System

Abstract

A system is provided.SOLUTION: A system comprising: means for using generated AI to analyze trends and trends in demand; means for using generated AI to identify target markets and plan marketing expansion strategies; means for using generated AI to automatically generate advertisement text, images, and videos; means for using automatically generated advertisement content to support marketing activities; and means for using generated demand data to automate customer responses.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] It is not easy for people who have difficulty finding employment to achieve economic independence. In particular, people with disabilities, the elderly, people who have experienced social withdrawal, and those serving time in prison face many challenges in the general labor market, limiting their employment opportunities. Furthermore, social enterprises such as social farms face difficulties in developing competitive products, promoting sales channels, and securing profits. This makes business operations difficult, and building a sustainable business model is a challenge. [Means for solving the problem]

[0005] This invention is a system that utilizes generative AI to support effective marketing activities and provide an environment where people who have difficulty finding employment can work with enthusiasm. Specifically, the following means are implemented.

[0006] Generative AI provides a means to analyze demand tendencies and trends, thereby identifying suitable markets and developing effective sales expansion strategies.

[0007] A means is provided to identify target markets based on demand forecast data and optimize promotional campaigns to target markets.

[0008] It uses generative AI to automatically generate ad text, images, and videos, creating effective marketing content with minimal effort.

[0009] We will provide a means to support marketing activities using automatically generated advertising content, enabling workers, including those with disabilities, to carry out their work efficiently.

[0010] In addition, it provides a means to automate customer support using generative AI, enabling quick and appropriate responses to customer inquiries.

[0011] This will help solve the economic and operational challenges faced by social farms and support sustainable business operations and economic independence.

[0012] "Generative AI" is a type of artificial intelligence technology that generates new data from a variety of data, including natural language processing and image generation.

[0013] "Demand forecasting" refers to analyzing past data and market trends to predict future demand.

[0014] A "target market" is a group of consumers or a geographic area that you specifically focus on to sell a particular product or service.

[0015] A "sales channel expansion strategy" refers to a strategy for expanding product distribution channels and delivering products to more consumers.

[0016] "Advertising text" refers to text created for sales promotion or marketing activities.

[0017] "Image" refers to visual content created to convey visual information.

[0018] "Video" refers to video content intended to convey dynamic visual information.

[0019] "Customer service" refers to activities to respond to inquiries and requests from customers and to improve customer satisfaction.

[0020] A "social farm" is a social enterprise that aims to enable people who have difficulty finding employment, such as people with disabilities or those who have experienced social withdrawal, to work alongside other employees.

[0021] "Automation" refers to the process of having machines or systems automatically perform tasks that are normally performed manually.

[0022] "Data collection" refers to the act of gathering information needed for a specific purpose.

[0023] "Data preprocessing" refers to the process of shaping and correcting collected data before applying it to an analysis or model.

[0024] A "machine learning model" refers to an algorithm that learns from collected data and makes predictions and classifications based on the results.

[0025] "Marketing activities" refers to a series of activities aimed at providing products and services appropriately to the market, such as product sales promotion, brand building, and communication with consumers.

[0026] A "chatbot" is an automated conversational program that simulates conversations using text or voice. [Brief explanation of the drawings]

[0027] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0028] 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.

[0029] First, the terms used in the following description will be explained.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] [First embodiment]

[0036] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0037] 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.

[0038] 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).

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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."

[0048] This invention is a system to support the economic independence of people who have difficulty finding employment, such as the disabled, the elderly, those who have experienced social withdrawal, and those serving time in prison, and it utilizes generative AI to match companies and brands with influencers, achieving effective marketing, expanding sales channels, and customer support. This system is operated mainly by three elements: a server, a terminal, and a user.

[0049] Server Roles and Operations

[0050] Demand forecasting

[0051] The server uses generative AI to collect and analyze market data and social media trend data to predict demand for specific regions and consumer segments.

[0052] Example: A server analyzes online shopping data and social media trend data from the past six months to predict increased demand for air conditioning equipment in the summer.

[0053] Market Targeting

[0054] The server identifies the target market based on the demand forecast results and develops a sales channel expansion strategy.

[0055] Example: A server designs a geo-specific advertising campaign to target the growing demand for air conditioning equipment in Okinawa Prefecture.

[0056] Content Generation

[0057] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos.

[0058] Example: The server generates advertising text such as "Buy now and enjoy a comfortable summer!" and images and videos of air conditioning equipment with a beach in Okinawa in the background.

[0059] Data storage

[0060] The server stores the generated demand forecast data, target market information, advertising content, etc. in a database and makes them accessible to the server.

[0061] Terminal roles and processing

[0062] Automating customer interactions

[0063] The device uses generative AI to build chatbots for customer service.

[0064] Example: The terminal provides instant responses to questions about how to install air conditioning equipment or the return procedure via an AI chatbot.

[0065] Customer interaction management

[0066] The terminal responds immediately to customer inquiries using automatically generated responses and records the information in a database.

[0067] User Roles and Actions

[0068] Business execution

[0069] Users (employees who are having difficulty finding work) download the promotional content generated by the server and use it in their actual marketing activities.

[0070] Example: A user prints the generated poster and distributes it in the community.

[0071] Support for marketing activities

[0072] Users can use the generated advertising text and images to promote their products on social media.

[0073] Example: A user posts the generated advertising image on social media to promote the product to their followers.

[0074] Following AI instructions

[0075] Users carry out their work in accordance with the sales expansion strategy recommended by the generation AI.

[0076] Example: The user plans and executes promotional activities in target areas as directed by AI.

[0077] conclusion

[0078] This system allows people who have difficulty finding employment to easily and efficiently carry out marketing activities. Furthermore, companies and NPOs can utilize the power of generative AI to effectively deliver competitive products to the market. We believe this system will make a significant contribution as a concrete means to improve the efficiency and sustainability of overall business operations.

[0079] The processing flow will be explained below.

[0080] Step 1:

[0081] Data collection

[0082] The server collects online shopping data, social media trend data, and more.

[0083] Specifically, data is automatically obtained using web scraping tools and APIs.

[0084] Step 2:

[0085] Data Preprocessing

[0086] The server cleanses the collected data, filling in missing values ​​and detecting outliers.

[0087] For example, importing data into an Excel spreadsheet or database and correcting inconsistent data.

[0088] Step 3:

[0089] Demand forecasting

[0090] The server analyzes the pre-processed data and uses machine learning models to predict demand.

[0091] For example, ARIMA models and LSTM networks are used to forecast future demand.

[0092] Step 4:

[0093] Saving prediction results

[0094] The server stores the prediction results in a database, making them available for subsequent processing steps.

[0095] Step 5:

[0096] Identifying your target market

[0097] The server identifies the optimal target market based on the demand forecast data.

[0098] For example, identify areas where demand for air conditioning equipment will increase (e.g., Okinawa Prefecture).

[0099] Step 6:

[0100] Planning sales channel expansion strategies

[0101] The server develops a sales expansion strategy for the identified target market.

[0102] For example, design an advertising campaign specifically for Okinawa Prefecture.

[0103] Step 7:

[0104] Content Generation

[0105] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos.

[0106] Specifically, it generates advertising text such as "Buy now and enjoy a comfortable summer!" and images and videos of air conditioning equipment with an Okinawa beach in the background.

[0107] Step 8:

[0108] Content Storage

[0109] The server stores the generated advertising text, images and videos in a database for users to access.

[0110] Step 9:

[0111] Chatbot Settings

[0112] The device uses generative AI to set up a chatbot that has learned common questions and their answers.

[0113] Specifically, prepare answers to questions about how to install air conditioning equipment and the return procedure.

[0114] Step 10:

[0115] Automating customer interactions

[0116] The terminal uses a chatbot to respond to customer inquiries immediately.

[0117] For example, an AI chatbot can provide answers to inquiries about how to use air conditioning equipment.

[0118] Step 11:

[0119] Content Use

[0120] The user downloads the generated promotional content and uses it in marketing activities.

[0121] Specifically, the generated posters are printed and distributed in the area.

[0122] Step 12:

[0123] Marketing Activities

[0124] Users can promote themselves on social media using advertising text and images generated by the server.

[0125] For example, the generated advertising image can be posted on social media to promote the product to followers.

[0126] Step 13:

[0127] Feedback collection

[0128] The user collects feedback from customers and sends it to the server.

[0129] For example, record customer opinions obtained through social media comments and messages.

[0130] Step 14:

[0131] Reanalyzing Data

[0132] The server analyzes the collected feedback data and uses it to forecast demand and develop marketing strategies for the next time.

[0133] For example, identifying areas for improvement in products and services based on customer feedback.

[0134] This series of processing steps enables people who have difficulty finding employment to carry out marketing activities efficiently, and companies and NPOs to achieve sustainable business operations.

[0135] Example 1

[0136] 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."

[0137] Existing marketing systems have the problem that it is difficult for people who have difficulty finding employment, such as people with disabilities, the elderly, people who have been socially withdrawn, and former prisoners, to carry out marketing activities efficiently. Furthermore, these systems are inadequate in market demand forecasting, targeting, and generating promotional content, making it difficult for companies and brands to effectively deliver products to the market. In addition, customer support is often done manually, requiring efficient use of human resources.

[0138] 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.

[0139] In this invention, the server includes: a means for analyzing demand trends using a generative AI; a means for identifying a target market based on demand forecast data and developing a sales channel expansion strategy; a means for automatically generating advertising text, images, and videos using the generative AI; a means for generating prompts for use with the generative AI model; a means for preprocessing collected data; a means for performing demand forecasts using the generative AI based on the preprocessed data; a means for saving promotional content generated for the identified target market; and a means for managing customer support based on the saved information. This enables efficient marketing activities for people who have difficulty finding employment, and enables highly accurate demand forecasts and effective promotional activities using the generative AI. Furthermore, automating customer support enables efficient use of human resources.

[0140] "Generative AI" is a technology that uses artificial intelligence to automatically generate content such as text, images, and videos.

[0141] "Demand forecasting" is the process of predicting future consumer demand based on collected data.

[0142] A "target market" refers to the region or consumer group that is predicted to have the greatest demand for a particular product or service.

[0143] A "market expansion strategy" is a plan or strategy for effectively promoting and selling a product or service to a specific market.

[0144] A "prompt" is an instruction used as input to a generative AI model that determines the content to be generated.

[0145] "Preprocessing" is the process of formatting data into an analyzable format, including filling in missing values ​​and removing outliers.

[0146] "Promotional Content" refers to content such as advertising text, images, and videos used to promote products and services.

[0147] "Customer interaction management" is the process of efficiently handling, recording, and managing customer inquiries and feedback.

[0148] This invention is a system to support the economic independence of people who have difficulty finding employment, such as the disabled, the elderly, those who have experienced social withdrawal, and those serving time in prison. It utilizes generative AI to match companies and brands with influencers, achieving effective marketing, expanding sales channels, and customer support. This system is operated mainly by three elements: a server, a terminal, and a user.

[0149] Server Roles and Operations

[0150] Market and trend data collection

[0151] The server uses a web crawler to collect online shopping data and social media trend data. Specifically, it uses Python's BeautifulSoup library to collect product sales data from multiple online shopping sites and the Twitter API to retrieve tweets related to specific keywords.

[0152] Data Preprocessing

[0153] The server cleanses the collected data and converts it into an analyzable format. It uses the pandas library to process missing values ​​and remove outliers. The collected data is then formatted as time series data.

[0154] Perform demand forecasts

[0155] The server uses a generative AI model to forecast demand. For example, it uses a time series analysis model on Amazon SageMaker to input preprocessed data into the model and predict future demand. An example of a prompt is, "Please analyze sales data for air conditioning equipment for the past six months and provide a demand forecast for next month."

[0156] Identifying your target market

[0157] The server identifies target markets based on the demand forecast results and develops a sales expansion strategy. It visualizes the forecast data using Microsoft Power BI to identify regions and consumer segments with the highest demand. As a specific example, it identifies high demand in Okinawa Prefecture.

[0158] Promotional content generation

[0159] The server uses generative AI to automatically generate ad text, images, and videos. For example, OpenAI's GPT-4 is used to input a prompt such as "Generate ad text and images for air conditioning equipment for the summer" and obtain the generated content. For example, ad text such as "Buy now and enjoy a comfortable summer!" and images and videos of air conditioning equipment with an Okinawa beach in the background are generated.

[0160] Data storage

[0161] The server stores the generated content and analysis data in a database, such as Amazon RDS, to store the generated ad text, images, videos, and demand forecast data for easy access in subsequent processing.

[0162] Terminal roles and processing

[0163] Automating customer interactions

[0164] The device uses generative AI to build a chatbot for customer support. Specifically, it uses Dialogflow to set up a dialogue flow that can immediately respond to customer questions based on the results of natural language processing by the generative AI.

[0165] Customer interaction management

[0166] The device responds to customer inquiries instantly using automatically generated responses and records the information in a database. Specifically, it automatically records the content of customer interactions using Salesforce.

[0167] User Roles and Actions

[0168] Business execution

[0169] Users (employees facing employment difficulties) download the promotional content generated by the server and use it in their marketing activities. Specifically, they print out the generated posters and distribute them locally.

[0170] Support for marketing activities

[0171] Users can use the generated advertising text and images to promote their products on social media. Specifically, they log in to their social media accounts and post advertising text and images to appeal to their followers.

[0172] Following AI instructions

[0173] The user carries out the sales expansion strategy recommended by the generation AI, specifically by planning and executing promotional activities in the target areas specified by the AI.

[0174] This system will enable people who have difficulty finding employment to carry out efficient marketing activities, enabling companies and brands to sustainably provide competitive products to the market. Furthermore, automating customer support will enable efficient use of human resources.

[0175] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0176] Step 1: Data collection

[0177] The server uses a web crawler to collect online shopping data and social media trend data. Specifically, it uses Python's BeautifulSoup library to collect product sales data from multiple online shopping sites and uses the Twitter API to obtain tweets related to specific keywords. The inputs are the "URL of the online shopping site" and the "Twitter API key," and the outputs are the "collected online shopping data" and the "collected social media data."

[0178] Step 2: Data Preprocessing

[0179] The server cleanses the collected data and converts it into an analyzable format. Specifically, it uses the pandas library to fill in missing values ​​and remove outliers. It then converts the collected data into time series data. The input is the collected data, and the output is the preprocessed data.

[0180] Step 3: Demand forecast

[0181] The server uses a generative AI model to perform demand forecasting. For example, it uses a time series analysis model on Amazon SageMaker to predict future demand based on preprocessed data. An example prompt is: "Analyze sales data for air conditioning equipment over the past six months and provide a demand forecast for next month." The inputs are "preprocessed data" and "prompt," and the output is "demand forecast data."

[0182] Step 4: Identify your target market

[0183] The server identifies target markets based on the demand forecast results and develops a sales expansion strategy. Specifically, it uses Microsoft Power BI to visualize the forecast data and identify the regions and consumer segments with the highest demand. The input is "demand forecast data," and the output is "identified target market data" and a "sales expansion strategy proposal."

[0184] Step 5: Generate promotional content

[0185] The server uses generative AI to automatically generate advertising text, images, and videos. Specifically, it uses OpenAI's generative AI to input a prompt such as "Please generate advertising text and images for air conditioning equipment for the summer" and obtains the generated content. The inputs are the generative AI model and the prompt, and the outputs are the generated advertising text and generated advertising images and videos.

[0186] Step 6: Save your data

[0187] The server stores the generated content and analysis data in a database. Specifically, it uses Amazon RDS to store the generated ad text, images, videos, demand forecast data, etc. The input is the "generated ad content" and "demand forecast data," and the output is the "stored data."

[0188] Step 7: Automate customer interactions

[0189] The device uses generative AI to build a chatbot for customer support. Specifically, it uses Dialogflow to set up a dialogue flow that can immediately respond to customer questions based on the results of natural language processing by the generative AI. The inputs are the generative AI model and customer question data, and the output is the chatbot's dialogue content.

[0190] Step 8: Manage customer interactions

[0191] The terminal responds immediately to customer inquiries using automatically generated responses and records the information in a database. Specifically, it automatically records the content of interactions with customers using Salesforce. The inputs are the "chatbot dialogue content" and "customer inquiry data," and the output is "recorded customer response data."

[0192] Step 9: Implement marketing activities

[0193] Users download the promotional content generated by the server and use it in their marketing activities. Specifically, they print out the generated posters and distribute them locally. They also log in to SNS and post the generated advertising text and images. The input is the "generated promotional content" and the output is the "results of the marketing activities."

[0194] (Application example 1)

[0195] 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."

[0196] The goal is to provide a system that enables people who have difficulty finding employment, such as people with disabilities, the elderly, those who have experienced social withdrawal, and those serving time in prison, to become financially independent and effectively participate in marketing activities. However, conventional systems do not adequately collect market data, forecast demand, or generate advertising content, making it difficult to identify target markets and develop sales channel expansion strategies. Furthermore, there is a lack of means to support social media promotions that effectively utilize the generated advertising content. As a result, it has been difficult for people with employment difficulties to effectively engage in marketing activities.

[0197] 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.

[0198] In this invention, the server includes means for analyzing demand trends using a generation AI, means for identifying a target market based on demand forecast data and formulating a sales channel expansion strategy, means for automatically generating advertising text, images, and videos using the generation AI, means for supporting marketing activities using the automatically generated advertising content, means for automating customer support using the generation AI, means for acquiring market data and generating region-specific advertising text using an application installed on a smartphone, means for forecasting demand based on online shopping data and social media trend data, and means for conducting promotions on social media using the generated advertising content. This enables people with employment difficulties to conduct marketing activities easily and efficiently, and companies to effectively deliver competitive products to the market.

[0199] "Generative AI" is an artificial intelligence technology that uses machine learning and natural language processing to automatically generate content such as text, images, and videos.

[0200] "Demand tendencies and trends" refer to the direction of demand and trends for a product within a specific market or consumer group over a certain period of time.

[0201] "Demand forecast data" is data that predicts future demand based on past market data and trend data.

[0202] A "target market" refers to a group of potential customers or a geographic area for a particular product or service.

[0203] A "market expansion strategy" is a plan or method for increasing sales in existing and new markets.

[0204] "Advertising text" is written content used to promote a product or service.

[0205] An "image" is any digital or printed visual content intended to convey visual information.

[0206] "Video" is a media format that provides dynamic visual information by playing multiple video frames in succession.

[0207] "Automatically generated advertising content" refers to advertising content, such as text, images, and videos, that are automatically created using generative AI.

[0208] "Marketing activities" refers to all strategic efforts to increase awareness of and promote sales of products and services.

[0209] "Customer service" refers to the work of responding to and supporting inquiries and complaints from customers.

[0210] An "application installed on a smartphone" is a software program that can be downloaded and used on a smartphone device.

[0211] "Market Data" means data about sales, consumer trends, competitor activities, etc. in a particular market.

[0212] "Localized ad text" is advertising text that focuses on a specific region and is tailored to the characteristics and needs of that region.

[0213] "Online shopping data" refers to data related to purchasing activities on the Internet.

[0214] "Social media trend data" refers to data on the popularity of topics and keywords on social media.

[0215] "SNS promotion" is a marketing technique that uses social networking services to promote products and services.

[0216] Server Roles and Operations

[0217] Demand forecasting

[0218] The server uses generative AI to collect and analyze market data and social media trend data to forecast demand for specific regions and consumer segments. Specifically, it builds a demand forecasting model based on online shopping data and social media trend data, and makes predictions using prompts such as:

[0219] "Please forecast the demand for air conditioning equipment in Okinawa Prefecture for the next three months."

[0220] Market Targeting

[0221] The server identifies target markets based on the demand forecast results and develops sales expansion strategies. For example, if it analyzes demand forecast data and determines that demand for air conditioning equipment will increase in Okinawa Prefecture, it will set Okinawa Prefecture as the target market and develop a region-specific marketing strategy.

[0222] Content Generation

[0223] The server uses generative AI to automatically generate promotional content such as ad text, images, and videos, using prompts such as:

[0224] "Generate advertising text for air conditioning equipment for Okinawa Prefecture."

[0225] Data storage

[0226] The server stores the generated demand forecast data, target market information, advertising content, etc. in a database and makes them accessible as needed.

[0227] Terminal roles and processing

[0228] Automating customer interactions

[0229] The device uses generative AI to build chatbots for customer service, such as providing immediate responses to customer questions about how to install air conditioners or the return procedure.

[0230] Customer interaction management

[0231] The terminal responds immediately to customer inquiries using automatically generated answers and records the information in a database, which allows for the management of customer response history.

[0232] User Roles and Actions

[0233] Business execution

[0234] Users (employees facing employment difficulties) download the promotional content generated by the server and use it in actual marketing activities, such as printing out posters and distributing them locally.

[0235] Support for marketing activities

[0236] The user can then use the generated advertising text and images to promote the product on social media. For example, the user can post the generated advertising image on social media to promote the product to their followers.

[0237] Following AI instructions

[0238] The user carries out operations in accordance with the sales expansion strategy recommended by the generation AI, for example, by planning and executing sales promotion activities in target areas as instructed by the AI.

[0239] Hardware and software used

[0240] The system uses the following hardware and software:

[0241] Hardware:

[0242] Servers, smartphones, terminals

[0243] software:

[0244] Generative AI API (e.g. OpenAI API)

[0245] HTTP request library (e.g., requests)

[0246] Specific examples

[0247] 1. Example prompt

[0248] "Please forecast the demand for air conditioning equipment in Okinawa Prefecture for the next three months."

[0249] "Generate advertising text for air conditioning equipment for Okinawa Prefecture."

[0250] This system allows people who have difficulty finding employment to easily and efficiently carry out marketing activities, and also enables companies to effectively deliver competitive products to the market.

[0251] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0252] Step 1:

[0253] The server collects online shopping data and social media trend data, which is obtained through an API.

[0254] Input: Online shopping data, social media trend data

[0255] Output: Collected market data

[0256] This step involves retrieving data from specific API endpoints and storing it in an internal database.

[0257] Step 2:

[0258] The server uses a generative AI model to forecast demand based on collected market data. The collected data is input into the model to generate a demand forecast.

[0259] Input: Collected market data, prompt: "Please forecast the demand for air conditioning equipment in Okinawa Prefecture for the next three months."

[0260] Output: Demand forecast data

[0261] In this step, the generative AI model is run and predicts results based on the prompt text.

[0262] Step 3:

[0263] The server identifies target markets based on demand forecast data and develops regionally specific sales channel expansion strategies.

[0264] Input: Demand forecast data

[0265] Output: Target market information, sales channel expansion strategy

[0266] In this step, the predictive data is analyzed to plan the optimal target market and effective marketing strategy.

[0267] Step 4:

[0268] The server uses generative AI to automatically generate advertising content such as ad text, images, and videos.

[0269] Input: Target market information, prompt "Generate advertising text for air conditioning equipment for Okinawa Prefecture."

[0270] Output: Ad text, images, videos

[0271] In this step, generative AI models are used to create advertising content tailored to a specified target market.

[0272] Step 5:

[0273] The server stores the generated advertising content in a database and makes it accessible to terminals and users as needed.

[0274] Input: Ad text, images, video

[0275] Output: Ad content stored in a database

[0276] In this step, the generated content is stored in a database for immediate access.

[0277] Step 6:

[0278] The device uses generative AI to build chatbots for customer interaction.

[0279] Input: Generative AI model, FAQ data

[0280] Output: Customer service chatbot

[0281] In this step, a generative AI model is used based on FAQ data to build a chatbot that provides immediate responses to customer questions.

[0282] Step 7:

[0283] The terminal records the history of customer interactions in a database.

[0284] Input: Customer inquiry, chatbot response

[0285] Output: Recorded customer interaction history

[0286] In this step, customer interactions are saved in a database for future reference.

[0287] Step 8:

[0288] The user downloads the generated promotional content from the server and uses it in actual marketing activities.

[0289] Input: Ad text, images, and videos downloaded from the database

[0290] Output: Marketing activities carried out (poster distribution, social media posts, etc.)

[0291] In this step, the user performs specific marketing activities using the generated advertising content.

[0292] Step 9:

[0293] The user uses the generated advertising content to promote the product on the SNS.

[0294] Input: Ad text, images, video

[0295] Output: Social media posts, follower reactions

[0296] In this step, the user posts the generated advertising content on the social networking site to promote the product to their followers.

[0297] Step 10:

[0298] Users carry out their work in accordance with the sales expansion strategy recommended by the generation AI.

[0299] Input: Sales channel expansion strategy

[0300] Output: Market expansion activities carried out

[0301] In this step, users plan and execute marketing activities based on AI recommendations.

[0302] 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.

[0303] This invention is a system that uses generative AI to forecast demand, identify target markets, automatically generate advertising content, support marketing activities, and automate customer support. Furthermore, by combining it with an emotion engine that recognizes user emotions, it adds the ability to analyze user emotions and generate responses and content based on them.

[0304] Server Roles and Operations

[0305] Data collection and preprocessing

[0306] The server collects online shopping data and social media trend data, and performs data cleansing, missing value imputation, and outlier detection.

[0307] Example: A server uses web scraping tools and APIs to collect online shopping data and social media trend data for the past six months.

[0308] Demand forecasting

[0309] The server applies machine learning models to the pre-processed data to predict future demand.

[0310] Example: A server uses an ARIMA model and an LSTM network to forecast demand for air conditioning equipment in summer.

[0311] Target market identification and strategy planning

[0312] The server identifies target markets based on demand forecast data and develops regionally specific sales expansion strategies.

[0313] Example: The server identifies an area where demand for air conditioners is high (e.g., Okinawa Prefecture) and plans an advertising campaign for that area.

[0314] Content Generation

[0315] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos.

[0316] Example: The server generates advertising text such as "Buy now and enjoy a comfortable summer!" and images and videos of air conditioning equipment with a beach in Okinawa in the background.

[0317] Emotion Engine Analysis

[0318] The server uses an emotion engine to analyze the user's emotions and generate responses and content based on those emotions.

[0319] Example: A server performs text analysis of a user's comments and feedback to determine whether the user is expressing positive or negative sentiment.

[0320] Terminal roles and processing

[0321] Chatbot Settings

[0322] The device uses generative AI to set up a chatbot that has learned common questions and their answers.

[0323] Example: The terminal prepares answers to questions about how to install a cooling unit or the return process.

[0324] Automating customer interactions

[0325] The terminal uses a chatbot to respond immediately to customer inquiries.

[0326] Example: The device responds to inquiries about how to use air conditioning equipment with an AI chatbot that provides accurate answers.

[0327] User Roles and Actions

[0328] Business execution

[0329] Users (employees who are having difficulty finding work) download the promotional content generated by the server and use it in their actual marketing activities.

[0330] Example: The user prints the generated posters and distributes them in the designated area. The user also promotes the posters on social media.

[0331] Sentiment Analysis Feedback

[0332] Users receive the results of customer sentiment analysis provided through the emotion engine and reflect them in their marketing strategies and customer service.

[0333] Example: Users consider rewards and promotions when customers express positive sentiment.

[0334] conclusion

[0335] By utilizing generative AI and an emotion engine, this invention makes it possible for people with employment difficulties to easily carry out marketing activities and customer service. This allows companies and nonprofit organizations to operate their businesses efficiently and effectively, increasing their sustainability. Based on specific processing steps, this system provides comprehensive and effective support.

[0336] The processing flow will be explained below.

[0337] Step 1:

[0338] Data collection

[0339] The server collects online shopping data, social media trend data, and more.

[0340] Specifically, data is automatically obtained using web scraping tools and APIs.

[0341] Step 2:

[0342] Data Preprocessing

[0343] The server cleanses the collected data, filling in missing values ​​and detecting outliers.

[0344] For example, importing data into an Excel spreadsheet or database and correcting inconsistent data.

[0345] Step 3:

[0346] Demand forecasting

[0347] The server analyzes the pre-processed data and uses machine learning models to predict demand.

[0348] For example, ARIMA models and LSTM networks are used to forecast future demand.

[0349] Step 4:

[0350] Saving prediction results

[0351] The server stores the prediction results in a database, making them available for subsequent processing steps.

[0352] Step 5:

[0353] Identifying your target market

[0354] The server identifies the optimal target market based on the demand forecast data.

[0355] For example, identify areas where demand for air conditioning equipment will increase (e.g., Okinawa Prefecture).

[0356] Step 6:

[0357] Planning sales channel expansion strategies

[0358] The server develops a sales expansion strategy for the identified target market.

[0359] For example, design an advertising campaign specifically for Okinawa Prefecture.

[0360] Step 7:

[0361] Content Generation

[0362] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos.

[0363] Specifically, it generates advertising text such as "Buy now and enjoy a comfortable summer!" and images and videos of air conditioning equipment with an Okinawa beach in the background.

[0364] Step 8:

[0365] Content Storage

[0366] The server stores the generated advertising text, images and videos in a database for users to access.

[0367] Step 9:

[0368] Emotion analysis

[0369] The server uses an emotion engine to analyze the user's emotions and generate responses and content based on those emotions.

[0370] Specifically, it performs text analysis of user comments and feedback to determine whether the user is expressing positive or negative sentiment.

[0371] Step 10:

[0372] Chatbot Settings

[0373] The device uses generative AI to set up a chatbot that has learned common questions and their answers.

[0374] Specifically, prepare answers to questions about how to install air conditioning equipment and the return procedure.

[0375] Step 11:

[0376] Automating customer interactions

[0377] The terminal uses a chatbot to respond to customer inquiries instantly.

[0378] For example, an AI chatbot can provide accurate answers to inquiries about how to use air conditioning equipment.

[0379] Step 12:

[0380] Content Use

[0381] The user downloads the generated promotional content and uses it in marketing activities.

[0382] Specifically, the generated posters are printed and distributed in the area.

[0383] Step 13:

[0384] Marketing Activities

[0385] Users can promote themselves on social media using advertising text and images generated by the server.

[0386] For example, the generated advertising image can be posted on social media to promote the product to followers.

[0387] Step 14:

[0388] Feedback collection

[0389] The user collects feedback from customers and sends it to the server.

[0390] For example, record customer opinions obtained through social media comments and messages.

[0391] Step 15:

[0392] Reanalyzing Data

[0393] The server analyzes the collected feedback data and uses it to forecast demand and develop marketing strategies for the next time.

[0394] For example, identifying areas for improvement in products and services based on customer feedback.

[0395] Step 16:

[0396] Optimizing emotional response

[0397] The server updates and optimizes appropriate emotional responses to customer feedback and inquiries based on the emotion engine.

[0398] For example, if a customer expresses negative feelings, offer an appropriate apology and suggest improvements.

[0399] This series of processing steps enables people who have difficulty finding employment to carry out efficient marketing activities, and companies and NPOs to achieve sustainable and effective business operations.In addition, the introduction of an emotion engine enables detailed responses based on customer emotions, and is expected to improve customer satisfaction.

[0400] Example 2

[0401] 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."

[0402] Traditional marketing systems required a significant amount of time and human resources to forecast demand, identify target markets, and generate advertising content, resulting in low operational efficiency. They also struggled to provide immediate customer service, making it difficult to improve customer satisfaction. Furthermore, they were unable to understand employee sentiment and generate marketing strategies and content based on that sentiment.

[0403] 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.

[0404] In this invention, the server includes a means for analyzing demand tendencies and trends using a generation AI, a means for identifying a target market based on demand forecast data and formulating a sales channel expansion strategy, a means for automatically generating advertising text, images, and videos using the generation AI, and a means for analyzing user emotions using an emotion engine and generating responses and content based on the analysis results, thereby enabling improved accuracy of demand forecasts, more efficient marketing strategies, faster generation of advertising content, faster customer responses, and emotion-based marketing activities.

[0405] "Generative AI" is an artificial intelligence technology that uses generative models to automatically generate content such as text, images, and videos.

[0406] "Demand tendency and trend analysis" refers to the use of past data to predict future demand and clarify its fluctuation patterns.

[0407] "Demand forecast data" is data on future demand predicted using machine learning models and analytical methods.

[0408] "Target market identification" refers to identifying the consumer market with the greatest demand or interest for a particular product or service.

[0409] A "sales channel expansion strategy" is a specific plan or method for expanding product distribution channels and widening the customer base.

[0410] "Automatic generation of advertising text, images, and videos" refers to the use of generative AI to automatically create text, graphics, and video for advertising.

[0411] "Support for marketing activities" means using the generated advertising content to support marketing initiatives such as market research, promotions, and sales promotions.

[0412] "Customer response automation" is a system that uses generative AI to provide instant answers to customer inquiries.

[0413] An "emotion engine" is a technology that analyzes a user's emotional state from text and voice data and generates responses and content based on the results.

[0414] "Sentiment analysis" is the process of determining the type and intensity of emotions from user comments and feedback.

[0415] This invention is a system that uses generative AI to forecast demand, identify target markets, automatically generate advertising content, support marketing activities, and automate customer support. Furthermore, by combining it with an emotion engine that recognizes user emotions, it adds the ability to analyze user emotions and generate responses and content based on them.

[0416] Server Roles and Operations

[0417] Data collection and preprocessing

[0418] The server collects online shopping data and social media trend data using APIs and web scraping tools. Specifically, it uses the Twitter API to collect tweets related to air conditioning equipment for the past six months and collects air conditioning equipment sales trend data through the APIs of online shopping sites. The server then cleanses this data, imputes missing values, and detects outliers. Missing data is imputed with the mean or median, and abnormally high or low values ​​are filtered out.

[0419] Demand forecasting

[0420] The server converts the cleansed data into a format suitable for machine learning models. Specifically, it converts the data into a time series format and standardizes features (e.g., date, sales volume, social media trend index). It then trains an ARIMA model or LSTM network to forecast future demand. For example, it predicts demand for air conditioning equipment in the summer of 2023 and estimates when peak demand will occur.

[0421] Target market identification and strategy planning

[0422] The server analyzes the predicted demand data and identifies target markets. For example, it predicts demand for air conditioning equipment in each region of Japan and determines that a specific region (e.g., Okinawa Prefecture) will have high demand. It then develops a sales expansion strategy optimized for that region. Specifically, it plans outdoor advertising and a region-specific online advertising campaign in Okinawa Prefecture.

[0423] Content Generation

[0424] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos. For example, it generates a catchphrase such as "Buy now and enjoy a comfortable summer!" and an advertising image with the beautiful beaches of Okinawa as the background.

[0425] Emotion Engine Analysis

[0426] The server uses an emotion engine to analyze users' emotions. Specifically, it collects customer reviews and comments on social media and analyzes the user's emotional state from text and voice data. For example, it identifies areas that need improvement based on negative comments, and identifies areas that need further strengthening based on positive comments.

[0427] Terminal roles and processing

[0428] Chatbot Settings

[0429] The device uses a generation AI to set up a chatbot that has learned frequently asked questions and their answers. Specifically, the generation AI creates FAQs about air conditioning equipment (e.g., installation methods, return procedures), and the chatbot learns them.

[0430] Automating customer interactions

[0431] The terminal uses a chatbot to respond to customer inquiries immediately. For example, an AI chatbot can provide an accurate answer to a question about how to use an air conditioner.

[0432] User Roles and Actions

[0433] Business execution

[0434] Users (employees facing employment difficulties) download the promotional content generated by the server and use it in actual marketing activities. For example, the user prints out the generated posters and distributes them in a designated area (e.g., Okinawa Prefecture).

[0435] Sentiment Analysis Feedback

[0436] Users can use the analysis results of the emotion engine to reflect them in their marketing strategies and customer service. For example, they can offer rewards to customers who express positive emotions and take measures to improve their customer service in response to negative comments.

[0437] Prompt Sentence Examples

[0438] Using a generative AI model, we present an example of a prompt sentence for predicting demand for air conditioning equipment in the summer.

[0439] Prompt statement

[0440] "Based on online shopping data and social media trend data from the past six months, please predict demand for air conditioning equipment for the summer of 2023."

[0441] As a result, this system enables improved accuracy in demand forecasting, more efficient marketing strategies, faster generation of advertising content, faster customer response, and emotion-based marketing activities.

[0442] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0443] Step 1:

[0444] Data collection and preprocessing

[0445] The server collects online shopping data and social media trend data using APIs and web scraping tools. The input data is online shopping sales data and social media post data from the past six months. The server cleanses the collected data, imputes missing values, and detects outliers. For example, missing data is imputed with the mean or median, and abnormally high or low values ​​are filtered. The output is a cleansed dataset.

[0446] Step 2:

[0447] Data preparation

[0448] The server converts the cleansed data into a format that can be applied to a machine learning model. The input data is the cleansed data generated in step 1. The server converts the data into a time series format and standardizes the features (e.g., date, sales volume, social media trend index). The output is a dataset converted into a format that can be applied to a machine learning model.

[0449] Step 3:

[0450] Training a demand forecasting model

[0451] The server trains a demand forecasting model using the training data. The input data is the training data generated in step 2. The server performs training using an ARIMA model or an LSTM network. Specifically, it optimizes the model parameters using past sales data and social media trend data. The output is a trained demand forecasting model.

[0452] Step 4:

[0453] Perform demand forecasts

[0454] The server uses the trained model to predict future demand. The input data is real-time or the latest sales data and social media trend data. The server inputs this data into the model to predict future demand. For example, it predicts demand for air conditioning equipment in the summer of 2023 and estimates when demand will peak. The output is predicted demand data.

[0455] Step 5:

[0456] Target market identification and strategy planning

[0457] The server analyzes the forecasted demand data by region and identifies target markets. The input data is the demand forecast data obtained in step 4. The server identifies regions where demand will increase and plans an optimal sales expansion strategy for those regions. For example, it plans outdoor advertising in Okinawa Prefecture and a region-specific online advertising campaign. The output is a target market list and a marketing strategy document.

[0458] Step 6:

[0459] Automatic generation of advertising content

[0460] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos. The input data is the marketing strategy document formulated in Step 5. Specifically, it generates a catchphrase such as "Buy now and enjoy a comfortable summer!" and an advertising image with Okinawa's beautiful beaches as the background. The output is the automatically generated advertising content.

[0461] Step 7:

[0462] Automating customer interactions

[0463] The device uses a generation AI to set up a chatbot that has learned frequently asked questions and their answers. The input data is an FAQ database and generated customer response templates. As a specific example, an answer to a question about how to install air conditioning equipment is prepared. The output is the trained chatbot.

[0464] Step 8:

[0465] Customer service using chatbots

[0466] The terminal uses a trained chatbot to immediately respond to customer inquiries. The input data is the customer's inquiry. The terminal provides an appropriate answer to the customer's question through the chatbot. For example, an accurate answer is provided to an inquiry about how to use air conditioning equipment. The output is the answer provided to the customer and the response history.

[0467] Step 9:

[0468] Conducting sentiment analysis

[0469] The server uses an emotion engine to analyze the user's emotions. The input data is customer reviews and comments on social media. The server analyzes the user's emotional state (positive, negative, etc.) from text and voice data. The output is the analyzed emotion data.

[0470] Step 10:

[0471] Sentiment Analysis Feedback

[0472] The user utilizes the analysis results of the emotion engine to reflect them in marketing strategies and customer service. The input data is the analyzed emotion data obtained in step 9. The user provides rewards to customers who show positive emotions and takes measures to improve negative opinions. The output is an improved marketing strategy and customer service policy.

[0473] (Application example 2)

[0474] 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."

[0475] In conventional marketing activities, it has been difficult to centrally perform demand forecasting, target market identification, advertising content generation, and efficient customer response. This has resulted in companies being unable to quickly develop effective advertising campaigns, and despite the need for rapid responses based on customer feedback, it has been difficult to do so. Another issue is the inability to effectively automatically generate and distribute specific advertising campaigns based on data collected using devices such as smartphones.

[0476] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0477] In this invention, the server includes means for analyzing demand tendencies and trends using a generation AI, means for identifying target markets based on demand forecast data and formulating sales channel expansion strategies, means for automatically generating advertising text, images, and videos using the generation AI, means for automatically generating and distributing advertising campaigns based on data collected via a smartphone application, and means for analyzing user sentiment and providing responses and content based on the analysis results. This enables efficient and centralized execution of a wide range of marketing activities, enabling companies to develop rapid and effective advertising campaigns and respond quickly to customer feedback.

[0478] "Generative AI" is artificial intelligence that creates new data and content using techniques such as generative adversarial networks (GANs) and transformer models.

[0479] "Demand forecasting" is the process of analyzing past data and trends to predict future consumer demand.

[0480] A "target market" is a group of consumers to whom a particular product or service should be promoted.

[0481] "Advertising Text" means text containing a specific marketing message used in an advertising campaign.

[0482] An "image" is a still image intended to convey visual information.

[0483] "Video" is a moving image intended to convey dynamic visual information.

[0484] A "marketing activity" is a set of strategies and actions taken to promote a product or service.

[0485] "Customer service" refers to the act of providing information in response to customer inquiries and questions and meeting their requests.

[0486] A "smartphone application" is software that runs on a smartphone and performs a specific task.

[0487] "Emotion analysis" is a technology that detects and analyzes a user's emotions from text or voice.

[0488] A "response" is a reaction or answer given in response to an input or inquiry from a user.

[0489] "Content" means text, images, video, and other materials used to advertise or convey information.

[0490] The present invention relates to a system for automatically generating and delivering advertising campaigns via a smartphone application. The system utilizes generative AI and sentiment analysis engines to forecast demand, identify target markets, generate advertising content, and automate customer interactions.

[0491] Server Roles and Operations

[0492] Data collection and preprocessing

[0493] The server uses APIs and web scraping to collect online shopping data and social media trend data, and then performs data cleansing, missing value imputation, and outlier detection. As a specific example, online shopping data and social media trend data from the past six months are collected.

[0494] Demand forecasting

[0495] The server applies machine learning models (such as ARIMA models and LSTM networks) to the preprocessed data to predict future demand, such as predicting demand for air conditioning equipment in the summer.

[0496] Target market identification and strategy planning

[0497] The server identifies target markets based on demand forecast data and develops region-specific sales expansion strategies. For example, it identifies areas where demand for air conditioning equipment is increasing and plans an advertising campaign for those areas.

[0498] Generative AI-based advertising content generation

[0499] The server uses generation AI (e.g., OpenAI API) to automatically generate promotional content such as advertising text, images, and videos. An example of a specific prompt is, "Please generate an advertisement for a summer air conditioner. Please create a text advertisement with an image of an air conditioner against the backdrop of a beach in Okinawa."

[0500] Emotion analysis

[0501] The server uses a sentiment analysis engine to analyze the user's sentiment and provides responses and content based on the results. It also performs text analysis of the user's comments and feedback to determine whether the sentiment is positive or negative.

[0502] Terminal roles and processing

[0503] Chatbot Settings

[0504] The device uses generative AI to set up a chatbot that is trained on frequently asked questions and their answers, and is ready to answer questions about how to install a cooling unit or the return process.

[0505] Automating customer interactions

[0506] The terminal uses an AI chatbot to respond to customer inquiries immediately, for example, by providing accurate answers to inquiries about how to use air conditioning equipment.

[0507] User Roles and Actions

[0508] Business execution

[0509] Users download the promotional content generated by the server and use it in their actual marketing activities. They print the generated posters and distribute them in designated areas. They also promote the content on social media.

[0510] Sentiment Analysis Feedback

[0511] Users can receive the results of customer sentiment analysis provided by the emotion engine and reflect them in their marketing strategies and customer service. For example, if a customer expresses positive sentiment, they can consider offering special offers or promotions.

[0512] conclusion

[0513] This invention provides a system that efficiently automates the generation and distribution of advertising campaigns via smartphone applications by utilizing generative AI and a sentiment analysis engine. This system centrally manages a wide range of marketing activities, enabling the rapid and effective development of advertising campaigns and customer responses.

[0514] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0515] Step 1:

[0516] The server collects online shopping data and social media trend data using APIs and web scraping. The data is obtained using the URL and API key provided as input and stored in internal storage. The specific data collected includes purchase history and social media posts from the past six months.

[0517] Step 2:

[0518] The server preprocesses the collected data. It uses the raw data stored in the internal storage as input and performs missing value completion, outlier detection and removal, and data cleansing. Specifically, it completes incomplete data records and removes records with clearly abnormal values. It also standardizes the data format to make it easier to analyze. After preprocessing is complete, the data is standardized and passed to the next step.

[0519] Step 3:

[0520] The server uses the preprocessed data to perform demand forecasting. It applies an ARIMA model or LSTM network to the time series data prepared as input to predict future demand. Specifically, it uses past purchase data as training data for the model and predicts demand values ​​for a certain period of time in the future. The forecast results are output as future demand trends.

[0521] Step 4:

[0522] The server identifies target markets based on demand forecast data and develops sales expansion strategies. Using the demand forecast results as input, it extracts regions and customer segments where demand will increase. Specifically, it calculates predicted demand values ​​for each region, identifies regions with high demand, and develops sales promotion strategies for those regions. The strategies developed here are used to plan advertising campaigns.

[0523] Step 5:

[0524] The server generates advertising content using generative AI. Using target market information and prompts as input, it instructs the generative AI model (e.g., OpenAI's API) to automatically generate advertising materials such as text, images, and videos. An example of a specific prompt is, "Please generate an advertisement for air conditioning equipment in the summer. Please create a text advertisement with an image of an air conditioning equipment with an Okinawa beach as the background." The generated advertising content is used in the next step.

[0525] Step 6:

[0526] The server creates an advertising campaign using automatically generated advertising content and distributes it via smartphones. Using the generated advertising content and target market data as input, the server designs the advertising campaign. Specifically, it plans content placement to distribute advertisements to specific user groups at appropriate times.

[0527] Step 7:

[0528] The server analyzes user feedback using a sentiment analysis engine. Using user comments and feedback data as input, the sentiment analysis model determines positive or negative sentiment. Specifically, it uses a text analysis algorithm to calculate the user's sentiment score and outputs the results as an analysis report.

[0529] Step 8:

[0530] The device uses a generative AI to set up a chatbot that has learned frequently asked questions and their answers. It uses an FAQ database and user inquiry data as input to provide the chatbot with learning data. Specifically, it generates answers to questions about how to install air conditioners and return procedures, and sets up the chatbot to automatically respond.

[0531] Step 9:

[0532] The terminal uses an AI chatbot to instantly respond to customer inquiries. Using the user's inquiry as input, the pre-configured chatbot automatically provides an accurate answer to the inquiry. Specifically, it returns an accurate answer to an inquiry about how to use air conditioning equipment.

[0533] Step 10:

[0534] The user downloads the generated promotional content from the server and uses it in actual marketing activities. The generated promotional content is used as input to print posters and carry out promotional activities on social media. Specifically, the generated advertising posters are distributed in designated areas and campaign posts are made on social media.

[0535] Step 11:

[0536] Users receive customer sentiment analysis results provided through the sentiment engine and reflect them in marketing strategies and customer interactions. Using the sentiment analysis results as input, users can plan rewards and promotions for positive customers. Specifically, strategies such as offering special discounts to customers who provide positive feedback can be implemented.

[0537] 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.

[0538] 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.

[0539] 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.

[0540] [Second embodiment]

[0541] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0542] 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.

[0543] 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).

[0544] 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.

[0545] 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.

[0546] 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).

[0547] 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.

[0548] 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.

[0549] 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.

[0550] 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.

[0551] 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.

[0552] 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."

[0553] This invention is a system to support the economic independence of people who have difficulty finding employment, such as the disabled, the elderly, those who have experienced social withdrawal, and those serving time in prison, and it utilizes generative AI to match companies and brands with influencers, achieving effective marketing, expanding sales channels, and customer support. This system is operated mainly by three elements: a server, a terminal, and a user.

[0554] Server Roles and Operations

[0555] Demand forecasting

[0556] The server uses generative AI to collect and analyze market data and social media trend data to predict demand for specific regions and consumer segments.

[0557] Example: A server analyzes online shopping data and social media trend data from the past six months to predict increased demand for air conditioning equipment in the summer.

[0558] Market Targeting

[0559] The server identifies the target market based on the demand forecast results and develops a sales channel expansion strategy.

[0560] Example: A server designs a geo-specific advertising campaign to target the growing demand for air conditioning equipment in Okinawa Prefecture.

[0561] Content Generation

[0562] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos.

[0563] Example: The server generates advertising text such as "Buy now and enjoy a comfortable summer!" and images and videos of air conditioning equipment with a beach in Okinawa in the background.

[0564] Data storage

[0565] The server stores the generated demand forecast data, target market information, advertising content, etc. in a database and makes them accessible to the server.

[0566] Terminal roles and processing

[0567] Automating customer interactions

[0568] The device uses generative AI to build chatbots for customer service.

[0569] Example: The terminal provides instant responses to questions about how to install air conditioning equipment or the return procedure via an AI chatbot.

[0570] Customer interaction management

[0571] The terminal responds immediately to customer inquiries using automatically generated responses and records the information in a database.

[0572] User Roles and Actions

[0573] Business execution

[0574] Users (employees who are having difficulty finding work) download the promotional content generated by the server and use it in their actual marketing activities.

[0575] Example: A user prints the generated poster and distributes it in the community.

[0576] Support for marketing activities

[0577] Users can use the generated advertising text and images to promote their products on social media.

[0578] Example: A user posts the generated advertising image on social media to promote the product to their followers.

[0579] Following AI instructions

[0580] Users carry out their work in accordance with the sales expansion strategy recommended by the generation AI.

[0581] Example: The user plans and executes promotional activities in target areas as directed by AI.

[0582] conclusion

[0583] This system allows people who have difficulty finding employment to easily and efficiently carry out marketing activities. Furthermore, companies and NPOs can utilize the power of generative AI to effectively deliver competitive products to the market. We believe this system will make a significant contribution as a concrete means to improve the efficiency and sustainability of overall business operations.

[0584] The processing flow will be explained below.

[0585] Step 1:

[0586] Data collection

[0587] The server collects online shopping data, social media trend data, and more.

[0588] Specifically, data is automatically obtained using web scraping tools and APIs.

[0589] Step 2:

[0590] Data Preprocessing

[0591] The server cleanses the collected data, filling in missing values ​​and detecting outliers.

[0592] For example, importing data into an Excel spreadsheet or database and correcting inconsistent data.

[0593] Step 3:

[0594] Demand forecasting

[0595] The server analyzes the pre-processed data and uses machine learning models to predict demand.

[0596] For example, ARIMA models and LSTM networks are used to forecast future demand.

[0597] Step 4:

[0598] Saving prediction results

[0599] The server stores the prediction results in a database, making them available for subsequent processing steps.

[0600] Step 5:

[0601] Identifying your target market

[0602] The server identifies the optimal target market based on the demand forecast data.

[0603] For example, identify areas where demand for air conditioning equipment will increase (e.g., Okinawa Prefecture).

[0604] Step 6:

[0605] Planning sales channel expansion strategies

[0606] The server develops a sales expansion strategy for the identified target market.

[0607] For example, design an advertising campaign specifically for Okinawa Prefecture.

[0608] Step 7:

[0609] Content Generation

[0610] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos.

[0611] Specifically, it generates advertising text such as "Buy now and enjoy a comfortable summer!" and images and videos of air conditioning equipment with an Okinawa beach in the background.

[0612] Step 8:

[0613] Content Storage

[0614] The server stores the generated advertising text, images and videos in a database for users to access.

[0615] Step 9:

[0616] Chatbot Settings

[0617] The device uses generative AI to set up a chatbot that has learned common questions and their answers.

[0618] Specifically, prepare answers to questions about how to install air conditioning equipment and the return procedure.

[0619] Step 10:

[0620] Automating customer interactions

[0621] The terminal uses a chatbot to respond to customer inquiries immediately.

[0622] For example, an AI chatbot can provide answers to inquiries about how to use air conditioning equipment.

[0623] Step 11:

[0624] Content Use

[0625] The user downloads the generated promotional content and uses it in marketing activities.

[0626] Specifically, the generated posters are printed and distributed in the area.

[0627] Step 12:

[0628] Marketing Activities

[0629] Users can promote themselves on social media using advertising text and images generated by the server.

[0630] For example, the generated advertising image can be posted on social media to promote the product to followers.

[0631] Step 13:

[0632] Feedback collection

[0633] The user collects feedback from customers and sends it to the server.

[0634] For example, record customer opinions obtained through social media comments and messages.

[0635] Step 14:

[0636] Reanalyzing Data

[0637] The server analyzes the collected feedback data and uses it to forecast demand and develop marketing strategies for the next time.

[0638] For example, identifying areas for improvement in products and services based on customer feedback.

[0639] This series of processing steps enables people who have difficulty finding employment to carry out marketing activities efficiently, and companies and NPOs to achieve sustainable business operations.

[0640] Example 1

[0641] 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."

[0642] Existing marketing systems have the problem that it is difficult for people who have difficulty finding employment, such as people with disabilities, the elderly, people who have been socially withdrawn, and former prisoners, to carry out marketing activities efficiently. Furthermore, these systems are inadequate in market demand forecasting, targeting, and generating promotional content, making it difficult for companies and brands to effectively deliver products to the market. In addition, customer support is often done manually, requiring efficient use of human resources.

[0643] 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.

[0644] In this invention, the server includes: a means for analyzing demand trends using a generative AI; a means for identifying a target market based on demand forecast data and developing a sales channel expansion strategy; a means for automatically generating advertising text, images, and videos using the generative AI; a means for generating prompts for use with the generative AI model; a means for preprocessing collected data; a means for performing demand forecasts using the generative AI based on the preprocessed data; a means for saving promotional content generated for the identified target market; and a means for managing customer support based on the saved information. This enables efficient marketing activities for people who have difficulty finding employment, and enables highly accurate demand forecasts and effective promotional activities using the generative AI. Furthermore, automating customer support enables efficient use of human resources.

[0645] "Generative AI" is a technology that uses artificial intelligence to automatically generate content such as text, images, and videos.

[0646] "Demand forecasting" is the process of predicting future consumer demand based on collected data.

[0647] A "target market" refers to the region or consumer group that is predicted to have the greatest demand for a particular product or service.

[0648] A "market expansion strategy" is a plan or strategy for effectively promoting and selling a product or service to a specific market.

[0649] A "prompt" is an instruction used as input to a generative AI model that determines the content to be generated.

[0650] "Preprocessing" is the process of formatting data into an analyzable format, including filling in missing values ​​and removing outliers.

[0651] "Promotional Content" refers to content such as advertising text, images, and videos used to promote products and services.

[0652] "Customer interaction management" is the process of efficiently handling, recording, and managing customer inquiries and feedback.

[0653] This invention is a system to support the economic independence of people who have difficulty finding employment, such as the disabled, the elderly, those who have experienced social withdrawal, and those serving time in prison. It utilizes generative AI to match companies and brands with influencers, achieving effective marketing, expanding sales channels, and customer support. This system is operated mainly by three elements: a server, a terminal, and a user.

[0654] Server Roles and Operations

[0655] Market and trend data collection

[0656] The server uses a web crawler to collect online shopping data and social media trend data. Specifically, it uses Python's BeautifulSoup library to collect product sales data from multiple online shopping sites and the Twitter API to retrieve tweets related to specific keywords.

[0657] Data Preprocessing

[0658] The server cleanses the collected data and converts it into an analyzable format. It uses the pandas library to process missing values ​​and remove outliers. The collected data is then formatted as time series data.

[0659] Perform demand forecasts

[0660] The server uses a generative AI model to forecast demand. For example, it uses a time series analysis model on Amazon SageMaker to input preprocessed data into the model and predict future demand. An example of a prompt is, "Please analyze sales data for air conditioning equipment for the past six months and provide a demand forecast for next month."

[0661] Identifying your target market

[0662] The server identifies target markets based on the demand forecast results and develops a sales expansion strategy. It visualizes the forecast data using Microsoft Power BI to identify regions and consumer segments with the highest demand. As a specific example, it identifies high demand in Okinawa Prefecture.

[0663] Promotional content generation

[0664] The server uses generative AI to automatically generate ad text, images, and videos. For example, OpenAI's GPT-4 is used to input a prompt such as "Generate ad text and images for air conditioning equipment for the summer" and obtain the generated content. For example, ad text such as "Buy now and enjoy a comfortable summer!" and images and videos of air conditioning equipment with an Okinawa beach in the background are generated.

[0665] Data storage

[0666] The server stores the generated content and analysis data in a database, such as Amazon RDS, to store the generated ad text, images, videos, and demand forecast data for easy access in subsequent processing.

[0667] Terminal roles and processing

[0668] Automating customer interactions

[0669] The device uses generative AI to build a chatbot for customer support. Specifically, it uses Dialogflow to set up a dialogue flow that can immediately respond to customer questions based on the results of natural language processing by the generative AI.

[0670] Customer interaction management

[0671] The device responds to customer inquiries instantly using automatically generated responses and records the information in a database. Specifically, it automatically records the content of customer interactions using Salesforce.

[0672] User Roles and Actions

[0673] Business execution

[0674] Users (employees facing employment difficulties) download the promotional content generated by the server and use it in their marketing activities. Specifically, they print out the generated posters and distribute them locally.

[0675] Support for marketing activities

[0676] Users can use the generated advertising text and images to promote their products on social media. Specifically, they log in to their social media accounts and post advertising text and images to appeal to their followers.

[0677] Following AI instructions

[0678] The user carries out the sales expansion strategy recommended by the generation AI, specifically by planning and executing promotional activities in the target areas specified by the AI.

[0679] This system will enable people who have difficulty finding employment to carry out efficient marketing activities, enabling companies and brands to sustainably provide competitive products to the market. Furthermore, automating customer support will enable efficient use of human resources.

[0680] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0681] Step 1: Data collection

[0682] The server uses a web crawler to collect online shopping data and social media trend data. Specifically, it uses Python's BeautifulSoup library to collect product sales data from multiple online shopping sites and uses the Twitter API to obtain tweets related to specific keywords. The inputs are the "URL of the online shopping site" and the "Twitter API key," and the outputs are the "collected online shopping data" and the "collected social media data."

[0683] Step 2: Data Preprocessing

[0684] The server cleanses the collected data and converts it into an analyzable format. Specifically, it uses the pandas library to fill in missing values ​​and remove outliers. It then converts the collected data into time series data. The input is the collected data, and the output is the preprocessed data.

[0685] Step 3: Demand forecast

[0686] The server uses a generative AI model to perform demand forecasting. For example, it uses a time series analysis model on Amazon SageMaker to predict future demand based on preprocessed data. An example prompt is: "Analyze sales data for air conditioning equipment over the past six months and provide a demand forecast for next month." The inputs are "preprocessed data" and "prompt," and the output is "demand forecast data."

[0687] Step 4: Identify your target market

[0688] The server identifies target markets based on the demand forecast results and develops a sales expansion strategy. Specifically, it uses Microsoft Power BI to visualize the forecast data and identify the regions and consumer segments with the highest demand. The input is "demand forecast data," and the output is "identified target market data" and a "sales expansion strategy proposal."

[0689] Step 5: Generate promotional content

[0690] The server uses generative AI to automatically generate advertising text, images, and videos. Specifically, it uses OpenAI's generative AI to input a prompt such as "Please generate advertising text and images for air conditioning equipment for the summer" and obtains the generated content. The inputs are the generative AI model and the prompt, and the outputs are the generated advertising text and generated advertising images and videos.

[0691] Step 6: Save your data

[0692] The server stores the generated content and analysis data in a database. Specifically, it uses Amazon RDS to store the generated ad text, images, videos, demand forecast data, etc. The input is the "generated ad content" and "demand forecast data," and the output is the "stored data."

[0693] Step 7: Automate customer interactions

[0694] The device uses generative AI to build a chatbot for customer support. Specifically, it uses Dialogflow to set up a dialogue flow that can immediately respond to customer questions based on the results of natural language processing by the generative AI. The inputs are the generative AI model and customer question data, and the output is the chatbot's dialogue content.

[0695] Step 8: Manage customer interactions

[0696] The terminal responds immediately to customer inquiries using automatically generated responses and records the information in a database. Specifically, it automatically records the content of interactions with customers using Salesforce. The inputs are the "chatbot dialogue content" and "customer inquiry data," and the output is "recorded customer response data."

[0697] Step 9: Implement marketing activities

[0698] Users download the promotional content generated by the server and use it in their marketing activities. Specifically, they print out the generated posters and distribute them locally. They also log in to SNS and post the generated advertising text and images. The input is the "generated promotional content" and the output is the "results of the marketing activities."

[0699] (Application example 1)

[0700] 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."

[0701] The goal is to provide a system that enables people who have difficulty finding employment, such as people with disabilities, the elderly, those who have experienced social withdrawal, and those serving time in prison, to become financially independent and effectively participate in marketing activities. However, conventional systems do not adequately collect market data, forecast demand, or generate advertising content, making it difficult to identify target markets and develop sales channel expansion strategies. Furthermore, there is a lack of means to support social media promotions that effectively utilize the generated advertising content. As a result, it has been difficult for people with employment difficulties to effectively engage in marketing activities.

[0702] 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.

[0703] In this invention, the server includes means for analyzing demand trends using a generation AI, means for identifying a target market based on demand forecast data and formulating a sales channel expansion strategy, means for automatically generating advertising text, images, and videos using the generation AI, means for supporting marketing activities using the automatically generated advertising content, means for automating customer support using the generation AI, means for acquiring market data and generating region-specific advertising text using an application installed on a smartphone, means for forecasting demand based on online shopping data and social media trend data, and means for conducting promotions on social media using the generated advertising content. This enables people with employment difficulties to conduct marketing activities easily and efficiently, and companies to effectively deliver competitive products to the market.

[0704] "Generative AI" is an artificial intelligence technology that uses machine learning and natural language processing to automatically generate content such as text, images, and videos.

[0705] "Demand tendencies and trends" refer to the direction of demand and trends for a product within a specific market or consumer group over a certain period of time.

[0706] "Demand forecast data" is data that predicts future demand based on past market data and trend data.

[0707] A "target market" refers to a group of potential customers or a geographic area for a particular product or service.

[0708] A "market expansion strategy" is a plan or method for increasing sales in existing and new markets.

[0709] "Advertising text" is written content used to promote a product or service.

[0710] An "image" is any digital or printed visual content intended to convey visual information.

[0711] "Video" is a media format that provides dynamic visual information by playing multiple video frames in succession.

[0712] "Automatically generated advertising content" refers to advertising content, such as text, images, and videos, that are automatically created using generative AI.

[0713] "Marketing activities" refers to all strategic efforts to increase awareness of and promote sales of products and services.

[0714] "Customer service" refers to the work of responding to and supporting inquiries and complaints from customers.

[0715] An "application installed on a smartphone" is a software program that can be downloaded and used on a smartphone device.

[0716] "Market Data" means data about sales, consumer trends, competitor activities, etc. in a particular market.

[0717] "Localized ad text" is advertising text that focuses on a specific region and is tailored to the characteristics and needs of that region.

[0718] "Online shopping data" refers to data related to purchasing activities on the Internet.

[0719] "Social media trend data" refers to data on the popularity of topics and keywords on social media.

[0720] "SNS promotion" is a marketing technique that uses social networking services to promote products and services.

[0721] Server Roles and Operations

[0722] Demand forecasting

[0723] The server uses generative AI to collect and analyze market data and social media trend data to forecast demand for specific regions and consumer segments. Specifically, it builds a demand forecasting model based on online shopping data and social media trend data, and makes predictions using prompts such as:

[0724] "Please forecast the demand for air conditioning equipment in Okinawa Prefecture for the next three months."

[0725] Market Targeting

[0726] The server identifies target markets based on the demand forecast results and develops sales expansion strategies. For example, if it analyzes demand forecast data and determines that demand for air conditioning equipment will increase in Okinawa Prefecture, it will set Okinawa Prefecture as the target market and develop a region-specific marketing strategy.

[0727] Content Generation

[0728] The server uses generative AI to automatically generate promotional content such as ad text, images, and videos, using prompts such as:

[0729] "Generate advertising text for air conditioning equipment for Okinawa Prefecture."

[0730] Data storage

[0731] The server stores the generated demand forecast data, target market information, advertising content, etc. in a database and makes them accessible as needed.

[0732] Terminal roles and processing

[0733] Automating customer interactions

[0734] The device uses generative AI to build chatbots for customer service, such as providing immediate responses to customer questions about how to install air conditioners or the return procedure.

[0735] Customer interaction management

[0736] The terminal responds immediately to customer inquiries using automatically generated answers and records the information in a database, which allows for the management of customer response history.

[0737] User Roles and Actions

[0738] Business execution

[0739] Users (employees facing employment difficulties) download the promotional content generated by the server and use it in actual marketing activities, such as printing out posters and distributing them locally.

[0740] Support for marketing activities

[0741] The user can then use the generated advertising text and images to promote the product on social media. For example, the user can post the generated advertising image on social media to promote the product to their followers.

[0742] Following AI instructions

[0743] The user carries out operations in accordance with the sales expansion strategy recommended by the generation AI, for example, by planning and executing sales promotion activities in target areas as instructed by the AI.

[0744] Hardware and software used

[0745] The system uses the following hardware and software:

[0746] Hardware:

[0747] Servers, smartphones, terminals

[0748] software:

[0749] Generative AI API (e.g. OpenAI API)

[0750] HTTP request library (e.g., requests)

[0751] Specific examples

[0752] 1. Example prompt

[0753] "Please forecast the demand for air conditioning equipment in Okinawa Prefecture for the next three months."

[0754] "Generate advertising text for air conditioning equipment for Okinawa Prefecture."

[0755] This system allows people who have difficulty finding employment to easily and efficiently carry out marketing activities, and also enables companies to effectively deliver competitive products to the market.

[0756] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0757] Step 1:

[0758] The server collects online shopping data and social media trend data, which is obtained through an API.

[0759] Input: Online shopping data, social media trend data

[0760] Output: Collected market data

[0761] This step involves retrieving data from specific API endpoints and storing it in an internal database.

[0762] Step 2:

[0763] The server uses a generative AI model to forecast demand based on collected market data. The collected data is input into the model to generate a demand forecast.

[0764] Input: Collected market data, prompt: "Please forecast the demand for air conditioning equipment in Okinawa Prefecture for the next three months."

[0765] Output: Demand forecast data

[0766] In this step, the generative AI model is run and predicts results based on the prompt text.

[0767] Step 3:

[0768] The server identifies target markets based on demand forecast data and develops regionally specific sales channel expansion strategies.

[0769] Input: Demand forecast data

[0770] Output: Target market information, sales channel expansion strategy

[0771] In this step, the predictive data is analyzed to plan the optimal target market and effective marketing strategy.

[0772] Step 4:

[0773] The server uses generative AI to automatically generate advertising content such as ad text, images, and videos.

[0774] Input: Target market information, prompt "Generate advertising text for air conditioning equipment for Okinawa Prefecture."

[0775] Output: Ad text, images, videos

[0776] In this step, generative AI models are used to create advertising content tailored to a specified target market.

[0777] Step 5:

[0778] The server stores the generated advertising content in a database and makes it accessible to terminals and users as needed.

[0779] Input: Ad text, images, video

[0780] Output: Ad content stored in a database

[0781] In this step, the generated content is stored in a database for immediate access.

[0782] Step 6:

[0783] The device uses generative AI to build chatbots for customer interaction.

[0784] Input: Generative AI model, FAQ data

[0785] Output: Customer service chatbot

[0786] In this step, a generative AI model is used based on FAQ data to build a chatbot that provides immediate responses to customer questions.

[0787] Step 7:

[0788] The terminal records the history of customer interactions in a database.

[0789] Input: Customer inquiry, chatbot response

[0790] Output: Recorded customer interaction history

[0791] In this step, customer interactions are saved in a database for future reference.

[0792] Step 8:

[0793] The user downloads the generated promotional content from the server and uses it in actual marketing activities.

[0794] Input: Ad text, images, and videos downloaded from the database

[0795] Output: Marketing activities carried out (poster distribution, social media posts, etc.)

[0796] In this step, the user performs specific marketing activities using the generated advertising content.

[0797] Step 9:

[0798] The user uses the generated advertising content to promote the product on the SNS.

[0799] Input: Ad text, images, video

[0800] Output: Social media posts, follower reactions

[0801] In this step, the user posts the generated advertising content on the social networking site to promote the product to their followers.

[0802] Step 10:

[0803] Users carry out their work in accordance with the sales expansion strategy recommended by the generation AI.

[0804] Input: Sales channel expansion strategy

[0805] Output: Market expansion activities carried out

[0806] In this step, users plan and execute marketing activities based on AI recommendations.

[0807] 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.

[0808] This invention is a system that uses generative AI to forecast demand, identify target markets, automatically generate advertising content, support marketing activities, and automate customer support. Furthermore, by combining it with an emotion engine that recognizes user emotions, it adds the ability to analyze user emotions and generate responses and content based on them.

[0809] Server Roles and Operations

[0810] Data collection and preprocessing

[0811] The server collects online shopping data and social media trend data, and performs data cleansing, missing value imputation, and outlier detection.

[0812] Example: A server uses web scraping tools and APIs to collect online shopping data and social media trend data for the past six months.

[0813] Demand forecasting

[0814] The server applies machine learning models to the pre-processed data to predict future demand.

[0815] Example: A server uses an ARIMA model and an LSTM network to forecast demand for air conditioning equipment in summer.

[0816] Target market identification and strategy planning

[0817] The server identifies target markets based on demand forecast data and develops regionally specific sales expansion strategies.

[0818] Example: The server identifies an area where demand for air conditioners is high (e.g., Okinawa Prefecture) and plans an advertising campaign for that area.

[0819] Content Generation

[0820] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos.

[0821] Example: The server generates advertising text such as "Buy now and enjoy a comfortable summer!" and images and videos of air conditioning equipment with a beach in Okinawa in the background.

[0822] Emotion Engine Analysis

[0823] The server uses an emotion engine to analyze the user's emotions and generate responses and content based on those emotions.

[0824] Example: A server performs text analysis of a user's comments and feedback to determine whether the user is expressing positive or negative sentiment.

[0825] Terminal roles and processing

[0826] Chatbot Settings

[0827] The device uses generative AI to set up a chatbot that has learned common questions and their answers.

[0828] Example: The terminal prepares answers to questions about how to install a cooling unit or the return process.

[0829] Automating customer interactions

[0830] The terminal uses a chatbot to respond immediately to customer inquiries.

[0831] Example: The device responds to inquiries about how to use air conditioning equipment with an AI chatbot that provides accurate answers.

[0832] User Roles and Actions

[0833] Business execution

[0834] Users (employees who are having difficulty finding work) download the promotional content generated by the server and use it in their actual marketing activities.

[0835] Example: The user prints the generated posters and distributes them in the designated area. The user also promotes the posters on social media.

[0836] Sentiment Analysis Feedback

[0837] Users receive the results of customer sentiment analysis provided through the emotion engine and reflect them in their marketing strategies and customer service.

[0838] Example: Users consider rewards and promotions when customers express positive sentiment.

[0839] conclusion

[0840] By utilizing generative AI and an emotion engine, this invention makes it possible for people with employment difficulties to easily carry out marketing activities and customer service. This allows companies and nonprofit organizations to operate their businesses efficiently and effectively, increasing their sustainability. Based on specific processing steps, this system provides comprehensive and effective support.

[0841] The processing flow will be explained below.

[0842] Step 1:

[0843] Data collection

[0844] The server collects online shopping data, social media trend data, and more.

[0845] Specifically, data is automatically obtained using web scraping tools and APIs.

[0846] Step 2:

[0847] Data Preprocessing

[0848] The server cleanses the collected data, filling in missing values ​​and detecting outliers.

[0849] For example, importing data into an Excel spreadsheet or database and correcting inconsistent data.

[0850] Step 3:

[0851] Demand forecasting

[0852] The server analyzes the pre-processed data and uses machine learning models to predict demand.

[0853] For example, ARIMA models and LSTM networks are used to forecast future demand.

[0854] Step 4:

[0855] Saving prediction results

[0856] The server stores the prediction results in a database, making them available for subsequent processing steps.

[0857] Step 5:

[0858] Identifying your target market

[0859] The server identifies the optimal target market based on the demand forecast data.

[0860] For example, identify areas where demand for air conditioning equipment will increase (e.g., Okinawa Prefecture).

[0861] Step 6:

[0862] Planning sales channel expansion strategies

[0863] The server develops a sales expansion strategy for the identified target market.

[0864] For example, design an advertising campaign specifically for Okinawa Prefecture.

[0865] Step 7:

[0866] Content Generation

[0867] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos.

[0868] Specifically, it generates advertising text such as "Buy now and enjoy a comfortable summer!" and images and videos of air conditioning equipment with an Okinawa beach in the background.

[0869] Step 8:

[0870] Content Storage

[0871] The server stores the generated advertising text, images and videos in a database for users to access.

[0872] Step 9:

[0873] Emotion analysis

[0874] The server uses an emotion engine to analyze the user's emotions and generate responses and content based on those emotions.

[0875] Specifically, it performs text analysis of user comments and feedback to determine whether the user is expressing positive or negative sentiment.

[0876] Step 10:

[0877] Chatbot Settings

[0878] The device uses generative AI to set up a chatbot that has learned common questions and their answers.

[0879] Specifically, prepare answers to questions about how to install air conditioning equipment and the return procedure.

[0880] Step 11:

[0881] Automating customer interactions

[0882] The terminal uses a chatbot to respond to customer inquiries instantly.

[0883] For example, an AI chatbot can provide accurate answers to inquiries about how to use air conditioning equipment.

[0884] Step 12:

[0885] Content Use

[0886] The user downloads the generated promotional content and uses it in marketing activities.

[0887] Specifically, the generated posters are printed and distributed in the area.

[0888] Step 13:

[0889] Marketing Activities

[0890] Users can promote themselves on social media using advertising text and images generated by the server.

[0891] For example, the generated advertising image can be posted on social media to promote the product to followers.

[0892] Step 14:

[0893] Feedback collection

[0894] The user collects feedback from customers and sends it to the server.

[0895] For example, record customer opinions obtained through social media comments and messages.

[0896] Step 15:

[0897] Reanalyzing Data

[0898] The server analyzes the collected feedback data and uses it to forecast demand and develop marketing strategies for the next time.

[0899] For example, identifying areas for improvement in products and services based on customer feedback.

[0900] Step 16:

[0901] Optimizing emotional response

[0902] The server updates and optimizes appropriate emotional responses to customer feedback and inquiries based on the emotion engine.

[0903] For example, if a customer expresses negative feelings, offer an appropriate apology and suggest improvements.

[0904] This series of processing steps enables people who have difficulty finding employment to carry out efficient marketing activities, and companies and NPOs to achieve sustainable and effective business operations.In addition, the introduction of an emotion engine enables detailed responses based on customer emotions, and is expected to improve customer satisfaction.

[0905] Example 2

[0906] 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."

[0907] Traditional marketing systems required a significant amount of time and human resources to forecast demand, identify target markets, and generate advertising content, resulting in low operational efficiency. They also struggled to provide immediate customer service, making it difficult to improve customer satisfaction. Furthermore, they were unable to understand employee sentiment and generate marketing strategies and content based on that sentiment.

[0908] 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.

[0909] In this invention, the server includes a means for analyzing demand tendencies and trends using a generation AI, a means for identifying a target market based on demand forecast data and formulating a sales channel expansion strategy, a means for automatically generating advertising text, images, and videos using the generation AI, and a means for analyzing user emotions using an emotion engine and generating responses and content based on the analysis results, thereby enabling improved accuracy of demand forecasts, more efficient marketing strategies, faster generation of advertising content, faster customer responses, and emotion-based marketing activities.

[0910] "Generative AI" is an artificial intelligence technology that uses generative models to automatically generate content such as text, images, and videos.

[0911] "Demand tendency and trend analysis" refers to the use of past data to predict future demand and clarify its fluctuation patterns.

[0912] "Demand forecast data" is data on future demand predicted using machine learning models and analytical methods.

[0913] "Target market identification" refers to identifying the consumer market with the greatest demand or interest for a particular product or service.

[0914] A "sales channel expansion strategy" is a specific plan or method for expanding product distribution channels and widening the customer base.

[0915] "Automatic generation of advertising text, images, and videos" refers to the use of generative AI to automatically create text, graphics, and video for advertising.

[0916] "Support for marketing activities" means using the generated advertising content to support marketing initiatives such as market research, promotions, and sales promotions.

[0917] "Customer response automation" is a system that uses generative AI to provide instant answers to customer inquiries.

[0918] An "emotion engine" is a technology that analyzes a user's emotional state from text and voice data and generates responses and content based on the results.

[0919] "Sentiment analysis" is the process of determining the type and intensity of emotions from user comments and feedback.

[0920] This invention is a system that uses generative AI to forecast demand, identify target markets, automatically generate advertising content, support marketing activities, and automate customer support. Furthermore, by combining it with an emotion engine that recognizes user emotions, it adds the ability to analyze user emotions and generate responses and content based on them.

[0921] Server Roles and Operations

[0922] Data collection and preprocessing

[0923] The server collects online shopping data and social media trend data using APIs and web scraping tools. Specifically, it uses the Twitter API to collect tweets related to air conditioning equipment for the past six months and collects air conditioning equipment sales trend data through the APIs of online shopping sites. The server then cleanses this data, imputes missing values, and detects outliers. Missing data is imputed with the mean or median, and abnormally high or low values ​​are filtered out.

[0924] Demand forecasting

[0925] The server converts the cleansed data into a format suitable for machine learning models. Specifically, it converts the data into a time series format and standardizes features (e.g., date, sales volume, social media trend index). It then trains an ARIMA model or LSTM network to forecast future demand. For example, it predicts demand for air conditioning equipment in the summer of 2023 and estimates when peak demand will occur.

[0926] Target market identification and strategy planning

[0927] The server analyzes the predicted demand data and identifies target markets. For example, it predicts demand for air conditioning equipment in each region of Japan and determines that a specific region (e.g., Okinawa Prefecture) will have high demand. It then develops a sales expansion strategy optimized for that region. Specifically, it plans outdoor advertising and a region-specific online advertising campaign in Okinawa Prefecture.

[0928] Content Generation

[0929] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos. For example, it generates a catchphrase such as "Buy now and enjoy a comfortable summer!" and an advertising image with the beautiful beaches of Okinawa as the background.

[0930] Emotion Engine Analysis

[0931] The server uses an emotion engine to analyze users' emotions. Specifically, it collects customer reviews and comments on social media and analyzes the user's emotional state from text and voice data. For example, it identifies areas that need improvement based on negative comments, and identifies areas that need further strengthening based on positive comments.

[0932] Terminal roles and processing

[0933] Chatbot Settings

[0934] The device uses a generation AI to set up a chatbot that has learned frequently asked questions and their answers. Specifically, the generation AI creates FAQs about air conditioning equipment (e.g., installation methods, return procedures), and the chatbot learns them.

[0935] Automating customer interactions

[0936] The terminal uses a chatbot to respond to customer inquiries immediately. For example, an AI chatbot can provide an accurate answer to a question about how to use an air conditioner.

[0937] User Roles and Actions

[0938] Business execution

[0939] Users (employees facing employment difficulties) download the promotional content generated by the server and use it in actual marketing activities. For example, the user prints out the generated posters and distributes them in a designated area (e.g., Okinawa Prefecture).

[0940] Sentiment Analysis Feedback

[0941] Users can use the analysis results of the emotion engine to reflect them in their marketing strategies and customer service. For example, they can offer rewards to customers who express positive emotions and take measures to improve their customer service in response to negative comments.

[0942] Prompt Sentence Examples

[0943] Using a generative AI model, we present an example of a prompt sentence for predicting demand for air conditioning equipment in the summer.

[0944] Prompt statement

[0945] "Based on online shopping data and social media trend data from the past six months, please predict demand for air conditioning equipment for the summer of 2023."

[0946] As a result, this system enables improved accuracy in demand forecasting, more efficient marketing strategies, faster generation of advertising content, faster customer response, and emotion-based marketing activities.

[0947] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0948] Step 1:

[0949] Data collection and preprocessing

[0950] The server collects online shopping data and social media trend data using APIs and web scraping tools. The input data is online shopping sales data and social media post data from the past six months. The server cleanses the collected data, imputes missing values, and detects outliers. For example, missing data is imputed with the mean or median, and abnormally high or low values ​​are filtered. The output is a cleansed dataset.

[0951] Step 2:

[0952] Data preparation

[0953] The server converts the cleansed data into a format that can be applied to a machine learning model. The input data is the cleansed data generated in step 1. The server converts the data into a time series format and standardizes the features (e.g., date, sales volume, social media trend index). The output is a dataset converted into a format that can be applied to a machine learning model.

[0954] Step 3:

[0955] Training a demand forecasting model

[0956] The server trains a demand forecasting model using the training data. The input data is the training data generated in step 2. The server performs training using an ARIMA model or an LSTM network. Specifically, it optimizes the model parameters using past sales data and social media trend data. The output is a trained demand forecasting model.

[0957] Step 4:

[0958] Perform demand forecasts

[0959] The server uses the trained model to predict future demand. The input data is real-time or the latest sales data and social media trend data. The server inputs this data into the model to predict future demand. For example, it predicts demand for air conditioning equipment in the summer of 2023 and estimates when demand will peak. The output is predicted demand data.

[0960] Step 5:

[0961] Target market identification and strategy planning

[0962] The server analyzes the forecasted demand data by region and identifies target markets. The input data is the demand forecast data obtained in step 4. The server identifies regions where demand will increase and plans an optimal sales expansion strategy for those regions. For example, it plans outdoor advertising in Okinawa Prefecture and a region-specific online advertising campaign. The output is a target market list and a marketing strategy document.

[0963] Step 6:

[0964] Automatic generation of advertising content

[0965] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos. The input data is the marketing strategy document formulated in Step 5. Specifically, it generates a catchphrase such as "Buy now and enjoy a comfortable summer!" and an advertising image with Okinawa's beautiful beaches as the background. The output is the automatically generated advertising content.

[0966] Step 7:

[0967] Automating customer interactions

[0968] The device uses a generation AI to set up a chatbot that has learned frequently asked questions and their answers. The input data is an FAQ database and generated customer response templates. As a specific example, an answer to a question about how to install air conditioning equipment is prepared. The output is the trained chatbot.

[0969] Step 8:

[0970] Customer service using chatbots

[0971] The terminal uses a trained chatbot to immediately respond to customer inquiries. The input data is the customer's inquiry. The terminal provides an appropriate answer to the customer's question through the chatbot. For example, an accurate answer is provided to an inquiry about how to use air conditioning equipment. The output is the answer provided to the customer and the response history.

[0972] Step 9:

[0973] Conducting sentiment analysis

[0974] The server uses an emotion engine to analyze the user's emotions. The input data is customer reviews and comments on social media. The server analyzes the user's emotional state (positive, negative, etc.) from text and voice data. The output is the analyzed emotion data.

[0975] Step 10:

[0976] Sentiment Analysis Feedback

[0977] The user utilizes the analysis results of the emotion engine to reflect them in marketing strategies and customer service. The input data is the analyzed emotion data obtained in step 9. The user provides rewards to customers who show positive emotions and takes measures to improve negative opinions. The output is an improved marketing strategy and customer service policy.

[0978] (Application example 2)

[0979] 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."

[0980] In conventional marketing activities, it has been difficult to centrally perform demand forecasting, target market identification, advertising content generation, and efficient customer response. This has resulted in companies being unable to quickly develop effective advertising campaigns, and despite the need for rapid responses based on customer feedback, it has been difficult to do so. Another issue is the inability to effectively automatically generate and distribute specific advertising campaigns based on data collected using devices such as smartphones.

[0981] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0982] In this invention, the server includes means for analyzing demand tendencies and trends using a generation AI, means for identifying target markets based on demand forecast data and formulating sales channel expansion strategies, means for automatically generating advertising text, images, and videos using the generation AI, means for automatically generating and distributing advertising campaigns based on data collected via a smartphone application, and means for analyzing user sentiment and providing responses and content based on the analysis results. This enables efficient and centralized execution of a wide range of marketing activities, enabling companies to develop rapid and effective advertising campaigns and respond quickly to customer feedback.

[0983] "Generative AI" is artificial intelligence that creates new data and content using techniques such as generative adversarial networks (GANs) and transformer models.

[0984] "Demand forecasting" is the process of analyzing past data and trends to predict future consumer demand.

[0985] A "target market" is a group of consumers to whom a particular product or service should be promoted.

[0986] "Advertising Text" means text containing a specific marketing message used in an advertising campaign.

[0987] An "image" is a still image intended to convey visual information.

[0988] "Video" is a moving image intended to convey dynamic visual information.

[0989] A "marketing activity" is a set of strategies and actions taken to promote a product or service.

[0990] "Customer service" refers to the act of providing information in response to customer inquiries and questions and meeting their requests.

[0991] A "smartphone application" is software that runs on a smartphone and performs a specific task.

[0992] "Emotion analysis" is a technology that detects and analyzes a user's emotions from text or voice.

[0993] A "response" is a reaction or answer given in response to an input or inquiry from a user.

[0994] "Content" means text, images, video, and other materials used to advertise or convey information.

[0995] The present invention relates to a system for automatically generating and delivering advertising campaigns via a smartphone application. The system utilizes generative AI and sentiment analysis engines to forecast demand, identify target markets, generate advertising content, and automate customer interactions.

[0996] Server Roles and Operations

[0997] Data collection and preprocessing

[0998] The server uses APIs and web scraping to collect online shopping data and social media trend data, and then performs data cleansing, missing value imputation, and outlier detection. As a specific example, online shopping data and social media trend data from the past six months are collected.

[0999] Demand forecasting

[1000] The server applies machine learning models (such as ARIMA models and LSTM networks) to the preprocessed data to predict future demand, such as predicting demand for air conditioning equipment in the summer.

[1001] Target market identification and strategy planning

[1002] The server identifies target markets based on demand forecast data and develops region-specific sales expansion strategies. For example, it identifies areas where demand for air conditioning equipment is increasing and plans an advertising campaign for those areas.

[1003] Generative AI-based advertising content generation

[1004] The server uses generation AI (e.g., OpenAI API) to automatically generate promotional content such as advertising text, images, and videos. An example of a specific prompt is, "Please generate an advertisement for a summer air conditioner. Please create a text advertisement with an image of an air conditioner against the backdrop of a beach in Okinawa."

[1005] Emotion analysis

[1006] The server uses a sentiment analysis engine to analyze the user's sentiment and provides responses and content based on the results. It also performs text analysis of the user's comments and feedback to determine whether the sentiment is positive or negative.

[1007] Terminal roles and processing

[1008] Chatbot Settings

[1009] The device uses generative AI to set up a chatbot that is trained on frequently asked questions and their answers, and is ready to answer questions about how to install a cooling unit or the return process.

[1010] Automating customer interactions

[1011] The terminal uses an AI chatbot to respond to customer inquiries immediately, for example, by providing accurate answers to inquiries about how to use air conditioning equipment.

[1012] User Roles and Actions

[1013] Business execution

[1014] Users download the promotional content generated by the server and use it in their actual marketing activities. They print the generated posters and distribute them in designated areas. They also promote the content on social media.

[1015] Sentiment Analysis Feedback

[1016] Users can receive the results of customer sentiment analysis provided by the emotion engine and reflect them in their marketing strategies and customer service. For example, if a customer expresses positive sentiment, they can consider offering special offers or promotions.

[1017] conclusion

[1018] This invention provides a system that efficiently automates the generation and distribution of advertising campaigns via smartphone applications by utilizing generative AI and a sentiment analysis engine. This system centrally manages a wide range of marketing activities, enabling the rapid and effective development of advertising campaigns and customer responses.

[1019] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1020] Step 1:

[1021] The server collects online shopping data and social media trend data using APIs and web scraping. The data is obtained using the URL and API key provided as input and stored in internal storage. The specific data collected includes purchase history and social media posts from the past six months.

[1022] Step 2:

[1023] The server preprocesses the collected data. It uses the raw data stored in the internal storage as input and performs missing value completion, outlier detection and removal, and data cleansing. Specifically, it completes incomplete data records and removes records with clearly abnormal values. It also standardizes the data format to make it easier to analyze. After preprocessing is complete, the data is standardized and passed to the next step.

[1024] Step 3:

[1025] The server uses the preprocessed data to perform demand forecasting. It applies an ARIMA model or LSTM network to the time series data prepared as input to predict future demand. Specifically, it uses past purchase data as training data for the model and predicts demand values ​​for a certain period of time in the future. The forecast results are output as future demand trends.

[1026] Step 4:

[1027] The server identifies target markets based on demand forecast data and develops sales expansion strategies. Using the demand forecast results as input, it extracts regions and customer segments where demand will increase. Specifically, it calculates predicted demand values ​​for each region, identifies regions with high demand, and develops sales promotion strategies for those regions. The strategies developed here are used to plan advertising campaigns.

[1028] Step 5:

[1029] The server generates advertising content using generative AI. Using target market information and prompts as input, it instructs the generative AI model (e.g., OpenAI's API) to automatically generate advertising materials such as text, images, and videos. An example of a specific prompt is, "Please generate an advertisement for air conditioning equipment in the summer. Please create a text advertisement with an image of an air conditioning equipment with an Okinawa beach as the background." The generated advertising content is used in the next step.

[1030] Step 6:

[1031] The server creates an advertising campaign using automatically generated advertising content and distributes it via smartphones. Using the generated advertising content and target market data as input, the server designs the advertising campaign. Specifically, it plans content placement to distribute advertisements to specific user groups at appropriate times.

[1032] Step 7:

[1033] The server analyzes user feedback using a sentiment analysis engine. Using user comments and feedback data as input, the sentiment analysis model determines positive or negative sentiment. Specifically, it uses a text analysis algorithm to calculate the user's sentiment score and outputs the results as an analysis report.

[1034] Step 8:

[1035] The device uses a generative AI to set up a chatbot that has learned frequently asked questions and their answers. It uses an FAQ database and user inquiry data as input to provide the chatbot with learning data. Specifically, it generates answers to questions about how to install air conditioners and return procedures, and sets up the chatbot to automatically respond.

[1036] Step 9:

[1037] The terminal uses an AI chatbot to instantly respond to customer inquiries. Using the user's inquiry as input, the pre-configured chatbot automatically provides an accurate answer to the inquiry. Specifically, it returns an accurate answer to an inquiry about how to use air conditioning equipment.

[1038] Step 10:

[1039] The user downloads the generated promotional content from the server and uses it in actual marketing activities. The generated promotional content is used as input to print posters and carry out promotional activities on social media. Specifically, the generated advertising posters are distributed in designated areas and campaign posts are made on social media.

[1040] Step 11:

[1041] Users receive customer sentiment analysis results provided through the sentiment engine and reflect them in marketing strategies and customer interactions. Using the sentiment analysis results as input, users can plan rewards and promotions for positive customers. Specifically, strategies such as offering special discounts to customers who provide positive feedback can be implemented.

[1042] 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.

[1043] 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.

[1044] 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.

[1045] [Third embodiment]

[1046] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1047] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1048] 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).

[1049] 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.

[1050] 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.

[1051] 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).

[1052] 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.

[1053] 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.

[1054] 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.

[1055] 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.

[1056] 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.

[1057] 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."

[1058] This invention is a system to support the economic independence of people who have difficulty finding employment, such as the disabled, the elderly, those who have experienced social withdrawal, and those serving time in prison, and it utilizes generative AI to match companies and brands with influencers, achieving effective marketing, expanding sales channels, and customer support. This system is operated mainly by three elements: a server, a terminal, and a user.

[1059] Server Roles and Operations

[1060] Demand forecasting

[1061] The server uses generative AI to collect and analyze market data and social media trend data to predict demand for specific regions and consumer segments.

[1062] Example: A server analyzes online shopping data and social media trend data from the past six months to predict increased demand for air conditioning equipment in the summer.

[1063] Market Targeting

[1064] The server identifies the target market based on the demand forecast results and develops a sales channel expansion strategy.

[1065] Example: A server designs a geo-specific advertising campaign to target the growing demand for air conditioning equipment in Okinawa Prefecture.

[1066] Content Generation

[1067] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos.

[1068] Example: The server generates advertising text such as "Buy now and enjoy a comfortable summer!" and images and videos of air conditioning equipment with a beach in Okinawa in the background.

[1069] Data storage

[1070] The server stores the generated demand forecast data, target market information, advertising content, etc. in a database and makes them accessible to the server.

[1071] Terminal roles and processing

[1072] Automating customer interactions

[1073] The device uses generative AI to build chatbots for customer service.

[1074] Example: The terminal provides instant responses to questions about how to install air conditioning equipment or the return procedure via an AI chatbot.

[1075] Customer interaction management

[1076] The terminal responds immediately to customer inquiries using automatically generated responses and records the information in a database.

[1077] User Roles and Actions

[1078] Business execution

[1079] Users (employees who are having difficulty finding work) download the promotional content generated by the server and use it in their actual marketing activities.

[1080] Example: A user prints the generated poster and distributes it in the community.

[1081] Support for marketing activities

[1082] Users can use the generated advertising text and images to promote their products on social media.

[1083] Example: A user posts the generated advertising image on social media to promote the product to their followers.

[1084] Following AI instructions

[1085] Users carry out their work in accordance with the sales expansion strategy recommended by the generation AI.

[1086] Example: The user plans and executes promotional activities in target areas as directed by AI.

[1087] conclusion

[1088] This system allows people who have difficulty finding employment to easily and efficiently carry out marketing activities. Furthermore, companies and NPOs can utilize the power of generative AI to effectively deliver competitive products to the market. We believe this system will make a significant contribution as a concrete means to improve the efficiency and sustainability of overall business operations.

[1089] The processing flow will be explained below.

[1090] Step 1:

[1091] Data collection

[1092] The server collects online shopping data, social media trend data, and more.

[1093] Specifically, data is automatically obtained using web scraping tools and APIs.

[1094] Step 2:

[1095] Data Preprocessing

[1096] The server cleanses the collected data, filling in missing values ​​and detecting outliers.

[1097] For example, importing data into an Excel spreadsheet or database and correcting inconsistent data.

[1098] Step 3:

[1099] Demand forecasting

[1100] The server analyzes the pre-processed data and uses machine learning models to predict demand.

[1101] For example, ARIMA models and LSTM networks are used to forecast future demand.

[1102] Step 4:

[1103] Saving prediction results

[1104] The server stores the prediction results in a database, making them available for subsequent processing steps.

[1105] Step 5:

[1106] Identifying your target market

[1107] The server identifies the optimal target market based on the demand forecast data.

[1108] For example, identify areas where demand for air conditioning equipment will increase (e.g., Okinawa Prefecture).

[1109] Step 6:

[1110] Planning sales channel expansion strategies

[1111] The server develops a sales expansion strategy for the identified target market.

[1112] For example, design an advertising campaign specifically for Okinawa Prefecture.

[1113] Step 7:

[1114] Content Generation

[1115] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos.

[1116] Specifically, it generates advertising text such as "Buy now and enjoy a comfortable summer!" and images and videos of air conditioning equipment with an Okinawa beach in the background.

[1117] Step 8:

[1118] Content Storage

[1119] The server stores the generated advertising text, images and videos in a database for users to access.

[1120] Step 9:

[1121] Chatbot Settings

[1122] The device uses generative AI to set up a chatbot that has learned common questions and their answers.

[1123] Specifically, prepare answers to questions about how to install air conditioning equipment and the return procedure.

[1124] Step 10:

[1125] Automating customer interactions

[1126] The terminal uses a chatbot to respond to customer inquiries immediately.

[1127] For example, an AI chatbot can provide answers to inquiries about how to use air conditioning equipment.

[1128] Step 11:

[1129] Content Use

[1130] The user downloads the generated promotional content and uses it in marketing activities.

[1131] Specifically, the generated posters are printed and distributed in the area.

[1132] Step 12:

[1133] Marketing Activities

[1134] Users can promote themselves on social media using advertising text and images generated by the server.

[1135] For example, the generated advertising image can be posted on social media to promote the product to followers.

[1136] Step 13:

[1137] Feedback collection

[1138] The user collects feedback from customers and sends it to the server.

[1139] For example, record customer opinions obtained through social media comments and messages.

[1140] Step 14:

[1141] Reanalyzing Data

[1142] The server analyzes the collected feedback data and uses it to forecast demand and develop marketing strategies for the next time.

[1143] For example, identifying areas for improvement in products and services based on customer feedback.

[1144] This series of processing steps enables people who have difficulty finding employment to carry out marketing activities efficiently, and companies and NPOs to achieve sustainable business operations.

[1145] Example 1

[1146] 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."

[1147] Existing marketing systems have the problem that it is difficult for people who have difficulty finding employment, such as people with disabilities, the elderly, people who have been socially withdrawn, and former prisoners, to carry out marketing activities efficiently. Furthermore, these systems are inadequate in market demand forecasting, targeting, and generating promotional content, making it difficult for companies and brands to effectively deliver products to the market. In addition, customer support is often done manually, requiring efficient use of human resources.

[1148] 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.

[1149] In this invention, the server includes: a means for analyzing demand trends using a generative AI; a means for identifying a target market based on demand forecast data and developing a sales channel expansion strategy; a means for automatically generating advertising text, images, and videos using the generative AI; a means for generating prompts for use with the generative AI model; a means for preprocessing collected data; a means for performing demand forecasts using the generative AI based on the preprocessed data; a means for saving promotional content generated for the identified target market; and a means for managing customer support based on the saved information. This enables efficient marketing activities for people who have difficulty finding employment, and enables highly accurate demand forecasts and effective promotional activities using the generative AI. Furthermore, automating customer support enables efficient use of human resources.

[1150] "Generative AI" is a technology that uses artificial intelligence to automatically generate content such as text, images, and videos.

[1151] "Demand forecasting" is the process of predicting future consumer demand based on collected data.

[1152] A "target market" refers to the region or consumer group that is predicted to have the greatest demand for a particular product or service.

[1153] A "market expansion strategy" is a plan or strategy for effectively promoting and selling a product or service to a specific market.

[1154] A "prompt" is an instruction used as input to a generative AI model that determines the content to be generated.

[1155] "Preprocessing" is the process of formatting data into an analyzable format, including filling in missing values ​​and removing outliers.

[1156] "Promotional Content" refers to content such as advertising text, images, and videos used to promote products and services.

[1157] "Customer interaction management" is the process of efficiently handling, recording, and managing customer inquiries and feedback.

[1158] This invention is a system to support the economic independence of people who have difficulty finding employment, such as the disabled, the elderly, those who have experienced social withdrawal, and those serving time in prison. It utilizes generative AI to match companies and brands with influencers, achieving effective marketing, expanding sales channels, and customer support. This system is operated mainly by three elements: a server, a terminal, and a user.

[1159] Server Roles and Operations

[1160] Market and trend data collection

[1161] The server uses a web crawler to collect online shopping data and social media trend data. Specifically, it uses Python's BeautifulSoup library to collect product sales data from multiple online shopping sites and the Twitter API to retrieve tweets related to specific keywords.

[1162] Data Preprocessing

[1163] The server cleanses the collected data and converts it into an analyzable format. It uses the pandas library to process missing values ​​and remove outliers. The collected data is then formatted as time series data.

[1164] Perform demand forecasts

[1165] The server uses a generative AI model to forecast demand. For example, it uses a time series analysis model on Amazon SageMaker to input preprocessed data into the model and predict future demand. An example of a prompt is, "Please analyze sales data for air conditioning equipment for the past six months and provide a demand forecast for next month."

[1166] Identifying your target market

[1167] The server identifies target markets based on the demand forecast results and develops a sales expansion strategy. It visualizes the forecast data using Microsoft Power BI to identify regions and consumer segments with the highest demand. As a specific example, it identifies high demand in Okinawa Prefecture.

[1168] Promotional content generation

[1169] The server uses generative AI to automatically generate ad text, images, and videos. For example, OpenAI's GPT-4 is used to input a prompt such as "Generate ad text and images for air conditioning equipment for the summer" and obtain the generated content. For example, ad text such as "Buy now and enjoy a comfortable summer!" and images and videos of air conditioning equipment with an Okinawa beach in the background are generated.

[1170] Data storage

[1171] The server stores the generated content and analysis data in a database, such as Amazon RDS, to store the generated ad text, images, videos, and demand forecast data for easy access in subsequent processing.

[1172] Terminal roles and processing

[1173] Automating customer interactions

[1174] The device uses generative AI to build a chatbot for customer support. Specifically, it uses Dialogflow to set up a dialogue flow that can immediately respond to customer questions based on the results of natural language processing by the generative AI.

[1175] Customer interaction management

[1176] The device responds to customer inquiries instantly using automatically generated responses and records the information in a database. Specifically, it automatically records the content of customer interactions using Salesforce.

[1177] User Roles and Actions

[1178] Business execution

[1179] Users (employees facing employment difficulties) download the promotional content generated by the server and use it in their marketing activities. Specifically, they print out the generated posters and distribute them locally.

[1180] Support for marketing activities

[1181] Users can use the generated advertising text and images to promote their products on social media. Specifically, they log in to their social media accounts and post advertising text and images to appeal to their followers.

[1182] Following AI instructions

[1183] The user carries out the sales expansion strategy recommended by the generation AI, specifically by planning and executing promotional activities in the target areas specified by the AI.

[1184] This system will enable people who have difficulty finding employment to carry out efficient marketing activities, enabling companies and brands to sustainably provide competitive products to the market. Furthermore, automating customer support will enable efficient use of human resources.

[1185] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1186] Step 1: Data collection

[1187] The server uses a web crawler to collect online shopping data and social media trend data. Specifically, it uses Python's BeautifulSoup library to collect product sales data from multiple online shopping sites and uses the Twitter API to obtain tweets related to specific keywords. The inputs are the "URL of the online shopping site" and the "Twitter API key," and the outputs are the "collected online shopping data" and the "collected social media data."

[1188] Step 2: Data Preprocessing

[1189] The server cleanses the collected data and converts it into an analyzable format. Specifically, it uses the pandas library to fill in missing values ​​and remove outliers. It then converts the collected data into time series data. The input is the collected data, and the output is the preprocessed data.

[1190] Step 3: Demand forecast

[1191] The server uses a generative AI model to perform demand forecasting. For example, it uses a time series analysis model on Amazon SageMaker to predict future demand based on preprocessed data. An example prompt is: "Analyze sales data for air conditioning equipment over the past six months and provide a demand forecast for next month." The inputs are "preprocessed data" and "prompt," and the output is "demand forecast data."

[1192] Step 4: Identify your target market

[1193] The server identifies target markets based on the demand forecast results and develops a sales expansion strategy. Specifically, it uses Microsoft Power BI to visualize the forecast data and identify the regions and consumer segments with the highest demand. The input is "demand forecast data," and the output is "identified target market data" and a "sales expansion strategy proposal."

[1194] Step 5: Generate promotional content

[1195] The server uses generative AI to automatically generate advertising text, images, and videos. Specifically, it uses OpenAI's generative AI to input a prompt such as "Please generate advertising text and images for air conditioning equipment for the summer" and obtains the generated content. The inputs are the generative AI model and the prompt, and the outputs are the generated advertising text and generated advertising images and videos.

[1196] Step 6: Save your data

[1197] The server stores the generated content and analysis data in a database. Specifically, it uses Amazon RDS to store the generated ad text, images, videos, demand forecast data, etc. The input is the "generated ad content" and "demand forecast data," and the output is the "stored data."

[1198] Step 7: Automate customer interactions

[1199] The device uses generative AI to build a chatbot for customer support. Specifically, it uses Dialogflow to set up a dialogue flow that can immediately respond to customer questions based on the results of natural language processing by the generative AI. The inputs are the generative AI model and customer question data, and the output is the chatbot's dialogue content.

[1200] Step 8: Manage customer interactions

[1201] The terminal responds immediately to customer inquiries using automatically generated responses and records the information in a database. Specifically, it automatically records the content of interactions with customers using Salesforce. The inputs are the "chatbot dialogue content" and "customer inquiry data," and the output is "recorded customer response data."

[1202] Step 9: Implement marketing activities

[1203] Users download the promotional content generated by the server and use it in their marketing activities. Specifically, they print out the generated posters and distribute them locally. They also log in to SNS and post the generated advertising text and images. The input is the "generated promotional content" and the output is the "results of the marketing activities."

[1204] (Application example 1)

[1205] 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."

[1206] The goal is to provide a system that enables people who have difficulty finding employment, such as people with disabilities, the elderly, those who have experienced social withdrawal, and those serving time in prison, to become financially independent and effectively participate in marketing activities. However, conventional systems do not adequately collect market data, forecast demand, or generate advertising content, making it difficult to identify target markets and develop sales channel expansion strategies. Furthermore, there is a lack of means to support social media promotions that effectively utilize the generated advertising content. As a result, it has been difficult for people with employment difficulties to effectively engage in marketing activities.

[1207] 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.

[1208] In this invention, the server includes means for analyzing demand trends using a generation AI, means for identifying a target market based on demand forecast data and formulating a sales channel expansion strategy, means for automatically generating advertising text, images, and videos using the generation AI, means for supporting marketing activities using the automatically generated advertising content, means for automating customer support using the generation AI, means for acquiring market data and generating region-specific advertising text using an application installed on a smartphone, means for forecasting demand based on online shopping data and social media trend data, and means for conducting promotions on social media using the generated advertising content. This enables people with employment difficulties to conduct marketing activities easily and efficiently, and companies to effectively deliver competitive products to the market.

[1209] "Generative AI" is an artificial intelligence technology that uses machine learning and natural language processing to automatically generate content such as text, images, and videos.

[1210] "Demand tendencies and trends" refer to the direction of demand and trends for a product within a specific market or consumer group over a certain period of time.

[1211] "Demand forecast data" is data that predicts future demand based on past market data and trend data.

[1212] A "target market" refers to a group of potential customers or a geographic area for a particular product or service.

[1213] A "market expansion strategy" is a plan or method for increasing sales in existing and new markets.

[1214] "Advertising text" is written content used to promote a product or service.

[1215] An "image" is any digital or printed visual content intended to convey visual information.

[1216] "Video" is a media format that provides dynamic visual information by playing multiple video frames in succession.

[1217] "Automatically generated advertising content" refers to advertising content, such as text, images, and videos, that are automatically created using generative AI.

[1218] "Marketing activities" refers to all strategic efforts to increase awareness of and promote sales of products and services.

[1219] "Customer service" refers to the work of responding to and supporting inquiries and complaints from customers.

[1220] An "application installed on a smartphone" is a software program that can be downloaded and used on a smartphone device.

[1221] "Market Data" means data about sales, consumer trends, competitor activities, etc. in a particular market.

[1222] "Localized ad text" is advertising text that focuses on a specific region and is tailored to the characteristics and needs of that region.

[1223] "Online shopping data" refers to data related to purchasing activities on the Internet.

[1224] "Social media trend data" refers to data on the popularity of topics and keywords on social media.

[1225] "SNS promotion" is a marketing technique that uses social networking services to promote products and services.

[1226] Server Roles and Operations

[1227] Demand forecasting

[1228] The server uses generative AI to collect and analyze market data and social media trend data to forecast demand for specific regions and consumer segments. Specifically, it builds a demand forecasting model based on online shopping data and social media trend data, and makes predictions using prompts such as:

[1229] "Please forecast the demand for air conditioning equipment in Okinawa Prefecture for the next three months."

[1230] Market Targeting

[1231] The server identifies target markets based on the demand forecast results and develops sales expansion strategies. For example, if it analyzes demand forecast data and determines that demand for air conditioning equipment will increase in Okinawa Prefecture, it will set Okinawa Prefecture as the target market and develop a region-specific marketing strategy.

[1232] Content Generation

[1233] The server uses generative AI to automatically generate promotional content such as ad text, images, and videos, using prompts such as:

[1234] "Generate advertising text for air conditioning equipment for Okinawa Prefecture."

[1235] Data storage

[1236] The server stores the generated demand forecast data, target market information, advertising content, etc. in a database and makes them accessible as needed.

[1237] Terminal roles and processing

[1238] Automating customer interactions

[1239] The device uses generative AI to build chatbots for customer service, such as providing immediate responses to customer questions about how to install air conditioners or the return procedure.

[1240] Customer interaction management

[1241] The terminal responds immediately to customer inquiries using automatically generated answers and records the information in a database, which allows for the management of customer response history.

[1242] User Roles and Actions

[1243] Business execution

[1244] Users (employees facing employment difficulties) download the promotional content generated by the server and use it in actual marketing activities, such as printing out posters and distributing them locally.

[1245] Support for marketing activities

[1246] The user can then use the generated advertising text and images to promote the product on social media. For example, the user can post the generated advertising image on social media to promote the product to their followers.

[1247] Following AI instructions

[1248] The user carries out operations in accordance with the sales expansion strategy recommended by the generation AI, for example, by planning and executing sales promotion activities in target areas as instructed by the AI.

[1249] Hardware and software used

[1250] The system uses the following hardware and software:

[1251] Hardware:

[1252] Servers, smartphones, terminals

[1253] software:

[1254] Generative AI API (e.g. OpenAI API)

[1255] HTTP request library (e.g., requests)

[1256] Specific examples

[1257] 1. Example prompt

[1258] "Please forecast the demand for air conditioning equipment in Okinawa Prefecture for the next three months."

[1259] "Generate advertising text for air conditioning equipment for Okinawa Prefecture."

[1260] This system allows people who have difficulty finding employment to easily and efficiently carry out marketing activities, and also enables companies to effectively deliver competitive products to the market.

[1261] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1262] Step 1:

[1263] The server collects online shopping data and social media trend data, which is obtained through an API.

[1264] Input: Online shopping data, social media trend data

[1265] Output: Collected market data

[1266] This step involves retrieving data from specific API endpoints and storing it in an internal database.

[1267] Step 2:

[1268] The server uses a generative AI model to forecast demand based on collected market data. The collected data is input into the model to generate a demand forecast.

[1269] Input: Collected market data, prompt: "Please forecast the demand for air conditioning equipment in Okinawa Prefecture for the next three months."

[1270] Output: Demand forecast data

[1271] In this step, the generative AI model is run and predicts results based on the prompt text.

[1272] Step 3:

[1273] The server identifies target markets based on demand forecast data and develops regionally specific sales channel expansion strategies.

[1274] Input: Demand forecast data

[1275] Output: Target market information, sales channel expansion strategy

[1276] In this step, the predictive data is analyzed to plan the optimal target market and effective marketing strategy.

[1277] Step 4:

[1278] The server uses generative AI to automatically generate advertising content such as ad text, images, and videos.

[1279] Input: Target market information, prompt "Generate advertising text for air conditioning equipment for Okinawa Prefecture."

[1280] Output: Ad text, images, videos

[1281] In this step, generative AI models are used to create advertising content tailored to a specified target market.

[1282] Step 5:

[1283] The server stores the generated advertising content in a database and makes it accessible to terminals and users as needed.

[1284] Input: Ad text, images, video

[1285] Output: Ad content stored in a database

[1286] In this step, the generated content is stored in a database for immediate access.

[1287] Step 6:

[1288] The device uses generative AI to build chatbots for customer interaction.

[1289] Input: Generative AI model, FAQ data

[1290] Output: Customer service chatbot

[1291] In this step, a generative AI model is used based on FAQ data to build a chatbot that provides immediate responses to customer questions.

[1292] Step 7:

[1293] The terminal records the history of customer interactions in a database.

[1294] Input: Customer inquiry, chatbot response

[1295] Output: Recorded customer interaction history

[1296] In this step, customer interactions are saved in a database for future reference.

[1297] Step 8:

[1298] The user downloads the generated promotional content from the server and uses it in actual marketing activities.

[1299] Input: Ad text, images, and videos downloaded from the database

[1300] Output: Marketing activities carried out (poster distribution, social media posts, etc.)

[1301] In this step, the user performs specific marketing activities using the generated advertising content.

[1302] Step 9:

[1303] The user uses the generated advertising content to promote the product on the SNS.

[1304] Input: Ad text, images, video

[1305] Output: Social media posts, follower reactions

[1306] In this step, the user posts the generated advertising content on the social networking site to promote the product to their followers.

[1307] Step 10:

[1308] Users carry out their work in accordance with the sales expansion strategy recommended by the generation AI.

[1309] Input: Sales channel expansion strategy

[1310] Output: Market expansion activities carried out

[1311] In this step, users plan and execute marketing activities based on AI recommendations.

[1312] 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.

[1313] This invention is a system that uses generative AI to forecast demand, identify target markets, automatically generate advertising content, support marketing activities, and automate customer support. Furthermore, by combining it with an emotion engine that recognizes user emotions, it adds the ability to analyze user emotions and generate responses and content based on them.

[1314] Server Roles and Operations

[1315] Data collection and preprocessing

[1316] The server collects online shopping data and social media trend data, and performs data cleansing, missing value imputation, and outlier detection.

[1317] Example: A server uses web scraping tools and APIs to collect online shopping data and social media trend data for the past six months.

[1318] Demand forecasting

[1319] The server applies machine learning models to the pre-processed data to predict future demand.

[1320] Example: A server uses an ARIMA model and an LSTM network to forecast demand for air conditioning equipment in summer.

[1321] Target market identification and strategy planning

[1322] The server identifies target markets based on demand forecast data and develops regionally specific sales expansion strategies.

[1323] Example: The server identifies an area where demand for air conditioners is high (e.g., Okinawa Prefecture) and plans an advertising campaign for that area.

[1324] Content Generation

[1325] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos.

[1326] Example: The server generates advertising text such as "Buy now and enjoy a comfortable summer!" and images and videos of air conditioning equipment with a beach in Okinawa in the background.

[1327] Emotion Engine Analysis

[1328] The server uses an emotion engine to analyze the user's emotions and generate responses and content based on those emotions.

[1329] Example: A server performs text analysis of a user's comments and feedback to determine whether the user is expressing positive or negative sentiment.

[1330] Terminal roles and processing

[1331] Chatbot Settings

[1332] The device uses generative AI to set up a chatbot that has learned common questions and their answers.

[1333] Example: The terminal prepares answers to questions about how to install a cooling unit or the return process.

[1334] Automating customer interactions

[1335] The terminal uses a chatbot to respond immediately to customer inquiries.

[1336] Example: The device responds to inquiries about how to use air conditioning equipment with an AI chatbot that provides accurate answers.

[1337] User Roles and Actions

[1338] Business execution

[1339] Users (employees who are having difficulty finding work) download the promotional content generated by the server and use it in their actual marketing activities.

[1340] Example: The user prints the generated posters and distributes them in the designated area. The user also promotes the posters on social media.

[1341] Sentiment Analysis Feedback

[1342] Users receive the results of customer sentiment analysis provided through the emotion engine and reflect them in their marketing strategies and customer service.

[1343] Example: Users consider rewards and promotions when customers express positive sentiment.

[1344] conclusion

[1345] By utilizing generative AI and an emotion engine, this invention makes it possible for people with employment difficulties to easily carry out marketing activities and customer service. This allows companies and nonprofit organizations to operate their businesses efficiently and effectively, increasing their sustainability. Based on specific processing steps, this system provides comprehensive and effective support.

[1346] The processing flow will be explained below.

[1347] Step 1:

[1348] Data collection

[1349] The server collects online shopping data, social media trend data, and more.

[1350] Specifically, data is automatically obtained using web scraping tools and APIs.

[1351] Step 2:

[1352] Data Preprocessing

[1353] The server cleanses the collected data, filling in missing values ​​and detecting outliers.

[1354] For example, importing data into an Excel spreadsheet or database and correcting inconsistent data.

[1355] Step 3:

[1356] Demand forecasting

[1357] The server analyzes the pre-processed data and uses machine learning models to predict demand.

[1358] For example, ARIMA models and LSTM networks are used to forecast future demand.

[1359] Step 4:

[1360] Saving prediction results

[1361] The server stores the prediction results in a database, making them available for subsequent processing steps.

[1362] Step 5:

[1363] Identifying your target market

[1364] The server identifies the optimal target market based on the demand forecast data.

[1365] For example, identify areas where demand for air conditioning equipment will increase (e.g., Okinawa Prefecture).

[1366] Step 6:

[1367] Planning sales channel expansion strategies

[1368] The server develops a sales expansion strategy for the identified target market.

[1369] For example, design an advertising campaign specifically for Okinawa Prefecture.

[1370] Step 7:

[1371] Content Generation

[1372] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos.

[1373] Specifically, it generates advertising text such as "Buy now and enjoy a comfortable summer!" and images and videos of air conditioning equipment with an Okinawa beach in the background.

[1374] Step 8:

[1375] Content Storage

[1376] The server stores the generated advertising text, images and videos in a database for users to access.

[1377] Step 9:

[1378] Emotion analysis

[1379] The server uses an emotion engine to analyze the user's emotions and generate responses and content based on those emotions.

[1380] Specifically, it performs text analysis of user comments and feedback to determine whether the user is expressing positive or negative sentiment.

[1381] Step 10:

[1382] Chatbot Settings

[1383] The device uses generative AI to set up a chatbot that has learned common questions and their answers.

[1384] Specifically, prepare answers to questions about how to install air conditioning equipment and the return procedure.

[1385] Step 11:

[1386] Automating customer interactions

[1387] The terminal uses a chatbot to respond to customer inquiries instantly.

[1388] For example, an AI chatbot can provide accurate answers to inquiries about how to use air conditioning equipment.

[1389] Step 12:

[1390] Content Use

[1391] The user downloads the generated promotional content and uses it in marketing activities.

[1392] Specifically, the generated posters are printed and distributed in the area.

[1393] Step 13:

[1394] Marketing Activities

[1395] Users can promote themselves on social media using advertising text and images generated by the server.

[1396] For example, the generated advertising image can be posted on social media to promote the product to followers.

[1397] Step 14:

[1398] Feedback collection

[1399] The user collects feedback from customers and sends it to the server.

[1400] For example, record customer opinions obtained through social media comments and messages.

[1401] Step 15:

[1402] Reanalyzing Data

[1403] The server analyzes the collected feedback data and uses it to forecast demand and develop marketing strategies for the next time.

[1404] For example, identifying areas for improvement in products and services based on customer feedback.

[1405] Step 16:

[1406] Optimizing emotional response

[1407] The server updates and optimizes appropriate emotional responses to customer feedback and inquiries based on the emotion engine.

[1408] For example, if a customer expresses negative feelings, offer an appropriate apology and suggest improvements.

[1409] This series of processing steps enables people who have difficulty finding employment to carry out efficient marketing activities, and companies and NPOs to achieve sustainable and effective business operations.In addition, the introduction of an emotion engine enables detailed responses based on customer emotions, and is expected to improve customer satisfaction.

[1410] Example 2

[1411] 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."

[1412] Traditional marketing systems required a significant amount of time and human resources to forecast demand, identify target markets, and generate advertising content, resulting in low operational efficiency. They also struggled to provide immediate customer service, making it difficult to improve customer satisfaction. Furthermore, they were unable to understand employee sentiment and generate marketing strategies and content based on that sentiment.

[1413] 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.

[1414] In this invention, the server includes a means for analyzing demand tendencies and trends using a generation AI, a means for identifying a target market based on demand forecast data and formulating a sales channel expansion strategy, a means for automatically generating advertising text, images, and videos using the generation AI, and a means for analyzing user emotions using an emotion engine and generating responses and content based on the analysis results, thereby enabling improved accuracy of demand forecasts, more efficient marketing strategies, faster generation of advertising content, faster customer responses, and emotion-based marketing activities.

[1415] "Generative AI" is an artificial intelligence technology that uses generative models to automatically generate content such as text, images, and videos.

[1416] "Demand tendency and trend analysis" refers to the use of past data to predict future demand and clarify its fluctuation patterns.

[1417] "Demand forecast data" is data on future demand predicted using machine learning models and analytical methods.

[1418] "Target market identification" refers to identifying the consumer market with the greatest demand or interest for a particular product or service.

[1419] A "sales channel expansion strategy" is a specific plan or method for expanding product distribution channels and widening the customer base.

[1420] "Automatic generation of advertising text, images, and videos" refers to the use of generative AI to automatically create text, graphics, and video for advertising.

[1421] "Support for marketing activities" means using the generated advertising content to support marketing initiatives such as market research, promotions, and sales promotions.

[1422] "Customer response automation" is a system that uses generative AI to provide instant answers to customer inquiries.

[1423] An "emotion engine" is a technology that analyzes a user's emotional state from text and voice data and generates responses and content based on the results.

[1424] "Sentiment analysis" is the process of determining the type and intensity of emotions from user comments and feedback.

[1425] This invention is a system that uses generative AI to forecast demand, identify target markets, automatically generate advertising content, support marketing activities, and automate customer support. Furthermore, by combining it with an emotion engine that recognizes user emotions, it adds the ability to analyze user emotions and generate responses and content based on them.

[1426] Server Roles and Operations

[1427] Data collection and preprocessing

[1428] The server collects online shopping data and social media trend data using APIs and web scraping tools. Specifically, it uses the Twitter API to collect tweets related to air conditioning equipment for the past six months and collects air conditioning equipment sales trend data through the APIs of online shopping sites. The server then cleanses this data, imputes missing values, and detects outliers. Missing data is imputed with the mean or median, and abnormally high or low values ​​are filtered out.

[1429] Demand forecasting

[1430] The server converts the cleansed data into a format suitable for machine learning models. Specifically, it converts the data into a time series format and standardizes features (e.g., date, sales volume, social media trend index). It then trains an ARIMA model or LSTM network to forecast future demand. For example, it predicts demand for air conditioning equipment in the summer of 2023 and estimates when peak demand will occur.

[1431] Target market identification and strategy planning

[1432] The server analyzes the predicted demand data and identifies target markets. For example, it predicts demand for air conditioning equipment in each region of Japan and determines that a specific region (e.g., Okinawa Prefecture) will have high demand. It then develops a sales expansion strategy optimized for that region. Specifically, it plans outdoor advertising and a region-specific online advertising campaign in Okinawa Prefecture.

[1433] Content Generation

[1434] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos. For example, it generates a catchphrase such as "Buy now and enjoy a comfortable summer!" and an advertising image with the beautiful beaches of Okinawa as the background.

[1435] Emotion Engine Analysis

[1436] The server uses an emotion engine to analyze users' emotions. Specifically, it collects customer reviews and comments on social media and analyzes the user's emotional state from text and voice data. For example, it identifies areas that need improvement based on negative comments, and identifies areas that need further strengthening based on positive comments.

[1437] Terminal roles and processing

[1438] Chatbot Settings

[1439] The device uses a generation AI to set up a chatbot that has learned frequently asked questions and their answers. Specifically, the generation AI creates FAQs about air conditioning equipment (e.g., installation methods, return procedures), and the chatbot learns them.

[1440] Automating customer interactions

[1441] The terminal uses a chatbot to respond to customer inquiries immediately. For example, an AI chatbot can provide an accurate answer to a question about how to use an air conditioner.

[1442] User Roles and Actions

[1443] Business execution

[1444] Users (employees facing employment difficulties) download the promotional content generated by the server and use it in actual marketing activities. For example, the user prints out the generated posters and distributes them in a designated area (e.g., Okinawa Prefecture).

[1445] Sentiment Analysis Feedback

[1446] Users can use the analysis results of the emotion engine to reflect them in their marketing strategies and customer service. For example, they can offer rewards to customers who express positive emotions and take measures to improve their customer service in response to negative comments.

[1447] Prompt Sentence Examples

[1448] Using a generative AI model, we present an example of a prompt sentence for predicting demand for air conditioning equipment in the summer.

[1449] Prompt statement

[1450] "Based on online shopping data and social media trend data from the past six months, please predict demand for air conditioning equipment for the summer of 2023."

[1451] As a result, this system enables improved accuracy in demand forecasting, more efficient marketing strategies, faster generation of advertising content, faster customer response, and emotion-based marketing activities.

[1452] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1453] Step 1:

[1454] Data collection and preprocessing

[1455] The server collects online shopping data and social media trend data using APIs and web scraping tools. The input data is online shopping sales data and social media post data from the past six months. The server cleanses the collected data, imputes missing values, and detects outliers. For example, missing data is imputed with the mean or median, and abnormally high or low values ​​are filtered. The output is a cleansed dataset.

[1456] Step 2:

[1457] Data preparation

[1458] The server converts the cleansed data into a format that can be applied to a machine learning model. The input data is the cleansed data generated in step 1. The server converts the data into a time series format and standardizes the features (e.g., date, sales volume, social media trend index). The output is a dataset converted into a format that can be applied to a machine learning model.

[1459] Step 3:

[1460] Training a demand forecasting model

[1461] The server trains a demand forecasting model using the training data. The input data is the training data generated in step 2. The server performs training using an ARIMA model or an LSTM network. Specifically, it optimizes the model parameters using past sales data and social media trend data. The output is a trained demand forecasting model.

[1462] Step 4:

[1463] Perform demand forecasts

[1464] The server uses the trained model to predict future demand. The input data is real-time or the latest sales data and social media trend data. The server inputs this data into the model to predict future demand. For example, it predicts demand for air conditioning equipment in the summer of 2023 and estimates when demand will peak. The output is predicted demand data.

[1465] Step 5:

[1466] Target market identification and strategy planning

[1467] The server analyzes the forecasted demand data by region and identifies target markets. The input data is the demand forecast data obtained in step 4. The server identifies regions where demand will increase and plans an optimal sales expansion strategy for those regions. For example, it plans outdoor advertising in Okinawa Prefecture and a region-specific online advertising campaign. The output is a target market list and a marketing strategy document.

[1468] Step 6:

[1469] Automatic generation of advertising content

[1470] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos. The input data is the marketing strategy document formulated in Step 5. Specifically, it generates a catchphrase such as "Buy now and enjoy a comfortable summer!" and an advertising image with Okinawa's beautiful beaches as the background. The output is the automatically generated advertising content.

[1471] Step 7:

[1472] Automating customer interactions

[1473] The device uses a generation AI to set up a chatbot that has learned frequently asked questions and their answers. The input data is an FAQ database and generated customer response templates. As a specific example, an answer to a question about how to install air conditioning equipment is prepared. The output is the trained chatbot.

[1474] Step 8:

[1475] Customer service using chatbots

[1476] The terminal uses a trained chatbot to immediately respond to customer inquiries. The input data is the customer's inquiry. The terminal provides an appropriate answer to the customer's question through the chatbot. For example, an accurate answer is provided to an inquiry about how to use air conditioning equipment. The output is the answer provided to the customer and the response history.

[1477] Step 9:

[1478] Conducting sentiment analysis

[1479] The server uses an emotion engine to analyze the user's emotions. The input data is customer reviews and comments on social media. The server analyzes the user's emotional state (positive, negative, etc.) from text and voice data. The output is the analyzed emotion data.

[1480] Step 10:

[1481] Sentiment Analysis Feedback

[1482] The user utilizes the analysis results of the emotion engine to reflect them in marketing strategies and customer service. The input data is the analyzed emotion data obtained in step 9. The user provides rewards to customers who show positive emotions and takes measures to improve negative opinions. The output is an improved marketing strategy and customer service policy.

[1483] (Application example 2)

[1484] 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."

[1485] In conventional marketing activities, it has been difficult to centrally perform demand forecasting, target market identification, advertising content generation, and efficient customer response. This has resulted in companies being unable to quickly develop effective advertising campaigns, and despite the need for rapid responses based on customer feedback, it has been difficult to do so. Another issue is the inability to effectively automatically generate and distribute specific advertising campaigns based on data collected using devices such as smartphones.

[1486] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1487] In this invention, the server includes means for analyzing demand tendencies and trends using a generation AI, means for identifying target markets based on demand forecast data and formulating sales channel expansion strategies, means for automatically generating advertising text, images, and videos using the generation AI, means for automatically generating and distributing advertising campaigns based on data collected via a smartphone application, and means for analyzing user sentiment and providing responses and content based on the analysis results. This enables efficient and centralized execution of a wide range of marketing activities, enabling companies to develop rapid and effective advertising campaigns and respond quickly to customer feedback.

[1488] "Generative AI" is artificial intelligence that creates new data and content using techniques such as generative adversarial networks (GANs) and transformer models.

[1489] "Demand forecasting" is the process of analyzing past data and trends to predict future consumer demand.

[1490] A "target market" is a group of consumers to whom a particular product or service should be promoted.

[1491] "Advertising Text" means text containing a specific marketing message used in an advertising campaign.

[1492] An "image" is a still image intended to convey visual information.

[1493] "Video" is a moving image intended to convey dynamic visual information.

[1494] A "marketing activity" is a set of strategies and actions taken to promote a product or service.

[1495] "Customer service" refers to the act of providing information in response to customer inquiries and questions and meeting their requests.

[1496] A "smartphone application" is software that runs on a smartphone and performs a specific task.

[1497] "Emotion analysis" is a technology that detects and analyzes a user's emotions from text or voice.

[1498] A "response" is a reaction or answer given in response to an input or inquiry from a user.

[1499] "Content" means text, images, video, and other materials used to advertise or convey information.

[1500] The present invention relates to a system for automatically generating and delivering advertising campaigns via a smartphone application. The system utilizes generative AI and sentiment analysis engines to forecast demand, identify target markets, generate advertising content, and automate customer interactions.

[1501] Server Roles and Operations

[1502] Data collection and preprocessing

[1503] The server uses APIs and web scraping to collect online shopping data and social media trend data, and then performs data cleansing, missing value imputation, and outlier detection. As a specific example, online shopping data and social media trend data from the past six months are collected.

[1504] Demand forecasting

[1505] The server applies machine learning models (such as ARIMA models and LSTM networks) to the preprocessed data to predict future demand, such as predicting demand for air conditioning equipment in the summer.

[1506] Target market identification and strategy planning

[1507] The server identifies target markets based on demand forecast data and develops region-specific sales expansion strategies. For example, it identifies areas where demand for air conditioning equipment is increasing and plans an advertising campaign for those areas.

[1508] Generative AI-based advertising content generation

[1509] The server uses generation AI (e.g., OpenAI API) to automatically generate promotional content such as advertising text, images, and videos. An example of a specific prompt is, "Please generate an advertisement for a summer air conditioner. Please create a text advertisement with an image of an air conditioner against the backdrop of a beach in Okinawa."

[1510] Emotion analysis

[1511] The server uses a sentiment analysis engine to analyze the user's sentiment and provides responses and content based on the results. It also performs text analysis of the user's comments and feedback to determine whether the sentiment is positive or negative.

[1512] Terminal roles and processing

[1513] Chatbot Settings

[1514] The device uses generative AI to set up a chatbot that is trained on frequently asked questions and their answers, and is ready to answer questions about how to install a cooling unit or the return process.

[1515] Automating customer interactions

[1516] The terminal uses an AI chatbot to respond to customer inquiries immediately, for example, by providing accurate answers to inquiries about how to use air conditioning equipment.

[1517] User Roles and Actions

[1518] Business execution

[1519] Users download the promotional content generated by the server and use it in their actual marketing activities. They print the generated posters and distribute them in designated areas. They also promote the content on social media.

[1520] Sentiment Analysis Feedback

[1521] Users can receive the results of customer sentiment analysis provided by the emotion engine and reflect them in their marketing strategies and customer service. For example, if a customer expresses positive sentiment, they can consider offering special offers or promotions.

[1522] conclusion

[1523] This invention provides a system that efficiently automates the generation and distribution of advertising campaigns via smartphone applications by utilizing generative AI and a sentiment analysis engine. This system centrally manages a wide range of marketing activities, enabling the rapid and effective development of advertising campaigns and customer responses.

[1524] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1525] Step 1:

[1526] The server collects online shopping data and social media trend data using APIs and web scraping. The data is obtained using the URL and API key provided as input and stored in internal storage. The specific data collected includes purchase history and social media posts from the past six months.

[1527] Step 2:

[1528] The server preprocesses the collected data. It uses the raw data stored in the internal storage as input and performs missing value completion, outlier detection and removal, and data cleansing. Specifically, it completes incomplete data records and removes records with clearly abnormal values. It also standardizes the data format to make it easier to analyze. After preprocessing is complete, the data is standardized and passed to the next step.

[1529] Step 3:

[1530] The server uses the preprocessed data to perform demand forecasting. It applies an ARIMA model or LSTM network to the time series data prepared as input to predict future demand. Specifically, it uses past purchase data as training data for the model and predicts demand values ​​for a certain period of time in the future. The forecast results are output as future demand trends.

[1531] Step 4:

[1532] The server identifies target markets based on demand forecast data and develops sales expansion strategies. Using the demand forecast results as input, it extracts regions and customer segments where demand will increase. Specifically, it calculates predicted demand values ​​for each region, identifies regions with high demand, and develops sales promotion strategies for those regions. The strategies developed here are used to plan advertising campaigns.

[1533] Step 5:

[1534] The server generates advertising content using generative AI. Using target market information and prompts as input, it instructs the generative AI model (e.g., OpenAI's API) to automatically generate advertising materials such as text, images, and videos. An example of a specific prompt is, "Please generate an advertisement for air conditioning equipment in the summer. Please create a text advertisement with an image of an air conditioning equipment with an Okinawa beach as the background." The generated advertising content is used in the next step.

[1535] Step 6:

[1536] The server creates an advertising campaign using automatically generated advertising content and distributes it via smartphones. Using the generated advertising content and target market data as input, the server designs the advertising campaign. Specifically, it plans content placement to distribute advertisements to specific user groups at appropriate times.

[1537] Step 7:

[1538] The server analyzes user feedback using a sentiment analysis engine. Using user comments and feedback data as input, the sentiment analysis model determines positive or negative sentiment. Specifically, it uses a text analysis algorithm to calculate the user's sentiment score and outputs the results as an analysis report.

[1539] Step 8:

[1540] The device uses a generative AI to set up a chatbot that has learned frequently asked questions and their answers. It uses an FAQ database and user inquiry data as input to provide the chatbot with learning data. Specifically, it generates answers to questions about how to install air conditioners and return procedures, and sets up the chatbot to automatically respond.

[1541] Step 9:

[1542] The terminal uses an AI chatbot to instantly respond to customer inquiries. Using the user's inquiry as input, the pre-configured chatbot automatically provides an accurate answer to the inquiry. Specifically, it returns an accurate answer to an inquiry about how to use air conditioning equipment.

[1543] Step 10:

[1544] The user downloads the generated promotional content from the server and uses it in actual marketing activities. The generated promotional content is used as input to print posters and carry out promotional activities on social media. Specifically, the generated advertising posters are distributed in designated areas and campaign posts are made on social media.

[1545] Step 11:

[1546] Users receive customer sentiment analysis results provided through the sentiment engine and reflect them in marketing strategies and customer interactions. Using the sentiment analysis results as input, users can plan rewards and promotions for positive customers. Specifically, strategies such as offering special discounts to customers who provide positive feedback can be implemented.

[1547] 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.

[1548] 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.

[1549] 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.

[1550] [Fourth embodiment]

[1551] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1552] 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.

[1553] 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).

[1554] 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.

[1555] 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.

[1556] 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).

[1557] 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.

[1558] 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.

[1559] 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.

[1560] 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.

[1561] 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.

[1562] 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.

[1563] 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."

[1564] This invention is a system to support the economic independence of people who have difficulty finding employment, such as the disabled, the elderly, those who have experienced social withdrawal, and those serving time in prison, and it utilizes generative AI to match companies and brands with influencers, achieving effective marketing, expanding sales channels, and customer support. This system is operated mainly by three elements: a server, a terminal, and a user.

[1565] Server Roles and Operations

[1566] Demand forecasting

[1567] The server uses generative AI to collect and analyze market data and social media trend data to predict demand for specific regions and consumer segments.

[1568] Example: A server analyzes online shopping data and social media trend data from the past six months to predict increased demand for air conditioning equipment in the summer.

[1569] Market Targeting

[1570] The server identifies the target market based on the demand forecast results and develops a sales channel expansion strategy.

[1571] Example: A server designs a geo-specific advertising campaign to target the growing demand for air conditioning equipment in Okinawa Prefecture.

[1572] Content Generation

[1573] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos.

[1574] Example: The server generates advertising text such as "Buy now and enjoy a comfortable summer!" and images and videos of air conditioning equipment with a beach in Okinawa in the background.

[1575] Data storage

[1576] The server stores the generated demand forecast data, target market information, advertising content, etc. in a database and makes them accessible to the server.

[1577] Terminal roles and processing

[1578] Automating customer interactions

[1579] The device uses generative AI to build chatbots for customer service.

[1580] Example: The terminal provides instant responses to questions about how to install air conditioning equipment or the return procedure via an AI chatbot.

[1581] Customer interaction management

[1582] The terminal responds immediately to customer inquiries using automatically generated responses and records the information in a database.

[1583] User Roles and Actions

[1584] Business execution

[1585] Users (employees who are having difficulty finding work) download the promotional content generated by the server and use it in their actual marketing activities.

[1586] Example: A user prints the generated poster and distributes it in the community.

[1587] Support for marketing activities

[1588] Users can use the generated advertising text and images to promote their products on social media.

[1589] Example: A user posts the generated advertising image on social media to promote the product to their followers.

[1590] Following AI instructions

[1591] Users carry out their work in accordance with the sales expansion strategy recommended by the generation AI.

[1592] Example: The user plans and executes promotional activities in target areas as directed by AI.

[1593] conclusion

[1594] This system allows people who have difficulty finding employment to easily and efficiently carry out marketing activities. Furthermore, companies and NPOs can utilize the power of generative AI to effectively deliver competitive products to the market. We believe this system will make a significant contribution as a concrete means to improve the efficiency and sustainability of overall business operations.

[1595] The processing flow will be explained below.

[1596] Step 1:

[1597] Data collection

[1598] The server collects online shopping data, social media trend data, and more.

[1599] Specifically, data is automatically obtained using web scraping tools and APIs.

[1600] Step 2:

[1601] Data Preprocessing

[1602] The server cleanses the collected data, filling in missing values ​​and detecting outliers.

[1603] For example, importing data into an Excel spreadsheet or database and correcting inconsistent data.

[1604] Step 3:

[1605] Demand forecasting

[1606] The server analyzes the pre-processed data and uses machine learning models to predict demand.

[1607] For example, ARIMA models and LSTM networks are used to forecast future demand.

[1608] Step 4:

[1609] Saving prediction results

[1610] The server stores the prediction results in a database, making them available for subsequent processing steps.

[1611] Step 5:

[1612] Identifying your target market

[1613] The server identifies the optimal target market based on the demand forecast data.

[1614] For example, identify areas where demand for air conditioning equipment will increase (e.g., Okinawa Prefecture).

[1615] Step 6:

[1616] Planning sales channel expansion strategies

[1617] The server develops a sales expansion strategy for the identified target market.

[1618] For example, design an advertising campaign specifically for Okinawa Prefecture.

[1619] Step 7:

[1620] Content Generation

[1621] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos.

[1622] Specifically, it generates advertising text such as "Buy now and enjoy a comfortable summer!" and images and videos of air conditioning equipment with an Okinawa beach in the background.

[1623] Step 8:

[1624] Content Storage

[1625] The server stores the generated advertising text, images and videos in a database for users to access.

[1626] Step 9:

[1627] Chatbot Settings

[1628] The device uses generative AI to set up a chatbot that has learned common questions and their answers.

[1629] Specifically, prepare answers to questions about how to install air conditioning equipment and the return procedure.

[1630] Step 10:

[1631] Automating customer interactions

[1632] The terminal uses a chatbot to respond to customer inquiries immediately.

[1633] For example, an AI chatbot can provide answers to inquiries about how to use air conditioning equipment.

[1634] Step 11:

[1635] Content Use

[1636] The user downloads the generated promotional content and uses it in marketing activities.

[1637] Specifically, the generated posters are printed and distributed in the area.

[1638] Step 12:

[1639] Marketing Activities

[1640] Users can promote themselves on social media using advertising text and images generated by the server.

[1641] For example, the generated advertising image can be posted on social media to promote the product to followers.

[1642] Step 13:

[1643] Feedback collection

[1644] The user collects feedback from customers and sends it to the server.

[1645] For example, record customer opinions obtained through social media comments and messages.

[1646] Step 14:

[1647] Reanalyzing Data

[1648] The server analyzes the collected feedback data and uses it to forecast demand and develop marketing strategies for the next time.

[1649] For example, identifying areas for improvement in products and services based on customer feedback.

[1650] This series of processing steps enables people who have difficulty finding employment to carry out marketing activities efficiently, and companies and NPOs to achieve sustainable business operations.

[1651] Example 1

[1652] 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."

[1653] Existing marketing systems have the problem that it is difficult for people who have difficulty finding employment, such as people with disabilities, the elderly, people who have been socially withdrawn, and former prisoners, to carry out marketing activities efficiently. Furthermore, these systems are inadequate in market demand forecasting, targeting, and generating promotional content, making it difficult for companies and brands to effectively deliver products to the market. In addition, customer support is often done manually, requiring efficient use of human resources.

[1654] 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.

[1655] In this invention, the server includes: a means for analyzing demand trends using a generative AI; a means for identifying a target market based on demand forecast data and developing a sales channel expansion strategy; a means for automatically generating advertising text, images, and videos using the generative AI; a means for generating prompts for use with the generative AI model; a means for preprocessing collected data; a means for performing demand forecasts using the generative AI based on the preprocessed data; a means for saving promotional content generated for the identified target market; and a means for managing customer support based on the saved information. This enables efficient marketing activities for people who have difficulty finding employment, and enables highly accurate demand forecasts and effective promotional activities using the generative AI. Furthermore, automating customer support enables efficient use of human resources.

[1656] "Generative AI" is a technology that uses artificial intelligence to automatically generate content such as text, images, and videos.

[1657] "Demand forecasting" is the process of predicting future consumer demand based on collected data.

[1658] A "target market" refers to the region or consumer group that is predicted to have the greatest demand for a particular product or service.

[1659] A "market expansion strategy" is a plan or strategy for effectively promoting and selling a product or service to a specific market.

[1660] A "prompt" is an instruction used as input to a generative AI model that determines the content to be generated.

[1661] "Preprocessing" is the process of formatting data into an analyzable format, including filling in missing values ​​and removing outliers.

[1662] "Promotional Content" refers to content such as advertising text, images, and videos used to promote products and services.

[1663] "Customer interaction management" is the process of efficiently handling, recording, and managing customer inquiries and feedback.

[1664] This invention is a system to support the economic independence of people who have difficulty finding employment, such as the disabled, the elderly, those who have experienced social withdrawal, and those serving time in prison. It utilizes generative AI to match companies and brands with influencers, achieving effective marketing, expanding sales channels, and customer support. This system is operated mainly by three elements: a server, a terminal, and a user.

[1665] Server Roles and Operations

[1666] Market and trend data collection

[1667] The server uses a web crawler to collect online shopping data and social media trend data. Specifically, it uses Python's BeautifulSoup library to collect product sales data from multiple online shopping sites and the Twitter API to retrieve tweets related to specific keywords.

[1668] Data Preprocessing

[1669] The server cleanses the collected data and converts it into an analyzable format. It uses the pandas library to process missing values ​​and remove outliers. The collected data is then formatted as time series data.

[1670] Perform demand forecasts

[1671] The server uses a generative AI model to forecast demand. For example, it uses a time series analysis model on Amazon SageMaker to input preprocessed data into the model and predict future demand. An example of a prompt is, "Please analyze sales data for air conditioning equipment for the past six months and provide a demand forecast for next month."

[1672] Identifying your target market

[1673] The server identifies target markets based on the demand forecast results and develops a sales expansion strategy. It visualizes the forecast data using Microsoft Power BI to identify regions and consumer segments with the highest demand. As a specific example, it identifies high demand in Okinawa Prefecture.

[1674] Promotional content generation

[1675] The server uses generative AI to automatically generate ad text, images, and videos. For example, OpenAI's GPT-4 is used to input a prompt such as "Generate ad text and images for air conditioning equipment for the summer" and obtain the generated content. For example, ad text such as "Buy now and enjoy a comfortable summer!" and images and videos of air conditioning equipment with an Okinawa beach in the background are generated.

[1676] Data storage

[1677] The server stores the generated content and analysis data in a database, such as Amazon RDS, to store the generated ad text, images, videos, and demand forecast data for easy access in subsequent processing.

[1678] Terminal roles and processing

[1679] Automating customer interactions

[1680] The device uses generative AI to build a chatbot for customer support. Specifically, it uses Dialogflow to set up a dialogue flow that can immediately respond to customer questions based on the results of natural language processing by the generative AI.

[1681] Customer interaction management

[1682] The device responds to customer inquiries instantly using automatically generated responses and records the information in a database. Specifically, it automatically records the content of customer interactions using Salesforce.

[1683] User Roles and Actions

[1684] Business execution

[1685] Users (employees facing employment difficulties) download the promotional content generated by the server and use it in their marketing activities. Specifically, they print out the generated posters and distribute them locally.

[1686] Support for marketing activities

[1687] Users can use the generated advertising text and images to promote their products on social media. Specifically, they log in to their social media accounts and post advertising text and images to appeal to their followers.

[1688] Following AI instructions

[1689] The user carries out the sales expansion strategy recommended by the generation AI, specifically by planning and executing promotional activities in the target areas specified by the AI.

[1690] This system will enable people who have difficulty finding employment to carry out efficient marketing activities, enabling companies and brands to sustainably provide competitive products to the market. Furthermore, automating customer support will enable efficient use of human resources.

[1691] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1692] Step 1: Data collection

[1693] The server uses a web crawler to collect online shopping data and social media trend data. Specifically, it uses Python's BeautifulSoup library to collect product sales data from multiple online shopping sites and uses the Twitter API to obtain tweets related to specific keywords. The inputs are the "URL of the online shopping site" and the "Twitter API key," and the outputs are the "collected online shopping data" and the "collected social media data."

[1694] Step 2: Data Preprocessing

[1695] The server cleanses the collected data and converts it into an analyzable format. Specifically, it uses the pandas library to fill in missing values ​​and remove outliers. It then converts the collected data into time series data. The input is the collected data, and the output is the preprocessed data.

[1696] Step 3: Demand forecast

[1697] The server uses a generative AI model to perform demand forecasting. For example, it uses a time series analysis model on Amazon SageMaker to predict future demand based on preprocessed data. An example prompt is: "Analyze sales data for air conditioning equipment over the past six months and provide a demand forecast for next month." The inputs are "preprocessed data" and "prompt," and the output is "demand forecast data."

[1698] Step 4: Identify your target market

[1699] The server identifies target markets based on the demand forecast results and develops a sales expansion strategy. Specifically, it uses Microsoft Power BI to visualize the forecast data and identify the regions and consumer segments with the highest demand. The input is "demand forecast data," and the output is "identified target market data" and a "sales expansion strategy proposal."

[1700] Step 5: Generate promotional content

[1701] The server uses generative AI to automatically generate advertising text, images, and videos. Specifically, it uses OpenAI's generative AI to input a prompt such as "Please generate advertising text and images for air conditioning equipment for the summer" and obtains the generated content. The inputs are the generative AI model and the prompt, and the outputs are the generated advertising text and generated advertising images and videos.

[1702] Step 6: Save your data

[1703] The server stores the generated content and analysis data in a database. Specifically, it uses Amazon RDS to store the generated ad text, images, videos, demand forecast data, etc. The input is the "generated ad content" and "demand forecast data," and the output is the "stored data."

[1704] Step 7: Automate customer interactions

[1705] The device uses generative AI to build a chatbot for customer support. Specifically, it uses Dialogflow to set up a dialogue flow that can immediately respond to customer questions based on the results of natural language processing by the generative AI. The inputs are the generative AI model and customer question data, and the output is the chatbot's dialogue content.

[1706] Step 8: Manage customer interactions

[1707] The terminal responds immediately to customer inquiries using automatically generated responses and records the information in a database. Specifically, it automatically records the content of interactions with customers using Salesforce. The inputs are the "chatbot dialogue content" and "customer inquiry data," and the output is "recorded customer response data."

[1708] Step 9: Implement marketing activities

[1709] Users download the promotional content generated by the server and use it in their marketing activities. Specifically, they print out the generated posters and distribute them locally. They also log in to SNS and post the generated advertising text and images. The input is the "generated promotional content" and the output is the "results of the marketing activities."

[1710] (Application example 1)

[1711] 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."

[1712] The goal is to provide a system that enables people who have difficulty finding employment, such as people with disabilities, the elderly, those who have experienced social withdrawal, and those serving time in prison, to become financially independent and effectively participate in marketing activities. However, conventional systems do not adequately collect market data, forecast demand, or generate advertising content, making it difficult to identify target markets and develop sales channel expansion strategies. Furthermore, there is a lack of means to support social media promotions that effectively utilize the generated advertising content. As a result, it has been difficult for people with employment difficulties to effectively engage in marketing activities.

[1713] 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.

[1714] In this invention, the server includes means for analyzing demand trends using a generation AI, means for identifying a target market based on demand forecast data and formulating a sales channel expansion strategy, means for automatically generating advertising text, images, and videos using the generation AI, means for supporting marketing activities using the automatically generated advertising content, means for automating customer support using the generation AI, means for acquiring market data and generating region-specific advertising text using an application installed on a smartphone, means for forecasting demand based on online shopping data and social media trend data, and means for conducting promotions on social media using the generated advertising content. This enables people with employment difficulties to conduct marketing activities easily and efficiently, and companies to effectively deliver competitive products to the market.

[1715] "Generative AI" is an artificial intelligence technology that uses machine learning and natural language processing to automatically generate content such as text, images, and videos.

[1716] "Demand tendencies and trends" refer to the direction of demand and trends for a product within a specific market or consumer group over a certain period of time.

[1717] "Demand forecast data" is data that predicts future demand based on past market data and trend data.

[1718] A "target market" refers to a group of potential customers or a geographic area for a particular product or service.

[1719] A "market expansion strategy" is a plan or method for increasing sales in existing and new markets.

[1720] "Advertising text" is written content used to promote a product or service.

[1721] An "image" is any digital or printed visual content intended to convey visual information.

[1722] "Video" is a media format that provides dynamic visual information by playing multiple video frames in succession.

[1723] "Automatically generated advertising content" refers to advertising content, such as text, images, and videos, that are automatically created using generative AI.

[1724] "Marketing activities" refers to all strategic efforts to increase awareness of and promote sales of products and services.

[1725] "Customer service" refers to the work of responding to and supporting inquiries and complaints from customers.

[1726] An "application installed on a smartphone" is a software program that can be downloaded and used on a smartphone device.

[1727] "Market Data" means data about sales, consumer trends, competitor activities, etc. in a particular market.

[1728] "Localized ad text" is advertising text that focuses on a specific region and is tailored to the characteristics and needs of that region.

[1729] "Online shopping data" refers to data related to purchasing activities on the Internet.

[1730] "Social media trend data" refers to data on the popularity of topics and keywords on social media.

[1731] "SNS promotion" is a marketing technique that uses social networking services to promote products and services.

[1732] Server Roles and Operations

[1733] Demand forecasting

[1734] The server uses generative AI to collect and analyze market data and social media trend data to forecast demand for specific regions and consumer segments. Specifically, it builds a demand forecasting model based on online shopping data and social media trend data, and makes predictions using prompts such as:

[1735] "Please forecast the demand for air conditioning equipment in Okinawa Prefecture for the next three months."

[1736] Market Targeting

[1737] The server identifies target markets based on the demand forecast results and develops sales expansion strategies. For example, if it analyzes demand forecast data and determines that demand for air conditioning equipment will increase in Okinawa Prefecture, it will set Okinawa Prefecture as the target market and develop a region-specific marketing strategy.

[1738] Content Generation

[1739] The server uses generative AI to automatically generate promotional content such as ad text, images, and videos, using prompts such as:

[1740] "Generate advertising text for air conditioning equipment for Okinawa Prefecture."

[1741] Data storage

[1742] The server stores the generated demand forecast data, target market information, advertising content, etc. in a database and makes them accessible as needed.

[1743] Terminal roles and processing

[1744] Automating customer interactions

[1745] The device uses generative AI to build chatbots for customer service, such as providing immediate responses to customer questions about how to install air conditioners or the return procedure.

[1746] Customer interaction management

[1747] The terminal responds immediately to customer inquiries using automatically generated answers and records the information in a database, which allows for the management of customer response history.

[1748] User Roles and Actions

[1749] Business execution

[1750] Users (employees facing employment difficulties) download the promotional content generated by the server and use it in actual marketing activities, such as printing out posters and distributing them locally.

[1751] Support for marketing activities

[1752] The user can then use the generated advertising text and images to promote the product on social media. For example, the user can post the generated advertising image on social media to promote the product to their followers.

[1753] Following AI instructions

[1754] The user carries out operations in accordance with the sales expansion strategy recommended by the generation AI, for example, by planning and executing sales promotion activities in target areas as instructed by the AI.

[1755] Hardware and software used

[1756] The system uses the following hardware and software:

[1757] Hardware:

[1758] Servers, smartphones, terminals

[1759] software:

[1760] Generative AI API (e.g. OpenAI API)

[1761] HTTP request library (e.g., requests)

[1762] Specific examples

[1763] 1. Example prompt

[1764] "Please forecast the demand for air conditioning equipment in Okinawa Prefecture for the next three months."

[1765] "Generate advertising text for air conditioning equipment for Okinawa Prefecture."

[1766] This system allows people who have difficulty finding employment to easily and efficiently carry out marketing activities, and also enables companies to effectively deliver competitive products to the market.

[1767] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1768] Step 1:

[1769] The server collects online shopping data and social media trend data, which is obtained through an API.

[1770] Input: Online shopping data, social media trend data

[1771] Output: Collected market data

[1772] This step involves retrieving data from specific API endpoints and storing it in an internal database.

[1773] Step 2:

[1774] The server uses a generative AI model to forecast demand based on collected market data. The collected data is input into the model to generate a demand forecast.

[1775] Input: Collected market data, prompt: "Please forecast the demand for air conditioning equipment in Okinawa Prefecture for the next three months."

[1776] Output: Demand forecast data

[1777] In this step, the generative AI model is run and predicts results based on the prompt text.

[1778] Step 3:

[1779] The server identifies target markets based on demand forecast data and develops regionally specific sales channel expansion strategies.

[1780] Input: Demand forecast data

[1781] Output: Target market information, sales channel expansion strategy

[1782] In this step, the predictive data is analyzed to plan the optimal target market and effective marketing strategy.

[1783] Step 4:

[1784] The server uses generative AI to automatically generate advertising content such as ad text, images, and videos.

[1785] Input: Target market information, prompt "Generate advertising text for air conditioning equipment for Okinawa Prefecture."

[1786] Output: Ad text, images, videos

[1787] In this step, generative AI models are used to create advertising content tailored to a specified target market.

[1788] Step 5:

[1789] The server stores the generated advertising content in a database and makes it accessible to terminals and users as needed.

[1790] Input: Ad text, images, video

[1791] Output: Ad content stored in a database

[1792] In this step, the generated content is stored in a database for immediate access.

[1793] Step 6:

[1794] The device uses generative AI to build chatbots for customer interaction.

[1795] Input: Generative AI model, FAQ data

[1796] Output: Customer service chatbot

[1797] In this step, a generative AI model is used based on FAQ data to build a chatbot that provides immediate responses to customer questions.

[1798] Step 7:

[1799] The terminal records the history of customer interactions in a database.

[1800] Input: Customer inquiry, chatbot response

[1801] Output: Recorded customer interaction history

[1802] In this step, customer interactions are saved in a database for future reference.

[1803] Step 8:

[1804] The user downloads the generated promotional content from the server and uses it in actual marketing activities.

[1805] Input: Ad text, images, and videos downloaded from the database

[1806] Output: Marketing activities carried out (poster distribution, social media posts, etc.)

[1807] In this step, the user performs specific marketing activities using the generated advertising content.

[1808] Step 9:

[1809] The user uses the generated advertising content to promote the product on the SNS.

[1810] Input: Ad text, images, video

[1811] Output: Social media posts, follower reactions

[1812] In this step, the user posts the generated advertising content on the social networking site to promote the product to their followers.

[1813] Step 10:

[1814] Users carry out their work in accordance with the sales expansion strategy recommended by the generation AI.

[1815] Input: Sales channel expansion strategy

[1816] Output: Market expansion activities carried out

[1817] In this step, users plan and execute marketing activities based on AI recommendations.

[1818] 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.

[1819] This invention is a system that uses generative AI to forecast demand, identify target markets, automatically generate advertising content, support marketing activities, and automate customer support. Furthermore, by combining it with an emotion engine that recognizes user emotions, it adds the ability to analyze user emotions and generate responses and content based on them.

[1820] Server Roles and Operations

[1821] Data collection and preprocessing

[1822] The server collects online shopping data and social media trend data, and performs data cleansing, missing value imputation, and outlier detection.

[1823] Example: A server uses web scraping tools and APIs to collect online shopping data and social media trend data for the past six months.

[1824] Demand forecasting

[1825] The server applies machine learning models to the pre-processed data to predict future demand.

[1826] Example: A server uses an ARIMA model and an LSTM network to forecast demand for air conditioning equipment in summer.

[1827] Target market identification and strategy planning

[1828] The server identifies target markets based on demand forecast data and develops regionally specific sales expansion strategies.

[1829] Example: The server identifies an area where demand for air conditioners is high (e.g., Okinawa Prefecture) and plans an advertising campaign for that area.

[1830] Content Generation

[1831] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos.

[1832] Example: The server generates advertising text such as "Buy now and enjoy a comfortable summer!" and images and videos of air conditioning equipment with a beach in Okinawa in the background.

[1833] Emotion Engine Analysis

[1834] The server uses an emotion engine to analyze the user's emotions and generate responses and content based on those emotions.

[1835] Example: A server performs text analysis of a user's comments and feedback to determine whether the user is expressing positive or negative sentiment.

[1836] Terminal roles and processing

[1837] Chatbot Settings

[1838] The device uses generative AI to set up a chatbot that has learned common questions and their answers.

[1839] Example: The terminal prepares answers to questions about how to install a cooling unit or the return process.

[1840] Automating customer interactions

[1841] The terminal uses a chatbot to respond immediately to customer inquiries.

[1842] Example: The device responds to inquiries about how to use air conditioning equipment with an AI chatbot that provides accurate answers.

[1843] User Roles and Actions

[1844] Business execution

[1845] Users (employees who are having difficulty finding work) download the promotional content generated by the server and use it in their actual marketing activities.

[1846] Example: The user prints the generated posters and distributes them in the designated area. The user also promotes the posters on social media.

[1847] Sentiment Analysis Feedback

[1848] Users receive the results of customer sentiment analysis provided through the emotion engine and reflect them in their marketing strategies and customer service.

[1849] Example: Users consider rewards and promotions when customers express positive sentiment.

[1850] conclusion

[1851] By utilizing generative AI and an emotion engine, this invention makes it possible for people with employment difficulties to easily carry out marketing activities and customer service. This allows companies and nonprofit organizations to operate their businesses efficiently and effectively, increasing their sustainability. Based on specific processing steps, this system provides comprehensive and effective support.

[1852] The processing flow will be explained below.

[1853] Step 1:

[1854] Data collection

[1855] The server collects online shopping data, social media trend data, and more.

[1856] Specifically, data is automatically obtained using web scraping tools and APIs.

[1857] Step 2:

[1858] Data Preprocessing

[1859] The server cleanses the collected data, filling in missing values ​​and detecting outliers.

[1860] For example, importing data into an Excel spreadsheet or database and correcting inconsistent data.

[1861] Step 3:

[1862] Demand forecasting

[1863] The server analyzes the pre-processed data and uses machine learning models to predict demand.

[1864] For example, ARIMA models and LSTM networks are used to forecast future demand.

[1865] Step 4:

[1866] Saving prediction results

[1867] The server stores the prediction results in a database, making them available for subsequent processing steps.

[1868] Step 5:

[1869] Identifying your target market

[1870] The server identifies the optimal target market based on the demand forecast data.

[1871] For example, identify areas where demand for air conditioning equipment will increase (e.g., Okinawa Prefecture).

[1872] Step 6:

[1873] Planning sales channel expansion strategies

[1874] The server develops a sales expansion strategy for the identified target market.

[1875] For example, design an advertising campaign specifically for Okinawa Prefecture.

[1876] Step 7:

[1877] Content Generation

[1878] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos.

[1879] Specifically, it generates advertising text such as "Buy now and enjoy a comfortable summer!" and images and videos of air conditioning equipment with an Okinawa beach in the background.

[1880] Step 8:

[1881] Content Storage

[1882] The server stores the generated advertising text, images and videos in a database for users to access.

[1883] Step 9:

[1884] Emotion analysis

[1885] The server uses an emotion engine to analyze the user's emotions and generate responses and content based on those emotions.

[1886] Specifically, it performs text analysis of user comments and feedback to determine whether the user is expressing positive or negative sentiment.

[1887] Step 10:

[1888] Chatbot Settings

[1889] The device uses generative AI to set up a chatbot that has learned common questions and their answers.

[1890] Specifically, prepare answers to questions about how to install air conditioning equipment and the return procedure.

[1891] Step 11:

[1892] Automating customer interactions

[1893] The terminal uses a chatbot to respond to customer inquiries instantly.

[1894] For example, an AI chatbot can provide accurate answers to inquiries about how to use air conditioning equipment.

[1895] Step 12:

[1896] Content Use

[1897] The user downloads the generated promotional content and uses it in marketing activities.

[1898] Specifically, the generated posters are printed and distributed in the area.

[1899] Step 13:

[1900] Marketing Activities

[1901] Users can promote themselves on social media using advertising text and images generated by the server.

[1902] For example, the generated advertising image can be posted on social media to promote the product to followers.

[1903] Step 14:

[1904] Feedback collection

[1905] The user collects feedback from customers and sends it to the server.

[1906] For example, record customer opinions obtained through social media comments and messages.

[1907] Step 15:

[1908] Reanalyzing Data

[1909] The server analyzes the collected feedback data and uses it to forecast demand and develop marketing strategies for the next time.

[1910] For example, identifying areas for improvement in products and services based on customer feedback.

[1911] Step 16:

[1912] Optimizing emotional response

[1913] The server updates and optimizes appropriate emotional responses to customer feedback and inquiries based on the emotion engine.

[1914] For example, if a customer expresses negative feelings, offer an appropriate apology and suggest improvements.

[1915] This series of processing steps enables people who have difficulty finding employment to carry out efficient marketing activities, and companies and NPOs to achieve sustainable and effective business operations.In addition, the introduction of an emotion engine enables detailed responses based on customer emotions, and is expected to improve customer satisfaction.

[1916] Example 2

[1917] 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."

[1918] Traditional marketing systems required a significant amount of time and human resources to forecast demand, identify target markets, and generate advertising content, resulting in low operational efficiency. They also struggled to provide immediate customer service, making it difficult to improve customer satisfaction. Furthermore, they were unable to understand employee sentiment and generate marketing strategies and content based on that sentiment.

[1919] 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.

[1920] In this invention, the server includes a means for analyzing demand tendencies and trends using a generation AI, a means for identifying a target market based on demand forecast data and formulating a sales channel expansion strategy, a means for automatically generating advertising text, images, and videos using the generation AI, and a means for analyzing user emotions using an emotion engine and generating responses and content based on the analysis results, thereby enabling improved accuracy of demand forecasts, more efficient marketing strategies, faster generation of advertising content, faster customer responses, and emotion-based marketing activities.

[1921] "Generative AI" is an artificial intelligence technology that uses generative models to automatically generate content such as text, images, and videos.

[1922] "Demand tendency and trend analysis" refers to the use of past data to predict future demand and clarify its fluctuation patterns.

[1923] "Demand forecast data" is data on future demand predicted using machine learning models and analytical methods.

[1924] "Target market identification" refers to identifying the consumer market with the greatest demand or interest for a particular product or service.

[1925] A "sales channel expansion strategy" is a specific plan or method for expanding product distribution channels and widening the customer base.

[1926] "Automatic generation of advertising text, images, and videos" refers to the use of generative AI to automatically create text, graphics, and video for advertising.

[1927] "Support for marketing activities" means using the generated advertising content to support marketing initiatives such as market research, promotions, and sales promotions.

[1928] "Customer response automation" is a system that uses generative AI to provide instant answers to customer inquiries.

[1929] An "emotion engine" is a technology that analyzes a user's emotional state from text and voice data and generates responses and content based on the results.

[1930] "Sentiment analysis" is the process of determining the type and intensity of emotions from user comments and feedback.

[1931] This invention is a system that uses generative AI to forecast demand, identify target markets, automatically generate advertising content, support marketing activities, and automate customer support. Furthermore, by combining it with an emotion engine that recognizes user emotions, it adds the ability to analyze user emotions and generate responses and content based on them.

[1932] Server Roles and Operations

[1933] Data collection and preprocessing

[1934] The server collects online shopping data and social media trend data using APIs and web scraping tools. Specifically, it uses the Twitter API to collect tweets related to air conditioning equipment for the past six months and collects air conditioning equipment sales trend data through the APIs of online shopping sites. The server then cleanses this data, imputes missing values, and detects outliers. Missing data is imputed with the mean or median, and abnormally high or low values ​​are filtered out.

[1935] Demand forecasting

[1936] The server converts the cleansed data into a format suitable for machine learning models. Specifically, it converts the data into a time series format and standardizes features (e.g., date, sales volume, social media trend index). It then trains an ARIMA model or LSTM network to forecast future demand. For example, it predicts demand for air conditioning equipment in the summer of 2023 and estimates when peak demand will occur.

[1937] Target market identification and strategy planning

[1938] The server analyzes the predicted demand data and identifies target markets. For example, it predicts demand for air conditioning equipment in each region of Japan and determines that a specific region (e.g., Okinawa Prefecture) will have high demand. It then develops a sales expansion strategy optimized for that region. Specifically, it plans outdoor advertising and a region-specific online advertising campaign in Okinawa Prefecture.

[1939] Content Generation

[1940] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos. For example, it generates a catchphrase such as "Buy now and enjoy a comfortable summer!" and an advertising image with the beautiful beaches of Okinawa as the background.

[1941] Emotion Engine Analysis

[1942] The server uses an emotion engine to analyze users' emotions. Specifically, it collects customer reviews and comments on social media and analyzes the user's emotional state from text and voice data. For example, it identifies areas that need improvement based on negative comments, and identifies areas that need further strengthening based on positive comments.

[1943] Terminal roles and processing

[1944] Chatbot Settings

[1945] The device uses a generation AI to set up a chatbot that has learned frequently asked questions and their answers. Specifically, the generation AI creates FAQs about air conditioning equipment (e.g., installation methods, return procedures), and the chatbot learns them.

[1946] Automating customer interactions

[1947] The terminal uses a chatbot to respond to customer inquiries immediately. For example, an AI chatbot can provide an accurate answer to a question about how to use an air conditioner.

[1948] User Roles and Actions

[1949] Business execution

[1950] Users (employees facing employment difficulties) download the promotional content generated by the server and use it in actual marketing activities. For example, the user prints out the generated posters and distributes them in a designated area (e.g., Okinawa Prefecture).

[1951] Sentiment Analysis Feedback

[1952] Users can use the analysis results of the emotion engine to reflect them in their marketing strategies and customer service. For example, they can offer rewards to customers who express positive emotions and take measures to improve their customer service in response to negative comments.

[1953] Prompt Sentence Examples

[1954] Using a generative AI model, we present an example of a prompt sentence for predicting demand for air conditioning equipment in the summer.

[1955] Prompt statement

[1956] "Based on online shopping data and social media trend data from the past six months, please predict demand for air conditioning equipment for the summer of 2023."

[1957] As a result, this system enables improved accuracy in demand forecasting, more efficient marketing strategies, faster generation of advertising content, faster customer response, and emotion-based marketing activities.

[1958] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1959] Step 1:

[1960] Data collection and preprocessing

[1961] The server collects online shopping data and social media trend data using APIs and web scraping tools. The input data is online shopping sales data and social media post data from the past six months. The server cleanses the collected data, imputes missing values, and detects outliers. For example, missing data is imputed with the mean or median, and abnormally high or low values ​​are filtered. The output is a cleansed dataset.

[1962] Step 2:

[1963] Data preparation

[1964] The server converts the cleansed data into a format that can be applied to a machine learning model. The input data is the cleansed data generated in step 1. The server converts the data into a time series format and standardizes the features (e.g., date, sales volume, social media trend index). The output is a dataset converted into a format that can be applied to a machine learning model.

[1965] Step 3:

[1966] Training a demand forecasting model

[1967] The server trains a demand forecasting model using the training data. The input data is the training data generated in step 2. The server performs training using an ARIMA model or an LSTM network. Specifically, it optimizes the model parameters using past sales data and social media trend data. The output is a trained demand forecasting model.

[1968] Step 4:

[1969] Perform demand forecasts

[1970] The server uses the trained model to predict future demand. The input data is real-time or the latest sales data and social media trend data. The server inputs this data into the model to predict future demand. For example, it predicts demand for air conditioning equipment in the summer of 2023 and estimates when demand will peak. The output is predicted demand data.

[1971] Step 5:

[1972] Target market identification and strategy planning

[1973] The server analyzes the forecasted demand data by region and identifies target markets. The input data is the demand forecast data obtained in step 4. The server identifies regions where demand will increase and plans an optimal sales expansion strategy for those regions. For example, it plans outdoor advertising in Okinawa Prefecture and a region-specific online advertising campaign. The output is a target market list and a marketing strategy document.

[1974] Step 6:

[1975] Automatic generation of advertising content

[1976] The server uses generative AI to automatically generate promotional content such as advertising text, images, and videos. The input data is the marketing strategy document formulated in Step 5. Specifically, it generates a catchphrase such as "Buy now and enjoy a comfortable summer!" and an advertising image with Okinawa's beautiful beaches as the background. The output is the automatically generated advertising content.

[1977] Step 7:

[1978] Automating customer interactions

[1979] The device uses a generation AI to set up a chatbot that has learned frequently asked questions and their answers. The input data is an FAQ database and generated customer response templates. As a specific example, an answer to a question about how to install air conditioning equipment is prepared. The output is the trained chatbot.

[1980] Step 8:

[1981] Customer service using chatbots

[1982] The terminal uses a trained chatbot to immediately respond to customer inquiries. The input data is the customer's inquiry. The terminal provides an appropriate answer to the customer's question through the chatbot. For example, an accurate answer is provided to an inquiry about how to use air conditioning equipment. The output is the answer provided to the customer and the response history.

[1983] Step 9:

[1984] Conducting sentiment analysis

[1985] The server uses an emotion engine to analyze the user's emotions. The input data is customer reviews and comments on social media. The server analyzes the user's emotional state (positive, negative, etc.) from text and voice data. The output is the analyzed emotion data.

[1986] Step 10:

[1987] Sentiment Analysis Feedback

[1988] The user utilizes the analysis results of the emotion engine to reflect them in marketing strategies and customer service. The input data is the analyzed emotion data obtained in step 9. The user provides rewards to customers who show positive emotions and takes measures to improve negative opinions. The output is an improved marketing strategy and customer service policy.

[1989] (Application example 2)

[1990] 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."

[1991] In conventional marketing activities, it has been difficult to centrally perform demand forecasting, target market identification, advertising content generation, and efficient customer response. This has resulted in companies being unable to quickly develop effective advertising campaigns, and despite the need for rapid responses based on customer feedback, it has been difficult to do so. Another issue is the inability to effectively automatically generate and distribute specific advertising campaigns based on data collected using devices such as smartphones.

[1992] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1993] In this invention, the server includes means for analyzing demand tendencies and trends using a generation AI, means for identifying target markets based on demand forecast data and formulating sales channel expansion strategies, means for automatically generating advertising text, images, and videos using the generation AI, means for automatically generating and distributing advertising campaigns based on data collected via a smartphone application, and means for analyzing user sentiment and providing responses and content based on the analysis results. This enables efficient and centralized execution of a wide range of marketing activities, enabling companies to develop rapid and effective advertising campaigns and respond quickly to customer feedback.

[1994] "Generative AI" is artificial intelligence that creates new data and content using techniques such as generative adversarial networks (GANs) and transformer models.

[1995] "Demand forecasting" is the process of analyzing past data and trends to predict future consumer demand.

[1996] A "target market" is a group of consumers to whom a particular product or service should be promoted.

[1997] "Advertising Text" means text containing a specific marketing message used in an advertising campaign.

[1998] An "image" is a still image intended to convey visual information.

[1999] "Video" is a moving image intended to convey dynamic visual information.

[2000] A "marketing activity" is a set of strategies and actions taken to promote a product or service.

[2001] "Customer service" refers to the act of providing information in response to customer inquiries and questions and meeting their requests.

[2002] A "smartphone application" is software that runs on a smartphone and performs a specific task.

[2003] "Emotion analysis" is a technology that detects and analyzes a user's emotions from text or voice.

[2004] A "response" is a reaction or answer given in response to an input or inquiry from a user.

[2005] "Content" means text, images, video, and other materials used to advertise or convey information.

[2006] The present invention relates to a system for automatically generating and delivering advertising campaigns via a smartphone application. The system utilizes generative AI and sentiment analysis engines to forecast demand, identify target markets, generate advertising content, and automate customer interactions.

[2007] Server Roles and Operations

[2008] Data collection and preprocessing

[2009] The server uses APIs and web scraping to collect online shopping data and social media trend data, and then performs data cleansing, missing value imputation, and outlier detection. As a specific example, online shopping data and social media trend data from the past six months are collected.

[2010] Demand forecasting

[2011] The server applies machine learning models (such as ARIMA models and LSTM networks) to the preprocessed data to predict future demand, such as predicting demand for air conditioning equipment in the summer.

[2012] Target market identification and strategy planning

[2013] The server identifies target markets based on demand forecast data and develops region-specific sales expansion strategies. For example, it identifies areas where demand for air conditioning equipment is increasing and plans an advertising campaign for those areas.

[2014] Generative AI-based advertising content generation

[2015] The server uses generation AI (e.g., OpenAI API) to automatically generate promotional content such as advertising text, images, and videos. An example of a specific prompt is, "Please generate an advertisement for a summer air conditioner. Please create a text advertisement with an image of an air conditioner against the backdrop of a beach in Okinawa."

[2016] Emotion analysis

[2017] The server uses a sentiment analysis engine to analyze the user's sentiment and provides responses and content based on the results. It also performs text analysis of the user's comments and feedback to determine whether the sentiment is positive or negative.

[2018] Terminal roles and processing

[2019] Chatbot Settings

[2020] The device uses generative AI to set up a chatbot that is trained on frequently asked questions and their answers, and is ready to answer questions about how to install a cooling unit or the return process.

[2021] Automating customer interactions

[2022] The terminal uses an AI chatbot to respond to customer inquiries immediately, for example, by providing accurate answers to inquiries about how to use air conditioning equipment.

[2023] User Roles and Actions

[2024] Business execution

[2025] Users download the promotional content generated by the server and use it in their actual marketing activities. They print the generated posters and distribute them in designated areas. They also promote the content on social media.

[2026] Sentiment Analysis Feedback

[2027] Users can receive the results of customer sentiment analysis provided by the emotion engine and reflect them in their marketing strategies and customer service. For example, if a customer expresses positive sentiment, they can consider offering special offers or promotions.

[2028] conclusion

[2029] This invention provides a system that efficiently automates the generation and distribution of advertising campaigns via smartphone applications by utilizing generative AI and a sentiment analysis engine. This system centrally manages a wide range of marketing activities, enabling the rapid and effective development of advertising campaigns and customer responses.

[2030] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2031] Step 1:

[2032] The server collects online shopping data and social media trend data using APIs and web scraping. The data is obtained using the URL and API key provided as input and stored in internal storage. The specific data collected includes purchase history and social media posts from the past six months.

[2033] Step 2:

[2034] The server preprocesses the collected data. It uses the raw data stored in the internal storage as input and performs missing value completion, outlier detection and removal, and data cleansing. Specifically, it completes incomplete data records and removes records with clearly abnormal values. It also standardizes the data format to make it easier to analyze. After preprocessing is complete, the data is standardized and passed to the next step.

[2035] Step 3:

[2036] The server uses the preprocessed data to perform demand forecasting. It applies an ARIMA model or LSTM network to the time series data prepared as input to predict future demand. Specifically, it uses past purchase data as training data for the model and predicts demand values ​​for a certain period of time in the future. The forecast results are output as future demand trends.

[2037] Step 4:

[2038] The server identifies target markets based on demand forecast data and develops sales expansion strategies. Using the demand forecast results as input, it extracts regions and customer segments where demand will increase. Specifically, it calculates predicted demand values ​​for each region, identifies regions with high demand, and develops sales promotion strategies for those regions. The strategies developed here are used to plan advertising campaigns.

[2039] Step 5:

[2040] The server generates advertising content using generative AI. Using target market information and prompts as input, it instructs the generative AI model (e.g., OpenAI's API) to automatically generate advertising materials such as text, images, and videos. An example of a specific prompt is, "Please generate an advertisement for air conditioning equipment in the summer. Please create a text advertisement with an image of an air conditioning equipment with an Okinawa beach as the background." The generated advertising content is used in the next step.

[2041] Step 6:

[2042] The server creates an advertising campaign using automatically generated advertising content and distributes it via smartphones. Using the generated advertising content and target market data as input, the server designs the advertising campaign. Specifically, it plans content placement to distribute advertisements to specific user groups at appropriate times.

[2043] Step 7:

[2044] The server analyzes user feedback using a sentiment analysis engine. Using user comments and feedback data as input, the sentiment analysis model determines positive or negative sentiment. Specifically, it uses a text analysis algorithm to calculate the user's sentiment score and outputs the results as an analysis report.

[2045] Step 8:

[2046] The device uses a generative AI to set up a chatbot that has learned frequently asked questions and their answers. It uses an FAQ database and user inquiry data as input to provide the chatbot with learning data. Specifically, it generates answers to questions about how to install air conditioners and return procedures, and sets up the chatbot to automatically respond.

[2047] Step 9:

[2048] The terminal uses an AI chatbot to instantly respond to customer inquiries. Using the user's inquiry as input, the pre-configured chatbot automatically provides an accurate answer to the inquiry. Specifically, it returns an accurate answer to an inquiry about how to use air conditioning equipment.

[2049] Step 10:

[2050] The user downloads the generated promotional content from the server and uses it in actual marketing activities. The generated promotional content is used as input to print posters and carry out promotional activities on social media. Specifically, the generated advertising posters are distributed in designated areas and campaign posts are made on social media.

[2051] Step 11:

[2052] Users receive customer sentiment analysis results provided through the sentiment engine and reflect them in marketing strategies and customer interactions. Using the sentiment analysis results as input, users can plan rewards and promotions for positive customers. Specifically, strategies such as offering special discounts to customers who provide positive feedback can be implemented.

[2053] 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.

[2054] 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.

[2055] 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 robot 414.

[2056] 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.

[2057] 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.

[2058] 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.

[2059] 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).

[2060] 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.

[2061] 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."

[2062] 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.

[2063] 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).

[2064] 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.

[2065] 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...

Claims

1. a means for analyzing demand tendencies and trends using generative AI; A means of identifying target markets based on demand forecast data and formulating sales channel expansion strategies; A means to automatically generate ad text, images, and videos using generative AI; A means for supporting marketing activities using automatically generated advertising content; A system that includes a means to automate customer interactions using generative AI.

2. 2. The system of claim 1, wherein the means for analyzing demand tendencies and trends collects and analyzes data based on online shopping data and social media trend data.

3. 2. The system according to claim 1, wherein the means for identifying target markets and formulating sales channel expansion strategies selects optimal markets based on a predictive model and formulates region-specific marketing strategies.

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