sales promotion plan" is a specific plan proposed by the system to enable the user to efficiently promote the sale of a product, and includes the sales promotion media to be used, advertising copy, budget allocation, campaign schedule, etc.

[0311] The "means for presenting to the user" is a method for displaying the generated sales promotion plan on the user's terminal so that the user can confirm it.

[0312] This invention is a system in which a generation AI creates an optimal sales promotion plan based on product information and sales promotion conditions entered by the user, and presents the plan to the user. In addition, by combining it with an emotion engine, it is possible to generate a sales promotion plan that takes into account the user's emotional information.

[0313] System Overview

[0314] User Input

[0315] Users use their devices to access a dedicated form and enter product information and promotion conditions. Product information includes the product name and product characteristics, while promotion conditions include the target demographic, promotion budget, promotion period, and specific conditions. For example, to create a promotion plan for a new smartwatch, users enter the target demographic as "men aged 25-45 living in urban areas," a promotion budget of "5 million yen," a promotion period of "3 months," and specific conditions as "mainly through online channels."

[0316] Incorporating an emotion engine

[0317] As users input and interact, the emotion engine kicks in. The emotion engine analyzes the user's input speed, interface operation patterns, and other biometric information to identify the user's emotional state. The emotion engine uses a software module that runs on a smartphone or computer.

[0318] Sending input to the server

[0319] The device sends the input information and the emotion information identified by the emotion engine to the server in JSON format. After receiving the information, the server analyzes the data.

[0320] Analysis on the server

[0321] The server parses the received JSON data, extracts each field according to the data structure using a JSON parser, and saves the product information, promotional terms, and sentiment information as structured data.

[0322] Applying generative AI

[0323] The server passes the analyzed data to the generation AI, which applies a learning model based on a database of past promotional cases and target attributes, and generates an optimal promotional plan taking into account the user's emotional information.

[0324] Proposal plan generation and presentation

[0325] The sales promotion plan created by the generation AI is sent back to the server. The generated plan includes the promotional media to be used, the advertising copy and message to be included, budget allocation, and a detailed campaign schedule. For example, if the emotional information indicates "excitement" or "interest," more aggressive advertising copy will be selected and a larger budget will be allocated to social media advertising and influencers.

[0326] final offer

[0327] The server sends the formatted promotion plan to the terminal and displays it to the user. The user can view the proposed promotion plan through the terminal and check details of budget allocation, advertising messages, and campaign period linked to emotional information. The user can also send a request for revisions as needed.

[0328] Specific example explanation

[0329] For example, if a user is selling a "new smartwatch," the process would proceed as follows: Using a device, the user enters the product name "new smartwatch," the target demographic "men aged 25-45 living in urban areas," the promotional budget "5 million yen," the promotion period "3 months," and the specific conditions "mainly online channels" into a form. This information, along with the user's emotional information (for example, if "excitement" is recognized during input), is sent from the device to the server. The server analyzes the data and passes it to the generation AI. The generation AI generates an optimal promotional plan based on past cases and target attributes, proposing, for example, 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing. The emotional information is reflected in the plan, and the proposal takes into account more impactful advertising copy and budget allocation.

[0330] Prompt Sentence Examples

[0331] "Product information: New smartwatch, Target demographic: Males aged 25-45 living in urban areas, Promotion budget: 5 million yen, Promotion period: 3 months, Specific conditions: Mainly online channels, Emotional information: Excitement. Please generate the optimal advertising plan for these conditions."

[0332] As a result, the present invention can provide a system in which a generation AI automatically generates an optimal sales promotion plan based on information and emotional information input by the user, thereby maximizing return on investment.

[0333] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0334] Step 1:

[0335] The user uses the device to input product information and promotion conditions. This input information is collected through an input form. The input data includes the product name, target demographic, promotion budget, promotion period, and specific conditions. For example, the product name is "New Smartwatch," the target demographic is "Men aged 25-45 living in urban areas," the promotion budget is "5 million yen," the promotion period is "3 months," and the specific condition is "mainly through online channels."

[0336] Step 2:

[0337] The device sends the user's input speed and operation patterns to the emotion engine. The emotion engine analyzes this data and identifies the user's emotional information. For example, if the input speed is fast and includes an exclamation mark, it is identified as "excited." The emotional information is added to the input data.

[0338] Step 3:

[0339] The device structures the input product information, promotional terms, and emotion information in JSON format and sends it to the server. Once the input data has been sent, the server receives it.

[0340] Step 4:

[0341] The server parses the received JSON data. Using a JSON parser, the server extracts product information, promotion terms, and sentiment information as individual fields and stores them as structured data. This data is then passed to the generative AI model.

[0342] Step 5:

[0343] The server inputs the analyzed data into a generative AI model. The generative AI model applies a learning model based on a database of past promotional cases and target attributes to generate an optimal promotional plan. Emotional information is also taken into consideration during this process. For example, a more impactful ad copy is selected for a user who is in an "excited" state.

[0344] Step 6:

[0345] The sales promotion plan generated by the generative AI model is sent back to the server. The generated sales promotion plan includes the promotional media to be used, advertising copy, budget allocation, and a detailed campaign schedule. For example, it may include 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing.

[0346] Step 7:

[0347] The server sends the formatted promotion plan to the terminal, which displays the proposed promotion plan to the user, who can review the plan through the terminal and, if necessary, send a request to modify it to the server.

[0348] In this way, the system of the present invention can generate an optimal sales promotion plan using a generative AI model based on the user's input information and emotional information, and provide it to the user.

[0349] 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.

[0350] 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.

[0351] 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.

[0352] [Second embodiment]

[0353] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0354] 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.

[0355] 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).

[0356] 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.

[0357] 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.

[0358] 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).

[0359] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0360] 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.

[0361] 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.

[0362] 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.

[0363] 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.

[0364] 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."

[0365] This invention is a system in which a generation AI creates an optimal sales promotion plan based on product information and sales promotion conditions entered by a user, and presents that plan to the user. This system consists of a terminal that receives user input, a server that processes the received data, and a generation AI that learns from past sales promotion cases and target attributes.

[0366] System Overview

[0367] User Input

[0368] A user accesses a dedicated form using a terminal and enters product information (product name, characteristics, etc.) and promotional conditions (target demographic, budget, period, specific conditions, etc.). For example, to sell a new smartwatch, the target demographic might be "men aged 25-45 living in urban areas," the promotional budget might be "5 million yen," the promotion period might be "three months," and the specific conditions might be "mainly through online channels."

[0369] Sending input to the server

[0370] The terminal sends the entered information to the server in JSON format.

[0371] Analysis on the server

[0372] The server parses the received JSON data, extracts each field, and structures it, so that product information and promotional terms are neatly organized and ready to be passed to the generation AI.

[0373] Applying generative AI

[0374] The server sends the analysis data to the generation AI, which uses a database of past promotional cases and a model trained based on target attributes to generate an optimal promotional plan. This plan generation includes a process of predicting the effectiveness of each medium, such as social media advertising, email marketing, and influencer marketing, and optimizing budget allocation and messaging.

[0375] Proposal plan generation and presentation

[0376] The sales promotion plan created by the generation AI is sent back to the server, which then presents it to the user. For example, it may include budget allocation, such as allocating 3 million yen to social media advertising, 1 million yen to email marketing, and 1 million yen to influencer marketing, as well as the start date and frequency of advertising.

[0377] Specific example explanation

[0378] For example, if a user is selling a "new smartwatch," the process would proceed as follows: Using a device, the user enters the product name "new smartwatch," the target demographic "men aged 25-45 living in urban areas," the promotional budget "5 million yen," the promotion period "3 months," and the specific conditions "mainly online channels." This information is sent from the device to the server, which analyzes the data and passes it on to the generation AI. The generation AI generates an optimal promotional plan based on past cases and target attributes, proposing a plan consisting of 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing. Finally, this plan is presented to the user, with specific advertising messages and schedules detailed.

[0379] In this way, the present invention provides a system that automatically generates an optimal sales promotion plan based on information entered by a user through advanced analysis and learning, thereby maximizing return on investment.

[0380] The processing flow will be explained below.

[0381] Step 1:

[0382] The user accesses a dedicated form on their device and enters product information and promotion conditions. Product information includes the product name and characteristics, while promotion conditions include the target demographic, promotion budget, promotion period, and other specific conditions. For example, to create a promotion plan for a new smartwatch, the user enters the target demographic as "men aged 25-45 living in urban areas," a promotion budget of "5 million yen," a promotion period of "3 months," and specific conditions as "mainly through online channels."

[0383] Step 2:

[0384] Once the user has completed the input, they press the "Submit" button, which causes the device to convert the information into JSON format and send it to the server as an HTTP POST request.

[0385] Step 3:

[0386] The server receives the HTTP request and parses the JSON data. Using a JSON parser, the server extracts each field according to the data structure and saves the product information and promotion terms as structured data.

[0387] Step 4:

[0388] The server passes the analyzed data to the generation AI, which then makes an API request to the generation AI and sends the analyzed product information and promotional terms.

[0389] Step 5:

[0390] The generation AI applies a learning model based on a database of past promotional cases and target attributes. The generation AI receives requests, simulates the effectiveness of each promotional method (social media advertising, email marketing, influencer marketing, etc.), and generates the optimal promotional plan.

[0391] Step 6:

[0392] The AI ​​then generates an optimal sales promotion plan and sends it back to the server. The plan includes the promotional media to be used, advertising copy and messaging, budget allocation, and a detailed campaign schedule. For example, a specific plan could be generated that allocates 3 million yen to social media advertising, 1 million yen to email marketing, and 1 million yen to influencer marketing.

[0393] Step 7:

[0394] The server analyzes the promotional plan received from the generation AI and formats it as necessary.

[0395] Step 8:

[0396] The server sends the formatted promotion plan to the terminal and displays it to the user. The user can view the proposed promotion plan through the terminal, and can check details of the budget allocation, advertising message, and campaign period, and can also submit a request for modification if necessary.

[0397] In this way, the server and generation AI work together to automatically generate the optimal sales promotion plan based on the product information and sales promotion conditions entered by the user, making it possible to quickly implement effective sales promotion activities.

[0398] Example 1

[0399] 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."

[0400] Creating a sales promotion plan has traditionally required a great deal of specialized knowledge and time, placing a significant burden on small and medium-sized enterprises and sole proprietors. Furthermore, there was a lack of methods for automatically generating effective plans that utilized past examples and target attributes. As a result, it was difficult to find the optimal marketing method, making it difficult to implement efficient sales promotion activities.

[0401] 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.

[0402] In this invention, the server includes means for receiving product information and promotional conditions entered by a user, means for analyzing the product information and promotional conditions and generating analysis data, means for transmitting the analysis data to the server in JSON format, means for applying a generative AI model that applies a learning model of past promotional cases and target attributes based on the analysis data to generate an optimal promotional plan, and means for presenting the generated promotional plan to the user. This allows users to easily automatically generate optimal promotional plans and enable efficient marketing activities.

[0403] "Product information" is information related to the product for sale provided by the user, and specifically includes the product name, characteristics, etc.

[0404] "Sales promotion conditions" are conditions for sales promotion activities set by the user, and specifically include the target demographic, sales promotion budget, sales promotion period, and specific conditions.

[0405] "Analysis data" refers to data that is analyzed and structured based on the product information and sales promotion conditions received by the server.

[0406] A "generative AI model" is an artificial intelligence model that learns from past promotional cases and target attributes, and generates the optimal promotional plan based on the input conditions.

[0407] "JSON format" is an abbreviation for JavaScript Object Notation, and is a standard format for expressing data in text format.

[0408] A "server" is a computing device that receives data entered by a user, analyzes it, and transmits it to a generative AI model.

[0409] A "terminal" is a device that allows a user to input data, and includes electronic devices such as computers and smartphones.

[0410] A "sales promotion plan" is a specific execution plan for sales promotion activities that is created by the generative AI model based on the analysis data.

[0411] The "HTTPS protocol" stands for Hypertext Transfer Protocol Secure, an internet communication protocol that encrypts data communications.

[0412] A "prompt" is a textual instruction entered into a generative AI model to instruct it to output appropriate data.

[0413] This invention is a system in which a generative AI model creates an optimal sales promotion plan based on product information and sales promotion conditions entered by a user, and presents that plan to the user. This system consists of a terminal that receives user input, a server that processes the received data, and a generative AI model that learns past sales promotion examples and target attributes.

[0414] System Overview

[0415] User Input

[0416] Users use their devices to access a dedicated form and enter product information and promotional conditions. Product information includes, for example, the name and characteristics of a new product, while promotional conditions include the target demographic, budget, period, and specific conditions. For example, to sell a new type of smartwatch, users might enter the target demographic as "men aged 25-45 living in urban areas," the promotional budget as "5 million yen," the promotion period as "3 months," and the specific conditions as "mainly through online channels."

[0417] Sending input to the server

[0418] The terminal converts the entered information into JSON format and sends it to the server using the HTTPS protocol, a procedure that ensures the security of the data.

[0419] Analysis on the server

[0420] The server parses the received JSON data, extracts each field, and structurizes it. This analysis is performed using Python libraries such as Pandas and NumPy. It also checks the integrity of the data and performs error handling if there is missing data.

[0421] Applying generative AI

[0422] The server passes the analysis data to the generative AI model. The generative AI model uses an artificial intelligence model trained based on a database of past promotional cases and target attributes. In particular, OpenAI's GPT-4 model is applied. The generative AI automatically generates the optimal promotional plan based on the input conditions.

[0423] Examples of prompts:

[0424] "Please create a sales promotion plan for a new smartwatch. The target demographic is men aged 25-45 living in urban areas. The sales promotion budget is 5 million yen, the sales promotion period is 3 months, and the specific conditions are that it will be mainly conducted through online channels."

[0425] Proposal plan generation and presentation

[0426] The server receives the promotional plan created by the generative AI model and presents it to the user. This plan includes budget allocation, such as allocating 3 million yen to social media advertising, 1 million yen to email marketing, and 1 million yen to influencer marketing, as well as the start date and frequency of advertising. The user can check this information on their device and view the detailed promotional plan.

[0427] Specific examples

[0428] For example, a user enters the following information to sell a "new smartwatch":

[0429] Product name: New smartwatch

[0430] Target demographic: Men aged 25-45, living in urban areas

[0431] Promotional budget: 5 million yen

[0432] Promotion period: 3 months

[0433] Specific conditions: Focus on online channels

[0434] This information is sent from the device to a server, where the data is analyzed and passed to a generative AI model. The generative AI model generates an optimal sales promotion plan based on past cases and target attributes. For example, a plan may be created that allocates 3 million yen to social media advertising, 1 million yen to email marketing, and 1 million yen to influencer marketing. This plan is then presented to the user, with specific advertising messages and schedules shown in detail.

[0435] In this way, the present invention provides a system that automatically generates an optimal sales promotion plan through advanced analysis and learning based on information entered by the user, thereby maximizing return on investment.

[0436] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0437] Step 1:

[0438] Users use their devices to access a dedicated form and enter product information and promotion conditions. This input includes the product name, target demographic, promotion budget, promotion period, and specific conditions. For example, for a new smartwatch, users would enter "new smartwatch," "male, aged 25-45, living in an urban area," "5 million yen," "3 months," and "mainly through online channels."

[0439] Input: Product information and promotion terms entered by the user into the form

[0440] Output: User input is stored on the terminal

[0441] Step 2:

[0442] The terminal converts the information entered by the user into JSON format. Specifically, it converts the entered data into a JSON object and prepares it for transmission.

[0443] Input: Product information and promotion terms entered by the user into the form

[0444] Output: Data converted to JSON format

[0445] Step 3:

[0446] The device then sends the converted JSON data to the server using the HTTPS protocol, with SSL / TLS encryption applied to the communication to ensure secure data transmission.

[0447] Input: Data converted to JSON format

[0448] Output: Data received by the server

[0449] Step 4:

[0450] The server parses the received JSON data, extracts each field, and structurizes it. This analysis is performed using Python libraries such as Pandas and NumPy. The server also checks the integrity of the data and performs error handling if there is missing data.

[0451] Input: JSON data received by the server

[0452] Output: Structured data (analysis data)

[0453] Step 5:

[0454] The server passes the structured data to a generative AI model. The generative AI model used is an artificial intelligence model trained based on past promotional cases and target attributes. In particular, OpenAI's GPT-4 model is applied. The generative AI model automatically generates the optimal promotional plan based on the prompt text.

[0455] Examples of prompts:

[0456] "Please create a sales promotion plan for a new smartwatch. The target demographic is men aged 25-45 living in urban areas. The sales promotion budget is 5 million yen, the sales promotion period is 3 months, and the specific conditions are that it will be mainly conducted through online channels."

[0457] Input: Structured data (analysis data), prompt statement

[0458] Output: Generated trade plan

[0459] Step 6:

[0460] The server receives the sales promotion plan created by the generative AI model and organizes its contents, specifically detailing budget allocation and advertising schedules for social media advertising, email marketing, and influencer marketing.

[0461] Input: Promotional plan received from the generative AI model

[0462] Output: Organized promotional plan

[0463] Step 7:

[0464] The server presents the organized sales promotion plan to the user, who can then check this information through his / her terminal and view the detailed sales promotion plan.

[0465] Input: Organized promotional plan

[0466] Output: Promotion offered to the user

[0467] These steps allow users to easily and automatically generate optimal sales promotion plans, enabling efficient marketing activities.

[0468] (Application example 1)

[0469] 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."

[0470] Conventional sales promotion plan generation systems require users to create detailed strategies themselves, requiring specialized knowledge and time. Furthermore, the data analysis and predictions required to generate optimal plans are often performed manually, limiting their efficiency and accuracy. Furthermore, it is difficult to provide the generated plans in a readily usable format, requiring a great deal of effort and cost. It is necessary to provide a system that can solve these issues and quickly and accurately generate and present optimal sales promotion plans.

[0471] 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.

[0472] In this invention, the server includes means for receiving product information and promotion conditions entered by a user, means for analyzing the product information and promotion conditions and generating analysis data, means for applying a learning model of past promotional cases and target attributes based on the analysis data to generate an optimal promotion plan, means for presenting the generated promotion plan to a user, and means for using a smartphone application to display the generated promotion plan, thereby enabling a user to quickly and accurately generate and present an optimal promotion plan without requiring specialized knowledge or time.

[0473] "Product information" is information indicating the name and characteristics of the product that the user is trying to sell.

[0474] "Sales promotion conditions" are requirements including the target group, budget, period, and specific conditions when carrying out sales promotion activities.

[0475] "Analysis data" refers to data obtained by analyzing the input product information and sales promotion conditions.

[0476] A "learning model" is a predictive model that the generation AI learns from past sales promotion cases and target attributes.

[0477] The "optimal sales promotion plan" is the most effective sales promotion strategy proposed by the generative AI based on analytical data and learning models.

[0478] "Smartphone application" means software that runs on a smartphone and allows users to display and operate promotional plans.

[0479] This invention is a system in which a generation AI creates an optimal sales promotion plan based on product information and sales promotion conditions entered by a user, and presents that plan to the user. This system consists of a terminal that receives user input, a server that processes the received data, and a generation AI that learns from past sales promotion cases and target attributes.

[0480] System Components

[0481] 1. User input receiving terminal

[0482] Users use their smartphones to access a dedicated form and enter product information (product name, characteristics, etc.) and promotional conditions (target demographic, budget, period, specific conditions, etc.).

[0483] 2. Data processing on the server

[0484] The smartphone sends the user's input in JSON format to the server, which then parses the received JSON data, extracts each field, and structures it. This neatly organizes product information and promotional terms, and prepares them for passing to the generation AI.

[0485] 3. Applying generative AI

[0486] The server sends the analysis data to the generation AI, which uses a database of past promotional cases and a model trained based on target attributes to generate the optimal promotional plan. For example, it predicts the effectiveness of each medium, such as social media advertising, email marketing, and influencer marketing, and optimizes budget allocation and messaging.

[0487] 4. Presentation of sales promotion plan

[0488] The generated AI sends the sales promotion plan back to the server, which then presents it to the user. This process includes displaying the plan via a smartphone application. Specific budget allocations are shown, such as 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing.

[0489] Examples and prompts

[0490] Specific examples

[0491] To sell a new type of smartwatch, a user inputs the target demographic as "men aged 25-45 living in urban areas," a promotional budget of "5 million yen," a promotional period of "3 months," and specific conditions of "mainly online channels." This information is sent from the smartphone to a server, which analyzes the data and passes it on to a generation AI. The generation AI generates an optimal promotional plan based on past cases and target attributes, proposing a plan consisting of 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing. Finally, this plan is presented to the user, with specific advertising messages and schedules shown in detail.

[0492] Prompt Sentence Examples

[0493] Product name: New smartwatch

[0494] Characteristics: High-end functionality, health management

[0495] Target demographic: Men aged 25-45, living in urban areas

[0496] Budget: 5 million yen

[0497] Duration: 3 months

[0498] Specific conditions: Focus on online channels

[0499] In this way, the present invention provides a system that automatically generates an optimal sales promotion plan through a smartphone application based on information entered by the user, thereby maximizing return on investment.

[0500] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0501] Step 1:

[0502] The user accesses a dedicated form using a device and enters product information and promotional conditions. Specifically, the user opens a smartphone application and enters information such as the product name, target demographic, budget, period, and specific conditions. An example of input data could be: new smartwatch, male aged 25-45, 5 million yen, 3 months, mainly online channels.

[0503] Step 2:

[0504] The device sends the entered information to the server in JSON format. The smartphone application compiles the product information and promotional conditions entered by the user and sends it to the server as an HTTP POST request. Examples of data to send include {"product_name": "New smartwatch", "target_audience": "Males aged 25-45", "budget": 5000000, "duration": "3 months", "conditions": "Mainly online channels"}.

[0505] Step 3:

[0506] The server analyzes the received JSON data, extracting and structuring each field. The server parses the received data and stores information such as the product name, target demographic, budget, period, and conditions in individual variables. This analysis neatly organizes the product information and sales promotion conditions. Specifically, the analyzed data will be formatted as "Product name: New smartwatch," "Target demographic: Men aged 25-45," and "Budget: 5 million yen."

[0507] Step 4:

[0508] The server sends the analysis data to the generation AI. The server passes the structured data to the generation AI model. This data becomes an input prompt, and the generation AI generates a sales promotion plan based on this. The generation AI predicts the optimal plan using a database of past sales promotion cases and target attributes.

[0509] Step 5:

[0510] The generation AI generates the optimal sales promotion plan. Specifically, the generation AI predicts the effectiveness of each medium, such as social media advertising, email marketing, and influencer marketing, and creates a detailed plan including budget allocation, messaging, and schedule. Examples of output data include social media advertising: 3 million yen, email marketing: 1 million yen, and influencer marketing: 1 million yen.

[0511] Step 6:

[0512] The server receives the sales promotion plan created by the generation AI. The server receives the data from the generation AI and formats it for presentation to the user. This format is JSON format and layout that is easy to read in smartphone applications.

[0513] Step 7:

[0514] The server sends the prepared sales promotion plan back to the device. The smartphone app receives the response from the server and displays it in a format that the user can view. Specifically, the budget allocation for each sales promotion medium, advertising messages, and campaign schedule are presented in a visually easy-to-understand format. The user is then ready to carry out specific sales promotion activities based on this information.

[0515] Through the above steps, a user can quickly obtain an efficient and effective sales promotion plan without having specialized knowledge.

[0516] 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.

[0517] The present invention is a system in which a generation AI creates an optimal sales promotion plan based on product information and sales promotion conditions entered by the user, and presents that plan to the user.By further combining it with an emotion engine, it is possible to take into account the user's emotional information.

[0518] System Overview

[0519] User Input

[0520] The user uses a terminal to access a dedicated form and enters product information and promotion conditions. Product information includes the product name and characteristics, while promotion conditions include the target demographic, promotion budget, promotion period, and other specific conditions. For example, to create a promotion plan for a new smartwatch, the user enters the target demographic as "men aged 25-45 living in urban areas," a promotion budget of "5 million yen," a promotion period of "3 months," and specific conditions as "mainly through online channels."

[0521] Incorporating an emotion engine

[0522] As the user types and interacts, the emotion engine kicks in. The emotion engine analyzes the user's input speed, interface manipulation patterns, and other biometric information to identify the user's emotional state.

[0523] Sending input to the server

[0524] The device sends the input information and the emotion information identified by the emotion engine to the server in JSON format.

[0525] Analysis on the server

[0526] The server parses the received JSON data, extracts each field according to the data structure using a JSON parser, and saves the product information, promotional terms, and sentiment information as structured data.

[0527] Applying generative AI

[0528] The server passes the analyzed data to the generation AI, which applies a learning model based on a database of past promotional cases and target attributes, and generates an optimal promotional plan taking into account the user's emotional information.

[0529] Proposal plan generation and presentation

[0530] The generated AI sends the promotional plan it has created back to the server. The plan includes the promotional media to be used, the advertising copy and message to be included, budget allocation, and a detailed campaign schedule. For example, if the emotional information indicates "excitement" or "interest," it will select more aggressive advertising copy and allocate a larger budget to social media advertising and influencers.

[0531] final offer

[0532] The server sends the formatted sales promotion plan to the terminal and displays it to the user. The user can view the proposed sales promotion plan through the terminal and check details of budget allocation, advertising messages, and campaign period linked to emotional information. The user can also send a request for revisions as needed.

[0533] Specific example explanation

[0534] For example, if a user is selling a "new smartwatch," the process would proceed as follows: The user uses a device to enter the product name "New Smartwatch," the target demographic "Men aged 25-45, living in urban areas," the promotion budget "5 million yen," the promotion period "3 months," and the specific conditions "mainly online channels" into a form. This information, along with the user's emotional information (for example, if "excitement" is recognized during input), is sent from the device to the server. The server analyzes the data and passes it to the generation AI. The generation AI generates an optimal promotional plan based on past cases and target attributes, proposing, for example, 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing. The emotional information is reflected in the plan, and the proposal is made taking into account more impactful advertising copy and budget allocation. Finally, this plan is presented to the user, with specific advertising messages and schedules detailed.

[0535] In this way, the present invention provides a system that automatically generates an optimal sales promotion plan through advanced analysis and learning based on information and emotional information input by the user, thereby maximizing return on investment.

[0536] The processing flow will be explained below.

[0537] Step 1:

[0538] The user accesses a dedicated form on their device and enters product information and promotion conditions. Product information includes the product name and characteristics, while promotion conditions include the target demographic, promotion budget, promotion period, and other specific conditions. For example, to create a promotion plan for a new smartwatch, the user enters the target demographic as "men aged 25-45 living in urban areas," a promotion budget of "5 million yen," a promotion period of "3 months," and specific conditions as "mainly through online channels."

[0539] Step 2:

[0540] Once the user has completed the input, they press the "Submit" button, which causes the device to convert the information into JSON format and send it to the server as an HTTP POST request.

[0541] Step 3:

[0542] The emotion engine analyzes user input and interactions in real time and identifies the user's emotional state (e.g., excitement, interest, stress, etc.) based on the user's typing speed, interface operation patterns, and additional data such as voice and facial expressions.

[0543] Step 4:

[0544] The device converts the emotional information recognized by the emotion engine into JSON format and sends it to the server, along with product information and sales promotion conditions.

[0545] Step 5:

[0546] The server receives the HTTP request and parses the JSON data. Using a JSON parser, the server extracts each field according to the data structure and saves the product information, promotion terms, and sentiment information as structured data.

[0547] Step 6:

[0548] The server passes the analyzed data to the generation AI. The server makes an API request to the generation AI and sends the analyzed product information, promotional conditions, and emotion information.

[0549] Step 7:

[0550] The generation AI applies a learning model based on a database of past promotional cases and target attributes, and also takes into account user emotional information. The generation AI simulates the effectiveness of each promotional method (social media advertising, email marketing, influencer marketing, etc.) and generates an optimal promotional plan. For example, if the user's emotion indicates "excitement," it selects more aggressive advertising copy and allocates a larger budget to social media advertising and influencers.

[0551] Step 8:

[0552] The AI ​​then generates an optimal promotional plan and sends it back to the server, which includes the promotional media to be used, the advertising copy and message to include, budget allocation, and a detailed campaign schedule.

[0553] Step 9:

[0554] The server analyzes the promotional plan received from the generation AI and formats it as necessary.

[0555] Step 10:

[0556] The server sends the formatted sales promotion plan to the terminal and displays it to the user. The user can view the proposed sales promotion plan through the terminal and check details of budget allocation, advertising messages, and campaign period linked to emotional information. The user can also send a request for revisions as needed.

[0557] In this way, the server and generation AI work together to automatically generate an optimal sales promotion plan based on the product information and sales promotion conditions entered by the user, as well as an emotion engine that recognizes emotions in real time, and present this to the user, making it possible to quickly implement effective sales promotion activities.

[0558] Example 2

[0559] 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."

[0560] Conventional sales promotion plan creation systems generate plans based only on product information and sales promotion conditions entered by the user. As a result, it is difficult to create optimal sales promotion plans because they are unable to take into account the user's emotions and interaction situations. There is also a need to provide more detailed and effective sales promotion plans by utilizing emotional information.

[0561] 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.

[0562] In this invention, the server includes means for receiving product information and promotion conditions entered by a user, means for analyzing the product information and promotion conditions and generating analysis data, means for analyzing the user's input speed and operation patterns and identifying the user's emotional state, means for applying a learning model of past promotional cases and target attributes based on the analysis data and emotional information to generate an optimal promotion plan, and means for presenting the generated promotion plan to the user. This enables the generation of a more sophisticated and effective promotion plan that reflects the user's emotional information.

[0563] "Product information" is data such as the name, characteristics, and specifications of the product sold by the user.

[0564] "Promotional conditions" are requirements related to promotional activities, including target audience, promotional budget, promotional period, and specific conditions.

[0565] "Analysis data" refers to data generated as a result of analyzing product information and sales promotion conditions.

[0566] "User's emotional state" is information that indicates the psychological state of the user when operating the system.

[0567] A "learning model" is an artificial intelligence model used to generate optimal sales promotion plans based on past data.

[0568] A "sales promotion plan" refers to a plan or strategy formulated to effectively sell a product.

[0569] "Emotion information" is information about the user's psychological state that is analyzed based on the user's input speed and operation patterns.

[0570] The system of the present invention uses a generation AI to create an optimal sales promotion plan based on product information and sales promotion conditions entered by the user, and presents the plan to the user. Furthermore, by combining it with an emotion engine, it is possible to take into account the user's emotional information. Specific embodiments of this system are described below.

[0571] System Overview

[0572] User Input

[0573] The user uses a terminal to access a dedicated form and enters product information and promotion conditions. Product information includes the product name and characteristics, while promotion conditions include the target demographic, promotion budget, promotion period, and other specific conditions. For example, to create a promotion plan for a new smartwatch, the user enters the target demographic as "men aged 25-45 living in urban areas," a promotion budget of "5 million yen," a promotion period of "3 months," and specific conditions as "mainly through online channels."

[0574] Incorporating an emotion engine

[0575] As the user types and interacts, the emotion engine kicks in. The emotion engine analyzes the user's input speed, interface manipulation patterns, and other biometric information to identify the user's emotional state.

[0576] Sending input to the server

[0577] The device sends the input information and the emotion information identified by the emotion engine to the server in JSON format.

[0578] Analysis on the server

[0579] The server parses the received JSON data, extracts each field according to the data structure using a JSON parser (e.g., Jackson or Gson), and saves the product information, promotional terms, and sentiment information as structured data.

[0580] Applying generative AI

[0581] The server passes the analyzed data to the generation AI, which applies a learning model (using TensorFlow, for example) based on a database of past promotional cases (e.g., Google Cloud Bigtable) and target attributes, and generates the optimal promotional plan by taking into account the user's emotional information.

[0582] Proposal plan generation and presentation

[0583] The generated AI sends the promotional plan it has created back to the server. The plan includes the promotional media to be used, the advertising copy and message to be included, budget allocation, and a detailed campaign schedule. For example, if the emotional information indicates "excitement" or "interest," it will select more aggressive advertising copy and allocate a larger budget to social media advertising and influencers.

[0584] final offer

[0585] The server sends the formatted sales promotion plan to the terminal and displays it to the user. The user can view the proposed sales promotion plan through the terminal and check details of budget allocation, advertising messages, and campaign period linked to emotional information. The user can also send a request for revisions as needed.

[0586] Specific example explanation

[0587] For example, if a user is selling a "new smartwatch," the process would proceed as follows: The user uses a device to enter the product name "New Smartwatch," the target demographic "Men aged 25-45, living in urban areas," the promotion budget "5 million yen," the promotion period "3 months," and the specific conditions "mainly online channels" into a form. This information, along with the user's emotional information (for example, if "excitement" is recognized during input), is sent from the device to the server. The server analyzes the data and passes it to the generation AI. The generation AI generates an optimal promotional plan based on past cases and target attributes, proposing, for example, 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing. The emotional information is reflected in the plan, and the proposal is made taking into account more impactful advertising copy and budget allocation. Finally, this plan is presented to the user, with specific advertising messages and schedules detailed.

[0588] Prompt Sentence Examples

[0589] An example prompt is:

[0590] Product name: New smartwatch

[0591] Target demographic: Men aged 25-45, living in urban areas

[0592] Promotional budget: 5 million yen

[0593] Promotion period: 3 months

[0594] Specific conditions: Focus on online channels

[0595] Emotion information: excitement

[0596] In this way, the present invention provides a system that automatically generates an optimal sales promotion plan through advanced analysis and learning based on information and emotional information input by the user, thereby maximizing return on investment.

[0597] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0598] Step 1:

[0599] The user uses a terminal to access a dedicated form and enters product information and promotion conditions. The information entered includes the product name, target demographic, promotion budget, promotion period, and specific conditions. This information is temporarily saved in the terminal.

[0600] Input: Product name "New smartwatch", target demographic "Men aged 25-45, living in urban areas", sales promotion budget "5 million yen", sales promotion period "3 months", specific conditions "mainly online channels"

[0601] Output: The product information and promotional terms entered by the user are saved in the terminal.

[0602] Step 2:

[0603] The device instantly verifies the product information and promotional conditions collected, and then activates the emotion engine, which collects and analyzes data such as the user's operation patterns and input speed in real time to identify the user's emotional state.

[0604] Input: User operation patterns, input speed, and other biometric information

[0605] Output: Emotion information identified by the emotion engine (e.g., excited, relaxed)

[0606] Step 3:

[0607] The terminal packages the product information and promotional conditions entered, as well as the emotional information identified by the emotion engine, in JSON format and sends it to the server.

[0608] Input: Product information, promotional conditions, emotional information

[0609] Output: JSON format data (e.g., product name "New Smartwatch", target demographic "Men aged 25-45", sales promotion budget "5 million yen", emotional information "Excitement")

[0610] Step 4:

[0611] The server parses the received JSON data and uses a JSON parser (e.g., Jackson or Gson) to extract product information, promotion terms, and sentiment information, and stores them as structured data in the database.

[0612] Input: JSON format data

[0613] Output: The extracted product information, promotional terms, and emotion information are stored in a database.

[0614] Step 5:

[0615] The server passes the analyzed data to a generative AI model, which applies a learning model (using TensorFlow, for example) based on a database of past promotional cases and target attribute data, and generates an optimal promotional plan taking into account the user's emotional information.

[0616] Input: Product information, promotional conditions, emotional information

[0617] Output: Generated optimal sales promotion plan (e.g., SNS advertising 3 million yen, email marketing 1 million yen, influencer marketing 1 million yen)

[0618] Step 6:

[0619] The server formats the sales promotion plan received from the generation AI and presents it to the user. It uses an HTML template engine (e.g., Thymeleaf) to convert it into a format that is easy for the user to understand.

[0620] Input: Generated promotion plan

[0621] Output: Formatted promotional plan

[0622] Step 7:

[0623] The server sends the formatted sales promotion plan to the terminal and displays it to the user. The user can view the proposed sales promotion plan through the terminal and check details of budget allocation linked to emotional information, advertising messages, and campaign period. In addition, the user can send a request for corrections as necessary.

[0624] Input: Formatted promotional plan

[0625] Output: Promotion plans that users can view, and the ability to submit amendment requests

[0626] (Application example 2)

[0627] 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."

[0628] Conventional sales promotion plan generation systems are unable to take into account the user's emotional information, and the generated sales promotion plan may not match the user's actual emotional state. As a result, the optimal sales promotion plan for the user is not provided, making it difficult to maximize the effectiveness of sales promotion activities. Furthermore, because they only use learning models based on past sales promotion cases and target attributes, they are unable to reflect dynamic user responses in real time. These problems need to be solved.

[0629] The specification processing by the specification 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 receiving product information and promotion conditions entered by the user, means for analyzing the product information and promotion conditions and generating analysis data, means for identifying the user's emotional information by combining an emotion engine and adding the identified emotional information to the analysis data, means for applying a learning model of past promotional cases and target attributes based on the analysis data to generate an optimal promotion plan, and means for presenting the generated promotion plan to the user. This makes it possible to provide a promotion plan that incorporates the user's emotional information.

[0630] "Product information" is information about the name and characteristics of a product that is input by the user when creating a sales promotion plan.

[0631] "Sales promotion conditions" are specific conditions for product sales promotion desired by the user, such as target demographic, sales promotion budget, sales promotion period, and specific conditions.

[0632] "Analysis data" refers to data generated as a result of analysis by the system based on the product information and sales promotion conditions entered by the user.

[0633] A "learning model" is a machine learning model used to create optimal sales promotion plans based on past sales promotion case data and target attribute data.

[0634] The "emotion engine" is a software module for identifying a user's emotional state based on the user's input speed, operation patterns, etc.

[0635] "Emotional information" is data about the user's emotional state as identified by the emotion engine.

[0636] A "sales promotion plan" is a specific plan proposed by the system to enable the user to efficiently promote the sale of a product, and includes the sales promotion media to be used, advertising copy, budget allocation, campaign schedule, etc.

[0637] The "means for presenting to the user" is a method for displaying the generated sales promotion plan on the user's terminal so that the user can confirm it.

[0638] This invention is a system in which a generation AI creates an optimal sales promotion plan based on product information and sales promotion conditions entered by the user, and presents the plan to the user. In addition, by combining it with an emotion engine, it is possible to generate a sales promotion plan that takes into account the user's emotional information.

[0639] System Overview

[0640] User Input

[0641] Users use their devices to access a dedicated form and enter product information and promotion conditions. Product information includes the product name and product characteristics, while promotion conditions include the target demographic, promotion budget, promotion period, and specific conditions. For example, to create a promotion plan for a new smartwatch, users enter the target demographic as "men aged 25-45 living in urban areas," a promotion budget of "5 million yen," a promotion period of "3 months," and specific conditions as "mainly through online channels."

[0642] Incorporating an emotion engine

[0643] As users input and interact, the emotion engine kicks in. The emotion engine analyzes the user's input speed, interface operation patterns, and other biometric information to identify the user's emotional state. The emotion engine uses a software module that runs on a smartphone or computer.

[0644] Sending input to the server

[0645] The device sends the input information and the emotion information identified by the emotion engine to the server in JSON format. After receiving the information, the server analyzes the data.

[0646] Analysis on the server

[0647] The server parses the received JSON data, extracts each field according to the data structure using a JSON parser, and saves the product information, promotional terms, and sentiment information as structured data.

[0648] Applying generative AI

[0649] The server passes the analyzed data to the generation AI, which applies a learning model based on a database of past promotional cases and target attributes, and generates an optimal promotional plan taking into account the user's emotional information.

[0650] Proposal plan generation and presentation

[0651] The sales promotion plan created by the generation AI is sent back to the server. The generated plan includes the promotional media to be used, the advertising copy and message to be included, budget allocation, and a detailed campaign schedule. For example, if the emotional information indicates "excitement" or "interest," more aggressive advertising copy will be selected and a larger budget will be allocated to social media advertising and influencers.

[0652] final offer

[0653] The server sends the formatted promotion plan to the terminal and displays it to the user. The user can view the proposed promotion plan through the terminal and check details of budget allocation, advertising messages, and campaign period linked to emotional information. The user can also send a request for revisions as needed.

[0654] Specific example explanation

[0655] For example, if a user is selling a "new smartwatch," the process would proceed as follows: Using a device, the user enters the product name "new smartwatch," the target demographic "men aged 25-45 living in urban areas," the promotional budget "5 million yen," the promotion period "3 months," and the specific conditions "mainly online channels" into a form. This information, along with the user's emotional information (for example, if "excitement" is recognized during input), is sent from the device to the server. The server analyzes the data and passes it to the generation AI. The generation AI generates an optimal promotional plan based on past cases and target attributes, proposing, for example, 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing. The emotional information is reflected in the plan, and the proposal takes into account more impactful advertising copy and budget allocation.

[0656] Prompt Sentence Examples

[0657] "Product information: New smartwatch, Target demographic: Males aged 25-45 living in urban areas, Promotion budget: 5 million yen, Promotion period: 3 months, Specific conditions: Mainly online channels, Emotional information: Excitement. Please generate the optimal advertising plan for these conditions."

[0658] As a result, the present invention can provide a system in which a generation AI automatically generates an optimal sales promotion plan based on information and emotional information input by the user, thereby maximizing return on investment.

[0659] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0660] Step 1:

[0661] The user uses the device to input product information and promotion conditions. This input information is collected through an input form. The input data includes the product name, target demographic, promotion budget, promotion period, and specific conditions. For example, the product name is "New Smartwatch," the target demographic is "Men aged 25-45 living in urban areas," the promotion budget is "5 million yen," the promotion period is "3 months," and the specific condition is "mainly through online channels."

[0662] Step 2:

[0663] The device sends the user's input speed and operation patterns to the emotion engine. The emotion engine analyzes this data and identifies the user's emotional information. For example, if the input speed is fast and includes an exclamation mark, it is identified as "excited." The emotional information is added to the input data.

[0664] Step 3:

[0665] The device structures the input product information, promotional terms, and emotion information in JSON format and sends it to the server. Once the input data has been sent, the server receives it.

[0666] Step 4:

[0667] The server parses the received JSON data. Using a JSON parser, the server extracts product information, promotion terms, and sentiment information as individual fields and stores them as structured data. This data is then passed to the generative AI model.

[0668] Step 5:

[0669] The server inputs the analyzed data into a generative AI model. The generative AI model applies a learning model based on a database of past promotional cases and target attributes to generate an optimal promotional plan. Emotional information is also taken into consideration during this process. For example, a more impactful ad copy is selected for a user who is in an "excited" state.

[0670] Step 6:

[0671] The sales promotion plan generated by the generative AI model is sent back to the server. The generated sales promotion plan includes the promotional media to be used, advertising copy, budget allocation, and a detailed campaign schedule. For example, it may include 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing.

[0672] Step 7:

[0673] The server sends the formatted promotion plan to the terminal, which displays the proposed promotion plan to the user, who can review the plan through the terminal and, if necessary, send a request to modify it to the server.

[0674] In this way, the system of the present invention can generate an optimal sales promotion plan using a generative AI model based on the user's input information and emotional information, and provide it to the user.

[0675] 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.

[0676] 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.

[0677] 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.

[0678] [Third embodiment]

[0679] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0680] 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.

[0681] 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).

[0682] 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.

[0683] 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.

[0684] 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).

[0685] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0686] 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.

[0687] 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.

[0688] 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.

[0689] 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.

[0690] 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."

[0691] This invention is a system in which a generation AI creates an optimal sales promotion plan based on product information and sales promotion conditions entered by a user, and presents that plan to the user. This system consists of a terminal that receives user input, a server that processes the received data, and a generation AI that learns from past sales promotion cases and target attributes.

[0692] System Overview

[0693] User Input

[0694] A user accesses a dedicated form using a terminal and enters product information (product name, characteristics, etc.) and promotional conditions (target demographic, budget, period, specific conditions, etc.). For example, to sell a new smartwatch, the target demographic might be "men aged 25-45 living in urban areas," the promotional budget might be "5 million yen," the promotion period might be "three months," and the specific conditions might be "mainly through online channels."

[0695] Sending input to the server

[0696] The terminal sends the entered information to the server in JSON format.

[0697] Analysis on the server

[0698] The server parses the received JSON data, extracts each field, and structures it, so that product information and promotional terms are neatly organized and ready to be passed to the generation AI.

[0699] Applying generative AI

[0700] The server sends the analysis data to the generation AI, which uses a database of past promotional cases and a model trained based on target attributes to generate an optimal promotional plan. This plan generation includes a process of predicting the effectiveness of each medium, such as social media advertising, email marketing, and influencer marketing, and optimizing budget allocation and messaging.

[0701] Proposal plan generation and presentation

[0702] The sales promotion plan created by the generation AI is sent back to the server, which then presents it to the user. For example, it may include budget allocation, such as allocating 3 million yen to social media advertising, 1 million yen to email marketing, and 1 million yen to influencer marketing, as well as the start date and frequency of advertising.

[0703] Specific example explanation

[0704] For example, if a user is selling a "new smartwatch," the process would proceed as follows: Using a device, the user enters the product name "new smartwatch," the target demographic "men aged 25-45 living in urban areas," the promotional budget "5 million yen," the promotion period "3 months," and the specific conditions "mainly online channels." This information is sent from the device to the server, which analyzes the data and passes it on to the generation AI. The generation AI generates an optimal promotional plan based on past cases and target attributes, proposing a plan consisting of 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing. Finally, this plan is presented to the user, with specific advertising messages and schedules detailed.

[0705] In this way, the present invention provides a system that automatically generates an optimal sales promotion plan based on information entered by a user through advanced analysis and learning, thereby maximizing return on investment.

[0706] The processing flow will be explained below.

[0707] Step 1:

[0708] The user accesses a dedicated form on their device and enters product information and promotion conditions. Product information includes the product name and characteristics, while promotion conditions include the target demographic, promotion budget, promotion period, and other specific conditions. For example, to create a promotion plan for a new smartwatch, the user enters the target demographic as "men aged 25-45 living in urban areas," a promotion budget of "5 million yen," a promotion period of "3 months," and specific conditions as "mainly through online channels."

[0709] Step 2:

[0710] Once the user has completed the input, they press the "Submit" button, which causes the device to convert the information into JSON format and send it to the server as an HTTP POST request.

[0711] Step 3:

[0712] The server receives the HTTP request and parses the JSON data. Using a JSON parser, the server extracts each field according to the data structure and saves the product information and promotion terms as structured data.

[0713] Step 4:

[0714] The server passes the analyzed data to the generation AI, which then makes an API request to the generation AI and sends the analyzed product information and promotional terms.

[0715] Step 5:

[0716] The generation AI applies a learning model based on a database of past promotional cases and target attributes. The generation AI receives requests, simulates the effectiveness of each promotional method (social media advertising, email marketing, influencer marketing, etc.), and generates the optimal promotional plan.

[0717] Step 6:

[0718] The AI ​​then generates an optimal sales promotion plan and sends it back to the server. The plan includes the promotional media to be used, advertising copy and messaging, budget allocation, and a detailed campaign schedule. For example, a specific plan could be generated that allocates 3 million yen to social media advertising, 1 million yen to email marketing, and 1 million yen to influencer marketing.

[0719] Step 7:

[0720] The server analyzes the promotional plan received from the generation AI and formats it as necessary.

[0721] Step 8:

[0722] The server sends the formatted promotion plan to the terminal and displays it to the user. The user can view the proposed promotion plan through the terminal, and can check details of the budget allocation, advertising message, and campaign period, and can also submit a request for modification if necessary.

[0723] In this way, the server and generation AI work together to automatically generate the optimal sales promotion plan based on the product information and sales promotion conditions entered by the user, making it possible to quickly implement effective sales promotion activities.

[0724] Example 1

[0725] 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."

[0726] Creating a sales promotion plan has traditionally required a great deal of specialized knowledge and time, placing a significant burden on small and medium-sized enterprises and sole proprietors. Furthermore, there was a lack of methods for automatically generating effective plans that utilized past examples and target attributes. As a result, it was difficult to find the optimal marketing method, making it difficult to implement efficient sales promotion activities.

[0727] 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.

[0728] In this invention, the server includes means for receiving product information and promotional conditions entered by a user, means for analyzing the product information and promotional conditions and generating analysis data, means for transmitting the analysis data to the server in JSON format, means for applying a generative AI model that applies a learning model of past promotional cases and target attributes based on the analysis data to generate an optimal promotional plan, and means for presenting the generated promotional plan to the user. This allows users to easily automatically generate optimal promotional plans and enable efficient marketing activities.

[0729] "Product information" is information related to the product for sale provided by the user, and specifically includes the product name, characteristics, etc.

[0730] "Sales promotion conditions" are conditions for sales promotion activities set by the user, and specifically include the target demographic, sales promotion budget, sales promotion period, and specific conditions.

[0731] "Analysis data" refers to data that is analyzed and structured based on the product information and sales promotion conditions received by the server.

[0732] A "generative AI model" is an artificial intelligence model that learns from past promotional cases and target attributes, and generates the optimal promotional plan based on the input conditions.

[0733] "JSON format" is an abbreviation for JavaScript Object Notation, and is a standard format for expressing data in text format.

[0734] A "server" is a computing device that receives data entered by a user, analyzes it, and transmits it to a generative AI model.

[0735] A "terminal" is a device that allows a user to input data, and includes electronic devices such as computers and smartphones.

[0736] A "sales promotion plan" is a specific execution plan for sales promotion activities that is created by the generative AI model based on the analysis data.

[0737] The "HTTPS protocol" stands for Hypertext Transfer Protocol Secure, an internet communication protocol that encrypts data communications.

[0738] A "prompt" is a textual instruction entered into a generative AI model to instruct it to output appropriate data.

[0739] This invention is a system in which a generative AI model creates an optimal sales promotion plan based on product information and sales promotion conditions entered by a user, and presents that plan to the user. This system consists of a terminal that receives user input, a server that processes the received data, and a generative AI model that learns past sales promotion examples and target attributes.

[0740] System Overview

[0741] User Input

[0742] Users use their devices to access a dedicated form and enter product information and promotional conditions. Product information includes, for example, the name and characteristics of a new product, while promotional conditions include the target demographic, budget, period, and specific conditions. For example, to sell a new type of smartwatch, users might enter the target demographic as "men aged 25-45 living in urban areas," the promotional budget as "5 million yen," the promotion period as "3 months," and the specific conditions as "mainly through online channels."

[0743] Sending input to the server

[0744] The terminal converts the entered information into JSON format and sends it to the server using the HTTPS protocol, a procedure that ensures the security of the data.

[0745] Analysis on the server

[0746] The server parses the received JSON data, extracts each field, and structurizes it. This analysis is performed using Python libraries such as Pandas and NumPy. It also checks the integrity of the data and performs error handling if there is missing data.

[0747] Applying generative AI

[0748] The server passes the analysis data to the generative AI model. The generative AI model uses an artificial intelligence model trained based on a database of past promotional cases and target attributes. In particular, OpenAI's GPT-4 model is applied. The generative AI automatically generates the optimal promotional plan based on the input conditions.

[0749] Examples of prompts:

[0750] "Please create a sales promotion plan for a new smartwatch. The target demographic is men aged 25-45 living in urban areas. The sales promotion budget is 5 million yen, the sales promotion period is 3 months, and the specific conditions are that it will be mainly conducted through online channels."

[0751] Proposal plan generation and presentation

[0752] The server receives the promotional plan created by the generative AI model and presents it to the user. This plan includes budget allocation, such as allocating 3 million yen to social media advertising, 1 million yen to email marketing, and 1 million yen to influencer marketing, as well as the start date and frequency of advertising. The user can check this information on their device and view the detailed promotional plan.

[0753] Specific examples

[0754] For example, a user enters the following information to sell a "new smartwatch":

[0755] Product name: New smartwatch

[0756] Target demographic: Men aged 25-45, living in urban areas

[0757] Promotional budget: 5 million yen

[0758] Promotion period: 3 months

[0759] Specific conditions: Focus on online channels

[0760] This information is sent from the device to a server, where the data is analyzed and passed to a generative AI model. The generative AI model generates an optimal sales promotion plan based on past cases and target attributes. For example, a plan may be created that allocates 3 million yen to social media advertising, 1 million yen to email marketing, and 1 million yen to influencer marketing. This plan is then presented to the user, with specific advertising messages and schedules shown in detail.

[0761] In this way, the present invention provides a system that automatically generates an optimal sales promotion plan through advanced analysis and learning based on information entered by the user, thereby maximizing return on investment.

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

[0763] Step 1:

[0764] Users use their devices to access a dedicated form and enter product information and promotion conditions. This input includes the product name, target demographic, promotion budget, promotion period, and specific conditions. For example, for a new smartwatch, users would enter "new smartwatch," "male, aged 25-45, living in an urban area," "5 million yen," "3 months," and "mainly through online channels."

[0765] Input: Product information and promotion terms entered by the user into the form

[0766] Output: User input is stored on the terminal

[0767] Step 2:

[0768] The terminal converts the information entered by the user into JSON format. Specifically, it converts the entered data into a JSON object and prepares it for transmission.

[0769] Input: Product information and promotion terms entered by the user into the form

[0770] Output: Data converted to JSON format

[0771] Step 3:

[0772] The device then sends the converted JSON data to the server using the HTTPS protocol, with SSL / TLS encryption applied to the communication to ensure secure data transmission.

[0773] Input: Data converted to JSON format

[0774] Output: Data received by the server

[0775] Step 4:

[0776] The server parses the received JSON data, extracts each field, and structurizes it. This analysis is performed using Python libraries such as Pandas and NumPy. The server also checks the integrity of the data and performs error handling if there is missing data.

[0777] Input: JSON data received by the server

[0778] Output: Structured data (analysis data)

[0779] Step 5:

[0780] The server passes the structured data to a generative AI model. The generative AI model used is an artificial intelligence model trained based on past promotional cases and target attributes. In particular, OpenAI's GPT-4 model is applied. The generative AI model automatically generates the optimal promotional plan based on the prompt text.

[0781] Examples of prompts:

[0782] "Please create a sales promotion plan for a new smartwatch. The target demographic is men aged 25-45 living in urban areas. The sales promotion budget is 5 million yen, the sales promotion period is 3 months, and the specific conditions are that it will be mainly conducted through online channels."

[0783] Input: Structured data (analysis data), prompt statement

[0784] Output: Generated trade plan

[0785] Step 6:

[0786] The server receives the sales promotion plan created by the generative AI model and organizes its contents, specifically detailing budget allocation and advertising schedules for social media advertising, email marketing, and influencer marketing.

[0787] Input: Promotional plan received from the generative AI model

[0788] Output: Organized promotional plan

[0789] Step 7:

[0790] The server presents the organized sales promotion plan to the user, who can then check this information through his / her terminal and view the detailed sales promotion plan.

[0791] Input: Organized promotional plan

[0792] Output: Promotion offered to the user

[0793] These steps allow users to easily and automatically generate optimal sales promotion plans, enabling efficient marketing activities.

[0794] (Application example 1)

[0795] 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."

[0796] Conventional sales promotion plan generation systems require users to create detailed strategies themselves, requiring specialized knowledge and time. Furthermore, the data analysis and predictions required to generate optimal plans are often performed manually, limiting their efficiency and accuracy. Furthermore, it is difficult to provide the generated plans in a readily usable format, requiring a great deal of effort and cost. It is necessary to provide a system that can solve these issues and quickly and accurately generate and present optimal sales promotion plans.

[0797] 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.

[0798] In this invention, the server includes means for receiving product information and promotion conditions entered by a user, means for analyzing the product information and promotion conditions and generating analysis data, means for applying a learning model of past promotional cases and target attributes based on the analysis data to generate an optimal promotion plan, means for presenting the generated promotion plan to a user, and means for using a smartphone application to display the generated promotion plan, thereby enabling a user to quickly and accurately generate and present an optimal promotion plan without requiring specialized knowledge or time.

[0799] "Product information" is information indicating the name and characteristics of the product that the user is trying to sell.

[0800] "Sales promotion conditions" are requirements including the target group, budget, period, and specific conditions when carrying out sales promotion activities.

[0801] "Analysis data" refers to data obtained by analyzing the input product information and sales promotion conditions.

[0802] A "learning model" is a predictive model that the generation AI learns from past sales promotion cases and target attributes.

[0803] The "optimal sales promotion plan" is the most effective sales promotion strategy proposed by the generative AI based on analytical data and learning models.

[0804] "Smartphone application" means software that runs on a smartphone and allows users to display and operate promotional plans.

[0805] This invention is a system in which a generation AI creates an optimal sales promotion plan based on product information and sales promotion conditions entered by a user, and presents that plan to the user. This system consists of a terminal that receives user input, a server that processes the received data, and a generation AI that learns from past sales promotion cases and target attributes.

[0806] System Components

[0807] 1. User input receiving terminal

[0808] Users use their smartphones to access a dedicated form and enter product information (product name, characteristics, etc.) and promotional conditions (target demographic, budget, period, specific conditions, etc.).

[0809] 2. Data processing on the server

[0810] The smartphone sends the user's input in JSON format to the server, which then parses the received JSON data, extracts each field, and structures it. This neatly organizes product information and promotional terms, and prepares them for passing to the generation AI.

[0811] 3. Applying generative AI

[0812] The server sends the analysis data to the generation AI, which uses a database of past promotional cases and a model trained based on target attributes to generate the optimal promotional plan. For example, it predicts the effectiveness of each medium, such as social media advertising, email marketing, and influencer marketing, and optimizes budget allocation and messaging.

[0813] 4. Presentation of sales promotion plan

[0814] The generated AI sends the sales promotion plan back to the server, which then presents it to the user. This process includes displaying the plan via a smartphone application. Specific budget allocations are shown, such as 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing.

[0815] Examples and prompts

[0816] Specific examples

[0817] To sell a new type of smartwatch, a user inputs the target demographic as "men aged 25-45 living in urban areas," a promotional budget of "5 million yen," a promotional period of "3 months," and specific conditions of "mainly online channels." This information is sent from the smartphone to a server, which analyzes the data and passes it on to a generation AI. The generation AI generates an optimal promotional plan based on past cases and target attributes, proposing a plan consisting of 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing. Finally, this plan is presented to the user, with specific advertising messages and schedules shown in detail.

[0818] Prompt Sentence Examples

[0819] Product name: New smartwatch

[0820] Characteristics: High-end functionality, health management

[0821] Target demographic: Men aged 25-45, living in urban areas

[0822] Budget: 5 million yen

[0823] Duration: 3 months

[0824] Specific conditions: Focus on online channels

[0825] In this way, the present invention provides a system that automatically generates an optimal sales promotion plan through a smartphone application based on information entered by the user, thereby maximizing return on investment.

[0826] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0827] Step 1:

[0828] The user accesses a dedicated form using a device and enters product information and promotional conditions. Specifically, the user opens a smartphone application and enters information such as the product name, target demographic, budget, period, and specific conditions. An example of input data could be: new smartwatch, male aged 25-45, 5 million yen, 3 months, mainly online channels.

[0829] Step 2:

[0830] The device sends the entered information to the server in JSON format. The smartphone application compiles the product information and promotional conditions entered by the user and sends it to the server as an HTTP POST request. Examples of data to send include {"product_name": "New smartwatch", "target_audience": "Males aged 25-45", "budget": 5000000, "duration": "3 months", "conditions": "Mainly online channels"}.

[0831] Step 3:

[0832] The server analyzes the received JSON data, extracting and structuring each field. The server parses the received data and stores information such as the product name, target demographic, budget, period, and conditions in individual variables. This analysis neatly organizes the product information and sales promotion conditions. Specifically, the analyzed data will be formatted as "Product name: New smartwatch," "Target demographic: Men aged 25-45," and "Budget: 5 million yen."

[0833] Step 4:

[0834] The server sends the analysis data to the generation AI. The server passes the structured data to the generation AI model. This data becomes an input prompt, and the generation AI generates a sales promotion plan based on this. The generation AI predicts the optimal plan using a database of past sales promotion cases and target attributes.

[0835] Step 5:

[0836] The generation AI generates the optimal sales promotion plan. Specifically, the generation AI predicts the effectiveness of each medium, such as social media advertising, email marketing, and influencer marketing, and creates a detailed plan including budget allocation, messaging, and schedule. Examples of output data include social media advertising: 3 million yen, email marketing: 1 million yen, and influencer marketing: 1 million yen.

[0837] Step 6:

[0838] The server receives the sales promotion plan created by the generation AI. The server receives the data from the generation AI and formats it for presentation to the user. This format is JSON format and layout that is easy to read in smartphone applications.

[0839] Step 7:

[0840] The server sends the prepared sales promotion plan back to the device. The smartphone app receives the response from the server and displays it in a format that the user can view. Specifically, the budget allocation for each sales promotion medium, advertising messages, and campaign schedule are presented in a visually easy-to-understand format. The user is then ready to carry out specific sales promotion activities based on this information.

[0841] Through the above steps, a user can quickly obtain an efficient and effective sales promotion plan without having specialized knowledge.

[0842] 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.

[0843] The present invention is a system in which a generation AI creates an optimal sales promotion plan based on product information and sales promotion conditions entered by the user, and presents that plan to the user.By further combining it with an emotion engine, it is possible to take into account the user's emotional information.

[0844] System Overview

[0845] User Input

[0846] The user uses a terminal to access a dedicated form and enters product information and promotion conditions. Product information includes the product name and characteristics, while promotion conditions include the target demographic, promotion budget, promotion period, and other specific conditions. For example, to create a promotion plan for a new smartwatch, the user enters the target demographic as "men aged 25-45 living in urban areas," a promotion budget of "5 million yen," a promotion period of "3 months," and specific conditions as "mainly through online channels."

[0847] Incorporating an emotion engine

[0848] As the user types and interacts, the emotion engine kicks in. The emotion engine analyzes the user's input speed, interface manipulation patterns, and other biometric information to identify the user's emotional state.

[0849] Sending input to the server

[0850] The device sends the input information and the emotion information identified by the emotion engine to the server in JSON format.

[0851] Analysis on the server

[0852] The server parses the received JSON data, extracts each field according to the data structure using a JSON parser, and saves the product information, promotional terms, and sentiment information as structured data.

[0853] Applying generative AI

[0854] The server passes the analyzed data to the generation AI, which applies a learning model based on a database of past promotional cases and target attributes, and generates an optimal promotional plan taking into account the user's emotional information.

[0855] Proposal plan generation and presentation

[0856] The generated AI sends the promotional plan it has created back to the server. The plan includes the promotional media to be used, the advertising copy and message to be included, budget allocation, and a detailed campaign schedule. For example, if the emotional information indicates "excitement" or "interest," it will select more aggressive advertising copy and allocate a larger budget to social media advertising and influencers.

[0857] final offer

[0858] The server sends the formatted sales promotion plan to the terminal and displays it to the user. The user can view the proposed sales promotion plan through the terminal and check details of budget allocation, advertising messages, and campaign period linked to emotional information. The user can also send a request for revisions as needed.

[0859] Specific example explanation

[0860] For example, if a user is selling a "new smartwatch," the process would proceed as follows: The user uses a device to enter the product name "New Smartwatch," the target demographic "Men aged 25-45, living in urban areas," the promotion budget "5 million yen," the promotion period "3 months," and the specific conditions "mainly online channels" into a form. This information, along with the user's emotional information (for example, if "excitement" is recognized during input), is sent from the device to the server. The server analyzes the data and passes it to the generation AI. The generation AI generates an optimal promotional plan based on past cases and target attributes, proposing, for example, 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing. The emotional information is reflected in the plan, and the proposal is made taking into account more impactful advertising copy and budget allocation. Finally, this plan is presented to the user, with specific advertising messages and schedules detailed.

[0861] In this way, the present invention provides a system that automatically generates an optimal sales promotion plan through advanced analysis and learning based on information and emotional information input by the user, thereby maximizing return on investment.

[0862] The processing flow will be explained below.

[0863] Step 1:

[0864] The user accesses a dedicated form on their device and enters product information and promotion conditions. Product information includes the product name and characteristics, while promotion conditions include the target demographic, promotion budget, promotion period, and other specific conditions. For example, to create a promotion plan for a new smartwatch, the user enters the target demographic as "men aged 25-45 living in urban areas," a promotion budget of "5 million yen," a promotion period of "3 months," and specific conditions as "mainly through online channels."

[0865] Step 2:

[0866] Once the user has completed the input, they press the "Submit" button, which causes the device to convert the information into JSON format and send it to the server as an HTTP POST request.

[0867] Step 3:

[0868] The emotion engine analyzes user input and interactions in real time and identifies the user's emotional state (e.g., excitement, interest, stress, etc.) based on the user's typing speed, interface operation patterns, and additional data such as voice and facial expressions.

[0869] Step 4:

[0870] The device converts the emotional information recognized by the emotion engine into JSON format and sends it to the server, along with product information and sales promotion conditions.

[0871] Step 5:

[0872] The server receives the HTTP request and parses the JSON data. Using a JSON parser, the server extracts each field according to the data structure and saves the product information, promotion terms, and sentiment information as structured data.

[0873] Step 6:

[0874] The server passes the analyzed data to the generation AI. The server makes an API request to the generation AI and sends the analyzed product information, promotional conditions, and emotion information.

[0875] Step 7:

[0876] The generation AI applies a learning model based on a database of past promotional cases and target attributes, and also takes into account user emotional information. The generation AI simulates the effectiveness of each promotional method (social media advertising, email marketing, influencer marketing, etc.) and generates an optimal promotional plan. For example, if the user's emotion indicates "excitement," it selects more aggressive advertising copy and allocates a larger budget to social media advertising and influencers.

[0877] Step 8:

[0878] The AI ​​then generates an optimal promotional plan and sends it back to the server, which includes the promotional media to be used, the advertising copy and message to include, budget allocation, and a detailed campaign schedule.

[0879] Step 9:

[0880] The server analyzes the promotional plan received from the generation AI and formats it as necessary.

[0881] Step 10:

[0882] The server sends the formatted sales promotion plan to the terminal and displays it to the user. The user can view the proposed sales promotion plan through the terminal and check details of budget allocation, advertising messages, and campaign period linked to emotional information. The user can also send a request for revisions as needed.

[0883] In this way, the server and generation AI work together to automatically generate an optimal sales promotion plan based on the product information and sales promotion conditions entered by the user, as well as an emotion engine that recognizes emotions in real time, and present this to the user, making it possible to quickly implement effective sales promotion activities.

[0884] Example 2

[0885] 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."

[0886] Conventional sales promotion plan creation systems generate plans based only on product information and sales promotion conditions entered by the user. As a result, it is difficult to create optimal sales promotion plans because they are unable to take into account the user's emotions and interaction situations. There is also a need to provide more detailed and effective sales promotion plans by utilizing emotional information.

[0887] 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.

[0888] In this invention, the server includes means for receiving product information and promotion conditions entered by a user, means for analyzing the product information and promotion conditions and generating analysis data, means for analyzing the user's input speed and operation patterns and identifying the user's emotional state, means for applying a learning model of past promotional cases and target attributes based on the analysis data and emotional information to generate an optimal promotion plan, and means for presenting the generated promotion plan to the user. This enables the generation of a more sophisticated and effective promotion plan that reflects the user's emotional information.

[0889] "Product information" is data such as the name, characteristics, and specifications of the product sold by the user.

[0890] "Promotional conditions" are requirements related to promotional activities, including target audience, promotional budget, promotional period, and specific conditions.

[0891] "Analysis data" refers to data generated as a result of analyzing product information and sales promotion conditions.

[0892] "User's emotional state" is information that indicates the psychological state of the user when operating the system.

[0893] A "learning model" is an artificial intelligence model used to generate optimal sales promotion plans based on past data.

[0894] A "sales promotion plan" refers to a plan or strategy formulated to effectively sell a product.

[0895] "Emotion information" is information about the user's psychological state that is analyzed based on the user's input speed and operation patterns.

[0896] The system of the present invention uses a generation AI to create an optimal sales promotion plan based on product information and sales promotion conditions entered by the user, and presents the plan to the user. Furthermore, by combining it with an emotion engine, it is possible to take into account the user's emotional information. Specific embodiments of this system are described below.

[0897] System Overview

[0898] User Input

[0899] The user uses a terminal to access a dedicated form and enters product information and promotion conditions. Product information includes the product name and characteristics, while promotion conditions include the target demographic, promotion budget, promotion period, and other specific conditions. For example, to create a promotion plan for a new smartwatch, the user enters the target demographic as "men aged 25-45 living in urban areas," a promotion budget of "5 million yen," a promotion period of "3 months," and specific conditions as "mainly through online channels."

[0900] Incorporating an emotion engine

[0901] As the user types and interacts, the emotion engine kicks in. The emotion engine analyzes the user's input speed, interface manipulation patterns, and other biometric information to identify the user's emotional state.

[0902] Sending input to the server

[0903] The device sends the input information and the emotion information identified by the emotion engine to the server in JSON format.

[0904] Analysis on the server

[0905] The server parses the received JSON data, extracts each field according to the data structure using a JSON parser (e.g., Jackson or Gson), and saves the product information, promotional terms, and sentiment information as structured data.

[0906] Applying generative AI

[0907] The server passes the analyzed data to the generation AI, which applies a learning model (using TensorFlow, for example) based on a database of past promotional cases (e.g., Google Cloud Bigtable) and target attributes, and generates the optimal promotional plan by taking into account the user's emotional information.

[0908] Proposal plan generation and presentation

[0909] The generated AI sends the promotional plan it has created back to the server. The plan includes the promotional media to be used, the advertising copy and message to be included, budget allocation, and a detailed campaign schedule. For example, if the emotional information indicates "excitement" or "interest," it will select more aggressive advertising copy and allocate a larger budget to social media advertising and influencers.

[0910] final offer

[0911] The server sends the formatted sales promotion plan to the terminal and displays it to the user. The user can view the proposed sales promotion plan through the terminal and check details of budget allocation, advertising messages, and campaign period linked to emotional information. The user can also send a request for revisions as needed.

[0912] Specific example explanation

[0913] For example, if a user is selling a "new smartwatch," the process would proceed as follows: The user uses a device to enter the product name "New Smartwatch," the target demographic "Men aged 25-45, living in urban areas," the promotion budget "5 million yen," the promotion period "3 months," and the specific conditions "mainly online channels" into a form. This information, along with the user's emotional information (for example, if "excitement" is recognized during input), is sent from the device to the server. The server analyzes the data and passes it to the generation AI. The generation AI generates an optimal promotional plan based on past cases and target attributes, proposing, for example, 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing. The emotional information is reflected in the plan, and the proposal is made taking into account more impactful advertising copy and budget allocation. Finally, this plan is presented to the user, with specific advertising messages and schedules detailed.

[0914] Prompt Sentence Examples

[0915] An example prompt is:

[0916] Product name: New smartwatch

[0917] Target demographic: Men aged 25-45, living in urban areas

[0918] Promotional budget: 5 million yen

[0919] Promotion period: 3 months

[0920] Specific conditions: Focus on online channels

[0921] Emotion information: excitement

[0922] In this way, the present invention provides a system that automatically generates an optimal sales promotion plan through advanced analysis and learning based on information and emotional information input by the user, thereby maximizing return on investment.

[0923] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0924] Step 1:

[0925] The user uses a terminal to access a dedicated form and enters product information and promotion conditions. The information entered includes the product name, target demographic, promotion budget, promotion period, and specific conditions. This information is temporarily saved in the terminal.

[0926] Input: Product name "New smartwatch", target demographic "Men aged 25-45, living in urban areas", sales promotion budget "5 million yen", sales promotion period "3 months", specific conditions "mainly online channels"

[0927] Output: The product information and promotional terms entered by the user are saved in the terminal.

[0928] Step 2:

[0929] The device instantly verifies the product information and promotional conditions collected, and then activates the emotion engine, which collects and analyzes data such as the user's operation patterns and input speed in real time to identify the user's emotional state.

[0930] Input: User operation patterns, input speed, and other biometric information

[0931] Output: Emotion information identified by the emotion engine (e.g., excited, relaxed)

[0932] Step 3:

[0933] The terminal packages the product information and promotional conditions entered, as well as the emotional information identified by the emotion engine, in JSON format and sends it to the server.

[0934] Input: Product information, promotional conditions, emotional information

[0935] Output: JSON format data (e.g., product name "New Smartwatch", target demographic "Men aged 25-45", sales promotion budget "5 million yen", emotional information "Excitement")

[0936] Step 4:

[0937] The server parses the received JSON data and uses a JSON parser (e.g., Jackson or Gson) to extract product information, promotion terms, and sentiment information, and stores them as structured data in the database.

[0938] Input: JSON format data

[0939] Output: The extracted product information, promotional terms, and emotion information are stored in a database.

[0940] Step 5:

[0941] The server passes the analyzed data to a generative AI model, which applies a learning model (using TensorFlow, for example) based on a database of past promotional cases and target attribute data, and generates an optimal promotional plan taking into account the user's emotional information.

[0942] Input: Product information, promotional conditions, emotional information

[0943] Output: Generated optimal sales promotion plan (e.g., SNS advertising 3 million yen, email marketing 1 million yen, influencer marketing 1 million yen)

[0944] Step 6:

[0945] The server formats the sales promotion plan received from the generation AI and presents it to the user. It uses an HTML template engine (e.g., Thymeleaf) to convert it into a format that is easy for the user to understand.

[0946] Input: Generated promotion plan

[0947] Output: Formatted promotional plan

[0948] Step 7:

[0949] The server sends the formatted sales promotion plan to the terminal and displays it to the user. The user can view the proposed sales promotion plan through the terminal and check details of budget allocation linked to emotional information, advertising messages, and campaign period. In addition, the user can send a request for corrections as necessary.

[0950] Input: Formatted promotional plan

[0951] Output: Promotion plans that users can view, and the ability to submit amendment requests

[0952] (Application example 2)

[0953] 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."

[0954] Conventional sales promotion plan generation systems are unable to take into account the user's emotional information, and the generated sales promotion plan may not match the user's actual emotional state. As a result, the optimal sales promotion plan for the user is not provided, making it difficult to maximize the effectiveness of sales promotion activities. Furthermore, because they only use learning models based on past sales promotion cases and target attributes, they are unable to reflect dynamic user responses in real time. These problems need to be solved.

[0955] The specification processing by the specification 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 receiving product information and promotion conditions entered by the user, means for analyzing the product information and promotion conditions and generating analysis data, means for identifying the user's emotional information by combining an emotion engine and adding the identified emotional information to the analysis data, means for applying a learning model of past promotional cases and target attributes based on the analysis data to generate an optimal promotion plan, and means for presenting the generated promotion plan to the user. This makes it possible to provide a promotion plan that incorporates the user's emotional information.

[0956] "Product information" is information about the name and characteristics of a product that is input by the user when creating a sales promotion plan.

[0957] "Sales promotion conditions" are specific conditions for product sales promotion desired by the user, such as target demographic, sales promotion budget, sales promotion period, and specific conditions.

[0958] "Analysis data" refers to data generated as a result of analysis by the system based on the product information and sales promotion conditions entered by the user.

[0959] A "learning model" is a machine learning model used to create optimal sales promotion plans based on past sales promotion case data and target attribute data.

[0960] The "emotion engine" is a software module for identifying a user's emotional state based on the user's input speed, operation patterns, etc.

[0961] "Emotional information" is data about the user's emotional state as identified by the emotion engine.

[0962] A "sales promotion plan" is a specific plan proposed by the system to enable the user to efficiently promote the sale of a product, and includes the sales promotion media to be used, advertising copy, budget allocation, campaign schedule, etc.

[0963] The "means for presenting to the user" is a method for displaying the generated sales promotion plan on the user's terminal so that the user can confirm it.

[0964] This invention is a system in which a generation AI creates an optimal sales promotion plan based on product information and sales promotion conditions entered by the user, and presents the plan to the user. In addition, by combining it with an emotion engine, it is possible to generate a sales promotion plan that takes into account the user's emotional information.

[0965] System Overview

[0966] User Input

[0967] Users use their devices to access a dedicated form and enter product information and promotion conditions. Product information includes the product name and product characteristics, while promotion conditions include the target demographic, promotion budget, promotion period, and specific conditions. For example, to create a promotion plan for a new smartwatch, users enter the target demographic as "men aged 25-45 living in urban areas," a promotion budget of "5 million yen," a promotion period of "3 months," and specific conditions as "mainly through online channels."

[0968] Incorporating an emotion engine

[0969] As users input and interact, the emotion engine kicks in. The emotion engine analyzes the user's input speed, interface operation patterns, and other biometric information to identify the user's emotional state. The emotion engine uses a software module that runs on a smartphone or computer.

[0970] Sending input to the server

[0971] The device sends the input information and the emotion information identified by the emotion engine to the server in JSON format. After receiving the information, the server analyzes the data.

[0972] Analysis on the server

[0973] The server parses the received JSON data, extracts each field according to the data structure using a JSON parser, and saves the product information, promotional terms, and sentiment information as structured data.

[0974] Applying generative AI

[0975] The server passes the analyzed data to the generation AI, which applies a learning model based on a database of past promotional cases and target attributes, and generates an optimal promotional plan taking into account the user's emotional information.

[0976] Proposal plan generation and presentation

[0977] The sales promotion plan created by the generation AI is sent back to the server. The generated plan includes the promotional media to be used, the advertising copy and message to be included, budget allocation, and a detailed campaign schedule. For example, if the emotional information indicates "excitement" or "interest," more aggressive advertising copy will be selected and a larger budget will be allocated to social media advertising and influencers.

[0978] final offer

[0979] The server sends the formatted promotion plan to the terminal and displays it to the user. The user can view the proposed promotion plan through the terminal and check details of budget allocation, advertising messages, and campaign period linked to emotional information. The user can also send a request for revisions as needed.

[0980] Specific example explanation

[0981] For example, if a user is selling a "new smartwatch," the process would proceed as follows: Using a device, the user enters the product name "new smartwatch," the target demographic "men aged 25-45 living in urban areas," the promotional budget "5 million yen," the promotion period "3 months," and the specific conditions "mainly online channels" into a form. This information, along with the user's emotional information (for example, if "excitement" is recognized during input), is sent from the device to the server. The server analyzes the data and passes it to the generation AI. The generation AI generates an optimal promotional plan based on past cases and target attributes, proposing, for example, 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing. The emotional information is reflected in the plan, and the proposal takes into account more impactful advertising copy and budget allocation.

[0982] Prompt Sentence Examples

[0983] "Product information: New smartwatch, Target demographic: Males aged 25-45 living in urban areas, Promotion budget: 5 million yen, Promotion period: 3 months, Specific conditions: Mainly online channels, Emotional information: Excitement. Please generate the optimal advertising plan for these conditions."

[0984] As a result, the present invention can provide a system in which a generation AI automatically generates an optimal sales promotion plan based on information and emotional information input by the user, thereby maximizing return on investment.

[0985] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0986] Step 1:

[0987] The user uses the device to input product information and promotion conditions. This input information is collected through an input form. The input data includes the product name, target demographic, promotion budget, promotion period, and specific conditions. For example, the product name is "New Smartwatch," the target demographic is "Men aged 25-45 living in urban areas," the promotion budget is "5 million yen," the promotion period is "3 months," and the specific condition is "mainly through online channels."

[0988] Step 2:

[0989] The device sends the user's input speed and operation patterns to the emotion engine. The emotion engine analyzes this data and identifies the user's emotional information. For example, if the input speed is fast and includes an exclamation mark, it is identified as "excited." The emotional information is added to the input data.

[0990] Step 3:

[0991] The device structures the input product information, promotional terms, and emotion information in JSON format and sends it to the server. Once the input data has been sent, the server receives it.

[0992] Step 4:

[0993] The server parses the received JSON data. Using a JSON parser, the server extracts product information, promotion terms, and sentiment information as individual fields and stores them as structured data. This data is then passed to the generative AI model.

[0994] Step 5:

[0995] The server inputs the analyzed data into a generative AI model. The generative AI model applies a learning model based on a database of past promotional cases and target attributes to generate an optimal promotional plan. Emotional information is also taken into consideration during this process. For example, a more impactful ad copy is selected for a user who is in an "excited" state.

[0996] Step 6:

[0997] The sales promotion plan generated by the generative AI model is sent back to the server. The generated sales promotion plan includes the promotional media to be used, advertising copy, budget allocation, and a detailed campaign schedule. For example, it may include 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing.

[0998] Step 7:

[0999] The server sends the formatted promotion plan to the terminal, which displays the proposed promotion plan to the user, who can review the plan through the terminal and, if necessary, send a request to modify it to the server.

[1000] In this way, the system of the present invention can generate an optimal sales promotion plan using a generative AI model based on the user's input information and emotional information, and provide it to the user.

[1001] 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.

[1002] 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.

[1003] 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.

[1004] [Fourth embodiment]

[1005] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1006] 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.

[1007] 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).

[1008] 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.

[1009] 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.

[1010] 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).

[1011] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1012] 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.

[1013] 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.

[1014] 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.

[1015] 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.

[1016] 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.

[1017] 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."

[1018] This invention is a system in which a generation AI creates an optimal sales promotion plan based on product information and sales promotion conditions entered by a user, and presents that plan to the user. This system consists of a terminal that receives user input, a server that processes the received data, and a generation AI that learns from past sales promotion cases and target attributes.

[1019] System Overview

[1020] User Input

[1021] A user accesses a dedicated form using a terminal and enters product information (product name, characteristics, etc.) and promotional conditions (target demographic, budget, period, specific conditions, etc.). For example, to sell a new smartwatch, the target demographic might be "men aged 25-45 living in urban areas," the promotional budget might be "5 million yen," the promotion period might be "three months," and the specific conditions might be "mainly through online channels."

[1022] Sending input to the server

[1023] The terminal sends the entered information to the server in JSON format.

[1024] Analysis on the server

[1025] The server parses the received JSON data, extracts each field, and structures it, so that product information and promotional terms are neatly organized and ready to be passed to the generation AI.

[1026] Applying generative AI

[1027] The server sends the analysis data to the generation AI, which uses a database of past promotional cases and a model trained based on target attributes to generate an optimal promotional plan. This plan generation includes a process of predicting the effectiveness of each medium, such as social media advertising, email marketing, and influencer marketing, and optimizing budget allocation and messaging.

[1028] Proposal plan generation and presentation

[1029] The sales promotion plan created by the generation AI is sent back to the server, which then presents it to the user. For example, it may include budget allocation, such as allocating 3 million yen to social media advertising, 1 million yen to email marketing, and 1 million yen to influencer marketing, as well as the start date and frequency of advertising.

[1030] Specific example explanation

[1031] For example, if a user is selling a "new smartwatch," the process would proceed as follows: Using a device, the user enters the product name "new smartwatch," the target demographic "men aged 25-45 living in urban areas," the promotional budget "5 million yen," the promotion period "3 months," and the specific conditions "mainly online channels." This information is sent from the device to the server, which analyzes the data and passes it on to the generation AI. The generation AI generates an optimal promotional plan based on past cases and target attributes, proposing a plan consisting of 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing. Finally, this plan is presented to the user, with specific advertising messages and schedules detailed.

[1032] In this way, the present invention provides a system that automatically generates an optimal sales promotion plan based on information entered by a user through advanced analysis and learning, thereby maximizing return on investment.

[1033] The processing flow will be explained below.

[1034] Step 1:

[1035] The user accesses a dedicated form on their device and enters product information and promotion conditions. Product information includes the product name and characteristics, while promotion conditions include the target demographic, promotion budget, promotion period, and other specific conditions. For example, to create a promotion plan for a new smartwatch, the user enters the target demographic as "men aged 25-45 living in urban areas," a promotion budget of "5 million yen," a promotion period of "3 months," and specific conditions as "mainly through online channels."

[1036] Step 2:

[1037] Once the user has completed the input, they press the "Submit" button, which causes the device to convert the information into JSON format and send it to the server as an HTTP POST request.

[1038] Step 3:

[1039] The server receives the HTTP request and parses the JSON data. Using a JSON parser, the server extracts each field according to the data structure and saves the product information and promotion terms as structured data.

[1040] Step 4:

[1041] The server passes the analyzed data to the generation AI, which then makes an API request to the generation AI and sends the analyzed product information and promotional terms.

[1042] Step 5:

[1043] The generation AI applies a learning model based on a database of past promotional cases and target attributes. The generation AI receives requests, simulates the effectiveness of each promotional method (social media advertising, email marketing, influencer marketing, etc.), and generates the optimal promotional plan.

[1044] Step 6:

[1045] The AI ​​then generates an optimal sales promotion plan and sends it back to the server. The plan includes the promotional media to be used, advertising copy and messaging, budget allocation, and a detailed campaign schedule. For example, a specific plan could be generated that allocates 3 million yen to social media advertising, 1 million yen to email marketing, and 1 million yen to influencer marketing.

[1046] Step 7:

[1047] The server analyzes the promotional plan received from the generation AI and formats it as necessary.

[1048] Step 8:

[1049] The server sends the formatted promotion plan to the terminal and displays it to the user. The user can view the proposed promotion plan through the terminal, and can check details of the budget allocation, advertising message, and campaign period, and can also submit a request for modification if necessary.

[1050] In this way, the server and generation AI work together to automatically generate the optimal sales promotion plan based on the product information and sales promotion conditions entered by the user, making it possible to quickly implement effective sales promotion activities.

[1051] Example 1

[1052] 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."

[1053] Creating a sales promotion plan has traditionally required a great deal of specialized knowledge and time, placing a significant burden on small and medium-sized enterprises and sole proprietors. Furthermore, there was a lack of methods for automatically generating effective plans that utilized past examples and target attributes. As a result, it was difficult to find the optimal marketing method, making it difficult to implement efficient sales promotion activities.

[1054] 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.

[1055] In this invention, the server includes means for receiving product information and promotional conditions entered by a user, means for analyzing the product information and promotional conditions and generating analysis data, means for transmitting the analysis data to the server in JSON format, means for applying a generative AI model that applies a learning model of past promotional cases and target attributes based on the analysis data to generate an optimal promotional plan, and means for presenting the generated promotional plan to the user. This allows users to easily automatically generate optimal promotional plans and enable efficient marketing activities.

[1056] "Product information" is information related to the product for sale provided by the user, and specifically includes the product name, characteristics, etc.

[1057] "Sales promotion conditions" are conditions for sales promotion activities set by the user, and specifically include the target demographic, sales promotion budget, sales promotion period, and specific conditions.

[1058] "Analysis data" refers to data that is analyzed and structured based on the product information and sales promotion conditions received by the server.

[1059] A "generative AI model" is an artificial intelligence model that learns from past promotional cases and target attributes, and generates the optimal promotional plan based on the input conditions.

[1060] "JSON format" is an abbreviation for JavaScript Object Notation, and is a standard format for expressing data in text format.

[1061] A "server" is a computing device that receives data entered by a user, analyzes it, and transmits it to a generative AI model.

[1062] A "terminal" is a device that allows a user to input data, and includes electronic devices such as computers and smartphones.

[1063] A "sales promotion plan" is a specific execution plan for sales promotion activities that is created by the generative AI model based on the analysis data.

[1064] The "HTTPS protocol" stands for Hypertext Transfer Protocol Secure, an internet communication protocol that encrypts data communications.

[1065] A "prompt" is a textual instruction entered into a generative AI model to instruct it to output appropriate data.

[1066] This invention is a system in which a generative AI model creates an optimal sales promotion plan based on product information and sales promotion conditions entered by a user, and presents that plan to the user. This system consists of a terminal that receives user input, a server that processes the received data, and a generative AI model that learns past sales promotion examples and target attributes.

[1067] System Overview

[1068] User Input

[1069] Users use their devices to access a dedicated form and enter product information and promotional conditions. Product information includes, for example, the name and characteristics of a new product, while promotional conditions include the target demographic, budget, period, and specific conditions. For example, to sell a new type of smartwatch, users might enter the target demographic as "men aged 25-45 living in urban areas," the promotional budget as "5 million yen," the promotion period as "3 months," and the specific conditions as "mainly through online channels."

[1070] Sending input to the server

[1071] The terminal converts the entered information into JSON format and sends it to the server using the HTTPS protocol, a procedure that ensures the security of the data.

[1072] Analysis on the server

[1073] The server parses the received JSON data, extracts each field, and structurizes it. This analysis is performed using Python libraries such as Pandas and NumPy. It also checks the integrity of the data and performs error handling if there is missing data.

[1074] Applying generative AI

[1075] The server passes the analysis data to the generative AI model. The generative AI model uses an artificial intelligence model trained based on a database of past promotional cases and target attributes. In particular, OpenAI's GPT-4 model is applied. The generative AI automatically generates the optimal promotional plan based on the input conditions.

[1076] Examples of prompts:

[1077] "Please create a sales promotion plan for a new smartwatch. The target demographic is men aged 25-45 living in urban areas. The sales promotion budget is 5 million yen, the sales promotion period is 3 months, and the specific conditions are that it will be mainly conducted through online channels."

[1078] Proposal plan generation and presentation

[1079] The server receives the promotional plan created by the generative AI model and presents it to the user. This plan includes budget allocation, such as allocating 3 million yen to social media advertising, 1 million yen to email marketing, and 1 million yen to influencer marketing, as well as the start date and frequency of advertising. The user can check this information on their device and view the detailed promotional plan.

[1080] Specific examples

[1081] For example, a user enters the following information to sell a "new smartwatch":

[1082] Product name: New smartwatch

[1083] Target demographic: Men aged 25-45, living in urban areas

[1084] Promotional budget: 5 million yen

[1085] Promotion period: 3 months

[1086] Specific conditions: Focus on online channels

[1087] This information is sent from the device to a server, where the data is analyzed and passed to a generative AI model. The generative AI model generates an optimal sales promotion plan based on past cases and target attributes. For example, a plan may be created that allocates 3 million yen to social media advertising, 1 million yen to email marketing, and 1 million yen to influencer marketing. This plan is then presented to the user, with specific advertising messages and schedules shown in detail.

[1088] In this way, the present invention provides a system that automatically generates an optimal sales promotion plan through advanced analysis and learning based on information entered by the user, thereby maximizing return on investment.

[1089] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1090] Step 1:

[1091] Users use their devices to access a dedicated form and enter product information and promotion conditions. This input includes the product name, target demographic, promotion budget, promotion period, and specific conditions. For example, for a new smartwatch, users would enter "new smartwatch," "male, aged 25-45, living in an urban area," "5 million yen," "3 months," and "mainly through online channels."

[1092] Input: Product information and promotion terms entered by the user into the form

[1093] Output: User input is stored on the terminal

[1094] Step 2:

[1095] The terminal converts the information entered by the user into JSON format. Specifically, it converts the entered data into a JSON object and prepares it for transmission.

[1096] Input: Product information and promotion terms entered by the user into the form

[1097] Output: Data converted to JSON format

[1098] Step 3:

[1099] The device then sends the converted JSON data to the server using the HTTPS protocol, with SSL / TLS encryption applied to the communication to ensure secure data transmission.

[1100] Input: Data converted to JSON format

[1101] Output: Data received by the server

[1102] Step 4:

[1103] The server parses the received JSON data, extracts each field, and structurizes it. This analysis is performed using Python libraries such as Pandas and NumPy. The server also checks the integrity of the data and performs error handling if there is missing data.

[1104] Input: JSON data received by the server

[1105] Output: Structured data (analysis data)

[1106] Step 5:

[1107] The server passes the structured data to a generative AI model. The generative AI model used is an artificial intelligence model trained based on past promotional cases and target attributes. In particular, OpenAI's GPT-4 model is applied. The generative AI model automatically generates the optimal promotional plan based on the prompt text.

[1108] Examples of prompts:

[1109] "Please create a sales promotion plan for a new smartwatch. The target demographic is men aged 25-45 living in urban areas. The sales promotion budget is 5 million yen, the sales promotion period is 3 months, and the specific conditions are that it will be mainly conducted through online channels."

[1110] Input: Structured data (analysis data), prompt statement

[1111] Output: Generated trade plan

[1112] Step 6:

[1113] The server receives the sales promotion plan created by the generative AI model and organizes its contents, specifically detailing budget allocation and advertising schedules for social media advertising, email marketing, and influencer marketing.

[1114] Input: Promotional plan received from the generative AI model

[1115] Output: Organized promotional plan

[1116] Step 7:

[1117] The server presents the organized sales promotion plan to the user, who can then check this information through his / her terminal and view the detailed sales promotion plan.

[1118] Input: Organized promotional plan

[1119] Output: Promotion offered to the user

[1120] These steps allow users to easily and automatically generate optimal sales promotion plans, enabling efficient marketing activities.

[1121] (Application example 1)

[1122] 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."

[1123] Conventional sales promotion plan generation systems require users to create detailed strategies themselves, requiring specialized knowledge and time. Furthermore, the data analysis and predictions required to generate optimal plans are often performed manually, limiting their efficiency and accuracy. Furthermore, it is difficult to provide the generated plans in a readily usable format, requiring a great deal of effort and cost. It is necessary to provide a system that can solve these issues and quickly and accurately generate and present optimal sales promotion plans.

[1124] 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.

[1125] In this invention, the server includes means for receiving product information and promotion conditions entered by a user, means for analyzing the product information and promotion conditions and generating analysis data, means for applying a learning model of past promotional cases and target attributes based on the analysis data to generate an optimal promotion plan, means for presenting the generated promotion plan to a user, and means for using a smartphone application to display the generated promotion plan, thereby enabling a user to quickly and accurately generate and present an optimal promotion plan without requiring specialized knowledge or time.

[1126] "Product information" is information indicating the name and characteristics of the product that the user is trying to sell.

[1127] "Sales promotion conditions" are requirements including the target group, budget, period, and specific conditions when carrying out sales promotion activities.

[1128] "Analysis data" refers to data obtained by analyzing the input product information and sales promotion conditions.

[1129] A "learning model" is a predictive model that the generation AI learns from past sales promotion cases and target attributes.

[1130] The "optimal sales promotion plan" is the most effective sales promotion strategy proposed by the generative AI based on analytical data and learning models.

[1131] "Smartphone application" means software that runs on a smartphone and allows users to display and operate promotional plans.

[1132] This invention is a system in which a generation AI creates an optimal sales promotion plan based on product information and sales promotion conditions entered by a user, and presents that plan to the user. This system consists of a terminal that receives user input, a server that processes the received data, and a generation AI that learns from past sales promotion cases and target attributes.

[1133] System Components

[1134] 1. User input receiving terminal

[1135] Users use their smartphones to access a dedicated form and enter product information (product name, characteristics, etc.) and promotional conditions (target demographic, budget, period, specific conditions, etc.).

[1136] 2. Data processing on the server

[1137] The smartphone sends the user's input in JSON format to the server, which then parses the received JSON data, extracts each field, and structures it. This neatly organizes product information and promotional terms, and prepares them for passing to the generation AI.

[1138] 3. Applying generative AI

[1139] The server sends the analysis data to the generation AI, which uses a database of past promotional cases and a model trained based on target attributes to generate the optimal promotional plan. For example, it predicts the effectiveness of each medium, such as social media advertising, email marketing, and influencer marketing, and optimizes budget allocation and messaging.

[1140] 4. Presentation of sales promotion plan

[1141] The generated AI sends the sales promotion plan back to the server, which then presents it to the user. This process includes displaying the plan via a smartphone application. Specific budget allocations are shown, such as 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing.

[1142] Examples and prompts

[1143] Specific examples

[1144] To sell a new type of smartwatch, a user inputs the target demographic as "men aged 25-45 living in urban areas," a promotional budget of "5 million yen," a promotional period of "3 months," and specific conditions of "mainly online channels." This information is sent from the smartphone to a server, which analyzes the data and passes it on to a generation AI. The generation AI generates an optimal promotional plan based on past cases and target attributes, proposing a plan consisting of 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing. Finally, this plan is presented to the user, with specific advertising messages and schedules shown in detail.

[1145] Prompt Sentence Examples

[1146] Product name: New smartwatch

[1147] Characteristics: High-end functionality, health management

[1148] Target demographic: Men aged 25-45, living in urban areas

[1149] Budget: 5 million yen

[1150] Duration: 3 months

[1151] Specific conditions: Focus on online channels

[1152] In this way, the present invention provides a system that automatically generates an optimal sales promotion plan through a smartphone application based on information entered by the user, thereby maximizing return on investment.

[1153] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1154] Step 1:

[1155] The user accesses a dedicated form using a device and enters product information and promotional conditions. Specifically, the user opens a smartphone application and enters information such as the product name, target demographic, budget, period, and specific conditions. An example of input data could be: new smartwatch, male aged 25-45, 5 million yen, 3 months, mainly online channels.

[1156] Step 2:

[1157] The device sends the entered information to the server in JSON format. The smartphone application compiles the product information and promotional conditions entered by the user and sends it to the server as an HTTP POST request. Examples of data to send include {"product_name": "New smartwatch", "target_audience": "Males aged 25-45", "budget": 5000000, "duration": "3 months", "conditions": "Mainly online channels"}.

[1158] Step 3:

[1159] The server analyzes the received JSON data, extracting and structuring each field. The server parses the received data and stores information such as the product name, target demographic, budget, period, and conditions in individual variables. This analysis neatly organizes the product information and sales promotion conditions. Specifically, the analyzed data will be formatted as "Product name: New smartwatch," "Target demographic: Men aged 25-45," and "Budget: 5 million yen."

[1160] Step 4:

[1161] The server sends the analysis data to the generation AI. The server passes the structured data to the generation AI model. This data becomes an input prompt, and the generation AI generates a sales promotion plan based on this. The generation AI predicts the optimal plan using a database of past sales promotion cases and target attributes.

[1162] Step 5:

[1163] The generation AI generates the optimal sales promotion plan. Specifically, the generation AI predicts the effectiveness of each medium, such as social media advertising, email marketing, and influencer marketing, and creates a detailed plan including budget allocation, messaging, and schedule. Examples of output data include social media advertising: 3 million yen, email marketing: 1 million yen, and influencer marketing: 1 million yen.

[1164] Step 6:

[1165] The server receives the sales promotion plan created by the generation AI. The server receives the data from the generation AI and formats it for presentation to the user. This format is JSON format and layout that is easy to read in smartphone applications.

[1166] Step 7:

[1167] The server sends the prepared sales promotion plan back to the device. The smartphone app receives the response from the server and displays it in a format that the user can view. Specifically, the budget allocation for each sales promotion medium, advertising messages, and campaign schedule are presented in a visually easy-to-understand format. The user is then ready to carry out specific sales promotion activities based on this information.

[1168] Through the above steps, a user can quickly obtain an efficient and effective sales promotion plan without having specialized knowledge.

[1169] 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.

[1170] The present invention is a system in which a generation AI creates an optimal sales promotion plan based on product information and sales promotion conditions entered by the user, and presents that plan to the user.By further combining it with an emotion engine, it is possible to take into account the user's emotional information.

[1171] System Overview

[1172] User Input

[1173] The user uses a terminal to access a dedicated form and enters product information and promotion conditions. Product information includes the product name and characteristics, while promotion conditions include the target demographic, promotion budget, promotion period, and other specific conditions. For example, to create a promotion plan for a new smartwatch, the user enters the target demographic as "men aged 25-45 living in urban areas," a promotion budget of "5 million yen," a promotion period of "3 months," and specific conditions as "mainly through online channels."

[1174] Incorporating an emotion engine

[1175] As the user types and interacts, the emotion engine kicks in. The emotion engine analyzes the user's input speed, interface manipulation patterns, and other biometric information to identify the user's emotional state.

[1176] Sending input to the server

[1177] The device sends the input information and the emotion information identified by the emotion engine to the server in JSON format.

[1178] Analysis on the server

[1179] The server parses the received JSON data, extracts each field according to the data structure using a JSON parser, and saves the product information, promotional terms, and sentiment information as structured data.

[1180] Applying generative AI

[1181] The server passes the analyzed data to the generation AI, which applies a learning model based on a database of past promotional cases and target attributes, and generates an optimal promotional plan taking into account the user's emotional information.

[1182] Proposal plan generation and presentation

[1183] The generated AI sends the promotional plan it has created back to the server. The plan includes the promotional media to be used, the advertising copy and message to be included, budget allocation, and a detailed campaign schedule. For example, if the emotional information indicates "excitement" or "interest," it will select more aggressive advertising copy and allocate a larger budget to social media advertising and influencers.

[1184] final offer

[1185] The server sends the formatted sales promotion plan to the terminal and displays it to the user. The user can view the proposed sales promotion plan through the terminal and check details of budget allocation, advertising messages, and campaign period linked to emotional information. The user can also send a request for revisions as needed.

[1186] Specific example explanation

[1187] For example, if a user is selling a "new smartwatch," the process would proceed as follows: The user uses a device to enter the product name "New Smartwatch," the target demographic "Men aged 25-45, living in urban areas," the promotion budget "5 million yen," the promotion period "3 months," and the specific conditions "mainly online channels" into a form. This information, along with the user's emotional information (for example, if "excitement" is recognized during input), is sent from the device to the server. The server analyzes the data and passes it to the generation AI. The generation AI generates an optimal promotional plan based on past cases and target attributes, proposing, for example, 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing. The emotional information is reflected in the plan, and the proposal is made taking into account more impactful advertising copy and budget allocation. Finally, this plan is presented to the user, with specific advertising messages and schedules detailed.

[1188] In this way, the present invention provides a system that automatically generates an optimal sales promotion plan through advanced analysis and learning based on information and emotional information input by the user, thereby maximizing return on investment.

[1189] The processing flow will be explained below.

[1190] Step 1:

[1191] The user accesses a dedicated form on their device and enters product information and promotion conditions. Product information includes the product name and characteristics, while promotion conditions include the target demographic, promotion budget, promotion period, and other specific conditions. For example, to create a promotion plan for a new smartwatch, the user enters the target demographic as "men aged 25-45 living in urban areas," a promotion budget of "5 million yen," a promotion period of "3 months," and specific conditions as "mainly through online channels."

[1192] Step 2:

[1193] Once the user has completed the input, they press the "Submit" button, which causes the device to convert the information into JSON format and send it to the server as an HTTP POST request.

[1194] Step 3:

[1195] The emotion engine analyzes user input and interactions in real time and identifies the user's emotional state (e.g., excitement, interest, stress, etc.) based on the user's typing speed, interface operation patterns, and additional data such as voice and facial expressions.

[1196] Step 4:

[1197] The device converts the emotional information recognized by the emotion engine into JSON format and sends it to the server, along with product information and sales promotion conditions.

[1198] Step 5:

[1199] The server receives the HTTP request and parses the JSON data. Using a JSON parser, the server extracts each field according to the data structure and saves the product information, promotion terms, and sentiment information as structured data.

[1200] Step 6:

[1201] The server passes the analyzed data to the generation AI. The server makes an API request to the generation AI and sends the analyzed product information, promotional conditions, and emotion information.

[1202] Step 7:

[1203] The generation AI applies a learning model based on a database of past promotional cases and target attributes, and also takes into account user emotional information. The generation AI simulates the effectiveness of each promotional method (social media advertising, email marketing, influencer marketing, etc.) and generates an optimal promotional plan. For example, if the user's emotion indicates "excitement," it selects more aggressive advertising copy and allocates a larger budget to social media advertising and influencers.

[1204] Step 8:

[1205] The AI ​​then generates an optimal promotional plan and sends it back to the server, which includes the promotional media to be used, the advertising copy and message to include, budget allocation, and a detailed campaign schedule.

[1206] Step 9:

[1207] The server analyzes the promotional plan received from the generation AI and formats it as necessary.

[1208] Step 10:

[1209] The server sends the formatted sales promotion plan to the terminal and displays it to the user. The user can view the proposed sales promotion plan through the terminal and check details of budget allocation, advertising messages, and campaign period linked to emotional information. The user can also send a request for revisions as needed.

[1210] In this way, the server and generation AI work together to automatically generate an optimal sales promotion plan based on the product information and sales promotion conditions entered by the user, as well as an emotion engine that recognizes emotions in real time, and present this to the user, making it possible to quickly implement effective sales promotion activities.

[1211] Example 2

[1212] 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."

[1213] Conventional sales promotion plan creation systems generate plans based only on product information and sales promotion conditions entered by the user. As a result, it is difficult to create optimal sales promotion plans because they are unable to take into account the user's emotions and interaction situations. There is also a need to provide more detailed and effective sales promotion plans by utilizing emotional information.

[1214] 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.

[1215] In this invention, the server includes means for receiving product information and promotion conditions entered by a user, means for analyzing the product information and promotion conditions and generating analysis data, means for analyzing the user's input speed and operation patterns and identifying the user's emotional state, means for applying a learning model of past promotional cases and target attributes based on the analysis data and emotional information to generate an optimal promotion plan, and means for presenting the generated promotion plan to the user. This enables the generation of a more sophisticated and effective promotion plan that reflects the user's emotional information.

[1216] "Product information" is data such as the name, characteristics, and specifications of the product sold by the user.

[1217] "Promotional conditions" are requirements related to promotional activities, including target audience, promotional budget, promotional period, and specific conditions.

[1218] "Analysis data" refers to data generated as a result of analyzing product information and sales promotion conditions.

[1219] "User's emotional state" is information that indicates the psychological state of the user when operating the system.

[1220] A "learning model" is an artificial intelligence model used to generate optimal sales promotion plans based on past data.

[1221] A "sales promotion plan" refers to a plan or strategy formulated to effectively sell a product.

[1222] "Emotion information" is information about the user's psychological state that is analyzed based on the user's input speed and operation patterns.

[1223] The system of the present invention uses a generation AI to create an optimal sales promotion plan based on product information and sales promotion conditions entered by the user, and presents the plan to the user. Furthermore, by combining it with an emotion engine, it is possible to take into account the user's emotional information. Specific embodiments of this system are described below.

[1224] System Overview

[1225] User Input

[1226] The user uses a terminal to access a dedicated form and enters product information and promotion conditions. Product information includes the product name and characteristics, while promotion conditions include the target demographic, promotion budget, promotion period, and other specific conditions. For example, to create a promotion plan for a new smartwatch, the user enters the target demographic as "men aged 25-45 living in urban areas," a promotion budget of "5 million yen," a promotion period of "3 months," and specific conditions as "mainly through online channels."

[1227] Incorporating an emotion engine

[1228] As the user types and interacts, the emotion engine kicks in. The emotion engine analyzes the user's input speed, interface manipulation patterns, and other biometric information to identify the user's emotional state.

[1229] Sending input to the server

[1230] The device sends the input information and the emotion information identified by the emotion engine to the server in JSON format.

[1231] Analysis on the server

[1232] The server parses the received JSON data, extracts each field according to the data structure using a JSON parser (e.g., Jackson or Gson), and saves the product information, promotional terms, and sentiment information as structured data.

[1233] Applying generative AI

[1234] The server passes the analyzed data to the generation AI, which applies a learning model (using TensorFlow, for example) based on a database of past promotional cases (e.g., Google Cloud Bigtable) and target attributes, and generates the optimal promotional plan by taking into account the user's emotional information.

[1235] Proposal plan generation and presentation

[1236] The generated AI sends the promotional plan it has created back to the server. The plan includes the promotional media to be used, the advertising copy and message to be included, budget allocation, and a detailed campaign schedule. For example, if the emotional information indicates "excitement" or "interest," it will select more aggressive advertising copy and allocate a larger budget to social media advertising and influencers.

[1237] final offer

[1238] The server sends the formatted sales promotion plan to the terminal and displays it to the user. The user can view the proposed sales promotion plan through the terminal and check details of budget allocation, advertising messages, and campaign period linked to emotional information. The user can also send a request for revisions as needed.

[1239] Specific example explanation

[1240] For example, if a user is selling a "new smartwatch," the process would proceed as follows: The user uses a device to enter the product name "New Smartwatch," the target demographic "Men aged 25-45, living in urban areas," the promotion budget "5 million yen," the promotion period "3 months," and the specific conditions "mainly online channels" into a form. This information, along with the user's emotional information (for example, if "excitement" is recognized during input), is sent from the device to the server. The server analyzes the data and passes it to the generation AI. The generation AI generates an optimal promotional plan based on past cases and target attributes, proposing, for example, 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing. The emotional information is reflected in the plan, and the proposal is made taking into account more impactful advertising copy and budget allocation. Finally, this plan is presented to the user, with specific advertising messages and schedules detailed.

[1241] Prompt Sentence Examples

[1242] An example prompt is:

[1243] Product name: New smartwatch

[1244] Target demographic: Men aged 25-45, living in urban areas

[1245] Promotional budget: 5 million yen

[1246] Promotion period: 3 months

[1247] Specific conditions: Focus on online channels

[1248] Emotion information: excitement

[1249] In this way, the present invention provides a system that automatically generates an optimal sales promotion plan through advanced analysis and learning based on information and emotional information input by the user, thereby maximizing return on investment.

[1250] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1251] Step 1:

[1252] The user uses a terminal to access a dedicated form and enters product information and promotion conditions. The information entered includes the product name, target demographic, promotion budget, promotion period, and specific conditions. This information is temporarily saved in the terminal.

[1253] Input: Product name "New smartwatch", target demographic "Men aged 25-45, living in urban areas", sales promotion budget "5 million yen", sales promotion period "3 months", specific conditions "mainly online channels"

[1254] Output: The product information and promotional terms entered by the user are saved in the terminal.

[1255] Step 2:

[1256] The device instantly verifies the product information and promotional conditions collected, and then activates the emotion engine, which collects and analyzes data such as the user's operation patterns and input speed in real time to identify the user's emotional state.

[1257] Input: User operation patterns, input speed, and other biometric information

[1258] Output: Emotion information identified by the emotion engine (e.g., excited, relaxed)

[1259] Step 3:

[1260] The terminal packages the product information and promotional conditions entered, as well as the emotional information identified by the emotion engine, in JSON format and sends it to the server.

[1261] Input: Product information, promotional conditions, emotional information

[1262] Output: JSON format data (e.g., product name "New Smartwatch", target demographic "Men aged 25-45", sales promotion budget "5 million yen", emotional information "Excitement")

[1263] Step 4:

[1264] The server parses the received JSON data and uses a JSON parser (e.g., Jackson or Gson) to extract product information, promotion terms, and sentiment information, and stores them as structured data in the database.

[1265] Input: JSON format data

[1266] Output: The extracted product information, promotional terms, and emotion information are stored in a database.

[1267] Step 5:

[1268] The server passes the analyzed data to a generative AI model, which applies a learning model (using TensorFlow, for example) based on a database of past promotional cases and target attribute data, and generates an optimal promotional plan taking into account the user's emotional information.

[1269] Input: Product information, promotional conditions, emotional information

[1270] Output: Generated optimal sales promotion plan (e.g., SNS advertising 3 million yen, email marketing 1 million yen, influencer marketing 1 million yen)

[1271] Step 6:

[1272] The server formats the sales promotion plan received from the generation AI and presents it to the user. It uses an HTML template engine (e.g., Thymeleaf) to convert it into a format that is easy for the user to understand.

[1273] Input: Generated promotion plan

[1274] Output: Formatted promotional plan

[1275] Step 7:

[1276] The server sends the formatted sales promotion plan to the terminal and displays it to the user. The user can view the proposed sales promotion plan through the terminal and check details of budget allocation linked to emotional information, advertising messages, and campaign period. In addition, the user can send a request for corrections as necessary.

[1277] Input: Formatted promotional plan

[1278] Output: Promotion plans that users can view, and the ability to submit amendment requests

[1279] (Application example 2)

[1280] 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."

[1281] Conventional sales promotion plan generation systems are unable to take into account the user's emotional information, and the generated sales promotion plan may not match the user's actual emotional state. As a result, the optimal sales promotion plan for the user is not provided, making it difficult to maximize the effectiveness of sales promotion activities. Furthermore, because they only use learning models based on past sales promotion cases and target attributes, they are unable to reflect dynamic user responses in real time. These problems need to be solved.

[1282] The specification processing by the specification 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 receiving product information and promotion conditions entered by the user, means for analyzing the product information and promotion conditions and generating analysis data, means for identifying the user's emotional information by combining an emotion engine and adding the identified emotional information to the analysis data, means for applying a learning model of past promotional cases and target attributes based on the analysis data to generate an optimal promotion plan, and means for presenting the generated promotion plan to the user. This makes it possible to provide a promotion plan that incorporates the user's emotional information.

[1283] "Product information" is information about the name and characteristics of a product that is input by the user when creating a sales promotion plan.

[1284] "Sales promotion conditions" are specific conditions for product sales promotion desired by the user, such as target demographic, sales promotion budget, sales promotion period, and specific conditions.

[1285] "Analysis data" refers to data generated as a result of analysis by the system based on the product information and sales promotion conditions entered by the user.

[1286] A "learning model" is a machine learning model used to create optimal sales promotion plans based on past sales promotion case data and target attribute data.

[1287] The "emotion engine" is a software module for identifying a user's emotional state based on the user's input speed, operation patterns, etc.

[1288] "Emotional information" is data about the user's emotional state as identified by the emotion engine.

[1289] A "sales promotion plan" is a specific plan proposed by the system to enable the user to efficiently promote the sale of a product, and includes the sales promotion media to be used, advertising copy, budget allocation, campaign schedule, etc.

[1290] The "means for presenting to the user" is a method for displaying the generated sales promotion plan on the user's terminal so that the user can confirm it.

[1291] This invention is a system in which a generation AI creates an optimal sales promotion plan based on product information and sales promotion conditions entered by the user, and presents the plan to the user. In addition, by combining it with an emotion engine, it is possible to generate a sales promotion plan that takes into account the user's emotional information.

[1292] System Overview

[1293] User Input

[1294] Users use their devices to access a dedicated form and enter product information and promotion conditions. Product information includes the product name and product characteristics, while promotion conditions include the target demographic, promotion budget, promotion period, and specific conditions. For example, to create a promotion plan for a new smartwatch, users enter the target demographic as "men aged 25-45 living in urban areas," a promotion budget of "5 million yen," a promotion period of "3 months," and specific conditions as "mainly through online channels."

[1295] Incorporating an emotion engine

[1296] As users input and interact, the emotion engine kicks in. The emotion engine analyzes the user's input speed, interface operation patterns, and other biometric information to identify the user's emotional state. The emotion engine uses a software module that runs on a smartphone or computer.

[1297] Sending input to the server

[1298] The device sends the input information and the emotion information identified by the emotion engine to the server in JSON format. After receiving the information, the server analyzes the data.

[1299] Analysis on the server

[1300] The server parses the received JSON data, extracts each field according to the data structure using a JSON parser, and saves the product information, promotional terms, and sentiment information as structured data.

[1301] Applying generative AI

[1302] The server passes the analyzed data to the generation AI, which applies a learning model based on a database of past promotional cases and target attributes, and generates an optimal promotional plan taking into account the user's emotional information.

[1303] Proposal plan generation and presentation

[1304] The sales promotion plan created by the generation AI is sent back to the server. The generated plan includes the promotional media to be used, the advertising copy and message to be included, budget allocation, and a detailed campaign schedule. For example, if the emotional information indicates "excitement" or "interest," more aggressive advertising copy will be selected and a larger budget will be allocated to social media advertising and influencers.

[1305] final offer

[1306] The server sends the formatted promotion plan to the terminal and displays it to the user. The user can view the proposed promotion plan through the terminal and check details of budget allocation, advertising messages, and campaign period linked to emotional information. The user can also send a request for revisions as needed.

[1307] Specific example explanation

[1308] For example, if a user is selling a "new smartwatch," the process would proceed as follows: Using a device, the user enters the product name "new smartwatch," the target demographic "men aged 25-45 living in urban areas," the promotional budget "5 million yen," the promotion period "3 months," and the specific conditions "mainly online channels" into a form. This information, along with the user's emotional information (for example, if "excitement" is recognized during input), is sent from the device to the server. The server analyzes the data and passes it to the generation AI. The generation AI generates an optimal promotional plan based on past cases and target attributes, proposing, for example, 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing. The emotional information is reflected in the plan, and the proposal takes into account more impactful advertising copy and budget allocation.

[1309] Prompt Sentence Examples

[1310] "Product information: New smartwatch, Target demographic: Males aged 25-45 living in urban areas, Promotion budget: 5 million yen, Promotion period: 3 months, Specific conditions: Mainly online channels, Emotional information: Excitement. Please generate the optimal advertising plan for these conditions."

[1311] As a result, the present invention can provide a system in which a generation AI automatically generates an optimal sales promotion plan based on information and emotional information input by the user, thereby maximizing return on investment.

[1312] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1313] Step 1:

[1314] The user uses the device to input product information and promotion conditions. This input information is collected through an input form. The input data includes the product name, target demographic, promotion budget, promotion period, and specific conditions. For example, the product name is "New Smartwatch," the target demographic is "Men aged 25-45 living in urban areas," the promotion budget is "5 million yen," the promotion period is "3 months," and the specific condition is "mainly through online channels."

[1315] Step 2:

[1316] The device sends the user's input speed and operation patterns to the emotion engine. The emotion engine analyzes this data and identifies the user's emotional information. For example, if the input speed is fast and includes an exclamation mark, it is identified as "excited." The emotional information is added to the input data.

[1317] Step 3:

[1318] The device structures the input product information, promotional terms, and emotion information in JSON format and sends it to the server. Once the input data has been sent, the server receives it.

[1319] Step 4:

[1320] The server parses the received JSON data. Using a JSON parser, the server extracts product information, promotion terms, and sentiment information as individual fields and stores them as structured data. This data is then passed to the generative AI model.

[1321] Step 5:

[1322] The server inputs the analyzed data into a generative AI model. The generative AI model applies a learning model based on a database of past promotional cases and target attributes to generate an optimal promotional plan. Emotional information is also taken into consideration during this process. For example, a more impactful ad copy is selected for a user who is in an "excited" state.

[1323] Step 6:

[1324] The sales promotion plan generated by the generative AI model is sent back to the server. The generated sales promotion plan includes the promotional media to be used, advertising copy, budget allocation, and a detailed campaign schedule. For example, it may include 3 million yen for social media advertising, 1 million yen for email marketing, and 1 million yen for influencer marketing.

[1325] Step 7:

[1326] The server sends the formatted promotion plan to the terminal, which displays the proposed promotion plan to the user, who can review the plan through the terminal and, if necessary, send a request to modify it to the server.

[1327] In this way, the system of the present invention can generate an optimal sales promotion plan using a generative AI model based on the user's input information and emotional information, and provide it to the user.

[1328] 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.

[1329] 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.

[1330] 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.

[1331] 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.

[1332] 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.

[1333] 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.

[1334] 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).

[1335] 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.

[1336] 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."

[1337] 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.

[1338] 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).

[1339] 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.

[1340] 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.

[1341] 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.

[1342] 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.

[1343] 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.

[1344] 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.

[1345] 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.

[1346] 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.

[1347] 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.

[1348] 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.

[1349] The following is further disclosed regarding the above embodiment.

[1350] (Claim 1)

[1351] means for receiving product information and promotion terms entered by a user;

[1352] means for analyzing the product information and sales promotion conditions and generating analysis data;

[1353] A means for applying a learning model of past sales promotion cases and target attributes based on the analysis data to generate an optimal sales promotion plan;

[1354] means for presenting the generated sales promotion plan to a user;

[1355] A system including:

[1356] (Claim 2)

[1357] 2. The system of claim 1, wherein the product information and promotion conditions input by the user include a product name, a target demographic, a promotion budget, a promotion period, and specific conditions.

[1358] (Claim 3)

[1359] 10. The system of claim 1, wherein the optimal promotional plan presented to the user includes promotional media to be used, messaging and advertising copy, budget allocation, and campaign schedule.

[1360] "Example 1"

[1361] (Claim 1)

[1362] means for receiving product information and promotion terms entered by a user;

[1363] means for analyzing the product information and sales promotion conditions and generating analysis data;

[1364] means for transmitting the analysis data in JSON format to a server;

[1365] A means for applying a generation AI model that applies a learning model of past sales promotion cases and target attributes based on the analysis data to generate an optimal sales promotion plan;

[1366] means for presenting the generated sales promotion plan to a user;

[1367] A system including:

[1368] (Claim 2)

[1369] 2. The system of claim 1, wherein the product information and promotion conditions input by the user include a product name, a target demographic, a promotion budget, a promotion period, and specific conditions.

[1370] (Claim 3)

[1371] 10. The system of claim 1, wherein the optimal promotional plan presented to the user includes promotional media to be used, messaging and advertising copy, budget allocation, and campaign schedule.

[1372] "Application Example 1"

[1373] (Claim 1)

[1374] means for receiving product information and promotion terms entered by a user;

[1375] means for analyzing the product information and sales promotion conditions and generating analysis data;

[1376] A means for applying a learning model of past sales promotion cases and target attributes based on the analysis data to generate an optimal sales promotion plan;

[1377] means for presenting the generated sales promotion plan to a user;

[1378] a means for using a smartphone application to display the generated promotional plan;

[1379] A system including:

[1380] (Claim 2)

[1381] 2. The system according to claim 1, wherein the product information and promotion conditions input by the user include a product name, a target demographic, a promotion budget, a promotion period, and specific conditions.

[1382] (Claim 3)

[1383] 2. The system of claim 1, wherein the optimal promotional plan presented to the user includes promotional media to be used, messaging and advertising copy, budget allocation, and campaign schedule.

[1384] "Example 2: Combining Emotion Engines"

[1385] (Claim 1)

[1386] means for receiving product information and promotion terms entered by a user;

[1387] means for analyzing the product information and sales promotion conditions and generating analysis data;

[1388] means for analyzing a user's input speed and operation pattern to identify the user's emotional state;

[1389] A means for applying a learning model of past sales promotion cases and target attributes based on the analysis data and emotion information to generate an optimal sales promotion plan;

[1390] means for presenting the generated sales promotion plan to a user;

[1391] A system including:

[1392] (Claim 2)

[1393] The product information and promotion conditions input by the user include a product name, a target demographic, a promotion budget, a promotion period, and specific conditions;

[1394] 10. The system of claim 1.

[1395] (Claim 3)

[1396] an optimal promotional plan presented to the user, including promotional media to be used, messaging and advertising copy, budget allocation, and campaign schedule;

[1397] 10. The system of claim 1.

[1398] "Application example 2 when combining emotion engines"

[1399] (Claim 1)

[1400] means for receiving product information and promotion terms entered by a user;

[1401] means for analyzing the product information and sales promotion conditions and generating analysis data;

[1402] A means for applying a learning model of past sales promotion cases and target attributes based on the analysis data to generate an optimal sales promotion plan;

[1403] means for identifying emotion information of the user by combining emotion engines and adding the identified emotion information to the analysis data;

[1404] means for presenting the generated sales promotion plan to a user;

[1405] A system including:

[1406] (Claim 2)

[1407] 2. The system of claim 1, wherein the product information and promotion conditions input by the user include a product name, a target demographic, a promotion budget, a promotion period, and specific conditions.

[1408] (Claim 3)

[1409] 10. The system of claim 1, wherein the optimal promotional plan presented to the user includes promotional media to be used, messaging and advertising copy, budget allocation, and campaign schedule. [Explanation of symbols]

[1410] 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. means for receiving product information and promotion terms entered by a user; means for analyzing the product information and sales promotion conditions and generating analysis data; A means for applying a learning model of past sales promotion cases and target attributes based on the analysis data to generate an optimal sales promotion plan; means for presenting the generated sales promotion plan to a user; A system including:

2. The system according to claim 1 , wherein the product information and promotion conditions input by the user include a product name, a target demographic, a promotion budget, a promotion period, and specific conditions.

3. The system of claim 1 , wherein the optimal promotional plan presented to the user includes promotional media to be used, messaging and advertising copy, budget allocation, and campaign schedule.

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